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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 56 Documents
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Search results for , issue "Vol. 5 No. 4 (2025): June 2025" : 56 Documents clear
Prediksi Penjualan Barang Menggunakan Metode K-Means dan Regresi Linear Henry Adam; Tukino; Elfina Novalia; Hananto, April Lia
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.541

Abstract

Sales data analysis plays an important role in supporting business decision making, especially to optimise stock management and improve operational efficiency. the main problem faced by Vapestore XYZ in Karawang is the difficulty in accurately predicting the number of product sales, so there is often an imbalance between inventory and market demand. This can cause losses due to overstocks or shortages of goods. Currently, the estimation of stock requirements still relies on intuition and personal experience, without the support of objective data analysis. This research aims to build a sales prediction model by combining the K-Means method for product clustering and Linear Regression for sales quantity prediction. Sales data is taken directly from the store POS application, then goes through the stages of cleaning, labelling, and clustering into three groups, namely ‘Less Sold’, “Sold”, and ‘Very Sold’. Sales prediction is performed using Linear Regression by utilising the clustering results and time variables as inputs. Model performance evaluation is performed using error metrics, namely Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Based on the test results, the developed Linear Regression model obtained MAE of 3.20, MSE of 52.34, and RMSE of 7.23. These error values indicate that the model is able to provide sales estimates that are close enough to the actual data to be reliable in stock planning. Visualisation of the prediction results in the form of tables and heatmaps makes it easy to identify sales trends and compare performance between products. The findings of this study prove that the combination of K-Means and Linear Regression methods is effectively used to support stock decision making and marketing strategies in vape retail stores. Further development is recommended by enriching the dataset and exploring other prediction methods to improve model performance.
Implementasi Algoritma Vigenere Cipher Untuk Keamanan Data Bantuan Sosial Di Desa Pazri; Abdul Halim Hasugian
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.544

Abstract

Data security in the digital era is very important. Village governments often manage sensitive data related to social assistance, such as recipient names, ID numbers, types of assistance, and other details. This data is very vulnerable to leaks or misuse, especially if there is no strong security system in place. This highlights the importance of implementing technology that supports data security. By implementing cryptographic algorithms such as the Vigenere Cipher, the village government can protect its community's data from potential threats. This algorithm uses a flexible secret key, making it ideal for securing small to medium-scale data, such as social assistance data. The Vigenere Cipher algorithm is applied to secure social assistance data in Lawe Sempilang Village, such as recipient names, ID numbers, and the types of assistance provided. This research aims to develop an application for the Implementation of the Vigenere Cipher Algorithm for Social Assistance Data Security in Lawe Sempilang Village. This application is designed to enhance the security of social assistance data by encrypting the data so that it can only be accessed by authorized parties, thereby preventing data leaks or misuse of information. The research method used is the Research and Development (R&D) method. This research successfully implemented the Vigenere Cipher algorithm in the data management system for social assistance recipients in Lawe Sempilang Village. With the presence of encryption, sensitive information such as Name, NIK, Phone Number, Email, Address, Dependents, and Income remains secure throughout the application process until approval by the Admin Department. The developed system is capable of maintaining data confidentiality and enhancing security in the distribution of social assistance. The results also show that the Vigenere Cipher algorithm succeeded in carrying out the encryption and decryption process correctly, so that only authorized parties can access the information in its original form.
Bird and Drone Image Classification Using ResNet CNN: A Deep Learning Approach for Aerial Surveillance Ahmad, Abdullah; Anjar Wanto; Adnan, Syed Muhammad
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.545

Abstract

Accurate classification of bird and drone images is crucial in supporting aerial surveillance and security systems, particularly to distinguish between natural objects such as birds and man-made objects such as drones. Manual classification methods have limitations in terms of speed and accuracy, thus necessitating a more efficient and reliable technology-based approach. This study aims to implement a ResNet-50 based Convolutional Neural Network (CNN) architecture to automatically classify bird and drone images. The dataset used was obtained from the Kaggle platform and consists of two classes: Bird and Drone, with a total of 22,407 images. The data was split into training (17,323 images), testing (844 images), and validation (1,740 images). All images underwent preprocessing and augmentation steps to enhance data quality and model training performance. The model was developed using the ResNet-50 architecture, which is well-regarded for handling complex image classification tasks. Evaluation results show that the model achieved an accuracy of 92%. For the Bird class, a precision of 0.83 and a recall of 0.99 were obtained, while for the Drone class, precision reached 0.99 and recall was 0.86. The average F1-score of 0.92 indicates that the model delivers balanced and reliable performance in the binary image classification task.
Analisis Sentimen Masyarakat Terhadap Pembatasan BBM Pertalite Menggunakan Random Forest dan K-Nearest Neighbor Muhammad Fadillah, Farhan; Cahyana, Yana; Rahmat; Fauzi, Ahmad
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.547

Abstract

This study aims to analyze public opinion regarding the policy of limiting the use of Pertalite fuel by examining user comments on the Instagram platform. To classify these opinions, classification approaches using K-Nearest Neighbor (KNN) and Random Forest algorithms were employed. Comments were categorized into three sentiment expressions: positive, negative, and neutral. The research stages included data collection (crawling), text cleaning and normalization, sentiment labeling, weighting using the TF-IDF technique, model development, and performance evaluation. A total of 2,081 comments were used, with 1,000 comments labeled by language experts as training data, and the remaining used for testing. Model evaluation was conducted using two data splitting ratios, 80:20 and 70:30, to assess classification stability and accuracy. The results indicate that the Random Forest algorithm consistently outperforms KNN, achieving the highest accuracy of 73% under the 80:20 scenario. The classification distribution suggests a dominance of negative sentiment in public opinion toward the policy. These findings reflect public dissatisfaction and serve as critical input for the government in reviewing the subsidized fuel distribution policy. This research also highlights the potential of social media as an alternative data source for real-time public perception analysis.
Perbandingan Algoritma Logistic Regression dan K-Nearest Neighbor Dalam Klasifikasi Kematangan Buah Pepaya Wildan Amin Wiharja; Tohirin Al Mudzakir; Hilda Yulia Novita; Jamaludin Indra
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.550

Abstract

Visual assessment of papaya ripeness often leads to inconsistent and low accuracy results. To address this, the study applies Logistic Regression and K-Nearest Neighbor (K-NN) algorithms for automatic classification using digital image processing. The initial dataset consisted of 300 images, which were expanded to 1,200 through preprocessing and augmentation. Features were extracted using the Gray Level Co-occurrence Matrix (GLCM) method, and the data was split into 80% for training and 20% for testing. The study aims to compare the performance of both algorithms and understand their classification mechanisms. Results show that K-NN with k=1 achieved an accuracy of 87%, while Logistic Regression with L2 regularization reached 73%, indicating that K-NN outperforms Logistic Regression in classifying papaya ripeness levels.
Prediksi Pola Pergerakan Saham Adro.Jk Melalui Model LSTM Berbasis Data Historis Iskandar, Muhammad Irsyad; Mudzakir, Tohirin Al; Cahyana, Yana; Pratama, Adi Rizky
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.554

Abstract

The fluctuating nature of stock price movements presents a significant challenge in investment decision-making. To address this issue, a predictive model capable of capturing historical patterns and accurately forecasting stock prices is required. This study aims to develop a stock price prediction model for PT Alamtri Resources Indonesia Tbk (ADRO.JK) using the Long Short-Term Memory (LSTM) algorithm. The dataset comprises daily closing prices from January 1, 2020, to December 30, 2024, obtained from Yahoo Finance. The data was processed in a time series format using a sliding window approach, employing 30 historical data points to predict the next price point. The model was constructed using two LSTM layers, one Dense layer, and techniques such as Dropout and EarlyStopping to prevent overfitting.The training and testing results indicate that the model performs exceptionally well, achieving a Mean Absolute Percentage Error (MAPE) of 0.0341 or 3.41%, corresponding to a prediction accuracy of 96.59%. In a short-term prediction scenario over seven days, the model achieved an accuracy of 99.07% (MAPE = 0.0093), while in a medium-term scenario up to May 19, 2025, it achieved an accuracy of 98.76% (MAPE = 0.0124). The predicted stock price on May 19, 2025, is estimated at IDR 1,913.76. With its high accuracy and low error rate, the LSTM model has proven to be a reliable tool for forecasting stock prices based on historical data.
Analisa Perbandingan Algorithma K-Nearest Neighbors dan Random Forest untuk Klasifikasi Tindakan Medis Persalinan pada Data Kehamilan Multi-Variabel Alfin Mahadi; Ema Utami
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.556

Abstract

Maternal mortality rate is still high in the most critical aspect affecting the quality of life of mothers and newborns. Very significant urgency considering the importance of proper medical in childbirth procedures. Kendal Islamic Hospital provides more complex data on maternal medical records of childbirth. Many optimization algorithms in classification have been proposed. Many swarm optimizations have been developed, particle swarm optimization is a superior optimization method. Comparison of K-Nearst Neighbors and Random Forest methods is often applied without optimization. This study compares the performance of the K-Nearest Neighbors (KNN) and Random Forest (RF) algorithms in classifying medical procedures for childbirth using medical records of maternity patients at RSI Kendal. The multivariable dataset includes age, weight, height, and more complete childbirth conditions. The preprocessing method involves imputation of empty values ??with KNN imputer, data normalization, and class oversampling using Synthetic Minority Over-sampling Technique (SMOTE). KNN and RF are optimized using Particle Swarm Optimization (PSO) to improve model accuracy. The results show that RF with an accuracy of 99.72% outperforms KNN with an accuracy of 97.03%. In the minority class, RF shows superiority with precision, recall, and F1-score reaching 100%, while KNN is more prone to errors in the minority class. This study confirms RF in handling complex multivariate data and highlights the importance of model optimization to improve accuracy in the classification of medical labor actions. These findings are expected to contribute to the development of machine learning-based decision support systems in the health sector.
Sistem Pendukung Keputusan Penentuan Kegiatan Ekstrakurikuler Sekolah Terbaik Menerapkan Metode TOPSIS dan ROC Adam Huda Nugraha; Imam Purwanto; Agus Turiyono
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.557

Abstract

Extracurricular activities (ekskul) are one of the additional components in the school education system, carried out outside of curriculum learning hours. The purpose of extracurricular activities is to hone students' abilities, add insight, improve skills, and arouse the enthusiasm of students through non-academic activities. However, there are still many students who are reluctant to join extracurricular activities, perhaps due to a lack of information or interest in the types of activities offered. In this research, a Decision Support System is introduced to help determine the best school extracurricular activities. This system is designed to provide recommendations for extracurricular activities that match the interests and potential of students. The method used in the development of this system is the AHP (Analytic Hierarchy Process) method to determine the priority of extracurricular activities that best fit the criteria that have been set. The results of this study show that the "A2 Martial Arts" activity gets the highest score of 0.7909 and is ranked first as the best extracurricular activity. This recommendation is expected to increase student participation in extracurricular activities and support the development of the potential and skills of students at school.
Implementasi Langchain dan Large Language Models Dalam Automatic Question Generation Untuk Computer Assisted Test Novri Rahman; Harahap, Nazruddin Safaat; Affandes, Muhammad; Pizaini
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.558

Abstract

The advancement of Artificial Intelligence (AI), particularly Large Language Models (LLM), presents new opportunities in transforming educational assessment systems. This study aims to implement the LangChain framework integrated with LLM for an Automatic Question Generation (AQG) system within a Computer Assisted Test (CAT) platform, using eleventh-grade Biology subject matter as a case study. The methodology includes data collection from PDF-based instructional materials, text embedding using Facebook AI Similarity Search (FAISS) as the knowledge base, and automatic question generation through the GPT-4o model. The system is developed using a microservices architecture comprising frontend and backend services built with the Next.js, FastAPI, and Express.js frameworks. System evaluation was conducted using the User Acceptance Test (UAT) and the DeepEval framework. The evaluation results show a teacher satisfaction rate of 92.7% and a positive response from students at 67.5%. Meanwhile, the DeepEval assessment reported average scores of 3,69% for hallucination, 97,44% for contextual precision, 83,30% for contextual relevancy, 70,63% for answer relevancy, and 92,47% for prompt alignment. These findings indicate that the integration of LangChain and LLM is effective in generating contextually accurate and relevant questions, although improvements are still needed in answer relevancy. This study is expected to provide an efficient solution for digital-based educational assessment and contribute to future developments in educational AI.
Sistem Pendukung Keputusan Berbasis Web Dalam Penentuan Susunan Personalia Organisasi Menggunakan Metode TOPSIS Irmansyah Lubis, Ahmadi; Purnamasari, Dwi Amalia; Ardi, Noper
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.560

Abstract

The determination of the composition of personnel in an organization, especially in the activities of the Branch Conference (Musran) of the Muhammadiyah Branch Executive of Belian Village, is still done manually and tends to be subjective. This has the potential to cause inefficiencies and internal conflicts in the decision-making process. This research aims to develop a web-based decision support system (SPK) that can help the management selection process objectively by using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The TOPSIS method was chosen because it is able to provide the best alternative ratings based on its proximity to positive and negative ideal solutions. The five main criteria used in decision-making include: organizational experience, level of education, loyalty to the organization, leadership, and availability of time. The system is designed using the PHP programming language and MySQL database. The results of the implementation show that the system is able to produce recommendations for a more systematic, transparent, and accountable management structure. With this TOPSIS-based SPK, the deliberation process can run faster and more efficiently without eliminating the principle of democracy in the organization. This research is expected to be an innovative solution in adaptive and sustainable information technology-based organizational governance.

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