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
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
Aplikasi Pencatatan Kalori Harian Berbasis Android Dengan Arsitektur MVVM
An imbalance between calorie intake and expenditure is considered the main cause of obesity or being overweight. So by controlling your calorie intake in a balanced manner and according to your needs you will be able to prevent obesity. The daily calorie recording application can help someone record, control and obtain information on their calorie intake. This article discusses the development of this type of application, named Nutrizen, using the waterfall method, during the development process by the CH2-PS076 team in the Bangkit 2023 batch 2 program. The application created is an Android-based application created with the Kotlin programming language and MVVM architecture as a design pattern that is easy to learn and makes the code easy to understand and manage. Testing this application uses the usability test method with the System Usability Scale tool to determine the level of user acceptance of usability. The results obtained include a marginal level of usability acceptance, so improvements are needed so that this application can be more accepted and relied on by the wider community.
Copyright (c) 2025 Alfi Zia Ulhaq, Abilawa Zulfiqar Adilukito, Sultan Muhamad Pascal Gadja Neru, Muhammad Daffa Agisfio https://creativecommons.org/licenses/by-sa/4.0
Analisa Komparasi Model Data Mining Algoritma C4.5, CHAID, dan Random Forest Untuk Penilaian Kelayakan Kredit
Credit has now become a trend in society. The problem with credit is the improper history of credit card usage. The resulting impact can lead to bad credit. If customers fail to pay off debts that have been agreed upon with the bank, they can increase their credit risk. This study aims to conduct a comparative analysis of three data mining classification methods: the C4.5 algorithm, Chi-Squared Automatic Interaction Detection (CHAID), and Random Forest. The goal is to classify creditworthiness status. The researcher used 481 vehicle credit records with "bad" and "good" reviews. In this study, the independent variables used are dependent status, age, marital status, occupation, income, employment status, company status, last education, length of stay, house condition, and down payment. For creditworthiness assessment, the C4.5 model shows a truth accuracy rate of 91.90% with an area under the curve (AUC) value of 0.915. The CHAID model shows a truth accuracy rate of 63.83% with an AUC value of 0.661, and the Random Forest model shows a truth accuracy rate of 78.60% with an AUC value of 0.907. The evaluation results show that both the Random Forest and C4.5 algorithms have high accuracy rates and AUC values.
Chi-Squared Automatic Interaction Detection (CHAID)
Copyright (c) 2025 Omar Pahlevi, Amrin, Harsih Rianto https://creativecommons.org/licenses/by-sa/4.0
Analisis Sentimen Cyberbullying Pada Komentar X Menggunakan Metode Naïve Bayes
Advances in communication technology and social media have made it easier to access global information, but have also increased cases of cyberbullying on platforms such as X. The impact of cyberbullying can include physical and psychological disorders, such as increased loneliness, anxiety, depression, and decreased self-esteem. In addition, victims of cyberbullying may feel distress that can increase the risk of suicidal ideation. This research utilizes the Naïve Bayes method to effectively and efficiently classify cyberbullying-related comments. This classification model was developed to detect cyberbullying in comments on X, using the Naïve Bayes algorithm and a dataset from Kaggle consisting of 650 comments that contain cyberbullying characteristics and those that do not. This research includes several preprocessing steps such as tokenization, normalization, and stemming. The data was then divided into two parts: 80% for training data and 20% for testing data. The evaluation results show a model accuracy of 80.77%, precision 81.25%, recall 70.91%, and AUC 0.794. The innovation in this research lies in the use of 2 (two) stemming operators, namely stemming dictionary and stemming snowball, where the stemming dictionary uses a special file containing abbreviations or slang words, which are often used in comments on the word becomes its basic form. This model tends to be more accurate in classifying comments as non-bullying than bullying. Suggestions for improvement include exploring other preprocessing methods and algorithms, as well as using larger and more varied datasets.
Copyright (c) 2025 Bany Wibisono, Aprizal Machmud, Nining Suryani, Yunita Yunita https://creativecommons.org/licenses/by-sa/4.0
Studi Perbandingan Algoritma Random Forest dan K-Nearest Neighbors (KNN) dalam Klasifikasi Gangguan Tidur
Sleep disorders such as insomnia and sleep apnea can significantly affect quality of life and increase the risk of chronic diseases. Early identification and classification of sleep disorders are crucial in preventing further impacts. This study aims to compare the performance of the Random Forest and K-Nearest Neighbors (KNN) algorithms in classifying sleep disorders using the Sleep Health and Lifestyle Dataset from Kaggle, which contains health and lifestyle data relevant to sleep patterns. The Random Forest and KNN algorithms were applied to classify sleep disorders into the categories 'None', 'Sleep Apnea', and 'Insomnia'. Based on the study results, the Random Forest algorithm achieved an accuracy of 89.69%, with the best performance in the 'None' category, reaching a recall of 96.08%. Meanwhile, KNN achieved an accuracy of 87.02% with K=5. Although Random Forest demonstrated superior results, challenges were still found in detecting the 'Sleep Apnea' category, where recall only reached 74.55%, likely due to data imbalance. This study shows that the Random Forest algorithm is more effective in classifying sleep disorders compared to KNN. Future research steps include data balancing and exploring other algorithms such as XGBoost to improve the performance of sleep disorder detection.
Copyright (c) 2025 Nurul Khasanah, Daniati Uki Eka Saputri , Faruq Aziz, Taopik Hidayat https://creativecommons.org/licenses/by-sa/4.0
Prediksi Risiko Alzheimer: Perbandingan Kinerja Algoritma Klasifikasi
Alzheimer's disease is one of the most common forms of dementia characterized by a gradual decline in the ability to think, remember, and behave. The disease progresses slowly and usually begins with short-term memory loss, followed by difficulties in language, disorientation, and personality changes. Early detection of Alzheimer's is important to slow its progression. This study implements Knowledge Discovery in Database (KDD) with data pre-processing stages, SMOTE data balance, classification. The dataset from Kaggle contains 2,149 data 35 features. This study uses five types of algorithm models. with the five algorithms tested. Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and K-Nearest Neighbor classification algorithms. Algorithm Implementation: The Random Forest algorithm is implemented during the classification process after data updates. This model is then assessed together with other algorithms using accuracy, precision, recall, F1-score, and ROC curve metrics to determine its effectiveness in detecting Alzheimer's. Meanwhile, the experiment was carried out by dividing the data into two parts, namely training data and test data of 70% and 30%. Research Results: The results showed that the Random Forest algorithm had the best performance with an Accuracy of around 0.98, Precision of 0.96, Recall of 1.00, and F1-score of 0.97, and an AUC value of around 0.99. This algorithm has proven to be superior to other algorithms in early detection of Alzheimer's disease.
Copyright (c) 2024 sidik https://creativecommons.org/licenses/by-sa/4.0
Prediksi Kualitas Tidur: Pendekatan Machine Learning yang Mengintegrasikan Faktor Kesehatan dan Lingkungan
Sleep disorders significantly affect an individual's sleep quality, which can lead to serious health problems. For the elderly, poor sleep quality can drastically reduce life expectancy. The main problem is the lack of effective predictive tools to improve sleep quality among the elderly, compounded by the numerous factors that can influence their sleep quality. Therefore, analysis and prediction are necessary to enhance sleep quality. This study aims to develop and test a predictive model for sleep quality in the elderly by integrating health and environmental factors using a machine learning approach. The dataset used is a new one available on the website Kaggle.com, namely the National Poll on Healthy Aging (NPHA) data, which provides insights into health issues, healthcare, and health policies affecting Americans aged 50 and above. The aim is to improve sleep quality among the elderly. A machine learning method, specifically deep learning with the Random Forest algorithm, was used in this study and showed good results with an accuracy rate of 94.00% and a training data accuracy of 44.44%. The results of this study are expected to provide a predictive tool that can be used by healthcare practitioners to improve the sleep quality of the elderly, thereby positively impacting their health and life expectancy.
Copyright (c) 2024 The Author(s) https://creativecommons.org/licenses/by-sa/4.0