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.
LPPM Universitas Bina Sarana Informatika
Enhancing FOMAML with Domain-Specific Residual Pretraining for Few-Shot Chili Disease Classification
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
Copyright (c) 2026 Rizal Amegia Saputra, Agus Buono, Karlisa Priandana, Samsuzana Abd Aziz, Muhamad Syukur https://creativecommons.org/licenses/by-sa/4.0
Collaborative Approaches to Enhancing Smart Vehicles Cybersecurity Through AI-Driven Threat Detection
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.
Copyright (c) 2026 Syed Atif Ali, Salwa Din https://creativecommons.org/licenses/by-sa/4.0
Performance of Distance Metrics in SMOTE for Binary Imbalanced Classification
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.
Copyright (c) 2026 Fandi Yulian Pamuji, Luthfi Indana, Mohammad Dwi Irfan Affandi https://creativecommons.org/licenses/by-sa/4.0
Cross-Project Defect Prediction on AEEEM Using SMOTE–Tomek and Ensemble learning
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.
Copyright (c) 2026 Fina Sifaul Nufus, Yan Rianto https://creativecommons.org/licenses/by-sa/4.0
Integrating MLOps for Monitoring, Drift Detection, and Adaptive Retraining in Sensor Systems
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.
Copyright (c) 2026 Novia Heriyani, Nita Merlina https://creativecommons.org/licenses/by-sa/4.0
Feature Comparison of RN-SMOTE and DBSCAN-SMOTE for Handling Imbalanced Data
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
Copyright (c) 2026 Rias Akmal Sembiring, Wawan Gunawan https://creativecommons.org/licenses/by-sa/4.0
Integrating LLM Intent-Aware Approach for Enhancing the Quality of Bundle Recommendations
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.
Copyright (c) 2026 Andy Maulana Yusuf, Musrinah Musrinah https://creativecommons.org/licenses/by-sa/4.0
UML-Based Design and Kano Evaluation of an Augmented Reality Library Application
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.
Copyright (c) 2026 Gerson Feoh, Ni Putu Ria Anggreni https://creativecommons.org/licenses/by-sa/4.0
Performance Evaluation of YOLOv8 for Railway Switching Operation Safety Monitoring
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.
Copyright (c) 2026 Aulya Anggita Putri Selendra, Teguh Arifianto, Fathurrozi Winjaya https://creativecommons.org/licenses/by-sa/4.0
Developing an Efficient PPDB System Integrating Payment Gateway and Secure Exams
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.
Copyright (c) 2026 Muhammad Rohman Syah, Deny Novianti M.Kom, Roynaldy Rosdiyanto https://creativecommons.org/licenses/by-sa/4.0
Optimization of Crop Recommendation Model Using Ensemble Learning Techniques for Multiclass Classification
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.
Copyright (c) 2026 Siti Marlina, Titik Misriati, Riska Aryanti https://creativecommons.org/licenses/by-sa/4.0
Saliency-Enhanced Deep Learning Framework for Stain-Robust White Blood Cell Segmentation and Classification
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.
Copyright (c) 2026 Mehdi Zekriyapanah Gashti, Mostafa Mohammadpour, Ghasem Farjamnia https://creativecommons.org/licenses/by-sa/4.0
SIBI-Based Gesture Recognition System Using Random Forest for Hearing-Impaired Communication
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.
Copyright (c) 2026 Andre Pratama, Ahmad Jurnaidi Wahidin https://creativecommons.org/licenses/by-sa/4.0
Analyzing Public Sentiment Toward Makanan Bergizi Gratis Program Using Machine Learning
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.
Copyright (c) 2026 Musriatun Napiah, Sujiliani Heristian, Mugi Raharjo, Rachmat Adi Purnama https://creativecommons.org/licenses/by-sa/4.0
Analysis of Student Academic Performance Using Random Forest and Support Vector Machines
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.
Copyright (c) 2026 Galih Mifta Agung, Robi Aziz Zuama, Eko Setia Budi https://creativecommons.org/licenses/by-sa/4.0
Development of Web and Mobile Health Services Information System Using Waterfall Method
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
Copyright (c) 2026 Yoga Pratrian, Yayan Hendriyan, Ahmad Hafidzul Kahfi https://creativecommons.org/licenses/by-sa/4.0
Design and Development of IdentifiKu: A Web-Based Diagnostic Model for Differentiated Learning
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.
Copyright (c) 2026 Muhammad Noor Hasan Siregar, Yulia Rizki Ramadhani, Yusra Fadhillah, Yoviansyah Rizki Pratama https://creativecommons.org/licenses/by-sa/4.0
Sistem Informasi Persediaan Obat Pada PT Mega Esa Farma Dengan Metode Extreme Programming
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.
Copyright (c) 2025 Biktra Rudianto, Sheren Prisscilya https://creativecommons.org/licenses/by-sa/4.0
Perancangan Sistem Informasi Surat Administrasi Penduduk Elektronik Menggunakan Metode Scrum
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.
Copyright (c) 2025 Luthfi Indana, Asri Samsiar Ilmananda https://creativecommons.org/licenses/by-sa/4.0
Studi Komparatif Algoritma K-Means dan K-Medoids untuk Segmentasi Informasi Kesehatan
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.
Copyright (c) 2025 Anis Masruriyah, Karenina Nurmelita Malik, Anis Fitri Nur Masruriyah, Mardiah Mardiah https://creativecommons.org/licenses/by-sa/4.0
Penerapan Model Scrum Pada Perancangan Aplikasi Penerimaan Siswa Baru MTS Nu 03 Suradadi
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.
Copyright (c) 2025 Prawira Diharja, Mia Rosmiati, Mulia Rahmayu https://creativecommons.org/licenses/by-sa/4.0
Evaluasi Dan Redesain Antarmuka Website megapolitanpos.com Menggunakan Metode Usability Testing Dan Heuristic Evaluation
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.
Copyright (c) 2025 Jonathan Tanuwijaya, Arfhan Prasetyo https://creativecommons.org/licenses/by-sa/4.0
Model Prediksi Risiko Kesehatan Perkotaan Berbasis Lingkungan dengan XGBoost
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
Copyright (c) 2025 Muhammad Kahfi Aulia, Eka Utaminingsih, Nanang Prihatin https://creativecommons.org/licenses/by-sa/4.0
Implementasi Firewall Mikrotik dalam Pembatasan Akses Situs Terlarang di RT/RW Net
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.
Copyright (c) 2025 Hanggoro Aji Al Kautsar, Ricki Sastra https://creativecommons.org/licenses/by-sa/4.0
Sistem Manajemen Proyek Berbasis Website Dengan Metode Kanban
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.
Copyright (c) 2025 Florence Aurelia Widjaja, Bhustomy Hakim https://creativecommons.org/licenses/by-sa/4.0
Optimalisasi Presensi Sekolah Berbasis QR Code dengan Metode Rapid Application Development
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.
Copyright (c) 2025 Eva Rahmawati, Herlambang Brawijaya, Doni Andriansyah, Elly Mufida https://creativecommons.org/licenses/by-sa/4.0
Analisis Sentimen Ulasan Pelanggan Menggunakan Algoritma Naive Bayes pada Aplikasi Gojek
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
Copyright (c) 2025 Sujiliani Heristian, Musriatun Napiah, Wati Erawati https://creativecommons.org/licenses/by-sa/4.0
Penerapan Integrasi Algoritma K-Means Dan Naïve Bayes Untuk Klasifikasi Wilayah Rawan Banjir Di Jakarta
Jakarta, as a metropolitan city in Indonesia, often experiences flooding caused by high rainfall, poor drainage systems, and rapid urbanization. This research aims to classify flood-prone areas in Jakarta using a combination of K-Means Clustering and Naïve Bayes Classifier algorithms. The research phase begins with data collection from the Satu Data Jakarta website, including attributes such as region, sub-district, village, average water level, number of affected RWs, number of affected families, number of affected people, and number of flood events. The collected data is then processed through cleaning and normalization stages before being analyzed using the K-Means algorithm to group areas based on their flooding characteristics. Furthermore, the Naïve Bayes algorithm was used to build a classification model that predicts flood-prone areas. The results showed that the combination of these two algorithms resulted in higher average accuracy compared to the use of conventional Naïve Bayes, having an accuracy of 98.18%% at training and testing data split ratios of 70:30, 80;20 and 90:10. The findings provide valuable insights for flood risk mitigation in Jakarta, assisting the government in taking more effective preventive measures.
Copyright (c) 2025 Irfan Maulana Sinatrya Sinatrya, Achmad Baroqah Pohan, Yunita Yunita, Hilda Amalia, Ade Fitria Lestari https://creativecommons.org/licenses/by-sa/4.0
Customer Churn Prediction Pada Sektor Perbankan Dengan Model Logistic Regression dan Random Forest
– Customer churn is a detrimental phenomenon in the banking sector because it can reduce revenue and increase the cost of acquiring new customers. This research aims to compare the performance of two models, Logistic Regression and Random Forest, to predict customer churn using datasets from Kaggle. The research process involves data preprocessing such as z-score normalization and dividing the dataset into training data (70%) and testing data (30%). The model was evaluated using a confusion matrix with Accuracy, precision, recall and F1-Score values. Logistic Regression achieved 76.85% Accuracy, 79% precision, 94% recall, and 86% F1-Score, showing quite good performance but susceptible to false positives. In contrast, Random Forest shows superior performance with 83.12% Accuracy, 84% precision, 96% recall, and 90% F1-Score. Random Forest is suitable for problems with high recall requirements because it is more reliable in detecting potential customer churn. To further improve model performance, it is recommended to perform hyperparameter optimization and feature importance analysis. This churn prediction model is expected to help banks reduce churn and increase customer retention.
Copyright (c) 2025 Ely Mufida, Doni Andriansyah, Hylenarty Hertyana https://creativecommons.org/licenses/by-sa/4.0
Analisis Performa Model ResNet-50 Pada Diagnosis Pneumonia Balita Berdasarkan Citra Radiografi Thorax
One of the most serious complications of ARI is pneumonia, where this disease causes sufferers to experience pain when breathing and limited oxygen intake. According to the World Health Organization (WHO), pneumonia is classified as a life-threatening disease due to the high mortality rate caused. To be able to diagnose this disease, patients usually undergo various medical examination methods, one of which is through chest radiography. However, the challenge in diagnosing pneumonia generally lies in the complexity and uncertainty in interpreting the results of these methods. Therefore, this study was conducted with the aim of building an image classification model based on the Chest radiography dataset from toddler patients using the ResNet-50 architecture, which is a variant of the Convolutional Neural Networks (CNN) algorithm. The combination of the two methods is applied to analyze and process images and obtain pattern recognition with high accuracy. The research methods used include the application of data augmentation, CNN architecture design, model training, and performance evaluation. The evaluation results show that the model has quite good performance with an accuracy of 85%, which indicates the model's ability to classify images with a fairly high level of accuracy, and has the potential to help the pneumonia diagnosis process more efficiently and accurately.
Copyright (c) 2025 Ami Rahmawati, Ita Yulianti, Siti Nurajizah, Taufik Hidayatulloh, Ani Oktarini Sari https://creativecommons.org/licenses/by-sa/4.0