Computer Science (CO-SCIENCE)
type: channel
description: Computer Science (CO-SCIENCE) was first published in 2021 with ISSN (Online): 2774-9711 and P-ISSN (Print): 2808-9065, and is published by the Indonesian Institute of Sciences (LIPI). Computer Science (CO-SCIENCE) has been accredited with SINTA Rank 4, based on Accreditation Decree No. 230/E/KPT/2022, effective from Volume 1 Issue 1 of 2021 to Volume 5 Issue 2 of 2025. and based on the Announcement of Reaccreditation of Scientific Journals Period II Year 2025, Computer Science (CO-SCIENCE) has obtained SINTA 3 accreditation with Number 295/C/C3/KPT/2026, effective from Volume 5 Issue 1 of 2025 to Volume 9 Issue 1 of 2029. Computer Science (CO-SCIENCE) is a journal published by LPPM Universitas Bina Sarana Informatika. Computer Science (CO-SCIENCE) is published twice a year (in January and July) in electronic format. Computer Science (CO-SCIENCE) is indexed in: Sinta, DOAJ, EBSCO, Google Scholar, Garuda, One Search, ROAD, BASE, Dimensions, Scilit, WorldCat and Crossref The editorial board accepts manuscripts in the form of scientific articles and research papers in the following fields: Networking, Mobile Applications, Software Engineering, Web Programming, Mobile Computing, Cloud Computing, Data Mining, and Scientific Applications.
publisher: LPPM Universitas Bina Sarana Informatika
language: en
publication name: Computer Science (CO-SCIENCE)
issn: 2808-9065
items: Enhancing FOMAML with Domain-Specific Residual Pretraining for Few-Shot Chili Disease Classification
items: Comparative Analysis of Machine Learning Models for Burnout Prediction in Generation-Z
items: Enhancing IPPEKas Digital Transformation for Effective Circular Economy Management
items: Collaborative Approaches to Enhancing Smart Vehicles Cybersecurity Through AI-Driven Threat Detection
items: Performance of Distance Metrics in SMOTE for Binary Imbalanced Classification
items: Cross-Project Defect Prediction on AEEEM Using SMOTE–Tomek and Ensemble learning
items: Integrating MLOps for Monitoring, Drift Detection, and Adaptive Retraining in Sensor Systems
items: Feature Comparison of RN-SMOTE and DBSCAN-SMOTE for Handling Imbalanced Data
items: Adventure Game Development for Environmental Conservation Using Game Development Life Cycle
items: Integrating LLM Intent-Aware Approach for Enhancing the Quality of Bundle Recommendations
items: UML-Based Design and Kano Evaluation of an Augmented Reality Library Application
items: Performance Evaluation of YOLOv8 for Railway Switching Operation Safety Monitoring
items: Developing an Efficient PPDB System Integrating Payment Gateway and Secure Exams
items: Optimization of Crop Recommendation Model Using Ensemble Learning Techniques for Multiclass Classification
items: Saliency-Enhanced Deep Learning Framework for Stain-Robust White Blood Cell Segmentation and Classification
items: SIBI-Based Gesture Recognition System Using Random Forest for Hearing-Impaired Communication
items: Analyzing Public Sentiment Toward Makanan Bergizi Gratis Program Using Machine Learning
items: Analysis of Student Academic Performance Using Random Forest and Support Vector Machines
items: Development of Web and Mobile Health Services Information System Using Waterfall Method
items: Design and Development of IdentifiKu: A Web-Based Diagnostic Model for Differentiated Learning
items: Sistem Informasi Persediaan Obat Pada PT Mega Esa Farma Dengan Metode Extreme Programming
items: Perancangan Sistem Informasi Surat Administrasi Penduduk Elektronik Menggunakan Metode Scrum
items: Studi Komparatif Algoritma K-Means dan K-Medoids untuk Segmentasi Informasi Kesehatan
items: Penerapan Model Scrum Pada Perancangan Aplikasi Penerimaan Siswa Baru MTS Nu 03 Suradadi
items: Evaluasi Dan Redesain Antarmuka Website megapolitanpos.com Menggunakan Metode Usability Testing Dan Heuristic Evaluation
items: Model Prediksi Risiko Kesehatan Perkotaan Berbasis Lingkungan dengan XGBoost
items: Implementasi Firewall Mikrotik dalam Pembatasan Akses Situs Terlarang di RT/RW Net
items: Sistem Manajemen Proyek Berbasis Website Dengan Metode Kanban
items: Optimalisasi Presensi Sekolah Berbasis QR Code dengan Metode Rapid Application Development
items: Analisis Sentimen Ulasan Pelanggan Menggunakan Algoritma Naive Bayes pada Aplikasi Gojek

Enhancing FOMAML with Domain-Specific Residual Pretraining for Few-Shot Chili Disease Classification
type: item
description: Meta-learning is an approach designed to address data limitations in few-shot learning scenarios. The performance of meta-learning is influenced by the quality of the initial weights used during the meta-training process. Initial weights derived from a relevant domain have the potential to produce more informative feature representations, thereby enabling the adaptation process to new tasks to proceed more effectively. This study analyzes the impact of domain-specific pretrained initialization on classification performance, learning stability, convergence behavior, and computational trade-offs within the First-Order Model-Agnostic Meta-Learning (FOMAML) framework using an enhanced ResNet-50 backbone. Experiments were conducted on a 3-way classification scenario with 1-shot, 5-shot, and 10-shot configurations. Model evaluation was performed using accuracy, precision, recall, and F1-score, while learning stability was analyzed using standard deviation (Std) and coefficient of variation (CV). The experimental results show that a chili-domain pretrained initialization consistently yields better performance than random initialization. Accuracy reached 95.33%, 95.60%, and 95.94% in the 1-shot, 5-shot, and 10-shot scenarios, respectively an increase of 17.20, 12.61, and 17.49 percentage points compared to random initialization. In terms of stability, the CV values decreased to 1.00%, 0.59%, and 1.03%, compared to 1.10%, 3.66%, and 2.42% with random initialization. These performance improvements were achieved with relatively small differences in training time 0.056 minutes, 0.170 minutes, and 0.876 minutes for the 1-shot, 5-shot, and 10-shot scenarios, respectively. Domain-specific pretrained initialization produces more relevant initial feature representations, thereby improving the effectiveness and stability of FOMAML adaptation while maintaining computational requirements comparable to those of random initialization
subject: Articles
  topic: section
subject: Chili leaf disease classification
  topic: keywords
subject: FOMAML
  topic: keywords
subject: Meta-learning
  topic: keywords
subject: Few-shot learning
  topic: keywords
subject: Pretrained initialization
  topic: keywords
creator: Rizal Amegia Saputra
creator: Agus Buono
creator: Karlisa Priandana
creator: Samsuzana Abd Aziz
creator: Muhamad Syukur
rights: Copyright (c) 2026 Rizal Amegia Saputra, Agus Buono, Karlisa Priandana, Samsuzana Abd Aziz, Muhamad Syukur https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-01
publication date: 2026-07-01
starting page: 94
ending page: 103
doi: 10.31294/co-science.v6i2.13101

Comparative Analysis of Machine Learning Models for Burnout Prediction in Generation-Z
type: item
description: Burnout has become an increasingly prevalent mental health issue among Generation Z due to the interaction of psychological and behavioral factors in a highly digitalized environment. This study aims to predict burnout risk levels using a multi-class machine learning classification approach. The research follows the Cross-Industry Standard Process for Data Mining (CRISP-DM), encompassing data understanding, preprocessing, modeling, and evaluation. A synthetic dataset containing 10,000 records and 22 psychological, behavioral, and lifestyle attributes was used to classify burnout risk into three categories: low, medium, and high. To address class imbalance and ensure reliable performance estimation, Stratified K-Fold cross-validation was employed. Logistic Regression was implemented as a baseline linear model, while Random Forest represented a non-linear approach. Experimental results demonstrate that Random Forest achieved the best performance, obtaining a macro F1-score of 0.988 and outperforming Logistic Regression in multi-class burnout prediction. Feature importance analysis further revealed that psychological variables, particularly the wellbeing index and anxiety score, contributed more substantially to burnout prediction than behavioral variables such as screen time. These findings indicate that internal psychological conditions are stronger predictors of burnout risk than external digital behaviors. This study provides a comparative evaluation of linear and non-linear machine learning models in an imbalanced multi-class setting and offers an interpretable framework to support data-driven strategies for early burnout detection and mental health intervention.
subject: Articles
  topic: section
subject: Class Imbalance
  topic: keywords
subject: CRISP-DM
  topic: keywords
subject: Generation Z
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Mental Health
  topic: keywords
creator: Anis Masruriyah
creator: Sanggi Bayu Ardika
creator: Ade Hikma Tiana
creator: Budi Arif Dermawan
rights: Copyright (c) 2026 Anis Masruriyah, Sanggi Bayu Ardika, Ade Hikma Tiana, Budi Arif Dermawan https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-31
publication date: 2026-07-31
starting page: 164
ending page: 173
doi: 10.31294/co-science.v6i2.13081

Enhancing IPPEKas Digital Transformation for Effective Circular Economy Management
type: item
description: Achieving successful digital transformation in community-based circular economy organizations requires more than the deployment of digital technologies. Sustainable organizational outcomes depend on the effective interaction of technological capability, organizational readiness, and user engagement. This study evaluates the effectiveness of the IPPEKas information system by integrating the DeLone and McLean Information Systems Success Model, the Human–Organization–Technology Fit (HOT-Fit) framework, and System Usability into a unified analytical model. Data were collected from 100 active IPPEKas users and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The empirical findings reveal that Information Quality, Organizational Support, and System Usability significantly enhance Net Benefits, whereas System Quality contributes indirectly through its positive influence on System Usability. These findings indicate that organizational value is created primarily through high-quality information and effective user interaction rather than through technical excellence alone. The proposed integrated framework advances information systems success research by combining technological, human, and organizational dimensions into a comprehensive evaluation model. From a practical perspective, the findings provide actionable recommendations for strengthening information governance, encouraging sustained system utilization, and improving organizational support to maximize the long-term value of digital transformation in community-based circular economy organizations.
subject: Articles
  topic: section
subject: digital transformation
  topic: keywords
subject: HOT-Fit
  topic: keywords
subject: DeLone and McLean
  topic: keywords
subject: system usability
  topic: keywords
subject: circular economy
  topic: keywords
creator: Lis Saumi Ramdhani
creator: Yusti Farlina
creator: Andi Riyanto
creator: Mochamad Wahyudi
creator: Lise Pujiastuti
creator: Rizal Amegia Saputra
rights: Copyright (c) 2026 Lis Saumi Ramdhani, Yusti Farlina, Andi Riyanto, Mochamad Wahyudi, Lise Pujiastuti, Rizal Amegia Saputra https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-31
publication date: 2026-07-31
starting page: 174
ending page: 183
doi: 10.31294/co-science.v6i2.12794

Collaborative Approaches to Enhancing Smart Vehicles Cybersecurity Through AI-Driven Threat Detection
type: item
description: This paper thoroughly investigates collaborative approaches to enhancing smart vehicles' related cybersecurity through AI-driven threat detection. As connected and automated vehicles (CAVs) become rapidly in demand, new vulnerabilities emerge alongside technological progress. We explored how integration of 5G networks, blockchain system, and quantum computing can address these security related challenges. Our study emphasizes the critical role of intrusion detection systems (IDS), AI-based pattern techniques, and interdisciplinary collaboration across academia, industry, and private sector. We present a roadmap incorporating secure hardware/software stacks and advanced threat intelligence to mitigate cybersecurity threats in autonomous vehicles. We address these challenges, by proposing a multi-layer AI-driven cybersecurity architecture by integrating in-vehicle anomaly detection, cloud-based correlation, and privacy-preserving federated learning. We validated the framework by using a hybrid simulation and edge-device testbed environment. Our results shows improved detection performance (F1-score: 0.97), as well as; enhanced adversarial robustness (89% under FGSM attack), and sub-50 ms real-time response capability while maintaining data privacy through local model training.
subject: Articles
  topic: section
subject: Cybersecurity in auto
  topic: keywords
subject: AI driven threat detection
  topic: keywords
subject: wsn
  topic: keywords
subject: wi-fi
  topic: keywords
subject: SMART Vehicle
  topic: keywords
creator: Syed Atif Ali
creator: Salwa Din
rights: Copyright (c) 2026 Syed Atif Ali, Salwa Din https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-21
publication date: 2026-07-21
starting page: 124
ending page: 133
doi: 10.31294/co-science.v6i2.12156

Performance of Distance Metrics in SMOTE for Binary Imbalanced Classification
type: item
description: Skewed class distribution continues to be one of the central obstacles in binary classification, since a learning model tends to lean toward the dominant class and consequently overlooks observations belonging to the under-represented class. The purpose of this research is to examine how the choice of distance measure inside SMOTE, specifically Euclidean, Manhattan, Chebyshev, and Hamming, affects predictive quality on imbalanced binary data. Ten publicly available binary datasets drawn from the KEEL repository, whose imbalance ratios span from 1.86 up to 15.80, were used in the experiment. Every dataset was preprocessed and partitioned into 80% for training and 20% for testing; oversampling with SMOTE was carried out on the training portion only, after which four learners, namely Naive Bayes, Decision Tree, Logistic Regression, and k-Nearest Neighbor, were assessed. Model quality was judged through the Matthews Correlation Coefficient (MCC) together with the G-Mean, as these two indicators describe imbalanced performance more faithfully than plain accuracy. The comparison revealed that pairing Euclidean-based SMOTE with Logistic Regression yielded the strongest average scores (MCC = 0.72; G-Mean = 0.79); Manhattan-based SMOTE reached its top MCC again with Logistic Regression (MCC = 0.68) and its top G-Mean with the Decision Tree (G-Mean = 0.79); Chebyshev-based SMOTE delivered the best overall combination together with the Decision Tree (MCC = 0.74; G-Mean = 0.84); and Hamming-based SMOTE performed best alongside Logistic Regression (MCC = 0.73; G-Mean = 0.81). Taken together, these outcomes suggest that the distance function chosen within SMOTE shapes the quality of the generated synthetic points and, in turn, the behavior of the trained classifier.
subject: Articles
  topic: section
subject: Binary Imbalanced Classification
  topic: keywords
subject: Distance Metrics
  topic: keywords
subject: SMOTE
  topic: keywords
subject: MCC
  topic: keywords
subject: G-Mean
  topic: keywords
creator: Fandi Yulian Pamuji
creator: Luthfi Indana
creator: Mohammad Dwi Irfan Affandi
rights: Copyright (c) 2026 Fandi Yulian Pamuji, Luthfi Indana, Mohammad Dwi Irfan Affandi https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-22
publication date: 2026-07-22
starting page: 134
ending page: 143
doi: 10.31294/co-science.v6i2.12543

Cross-Project Defect Prediction on AEEEM Using SMOTE–Tomek and Ensemble learning
type: item
description: Cross-project defect prediction (CPDP) aims to predict software defects in a target project using data from other projects. However, most existing CPDP approaches rely on complex frameworks, such as transfer learning or sophisticated multi-source integration, making them difficult to reproduce and apply in practical software engineering environments. This study proposes a simple yet integrated hybrid preprocessing pipeline consisting of feature normalization, Principal Component Analysis (PCA), SMOTE–Tomek balancing, and decision threshold tuning to improve CPDP performance on the AEEEM dataset. Experiments were conducted under both single-source and multi-source CPDP scenarios using Random Forest (RF) and Support Vector Machine (SVM) classifiers. Performance was evaluated using the F1 Score and the Area Under the Curve (AUC). The experimental results demonstrate that the proposed approach improves prediction performance, particularly under the multi-source CPDP scenario. Compared with the more complex MSCPDP approach, the proposed method achieved a higher F1-score on four out of five target projects and consistently outperformed MSCPDP on all five projects in terms of AUC. Furthermore, the experimental analysis indicates that decision threshold tuning contributed more significantly to performance improvement than class balancing alone. In contrast, the combination of threshold tuning and SMOTE–Tomek yielded the best overall performance. These findings provide empirical evidence that a simple, reproducible preprocessing pipeline can effectively improve CPDP performance without requiring complex learning frameworks.
subject: Articles
  topic: section
subject: CPDP
  topic: keywords
subject: Imbalanced data
  topic: keywords
subject: Hybrid preprocessing
  topic: keywords
subject: SMOTE–Tomek
  topic: keywords
subject: Threshold tuning
  topic: keywords
creator: Fina Sifaul Nufus
creator: Yan Rianto
rights: Copyright (c) 2026 Fina Sifaul Nufus, Yan Rianto https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-16
publication date: 2026-07-16
starting page: 104
ending page: 113
doi: 10.31294/co-science.v6i2.12521

Integrating MLOps for Monitoring, Drift Detection, and Adaptive Retraining in Sensor Systems
type: item
description: Machine learning models deployed in sensor-based production environments are prone to performance degradation due to evolving data distributions, commonly known as data drift. In gas sensor systems, such drift often arises from environmental variability and sensor aging, which can significantly reduce predictive reliability if left unaddressed. This study presents an integrated Machine Learning Operations (MLOps) framework that combines performance monitoring, distribution-based drift detection, and adaptive retraining within a unified production pipeline. Experiments are conducted using the Gas Sensor Array Drift Dataset, organized into sequential batches to emulate real-world deployment conditions. Data drift is quantified using Population Stability Index (PSI) and Kullback–Leibler Divergence (KL), which serve as decision thresholds for triggering retraining. The proposed adaptive retraining strategy is systematically compared with baseline (no retraining) and periodic retraining approaches. The results indicate that the adaptive strategy maintains more stable performance across data batches while minimizing unnecessary retraining processes. Additionally, the use of containerization and experiment tracking ensures reproducibility and supports full lifecycle traceability. Overall, this study demonstrates that integrating MLOps practices into sensor-based machine learning systems is essential for improving robustness and ensuring long-term operational sustainability in dynamic environments.
subject: Articles
  topic: section
subject: Adaptive Retraining
  topic: keywords
subject: Data Drift Detection
  topic: keywords
subject: Gas Sensor Data
  topic: keywords
subject: Machine Learning Operations (MLOps
  topic: keywords
subject: Production Machine Learning
  topic: keywords
creator: Novia Heriyani
creator: Nita Merlina
rights: Copyright (c) 2026 Novia Heriyani, Nita Merlina https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-21
publication date: 2026-07-21
starting page: 154
ending page: 163
doi: 10.31294/co-science.v6i2.12409

Feature Comparison of RN-SMOTE and DBSCAN-SMOTE for Handling Imbalanced Data
type: item
description: HIV classification using machine learning is often challenged by severe class imbalance, which may reduce predictive reliability and bias classification models toward the majority class. This study aims to compare the effectiveness of two hybrid oversampling techniques, namely SMOTE-DBSCAN and RN-SMOTE, in improving HIV classification performance using an Indonesian HIV dataset. The dataset was obtained from a Non-Governmental Organization (NGO) and initially consisted of 707,389 records with 90 attributes. After data preprocessing, 148,408 validated records with 30 attributes were retained, comprising 135,739 non-reactive and 12,668 reactive HIV cases, indicating a substantial class imbalance. Two feature configurations derived from previous HIV studies were evaluated using SMOTE-DBSCAN and RN-SMOTE. To prevent data leakage, the dataset was partitioned using a stratified 80:20 train-test split before oversampling, while model validation was performed using 10-fold cross-validation on the training data. The balanced datasets were subsequently evaluated using eight classification algorithms. Experimental results demonstrate that RN-SMOTE consistently outperformed SMOTE-DBSCAN across both feature configurations, while Random Forest achieved the best overall predictive performance. Although SMOTE-DBSCAN required less computational time during the balancing process, RN-SMOTE produced more robust classification performance by generating cleaner and more representative minority-class samples. These findings demonstrate the effectiveness of noise-aware hybrid oversampling strategies for improving HIV classification on highly imbalanced datasets and provide practical insights for developing reliable machine learning models to support HIV surveillance and public health decision-making
subject: Articles
  topic: section
subject: HIV classification
  topic: keywords
subject: class imbalance
  topic: keywords
subject: hybrid oversampling
  topic: keywords
subject: RN-SMOTE
  topic: keywords
subject: machine learning
  topic: keywords
creator: Rias Akmal Sembiring
creator: Wawan Gunawan
rights: Copyright (c) 2026 Rias Akmal Sembiring, Wawan Gunawan https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-16
publication date: 2026-07-16
starting page: 114
ending page: 123
doi: 10.31294/co-science.v6i2.12250

Adventure Game Development for Environmental Conservation Using Game Development Life Cycle
type: item
description: The rapid development of information and communication technology has driven innovation in various fields, including digital entertainment through video games. This research develops an educational adventure game themed on environmental conservation titled Green Solaris, designed as an interactive medium to raise awareness about environmental issues among children and teenagers. The game adopts a 3D isometric format with a pixelated visual style, focusing on four main themes: Clean Water and Sanitation, Affordable and Clean Energy, Climate Action, and Life on Land. The development process employs the Game Development Life Cycle (GDLC) methodology combined with Agile-SCRUM approach, encompassing six main phases: initiation, pre-production, production, testing, beta, and release. The game features core mechanics including exploration, interaction with NPCs, waste collection and recycling systems, point-based upgrades, and environmental restoration missions across multiple islands. Testing results using Likert scale instruments show that 90% of respondents rated the game as "Very Satisfying" and 10% as "Quite Satisfying" with no negative assessments, indicating the game's potential as an effective educational medium. Green Solaris has been successfully published on the itch.io platform and is accessible to the public, demonstrating that video games can serve as an innovative and engaging alternative learning tool for instilling environmental awareness through creative, interactive, and educational digital experiences.
subject: Articles
  topic: section
subject: educational game
  topic: keywords
subject: environmental conservation
  topic: keywords
subject: GDLC
  topic: keywords
subject: 3D isometric
  topic: keywords
subject: pixelate shader
  topic: keywords
creator: Nisa Rizqiya Fadhliana
creator: Naufal Andrian
creator: Diva Rajestiadi
creator: M. Bintang Kurninawan
rights: Copyright (c) 2026 Nisa Rizqiya Fadhliana, Naufal Andrian, Diva Rajestiadi, M. Bintang Kurninawan https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-31
publication date: 2026-07-31
starting page: 184
ending page: 193
doi: 10.31294/co-science.v6i2.12173

Integrating LLM Intent-Aware Approach for Enhancing the Quality of Bundle Recommendations
type: item
description: Digital grocery shopping has shifted consumer patterns toward multi- item purchasing. While bundle recommendation systems address this, existing models relying on product ID co-occurrence fail to capture the mixed shopping intentions inherent in consumer baskets. To address these limitations, we propose Intent-aware Bundle Recommendation (IABR), a framework shifting from structural matching to semantic intent reasoning. IABR utilizes Large Language Models to decompose baskets into coherent sub-packages. Subsequently, we fine- tuned Gemma-3-4B using Parameter-Efficient Fine-Tuning to generate narrative intent descriptions regarding short-term shopping missions and long-term sustainable user lifestyles. These intents are encoded via Sentence Transformers for semantic retrieval. Extensive testing on the Instacart dataset demonstrates IABR’s significance against baselines like BGCN. Our IABR method achieved a Recall@20 of 28.15% while improving diversity scores by 12% (p < 0.05). This validates that generative semantic modeling enables accurate next- bundle predictions, effectively balancing precision with thematic variation and personalization.
subject: Articles
  topic: section
subject: Bundle Recommendation
  topic: keywords
subject: Instacart
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Intent-aware
  topic: keywords
subject: Parameter-Efficient Fine-Tuning
  topic: keywords
creator: Andy Maulana Yusuf
creator: Musrinah Musrinah
rights: Copyright (c) 2026 Andy Maulana Yusuf, Musrinah Musrinah https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-21
publication date: 2026-07-21
starting page: 144
ending page: 153
doi: 10.31294/co-science.v6i2.12101

UML-Based Design and Kano Evaluation of an Augmented Reality Library Application
type: item
description: The application of Augmented Reality (AR) technology in library services has the potential to enhance the user experience through more interactive and engaging information presentations. This study aims to create an augmented reality (AR) book search system that uses marker-based tracking to help students find references at the Dhyana Pura University Library, while also measuring how satisfied users are using the Kano Model. The system was created using the waterfall approach for developing software and used Unified Modeling Language (UML) to outline how activities flow and how different parts of the system connect. The satisfaction test involved 54 respondents, with attribute analysis using the Kano Model and Customer Satisfaction Coefficient (CSC). The results of the study show that most of the application features fall into the Attractive category, which indicates that these features provide added value and a positive experience for users. The combination of UML-based system design and evaluation with the Kano Model was successful in creating a library application that is functional, easy to use, and focused on user satisfaction.
subject: Articles
  topic: section
subject: Augmented Reality
  topic: keywords
subject: Kano Model
  topic: keywords
subject: Library
  topic: keywords
subject: Marker-Based Tracking
  topic: keywords
subject: UML
  topic: keywords
creator: Gerson Feoh
creator: Ni Putu Ria Anggreni
rights: Copyright (c) 2026 Gerson Feoh, Ni Putu Ria Anggreni https://creativecommons.org/licenses/by-sa/4.0
date: 2026-01-28
publication date: 2026-01-28
starting page: 84
ending page: 93
doi: 10.31294/co-science.v6i1.11637

Performance Evaluation of YOLOv8 for Railway Switching Operation Safety Monitoring
type: item
description: Safety in railway shunting operations requires continuous monitoring of train distance and speed to reduce the risk of operational accidents. In practice, shunting activities are still highly dependent on manual observation and verbal communication, while the performance of vision based safety systems under real operational conditions remains uncertain. In addition, comprehensive performance evaluations of deep learning based object detection models in real shunting environments, particularly under different hardware capabilities and lighting conditions, are still limited. This study aims to evaluate the performance of the YOLOv8 algorithm for real-time distance and speed monitoring during railway shunting operations. The system was tested using a camera-based detection approach under different processor configurations, namely an internal CPU and an RTX GPU, and under morning, daytime, and nighttime lighting conditions. System performance was evaluated based on accuracy, precision, and real-time detection capability across these conditions. The results show that the system achieved an average accuracy of 87.32% when operating on a CPU which increased to 91.30% when using a GPU. Optimal performance was observed under adequate daylight conditions, while reduced lighting led to a decline in performance, particularly on CPU-based processing. These findings indicate that hardware configuration and lighting conditions play a critical role in determining the reliability of YOLOv8-based safety monitoring systems for railway shunting operations.
subject: Articles
  topic: section
subject: safety
  topic: keywords
subject: shunting
  topic: keywords
subject: railway
  topic: keywords
subject: YOLOv8
  topic: keywords
subject: real-time
  topic: keywords
creator: Aulya Anggita Putri Selendra
creator: Teguh Arifianto
creator: Fathurrozi Winjaya
rights: Copyright (c) 2026 Aulya Anggita Putri Selendra, Teguh Arifianto, Fathurrozi Winjaya https://creativecommons.org/licenses/by-sa/4.0
date: 2026-01-27
publication date: 2026-01-27
starting page: 75
ending page: 83
doi: 10.31294/co-science.v6i1.11674

Developing an Efficient PPDB System Integrating Payment Gateway and Secure Exams
type: item
description: The New Student Admission (PPDB) process is a critical gateway for educational institutions. However, SMKS Jakarta 1 Pondok Kopi faced a significant 35.4% decline in applicants between 2021 and 2024 due to an inefficient and fragmented PPDB system. Key issues included an outdated information platform, a complex multi-platform registration flow, error-prone manual payment verification, and an insecure online exam system. This research aimed to develop a web-based integrated PPDB information system that consolidates all stages: registration, digital payments via a payment gateway, an online examination with anti-cheating features, and centralized announcements. Using the Waterfall development model, the system was evaluated in two stages. Black Box testing confirmed that all functionalities performed as expected. Furthermore, User Acceptance Testing (UAT) revealed a very high acceptance rate, achieving average scores of 4.48 out of 5.00 from student proxies and 4.50 from the committee, yielding a final interpretation of “Very Good.” The practical implication of this system is a significant improvement in administrative efficiency, data accuracy, and modernization of the school's services. This research contributes by providing and validating an effective integrated PPDB system model, proving it to be a comprehensive solution for vocational education institutions.
subject: Articles
  topic: section
subject: PPDB Information System
  topic: keywords
subject: Waterfall Method
  topic: keywords
subject: Payment Gateway
  topic: keywords
subject: Online Exam
  topic: keywords
creator: Muhammad Rohman Syah
creator: Deny Novianti
creator: Roynaldy Rosdiyanto
rights: Copyright (c) 2026 Muhammad Rohman Syah, Deny Novianti M.Kom, Roynaldy Rosdiyanto https://creativecommons.org/licenses/by-sa/4.0
date: 2026-01-01
publication date: 2026-01-01
starting page: 48
ending page: 56
doi: 10.31294/co-science.v6i1.10103

Optimization of Crop Recommendation Model Using Ensemble Learning Techniques for Multiclass Classification
type: item
description: Crop recommendation systems play a crucial role in modern agriculture by helping farmers make data-driven decisions to maximize yield, optimize resource use, and ensure sustainable farming practices. By analyzing environmental and soil parameters, these systems can suggest the most suitable crops for specific conditions, reducing the risks of crop failure and improving overall productivity. This study evaluates the performance of five ensemble learning algorithms—Random Forest, Extra Trees, CatBoost, XGBoost, and LightGBM—for multiclass classification in a crop recommendation system. All models achieved high accuracy above 98%, with Random Forest demonstrating the best and most stable performance. The feature importance analysis revealed that climatic factors, particularly rainfall and humidity, contributed the most to prediction outcomes, followed by macronutrients such as potassium, phosphorus, and nitrogen. In contrast, temperature and soil pH showed relatively lower influence. These findings highlight the dominance of climatic factors over soil chemical properties and demonstrate the capability of ensemble learning methods to capture complex data patterns. Random Forest is recommended as the primary model to support more effective land management and crop cultivation strategies.
subject: Articles
  topic: section
subject: Ensemble Learning
  topic: keywords
subject: Classification
  topic: keywords
subject: Crop Recommendation
  topic: keywords
subject: Random Forest Algorithm
  topic: keywords
subject: Multiclass
  topic: keywords
creator: Siti Marlina
creator: Titik Misriati
creator: Riska Aryanti
rights: Copyright (c) 2026 Siti Marlina, Titik Misriati, Riska Aryanti https://creativecommons.org/licenses/by-sa/4.0
date: 2026-01-01
publication date: 2026-01-01
starting page: 12
ending page: 18
doi: 10.31294/co-science.v6i1.10044

Saliency-Enhanced Deep Learning Framework for Stain-Robust White Blood Cell Segmentation and Classification
type: item
description: Accurate segmentation and classification of white blood cell (WBC) are essential for clinical hematology, yet remain challenging due to staining variability, complex backgrounds, and class imbalance. This study introduces an explainable, saliency-enhanced deep learning framework designed to achieve stain-robust leukocyte analysis. The framework integrates a saliency-driven preprocessing module, a lightweight EfficientSwin hybrid backbone, and a ResNeXt-CC–inspired cross-layer feature fusion block to capture complementary fine-grained and global features. A multi-task head jointly performs WBC segmentation and subtype classification, while a saliency-alignment loss enforces consistency between learned attention and saliency priors, providing training-time interpretability rather than post-hoc visualization alone. SG-CLDFF was evaluated on three public datasets (BCCD, LISC, ALL-IDB) and further tested under cross-stain and cross-dataset conditions. The framework achieved 95.8% accuracy, 0.94 F1-score, and 0.82 IoU, improving over strong CNN and transformer baselines. Ablation studies confirmed that both saliency preprocessing and cross-layer fusion contribute independently to performance, with saliency alignment yielding ≥2 IoU improvement in cross-stain scenarios (p < 0.05). Qualitative results using saliency maps and Grad-CAM demonstrate focused attention on diagnostically meaningful regions. These findings validate SG-CLDFF as a robust, interpretable, and stain-resilient solution for automated WBC analysis, offering a practical foundation for deployment in digital hematology workflows.
subject: Articles
  topic: section
subject: White blood cell Segmentation
  topic: keywords
subject: Stain-Robust Deep Learning
  topic: keywords
subject: Saliency-Guided Feature Fusion
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Biomedical Image Analysis
  topic: keywords
creator: Mehdi Zekriyapanah Gashti
creator: Mostafa Mohammadpour
creator: Ghasem Farjamnia
rights: Copyright (c) 2026 Mehdi Zekriyapanah Gashti, Mostafa Mohammadpour, Ghasem Farjamnia https://creativecommons.org/licenses/by-sa/4.0
date: 2026-01-01
publication date: 2026-01-01
starting page: 19
ending page: 29
doi: 10.31294/co-science.v6i1.10182

SIBI-Based Gesture Recognition System Using Random Forest for Hearing-Impaired Communication
type: item
description: Individuals with hearing impairments often face communication barriers when interacting with people unfamiliar with sign language. One officially recognized sign system in Indonesia is the Indonesian Sign System (SIBI), which conveys meaning through hand gestures. Most existing sign language recognition studies focus on single-hand gestures, limiting expressiveness. This study proposes a two-hand gesture recognition system based on digital image processing to translate SIBI gestures into alphabetic letters, while additional gestures enable text control functions. The dataset consists of 29 gesture classes with 1,000 images per class, totaling 29,000 images, and is divided into training and testing sets using a train–test split. A Random Forest classifier is employed to handle high-dimensional landmark coordinate data. Experimental results demonstrate a classification accuracy of 99.97%. The system is implemented as a real-time, user-friendly application. Although high accuracy is achieved, potential overfitting due to the controlled dataset is identified as a limitation. Future work will focus on improving generalization using more diverse real-world data.
subject: Articles
  topic: section
subject: Hearing Impairment
  topic: keywords
subject: Sign Language
  topic: keywords
subject: SIBI
  topic: keywords
subject: Gesture Recognition
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Andre Pratama
creator: Ahmad Jurnaidi Wahidin
rights: Copyright (c) 2026 Andre Pratama, Ahmad Jurnaidi Wahidin https://creativecommons.org/licenses/by-sa/4.0
date: 2026-01-10
publication date: 2026-01-10
starting page: 66
ending page: 74
doi: 10.31294/co-science.v6i1.10570

Analyzing Public Sentiment Toward Makanan Bergizi Gratis Program Using Machine Learning
type: item
description: Makanan Bergizi Gratis (MBG) program is a strategic initiative of the Indonesian government to improve the nutritional quality of schoolchildren. This research seeks to examine public sentiment regarding the MBG program by leveraging 10,000 tweets obtained from Kaggle. The method used combines Natural Language Processing (NLP) and Machine Learning approaches, several algorithms such as Logistic Regression, Support Vector Machine (SVM), Random Forest, Naive Bayes, XGBoost, and LightGBM were tested to compare classification performance. The dataset contains a collection of public reviews categorized into three sentiment classes: positive, negative, and neutral. The analysis process includes text cleaning, tokenization, stopword removal, and stemming to obtain a cleaner text representation. Text features were then extracted using the Term Frequency–Inverse Document Frequency (TF-IDF) method. The results showed that the Logistic Regression 97% with an F1-score of 0.9552 models showed the most optimal performance. Sentiment analysis revealed 65% positive responses, 25% neutral, and 10% negative, with the dominant keywords being “nutrisi,” “sehat,” “anak sekolah,” and “gratis.” The results visualization, in the form of a Word Cloud and a bar chart, indicate that public opinion tends to be positive towards the implementation of the MBG program, particularly regarding improving the nutrition of schoolchildren. This research is expected to provide input for policymakers in evaluating public perceptions of the implementation of food-based social programs.
subject: Articles
  topic: section
subject: MBG
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Natural Language Processing
  topic: keywords
creator: Musriatun Napiah
creator: Sujiliani Heristian
creator: Mugi Raharjo
creator: Rachmat Adi Purnama
rights: Copyright (c) 2026 Musriatun Napiah, Sujiliani Heristian, Mugi Raharjo, Rachmat Adi Purnama https://creativecommons.org/licenses/by-sa/4.0
date: 2026-01-01
publication date: 2026-01-01
starting page: 30
ending page: 38
doi: 10.31294/co-science.v6i1.10445

Analysis of Student Academic Performance Using Random Forest and Support Vector Machines
type: item
description: Assessing student academic performance objectively remains a challenge at SMP Negeri 16 Bogor due to diverse internal and external factors in student records. This study aims to compare the classification performance of the Random Forest and Support Vector Machine (SVM) algorithms using a dataset of 403 students containing demographic, socioeconomic, and school-related attributes. Although the attributes are not traditional academic indicators (e.g., assignment or exam scores), they are used to explore whether non-academic features can contribute to predictive models. Following data preprocessing—handling missing values, encoding categorical variables, and managing class imbalance—both algorithms were evaluated using accuracy, precision, recall, and confusion matrix analysis. Results show that SVM outperforms Random Forest with 78.00% accuracy, 89.98% precision, and 70.24% recall. These findings indicate that SVM is more robust for imbalanced classification tasks and can provide useful insights even when academic-performance labels are predicted from non-academic attributes.
subject: Articles
  topic: section
subject: Academic Performance
  topic: keywords
subject: SVM
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Classification
  topic: keywords
subject: Confusion Matrix
  topic: keywords
creator: Galih Mifta Agung
creator: Robi Aziz Zuama
creator: Eko Setia Budi
rights: Copyright (c) 2026 Galih Mifta Agung, Robi Aziz Zuama, Eko Setia Budi https://creativecommons.org/licenses/by-sa/4.0
date: 2026-01-01
publication date: 2026-01-01
starting page: 57
ending page: 65
doi: 10.31294/co-science.v6i1.10123

Development of Web and Mobile Health Services Information System Using Waterfall Method
type: item
description: The increasing demand for fast, accurate, and efficient healthcare services has encouraged the development of integrated information systems. This study aims to design and develop a Health Service Information System (SILAKES) based on web and mobile platforms at the Oputa Yi Koo Heart and Blood Vessel Hospital (RSJPD) in Kendari. The system is developed to facilitate patients in accessing doctor schedules, taking queue numbers online, consulting services, checking blood stock availability, and filling out satisfaction surveys. The development process adopts the waterfall (linear sequential model) method, which consists of five stages (requirement definition, system and software design, implementation and unit testing, integration and system testing, operation and maintenance). The results of this study show that SILAKES improves the efficiency of hospital staff and provides convenience for patients to access health services without space and time constraints. The implementation of this system also contributes to the digitalization of hospital services and supports the enhancement of overall healthcare service quality
subject: Articles
  topic: section
subject: Health Information System
  topic: keywords
subject: Codeigniter
  topic: keywords
subject: Web
  topic: keywords
subject: Mobile
  topic: keywords
subject: Waterfall
  topic: keywords
creator: Yoga Pratrian
creator: Yayan Hendriyan
creator: Ahmad Hafidzul Kahfi
rights: Copyright (c) 2026 Yoga Pratrian, Yayan Hendriyan, Ahmad Hafidzul Kahfi https://creativecommons.org/licenses/by-sa/4.0
date: 2026-01-01
publication date: 2026-01-01
starting page: 39
ending page: 47
doi: 10.31294/co-science.v6i1.10072

Design and Development of IdentifiKu: A Web-Based Diagnostic Model for Differentiated Learning
type: item
description: This study aims to develop and evaluate IdentifiKu, a web-based diagnostic assessment platform designed to support differentiated learning within the Kurikulum Merdeka framework in Indonesia. Specifically, the research seeks to bridge the gap in existing assessment platforms that predominantly focus on cognitive dimensions by integrating cognitive and non-cognitive domains—learning styles, personality traits, and multiple intelligences—into a unified scoring model. The platform was developed using a Design and Development Research (DDR) approach combined with the Waterfall Software Development Life Cycle (SDLC), encompassing requirements analysis, system design, implementation, testing, and deployment. The architecture adopts a three-tier client–server model, with a Laravel-based application layer and a MySQL database optimized to the third normal form. Performance evaluation involved functional testing and user feedback from twelve teachers across diverse subject areas. Quantitative results indicated that the system met or exceeded all operational benchmarks, including an average page load time of 2.4 seconds, 99.8% uptime, 100% scoring accuracy, and a System Usability Scale (SUS) score of 85.3. Teachers reported that the platform’s comprehensive learner profiles facilitated targeted instructional strategies, improved student engagement, and streamlined assessment processes. This research contributes a scalable, pedagogically aligned model for integrating multidimensional diagnostics into differentiated learning practices, which may be adapted to other educational contexts to enhance data-driven instruction.
subject: Articles
  topic: section
subject: Diagnostic assessment
  topic: keywords
subject: differentiated learning
  topic: keywords
subject: web-based platform
  topic: keywords
subject: educational technology
  topic: keywords
subject: learner profiling
  topic: keywords
creator: Muhammad Noor Hasan Siregar
creator: Yulia Rizki Ramadhani
creator: Yusra Fadhillah
creator: Yoviansyah Rizki Pratama
rights: Copyright (c) 2026 Muhammad Noor Hasan Siregar, Yulia Rizki Ramadhani, Yusra Fadhillah, Yoviansyah Rizki Pratama https://creativecommons.org/licenses/by-sa/4.0
date: 2026-01-01
publication date: 2026-01-01
starting page: 1
ending page: 11
doi: 10.31294/co-science.v6i1.9762

Sistem Informasi Persediaan Obat Pada PT Mega Esa Farma Dengan Metode Extreme Programming
type: item
description: PT. Mega Esa Farma is one of the leading pharmaceutical companies in Indonesia, facing significant challenges in managing inventory information. The main issues lie in recording incoming and outgoing goods, final stock, and other inventory-related problems. These obstacles result in slow data processing and delays in providing accurate information, ultimately hindering the efficiency of product sales and distribution. This study aims to develop a web-based inventory information system capable of efficiently managing, processing, organizing, and storing data, while presenting accurate and real-time information. The system development adopts the Extreme Programming (XP) methodology, which emphasizes short iterations, automated testing, simple design, and close collaboration between users and developers. System analysis is carried out using the Unified Modeling Language (UML) to visualize the structure and functionality of the system. The implementation results show that the developed system simplifies inventory recording, accelerates information access, and improves data accuracy. It also provides features such as minimum stock notifications and real-time reporting, which were not available in the previous manual system. The main contribution of this study lies in applying the XP methodology in the context of pharmaceutical inventory system development, along with the integration of real-time reporting and stock alert features as a form of novelty. This system is expected to serve as an innovative solution to enhance inventory management efficiency in the pharmaceutical industry, particularly at PT. Mega Esa Farma.
subject: Articles
  topic: section
subject: Inventory
  topic: keywords
subject: UML
  topic: keywords
subject: Information Systems
  topic: keywords
subject: Web
  topic: keywords
creator: Biktra Rudianto
creator: Sheren Prisscilya
rights: Copyright (c) 2025 Biktra Rudianto, Sheren Prisscilya https://creativecommons.org/licenses/by-sa/4.0
date: 2025-07-30
publication date: 2025-07-30
starting page: 153
ending page: 162
doi: 10.31294/co-science.v5i2.8973

Perancangan Sistem Informasi Surat Administrasi Penduduk Elektronik Menggunakan Metode Scrum
type: item
description: Letter management is a routine activity carried out at the Kerta Jaya Village office in Banyuasin. Currently, the administration of administrative documents in this village has been done manually, resulting in long queues. This study aims to develop an information system for managing electronic administrative documents for residents, such as domicile certificates, death certificates, and single-marriage certificates. This research is a research and development (R&D) study using the Scrum method. The Scrum method divides tasks into small parts (sprints). In each sprint, researchers focus on completing several features that can be used immediately. The Scrum method was used because it has a structured framework with sprints that produce functional incremental products. This method is suitable because it allows for rapid adaptation so that application development does not take too long. The result of this study is an information system for managing electronic administrative letters in Kerta Jaya Village, Banyuasin District. From the results of the application's usability aspect, the percentage obtained on 30 respondents was 79.16%, meaning this application is suitable for use by the community.
subject: Articles
  topic: section
subject: Digital civil administration documents
  topic: keywords
subject: research and development
  topic: keywords
subject: scrum
  topic: keywords
creator: Luthfi Indana
creator: Asri Samsiar Ilmananda
rights: Copyright (c) 2025 Luthfi Indana, Asri Samsiar Ilmananda https://creativecommons.org/licenses/by-sa/4.0
date: 2025-07-30
publication date: 2025-07-30
starting page: 143
ending page: 152
doi: 10.31294/co-science.v5i2.9511

Studi Komparatif Algoritma K-Means dan K-Medoids untuk Segmentasi Informasi Kesehatan
type: item
description: In analyzing medical data to support clinical decisions, segmentation of health information plays a crucial role. This study presents a comparative analysis of K-Means and K-Medoids algorithms in clustering Medical Examination data. This evaluation is conducted using two main internal approaches, namely Silhouette Score and Davies-Bouldin Index in measuring the quality of separation as well as cohesion between clusters. The experiment involved varying the number of clusters to determine the optimal configuration of each algorithm. The results show that K-Means provides representative performance and is more stable against data complexity, compared to the K-Medoids algorithm which is only optimal in a small number of clusters. Statistical analysis using one-way ANOVA was applied to test the significance of performance differences between algorithms based on the average Silhouette Score value, yielding an F-value of 4.8594 with a P-value of 0.0447. This indicates that the performance difference between the two algorithms is statistically significant at 5% significance rate. This research confirms the K-Means algorithm for segmenting health data with diverse distributions and is expected to serve as a foundation for the development of more efficient health data classification systems in the future.
subject: Articles
  topic: section
subject: Information Segmentation
  topic: keywords
subject: Clustering
  topic: keywords
subject: K-Means
  topic: keywords
subject: K-Medoids
  topic: keywords
subject: Health Data
  topic: keywords
creator: Muhammad Dwi Ananda
creator: Mardiah Mardiah
creator: Anis Fitri Nur Masruriyah
creator: Karenina Nurmelita Malik
rights: Copyright (c) 2025 Anis Masruriyah, Karenina Nurmelita Malik, Anis Fitri Nur Masruriyah, Mardiah Mardiah https://creativecommons.org/licenses/by-sa/4.0
date: 2025-07-22
publication date: 2025-07-22
starting page: 103
ending page: 112
doi: 10.31294/coscience.v5i2.9207

Penerapan Model Scrum Pada Perancangan Aplikasi Penerimaan Siswa Baru MTS Nu 03 Suradadi
type: item
description: Digital transformation in the education sector demands innovation in service delivery, including the student admission process. This study presents an innovative approach through the application of the Scrum software development model to build a new student admission application at MTs NU 03 Suradadi. Unlike conventional methods, Scrum offers advantages in rapid iteration, adaptability to changing requirements, and close collaboration between the development team and stakeholders. The development process was carried out in stages through Scrum phases such as product backlog, sprint planning, development sprints, and sprint reviews and retrospectives. The result is a web-based application featuring key functionalities such as online registration, document uploads, registration proof printing, and user account management. The implementation results show a significant improvement in administrative efficiency and service accessibility, indicated by a 35% increase in student applicants, a reduction in data verification time from 3 days to 1 day, and a user satisfaction rate of 87%. These findings demonstrate that the application of Scrum not only improves product quality but also has a tangible impact in effectively and sustainably supporting the digitalization of educational services.
subject: Articles
  topic: section
subject: Scrum
  topic: keywords
subject: web application
  topic: keywords
subject: information system
  topic: keywords
subject: student admission
  topic: keywords
subject: MTS NU 03 Suradadi
  topic: keywords
creator: Prawira Diharja
creator: Mia Rosmiati
creator: Mulia Rahmayu
rights: Copyright (c) 2025 Prawira Diharja, Mia Rosmiati, Mulia Rahmayu https://creativecommons.org/licenses/by-sa/4.0
date: 2025-07-22
publication date: 2025-07-22
starting page: 113
ending page: 122
doi: 10.31294/coscience.v5i2.9166

Evaluasi Dan Redesain Antarmuka Website megapolitanpos.com Menggunakan Metode Usability Testing Dan Heuristic Evaluation
type: item
description: Megapolitanpos.com, a Press Council-verified online news portal, faces usability problems such as complex navigation, unorganized layout, and less attractive visual design. This research aims to identify problems, redesign the interface, and improve user satisfaction through Usability Testing and Heuristic Evaluation. From 373 respondents, major problems such as unintuitive navigation, inconsistent news categories, unstructured information, absence of page loading indicators, and lack of user guidance were found. Most of the issues had a severity rating of 2, requiring redesign. The average severity rating obtained was 2.0. Recommendations include improving the category layout, adding loading indicators, tooltips, and arranging information elements logically, while maintaining the blue color as the visual identity. HTML and Bootstrap-based design prototypes designed based on valid and reliable analysis are expected to improve user experience and present information more intuitively.
subject: Articles
  topic: section
subject: Interface Design
  topic: keywords
subject: Heuristic Evaluation
  topic: keywords
subject: Usability Testing
  topic: keywords
subject: Severity Rating
  topic: keywords
creator: Jonathan Tanuwijaya
creator: Arfhan Prasetyo
rights: Copyright (c) 2025 Jonathan Tanuwijaya, Arfhan Prasetyo https://creativecommons.org/licenses/by-sa/4.0
date: 2025-07-30
publication date: 2025-07-30
starting page: 133
ending page: 142
doi: 10.31294/co-science.v5i2.9116

Model Prediksi Risiko Kesehatan Perkotaan Berbasis Lingkungan dengan XGBoost
type: item
description: Poor urban air quality is a major public health concern, especially in highly urbanized areas. This study aims to predict health risks associated with air pollution using machine learning techniques based on environmental variables. The dataset used, Urban Air Quality and Health Impact, contains 1,000 rows and 46 columns, including temperature, humidity, wind speed, dew point, ultraviolet (UV) index, and health risk scores from major U.S. cities. As an improvement over previous studies using linear regression and Random Forest (R-squared 0.89; Mean Squared Error/MSE 0.65), this research implements an optimized Extreme Gradient Boosting (XGBoost) model. The model was fine-tuned using Randomized Search on key hyperparameters and evaluated with an 80:20 data split. It achieved an R-squared of 0.9692 and MSE of 0.0122. Dew point and wind speed were identified as the most influential features. Although synthetic, the dataset reflects environmental patterns similar to Indonesian urban areas. This study does not adopt a text mining framework but instead uses a supervised regression approach based on environmental features. Its main novelty lies in the first application of an optimized XGBoost model using complex variables such as feels-like temperature to estimate urban health risk. Limitations include the absence of real-world validation with Indonesian data and the lack of analysis on interactions between variables
subject: Articles
  topic: section
subject: air quality
  topic: keywords
subject: health risk
  topic: keywords
subject: XGBoost
  topic: keywords
subject: fine-tuning
  topic: keywords
subject: machine learning
  topic: keywords
subject: environmental variables
  topic: keywords
creator: Nanang Prihatin
creator: Muhammad Kahfi Aulia
creator: Eka Utaminingsih
rights: Copyright (c) 2025 Muhammad Kahfi Aulia, Eka Utaminingsih, Nanang Prihatin https://creativecommons.org/licenses/by-sa/4.0
date: 2025-07-11
publication date: 2025-07-11
starting page: 95
ending page: 102
doi: 10.31294/coscience.v5i2.9109

Implementasi Firewall Mikrotik dalam Pembatasan Akses Situs Terlarang di RT/RW Net
type: item
description: The increasing use of the Internet in RT/RW Net environments has brought both positive and negative impacts. One issue is the unrestricted access to inappropriate content, especially among students, such as online games, pornography, and gambling websites. This study aims to restrict internet user access rights in RT/RW Net networks by implementing firewall filters on MikroTik routers. The method used is firewall filtering configuration based on IP, port, and domain restrictions. The results show that the implementation of the firewall filter on MikroTik routers effectively blocks access to restricted sites and improves network security. Tests were carried out before and after configuration, showing the successful blocking of unwanted content. standards.
subject: Articles
  topic: section
subject: firewall
  topic: keywords
subject: rt/rw net
  topic: keywords
subject: filtering
  topic: keywords
subject: mikrotik
  topic: keywords
creator: Hanggoro Aji Al Kautsar
creator: Ricki Sastra
rights: Copyright (c) 2025 Hanggoro Aji Al Kautsar, Ricki Sastra https://creativecommons.org/licenses/by-sa/4.0
date: 2025-07-23
publication date: 2025-07-23
starting page: 123
ending page: 132
doi: 10.31294/coscience.v5i2.8897

Sistem Manajemen Proyek Berbasis Website Dengan Metode Kanban
type: item
description: The advancement of digital technology has created a growing demand for systems that support work effectiveness, particularly in project management. The Digital Learning Division at PT. Kurnia Ciptamoda Gemilang faces challenges in monitoring projects in a structured and timely manner, necessitating a web-based project management system tailored to the company’s specific needs. This study aims to design a standalone web-based project management system that is not yet integrated with existing systems but is structured to allow future integration. The system was developed using the Next.js framework, applying the Kanban method and the SDLC Waterfall development model. The research involved problem identification, literature review, and interviews with project managers to gather both functional and non-functional requirements. The results show that the developed system includes key features such as workspace management, member management, project and task management, task prioritization, deadline setting, and visualizations in the form of Kanban boards and calendars. Work efficiency is measured through the availability of a more structured workflow and a reduction in miscommunication during task assignment. The system was evaluated using the Black Box Testing method, and the results confirm that all features function according to specifications and meet the users' actual needs in the work environment. The system has proven effective in improving work efficiency, facilitating team coordination, and ensuring that projects proceed as planned.
subject: Articles
  topic: section
subject: Project Management
  topic: keywords
subject: Website
  topic: keywords
subject: Next.js
  topic: keywords
subject: Kanban
  topic: keywords
subject: waterfall
  topic: keywords
creator: Florence Aurelia Widjaja
creator: Bhustomy Hakim
rights: Copyright (c) 2025 Florence Aurelia Widjaja, Bhustomy Hakim https://creativecommons.org/licenses/by-sa/4.0
date: 2025-07-09
publication date: 2025-07-09
starting page: 77
ending page: 86
doi: 10.31294/coscience.v5i2.8864

Optimalisasi Presensi Sekolah Berbasis QR Code dengan Metode Rapid Application Development
type: item
description: High School attendance systems play an important role in monitoring student attendance and enforcing discipline in the academic environment. However, many schools still use manual methods such as written attendance lists or teacher name calling, which are inefficient, time-consuming, and prone to manipulation and fraud. These methods present challenges for teachers and administrative staff, leading to inaccurate recording, data loss, and falsification of attendance. To address these issues, this study proposes the development of a QR Code-based school attendance system using the Rapid Application Development (RAD) methodology. RAD was chosen because of its ability to produce prototypes quickly and allow for iterative system improvements according to user needs. The proposed system allows students to scan a unique QR Code to automatically record their attendance, thereby reducing human intervention and minimizing errors. The expected outcomes of this study include increased accuracy, efficiency, and security in recording student attendance. The RAD approach is predicted to accelerate the development process without sacrificing ease of use and system reliability. In addition, this system is expected to be able to prevent fraud in attendance, because QR Code-based authentication provides a more secure validation mechanism. Through a series of trials and evaluations, this study aims to prove that the integration of RAD with QR Code technology can improve the effectiveness of attendance recording compared to conventional methods. Based on the results of the trials and evaluations, it can be concluded that the QR Code-based attendance system with the RAD approach has been proven to improve the efficiency, accuracy, and security of the attendance system in schools.
subject: Articles
  topic: section
subject: School attendance system
  topic: keywords
subject: QR Code
  topic: keywords
subject: RAD methodology
  topic: keywords
subject: school management
  topic: keywords
subject: automation
  topic: keywords
subject: security
  topic: keywords
creator: Eva Rahmawati
creator: Herlambang Brawijaya
creator: Doni Andriansyah
creator: Elly Mufida
rights: Copyright (c) 2025 Eva Rahmawati, Herlambang Brawijaya, Doni Andriansyah, Elly Mufida https://creativecommons.org/licenses/by-sa/4.0
date: 2025-07-09
publication date: 2025-07-09
starting page: 87
ending page: 94
doi: 10.31294/coscience.v5i2.8505

Analisis Sentimen Ulasan Pelanggan Menggunakan Algoritma Naive Bayes pada Aplikasi Gojek
type: item
description: Transportation is a means that a person uses to move from one place to another. One mode of transportation that is popular among the public is online motorcycle taxis, such as Gojek. Gojek continues to innovate to meet customer needs more effectively, as well as expand the scope of its services. This research aims to identify the number of positive and negative sentiments in the user review dataset, evaluate the performance of the algorithm used, and measure the level of customer satisfaction with Gojek services. Analysis was carried out on 6,485 customer reviews, which resulted in 4,387 positive sentiments and 2,098 negative sentiments. The classification model used, namely Naive Bayes, shows an accuracy of 88.5%, precision of 88.1%, and recall of 89.0%. The results of this research indicate that the Naive Bayes method provides good performance in analyzing the sentiment of user reviews of Gojek services
subject: Articles
  topic: section
subject: Gojek
  topic: keywords
subject: Sentiment analysis
  topic: keywords
subject: Naive Bayes
  topic: keywords
creator: Sujiliani Heristian
creator: Musriatun Napiah
creator: Wati Erawati
rights: Copyright (c) 2025 Sujiliani Heristian, Musriatun Napiah, Wati Erawati https://creativecommons.org/licenses/by-sa/4.0
date: 2025-01-31
publication date: 2025-01-31
starting page: 35
ending page: 41
doi: 10.31294/coscience.v5i1.7775