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Bulletin of Computer Science Research
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27743659     DOI : -
Core Subject : Science,
Computer Science & IT
Bulletin of Computer Science Research covers the whole spectrum of Computer Science, which includes, but is not limited to : • Artificial Immune Systems, Ant Colonies, and Swarm Intelligence • Bayesian Networks and Probabilistic Reasoning • Biologically Inspired Intelligence • Brain-Computer Interfacing • Business Intelligence • Chaos theory and intelligent control systems • Clustering and Data Analysis • Complex Systems and Applications • Computational Intelligence and Soft Computing • Distributed Intelligent Systems • Database Management and Information Retrieval • Evolutionary computation and DNA/cellular/molecular computing • Expert Systems • Fault detection, Fault analysis, and Diagnostics • Fusion of Neural Networks and Fuzzy Systems • Green and Renewable Energy Systems • Human Interface, Human-Computer Interaction, Human Information Processing • Hybrid and Distributed Algorithms • High-Performance Computing • Information storage, security, integrity, privacy, and trust • Image and Speech Signal Processing • Knowledge-Based Systems, Knowledge Networks • Knowledge discovery and ontology engineering • Machine Learning, Reinforcement Learning • Networked Control Systems • Neural Networks and Applications • Natural Language Processing • Optimization and Decision Making • Pattern Classification, Recognition, speech recognition, and synthesis • Robotic Intelligence • Rough sets and granular computing • Robustness Analysis • Self-Organizing Systems • Social Intelligence • Soft computing in P2P, Grid, Cloud and Internet Computing Technologies • Support Vector Machines • Ubiquitous, grid and high-performance computing • Virtual Reality in Engineering Applications • Web and mobile Intelligence, and Big Data • Cryptography • Model and Simulation • Image Processing
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 462 Documents
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Implementasi Data Mining untuk Menentukan Pola Pembelian Obat Menggunakan Metode Apriori Muhtajuddin Danny; Isarianto Isarianto
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1133

Abstract

The development of information technology has increased the amount of drug sales transaction data in the pharmacy sector. However, transaction data are generally used only as administrative archives and have not been optimally utilized to produce strategic information. This study aims to implement data mining using the Apriori method to determine drug purchasing patterns based on pharmaceutical transaction data. This research employed a quantitative approach using the Pharmacy Transactional Dataset obtained from the Kaggle platform. The research stages were conducted using the Cross Industry Standard Process for Data Mining (CRISP-DM), including business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The analysis process was carried out using the Python programming language with the assistance of the pandas and mlxtend libraries. The results showed that the purchasing relationship between Paracetamol and Vitamin C had the highest association value with a support value of 32% and a confidence value of 78%. These results indicate that the Apriori algorithm is capable of identifying relationships among drug products based on pharmaceutical transaction data. The resulting information can be utilized to support promotional strategies, drug inventory management, and business decision-making in the pharmaceutical sector.
Pengembangan Sistem Informasi Akademik Terintegrasi Berbasis Web Mobile Pada Lembaga Pendidikan Menengah Atas Sandy Maulana Rifqi; Ledy Elsera Astrianty
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1136

Abstract

Academic information dissemination at Senior High School has been carried out conventionally, often resulting in delays in providing attendance, class schedules, and grade reports to students and parents. The limitation of non-real-time information access impacts the lack of parental supervision regarding student discipline and academic progress. This research aims to design and build a web and mobile-based Academic Information System capable of integrating school data management digitally, quickly, and accurately. The system development method used is the Waterfall method, including requirement analysis, system design, implementation, testing, and maintenance. The system was developed using the Laravel framework for the web-based admin dashboard and the Flutter framework for the mobile application for students and parents, supported by a MySql database. This research provides a scientific contribution in the form of a cross-platform integration architecture (dual-framework) connected via RESTful API to overcome data communication asymmetry in educational institutions. This cross-platform integration is implemented to facilitate administrators' needs in large master data management while providing enhanced accessibility for mobile users. Functional testing using the Black Box Testing method showed that all main features operate as expected, and User Acceptance Testing (UAT) resulted in an average score of 86.0%, falling into the "Very Good" category. The results of this study indicate that this integrated information system is effective in facilitating access to academic information transparently and improving administrative efficiency in educational institutions.
Prediksi Minat Pencarian Layanan Pesan-Antar Makanan Online (GoFood dan GrabFood) di Indonesia Menggunakan Algoritma Random Forest Regression dengan Walk-Forward Validation Sinta Bella; Achmad Baijuri; Fajriyanto Fajriyanto
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1137

Abstract

The rapid growth of online food delivery services in Indonesia, particularly GoFood and GrabFood, creates significant operational challenges due to unpredictable fluctuations in user interest that cause driver and merchant capacity imbalances. Actual transaction data is proprietary, necessitating a proxy-data approach using Google Trends search interest indices. This study predicts GoFood search interest in Indonesia using Random Forest Regression based on Google Trends data from January 2018 to December 2025 (96 monthly records). The primary contributions of this study are threefold: first, the application of walk-forward validation as a methodologically sound evaluation approach for time-series data that eliminates temporal data leakage; second, the use of lag features (GoFood_lag1 and GrabFood_lag1) ensuring all predictor variables are practically available at prediction time; and third, empirical validation that this approach yields more conservative and scientifically defensible evaluations compared to conventional random split methods. Evaluation results yield MSE 1.4510, RMSE 1.2046, R² 0.5065, and MAPE 6.55%, demonstrating adequate generalization capability for data-driven operational planning.
Perancangan Sistem Informasi Pengelolaan Pakan Ternak Berbasis Web Menggunakan Metode Waterfall Hizki Alawiyah; Bayu Pamungkas
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1140

Abstract

Animal feed management plays a crucial role in supporting the smooth operation of livestock farms, particularly in government agencies involved in livestock development and breeding. The Margawati Sheep and Goat Livestock Breeding Development Center (BPPTDK) still employs manual feed data management processes, from recording feed types, stock management, to report preparation. This situation results in a suboptimal data management process due to the potential for recording errors, delays in information delivery, and difficulties in accurately monitoring feed stocks. This study aims to design a Web-Based Animal Feed Management Information System to support a more effective, integrated, and computerized feed data management process. The system development method used is the Waterfall method, which includes the stages of needs analysis, system design, implementation, testing, and maintenance. The system design process was carried out using the Unified Modeling Language (UML) in the form of use case diagrams, activity diagrams, and class diagrams. The results of the study indicate that the developed system is able to assist in managing feed type data, controlling feed stock, recording incoming and outgoing feed data, and preparing reports more effectively compared to the previous manual system. Test results using the Black Box Testing method indicate that all the system's main functions operate according to user requirements. The designed system also supports real-time feed stock monitoring and facilitates integrated data management according to operational requirements at the Margawati BPPTDK UPTD. Therefore, the developed information system is expected to improve the effectiveness and efficiency of the livestock feed management process in a sustainable manner.
Klasifikasi Penyakit Pneumonia Menggunakan Regresi Logistik, SVM, dan Fitur Deep Learning Ahmad Bagus Muzakki; Imam Yuadi
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1141

Abstract

This study evaluates the integration of deep learning-based feature extraction with conventional machine learning algorithms for pneumonia disease classification from chest X-ray images. Two pre-trained models, Inception V3 and SqueezeNet, are used as feature extractors due to their ability to generate effective feature representations from medical images. Inception V3 was chosen because it is able to capture complex visual patterns, while SqueezeNet offers computational efficiency with a smaller number of parameters. Meanwhile, Logistic Regression and Support Vector Machine (SVM) are used as classification algorithms due to their ability to handle high-dimensional data extracted from deep learning features. The dataset used consists of two categories, namely normal images and pneumonia images, with the entire analysis process carried out using Orange Data Mining. The experimental results show that the combination of Inception V3 and SVM provides the best performance with an Area Under Curve (AUC) of 0.731, Classification Accuracy (CA) of 0.835, F1-score of 0.805, Precision of 0.810, Recall of 0.835, and Matthews Correlation Coefficient (MCC) of 0.321. Meanwhile, the combination of SqueezeNet and Logistic Regression produces a CA of 0.771, an F1-score of 0.751, and an MCC of 0.118, showing quite competitive performance although still below Inception V3 and SVM. The results show that the quality of feature embedding generated by the deep learning model has a significant influence on classification performance. The integration of transfer learning and conventional machine learning has been proven to improve the accuracy of pneumonia detection and has the potential to support the development of an efficient and accurate artificial intelligence-based diagnostic system.
Pengembangan Aplikasi Smart Transportation Berbasis Real-Time Tracking dan Pembayaran Digital untuk Bus Listrik Menggunakan Metode Research and Development (R&D) Dolly Brams Matondang; Susilo Immanuel Situmorang
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1142

Abstract

The development of digital technology has encouraged innovation in public transportation services through the concept of smart transportation. However, electric bus services still face limitations in providing real-time bus location information and have not yet integrated digital payment systems within a single platform. These conditions make it difficult for users to obtain arrival time information and perform payment transactions efficiently. This study aims to develop a mobile-based smart transportation application that integrates real-time tracking, Estimated Time of Arrival (ETA), route and bus stop information, and digital payment services through mobile banking, e-wallets, and QR code. The Research and Development (R&D) method was applied because it supports a systematic system development process starting from needs analysis, system design, prototype development, and usability testing. The contribution of this research lies in the development of an integrated application to facilitate users in accessing electric bus services more effectively and efficiently, considering that there is currently no application that completely integrates these features into a single platform. Usability testing using the System Usability Scale (SUS) method involving 30 respondents produced an average score of 87.3, which falls into the Excellent, Grade A, and Acceptable categories. These results indicate that the application has a very good level of usability and is feasible for further development.
Implementasi Frontend Sistem Pelaporan Tugas Harian Berbasis Web dengan Pendekatan Gamifikasi Menggunakan Vue.js Riza Al Hambra; Novi Tristanti
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1143

Abstract

The phenomenon of students' lack of discipline in completing and reporting daily tasks is an academic problem that impacts learning achievement. Monotonous manual reporting systems often reduce intrinsic motivation and student participation rates. This study aims to design and implement a web-based daily task reporting system frontend with a gamification approach using the Vue.js framework, and analyze its impact on improving discipline. The research location is at Universitas Muhammadiyah Karanganyar involving 10 active students as trial respondents. The research method used is a qualitative approach with an implementative case study type. The system interface was developed using Vue.js 3, Pinia as state management, and Vue Router. Data collection techniques included participatory observation, in-depth interviews, and User Acceptance Test (UAT) questionnaires with a 1 to 5 Likert scale. Data testing was analyzed using the Miles and Huberman model consisting of data reduction, data display, and drawing conclusions. Interim research results show the functional success rate of the frontend reached 88 percent in the localhost environment. The UAT questionnaire obtained an overall average score of 4.21 which is classified as very good. Gamification features became the most preferred element by 60 percent of respondents, which effectively encouraged students' disciplined behavior in submitting assignments on time.
Implementation of PROMETHEE Method in Decision Support System for Student Competency Competition Participant Selection Muhamad Rezka Al Anshori; Hadi Zakaria
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1145

Abstract

The Student Competency Competition (Lomba Kompetensi Siswa/LKS) is a prestigious national-level event requiring vocational schools to select participants in an objective and measurable manner. However, the selection process at SMKN 1 Tangerang Selatan still relies on subjective teacher judgment and lacks comprehensive evaluation criteria, risking suboptimal decisions that may result in selected students being insufficiently competent for the competition. This research aims to design and implement a web-based Decision Support System (DSS) using the PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) method to support objective, structured, and transparent LKS participant selection. Three evaluation criteria were defined based on field interviews: Practicum Subject (K1), Logic Subject (K2), and Attendance (K3), each processed through pairwise preference comparisons using linear and usual preference functions with equal criterion weights. The system was developed using JavaScript (Node.js) and MongoDB, supporting four user roles with distinct access rights. System correctness was verified through Black Box Testing across 16 functional scenarios and White Box Testing of four algorithmic modules using Cyclomatic Complexity analysis, all of which passed successfully. The ranking results identified A3 as the top-recommended candidate with the highest Net Flow value of +0.2083, demonstrating the system's ability to surface competency dimensions overlooked by manual evaluation. Based on a questionnaire involving 20 respondents, the system achieved a user satisfaction score of 87.6%, categorized as very strong. These findings confirm that the PROMETHEE-based DSS is a reliable, practical, and deployment-ready tool for supporting participant selection in vocational school environments. The findings of this study offer a replicable methodological framework for vocational institutions seeking to standardize competition-based participant selection through multi-criteria decision support.
Analisis Pengembangan dan Evaluasi Virtual World Bertema Lingkungan Pegunungan Menggunakan Metode Research and Development (R&D) Robby Firmansyah; Andri Firmansyah
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1146

Abstract

The development of metaverse technology and virtual worlds has created opportunities for utilizing virtual environments as media for digital exploration, simulation, and interactive learning. However, most previous studies have primarily focused on pedagogical aspects and learning simulations, while research discussing the technical development of terrain modeling, lighting, and mountainous environmental atmospheres on the Roblox Studio platform remains limited. This study aims to develop and evaluate a mountainous environment-themed virtual world using Roblox Studio and to analyze user acceptance of the resulting virtual environment. The research employed the Research and Development (R&D) method, consisting of needs analysis, design, implementation, testing, and evaluation stages. The development process utilized Terrain Editor, Future Lighting, skybox, fog, and Lua scripting features to create an interactive virtual environment. Evaluation was conducted using a Likert-scale questionnaire distributed to 67 respondents. The results showed a feasibility score of 79.88%, which falls into the good category. The highest-rated indicator was the suitability of the virtual world to the mountainous environment concept (4.15), while the lowest-rated indicator was the structured layout of the environment (3.83). These findings indicate that the developed virtual world was able to provide a positive exploration experience for users. The contribution of this research lies in establishing a systematic development framework for a mountainous environment-themed virtual world through the implementation of terrain modeling, lighting and atmospheric configuration, and user experience evaluation using the Research and Development (R&D) approach. In addition, this study provides a practical reference for the development of metaverse-based virtual environments that can be utilized for simulation, digital exploration, and interactive learning applications.
Analisis Kinerja Recursive Feature Elimination pada Support Vector Machine untuk Klasifikasi Penyakit Stroke pada Data Tidak Seimbang Faridatul Jannah; Siska Kurnia Gusti; Elin Haerani; Teddie Darmizal
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1147

Abstract

Stroke is a non-communicable disease with high mortality and disability rates, necessitating a classification approach that can facilitate more effective detection. Class imbalance in stroke datasets causes classification models to be biased toward the majority class, resulting in suboptimal classification performance. This study aims to analyze the performance of Recursive Feature Elimination (RFE) in a Support Vector Machine (SVM) model with data imbalance handling using Adaptive Synthetic Sampling (ADASYN) in stroke classification. The dataset used is a secondary dataset from Kaggle consisting of 5109 data points after the preprocessing stage. The modeling process was conducted by testing various data split ratios as well as combinations of kernels and SVM parameters using a 5-fold cross-validation approach. The results show that the best model was obtained with an 80:20 split ratio, a polynomial kernel, and a C parameter of 0.1, yielding an accuracy of 0.75, precision of 0.14, recall of 0.82, an F1-score of 0.24, and an AUC of 0.8245. The application of RFE resulted in improved model performance compared to without RFE, although the magnitude of the improvement was relatively small. The still low precision value indicates that the model still produces many false positives, so the classification challenge on the stroke dataset has not been fully resolved. On the other hand, an AUC value of 0.8245 indicates that the model performs reasonably well in distinguishing between the two classes overall, although its application in a clinical context still requires further refinement.

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