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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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Perancangan Aplikasi Layanan Foto Prewedding Berbasis Mobile Menggunakan Model Waterfall Aqilla Fadia Hafizhah; Karnadi Karnadi; Muhammad Ihsan
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.916

Abstract

This research aims to design a mobile-based pre-wedding photo service application that can simplify the ordering process, speed up communication between customers and photo studios, and improve operational efficiency. The research method used is a qualitative method with data collection techniques through observation and literature studies. The system development method used by the waterfall model. The application is implemented using Android Studio with the kotlin and java programming languages and the firebase database. The result of this implementation is in the form of a mobile-based prewedding photo service application that has registration, login, photo selection, ordering, order confirmation, transaction history, package management and photo collection by admins, as well as revenue reports that can be accessed by the owner. Based on the results of tests using the blackbox method, all application features can run well and according to user needs. With this application, it is hoped that Studio Photo Holic can improve the quality of service and provide convenience for customers in ordering prewedding photo services.
Analisis SWOT dan Critical Success Factor pada Sistem Informasi Logistik IGD Berbasis Google Workspace Ganef Tri Wijayatno; Fahreza Dandy Sihmawanto; Prind Triajeng Pungkasanti
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.917

Abstract

The Emergency Department (ED) Logistics Information System (IGD) is a crucial element in ensuring the smooth distribution of medical supplies, thus maintaining the quality of healthcare services. Utilizing cloud-based services such as Google Workspace offers an innovative solution for integrating various logistics functions into one platform, from stock management to real-time reporting. This study aims to evaluate the internal and external conditions of the Google Workspace-based ED Logistics Information System using a SWOT approach and to determine Critical Success Factors (CSFs) as the basis for development strategies. The research method used quantitative and qualitative descriptive analysis. Based on observations, questionnaires, and interviews with ED logistics staff, nurses, and management directly involved in system management, the IFAS analysis showed that the total score for weaknesses (0.75) was slightly higher than the strengths (0.73), indicating persistent internal constraints, particularly in data security, limited specific logistics features, and reliance on internet connectivity. Meanwhile, the EFAS analysis showed that external threats (0.75) outweighed opportunities (0.74), with the primary threats being dependence on third parties, the risk of data breaches, and staff resistance to the new system. The CSF analysis identified that system success is influenced by improved data security, the development of specialized logistics features, and ongoing management support. The results of this study are expected to serve as a reference in developing strategies to improve the effectiveness and reliability of cloud computing-based emergency room logistics systems.
Pendekatan Interpretatif dalam Prediksi Persalinan Caesar Menggunakan Decision Tree pada Data Pelayanan Kesehatan Primer Arif Susilo; Asep Arwan Sulaeman
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.919

Abstract

Caesarean delivery is a medical procedure performed under specific conditions to reduce risks for both mother and baby. However, the increasing rate of caesarean deliveries, which is not always based on medical indications, highlights the need to systematically understand the factors influencing delivery methods. This study aims to explore the relationship between clinical variables of pregnant women and delivery methods using a data mining approach based on Decision Tree and Random Forest algorithms. The dataset consists of secondary data collected from three primary healthcare centers (Puskesmas), namely Mranti, Banyuurip, and Bayan, with a total of 390 records. The study follows the Knowledge Discovery in Database (KDD) framework, including data selection, preprocessing, transformation, dataset splitting, handling class imbalance using Synthetic Minority Over-sampling Technique (SMOTE), modeling, and evaluation. The results show that the model achieved an accuracy of 88%, precision of 58.82%, recall of 66.67%, and an F1-score of 62.50%. Although the accuracy appears relatively high, the model’s performance in identifying caesarean cases remains moderate. This indicates that the model is more effective in classifying the majority class than the minority class. This study highlights that data mining applied to primary healthcare data can provide valuable insights for early pattern identification. However, the obtained results are not sufficient for direct clinical decision-making. Future research with larger datasets and more adaptive methods is required to improve model performance.
Prediksi Minat Belajar Siswa Berdasarkan Nilai Dan Intensitas Bermain Game Menggunakan Algoritma K-Nearest Neighbor Karimah Agustin; Gina Purnama Insany
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.949

Abstract

This study aims to predict students' learning interest based on Islamic creed scores and their frequency of playing the Free Fire game using the KNN algorithm. The dataset used includes Islamic creed assignment scores, final exam (UAS) scores, game play time, frequency of play, and learning interest categories. The analysis process was carried out through several stages: data exploration, data cleaning and processing, selecting the best values, modeling, and disseminating the model results. The model was tested using the 5-fold cross validation method and confusion matrix to ensure that the prediction performance was adequate. The results showed that academic scores, especially Islamic creed final exam scores, had a greater influence on learning interest than the intensity of playing the Free Fire game. The KNN model with a k value of 6 produced an accuracy of 94.73% and a very small prediction error, as seen in the Confusion Matrix and Classification Report. These results indicate that the model is capable of working well in classifying students' learning interest into medium or high categories. This prediction model is expected to be used as a tool to understand student learning interest patterns in the school environment. The contribution of this research is the application of KNN-based machine learning methods in analyzing student learning interests, which were previously dominated by conventional approaches such as regression and questionnaires.
Analisis Sentimen Publik Terhadap Progres Pembangunan IKN di TikTok Menggunakan Naïve Bayes dan SVM Candra Naya; Ermanto
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.969

Abstract

The rapid development of social media has created new spaces for the public to express opinions regarding various public policies, including the development of Indonesia’s new capital city, Nusantara (IKN). TikTok, as one of the platforms with high user interaction, provides a valuable data source for analyzing public perceptions of this national development project. This study aims to analyze sentiment in TikTok comments related to the progress of IKN development and to compare the performance of the Naïve Bayes and Support Vector Machine (SVM) classification algorithms. The research employs a quantitative approach using a data mining framework based on the SEMMA methodology, which includes the stages of Sample, Explore, Modify, Model, and Assess. The dataset was collected through web scraping using Apify, resulting in 2,000 comments, of which 1,850 valid comments remained after the selection process. Text preprocessing was performed through cleaning, case folding, tokenizing, stopword removal, and filtering, followed by feature extraction using the TF-IDF method. The dataset was divided into training and testing sets using an 80:20 ratio. Model evaluation was conducted using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that the SVM algorithm outperformed Naïve Bayes with an accuracy of 91.25%, precision of 90.70%, recall of 92.86%, and F1-score of 91.77%, while Naïve Bayes achieved an accuracy of 84.25%, precision of 83.87%, recall of 86.67%, and F1-score of 85.24%. The sentiment distribution indicates that positive sentiment toward the development of IKN slightly dominates negative sentiment. These findings suggest that SVM is more effective for classifying sentiment in informal social media text such as TikTok comments.
Analisis Pengelompokan Jenis Anomali Aktivitas Pengguna Pada Log Sistem Informasi Klinik Menggunakan Lof Dan K-Means Puja M Alca; Sumijan Sumijan; Rini Sovia
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.v6i4.972

Abstract

Digital transformation in the healthcare sector has driven the adoption of clinic information systems for computerized management of patient medical records. Sensitive data security is threatened by user behavior deviations, requiring immediate detection mechanisms. This study aims to identify anomalous activity patterns and indicators from user log records, including unusual database operation frequencies, abnormal access times, and suspicious data manipulation patterns.The Local Outlier Factor algorithm functions to systematically calculate the local density score of each data point relative to its nearest neighbors. This method detects user activities that deviate significantly from normal patterns in daily clinic operational systems. The K-Means Clustering algorithm groups detected anomalous data into clusters based on similarity of user activity feature characteristics. The clustering facilitates administrator categorization of occurring anomaly types along with threat severity levels to the system.Research data were obtained from user activity log records of the clinic information system at Klinik Utama RIDDA Payakumbuh, which underwent preprocessing stages including data cleaning, feature transformation, value normalization, and handling of missing values.Test results demonstrate that the combination of LOF and K-Means achieved accuracy of 89.5%, precision of 87.3%, and recall of 85.7% on the test dataset. These validation metrics prove that the method effectively addresses user behavior deviation detection in the clinic environment. The test results affirm that the hybrid approach can identify suspicious activities with minimal error rates, ensuring reliability. The research contribution provides practical impact for clinic information system administrators in supervising patient data security through integrated early warning mechanisms.
Analisis Komparasi Convolutional Neural Network dan Learning Vector Quantization dalam Klasifikasi Khat Arab Digital Sabri T Rahman; Yuhandri Yuhandri; Sumijan Sumijan
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.v6i4.976

Abstract

Arabic khat is a form of writing that possesses complex visual characteristics, such as variations in letter shapes, stroke thickness, texture, and stylistic differences. This complexity creates challenges in manually recognizing different types of khat. This study aims to analyze and compare the performance of Convolutional Neural Network (CNN) and Learning Vector Quantization (LVQ) methods in classifying five types of Arabic khat digital images, namely Diwani, Farsi, Naskh, Ruqaa, and Tuluth. The dataset was obtained from the Kaggle.com platform. CNN architecture consists of an input layer of 100×100×1, followed by two convolutional layers with 32 and 64 filters of size 3×3, each followed by ReLU activation and max pooling with stride 2. The network then includes a fully connected layer with 64 neurons, a final fully connected layer corresponding to the number of classes, a softmax layer, and a classification layer. CNN training was conducted using 5-fold cross-validation, applying data augmentation in each fold. For the LVQ method, Local Binary Pattern (LBP) was used for feature extraction from 100×100 images with parameters: radius 1, 8 neighbors, cell size [48 48], and L2 normalization. The extracted features were used for training with an initialization of 25 prototypes from 5 classes. The process also employed 5-fold cross-validation. From 40 testing samples, the CNN model achieved an accuracy of 87.5%, while the LVQ model achieved an accuracy of 85%. The CNN algorithm demonstrated better performance in handling the complex visual patterns of Arabic khat. Meanwhile, LVQ showed advantages in architectural simplicity and computational efficiency. This research is expected to contribute to the development of Arabic khat image classification systems and serve as a reference in selecting optimal methods for Arabic khat recognition.
Impelementasi Sistem Presensi Wisuda Berbasis QR Code untuk Meningkatkan Layanan Menggunakan MERN Stack Puja Hanifah; Felly Chandra; Dini Hidayatul Qudsi; Meilany Dewi
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.v6i4.979

Abstract

Graduation is a ceremonial activity routinely conducted at a university, including at Polytechnic Caltex Riau. The process of conferring degrees at Polytechnic Caltex Riau has been experiencing an increase each year. In the registration process, a website has already been used to facilitate the registration, but on the implementation day, it is still done manually. Like the attendance process for graduates still using paper, as well as parents who must sign in when they arrive. This takes a long time in the process, not to mention the loss of attendance records, duplicate signatures, and so on. Because of these issues, a graduation attendance system was built that will use QR codes in the registration process and was developed using MERN Stack technology. The purpose of developing this system is to facilitate the graduation committee in coordinating activities with parents and graduates. The use of MERN Stack technology is very suitable for development for real-time data, and using QR codes speeds up the existing process. From the research results, all features of the system are operational and can be used based on the UAT testing conducted on three users, namely the admin, super admin, and BAAK. The usability testing yielded a score of 63%, indicating that the system is somewhat difficult to use and the interface needs further improvement.
Analisis Komentar Youtube Terhadap Kebijakan Bebas Impor Oleh Pemerintah Pusat Menggunakan Support Vector Machine Ignasius Aditya Anggoro Putra; Salmon Salmon; Kusnandar Kusnandar
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.995

Abstract

YouTube has become an important platform for expressing public opinion on government policies, including the free import policy. This study aims to analyze the sentiment of YouTube user comments regarding the free import policy using the Support Vector Machine (SVM) algorithm. The data were collected through web scraping using the YouTube Data API v3 from a Kompas.com video, resulting in 3,267 raw comments. The research stages include text preprocessing, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), lexicon-based sentiment labeling, and sentiment classification using SVM. To address data imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied. Model performance was evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that the SVM model achieved an accuracy of 77.00% without tuning and 75.15% after hyperparameter optimization, with improved balance across sentiment classes. These findings indicate that SVM is effective for sentiment classification of YouTube comments.
Influence of Imbalanced Data on Text Classification Using Recurrent Neural Network Rina Septiriana; Tursina Tursina
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.v6i4.996

Abstract

Recurrent Neural Networks (RNNs) such as LSTM and GRU are designed for sequential data. However, their performance in emotion detection is often compromised by class imbalance. This study compares LSTM and GRU architectures for classifying emotional states using a dataset of 4,386 Indonesian tweets. The dataset exhibits a mild imbalance (approximately 1.7:1) across five classes: Anger, Happy, Sadness, Love, and Fear. However, the effectiveness of these models is often hindered by class imbalance in datasets, which biases predictions toward majority classes and compromises the reliability of standard metrics. This study aims to systematically evaluate the comparison of LSTM and GRU architectures in processing imbalanced Indonesian emotional tweet data. The methodology involves evaluating these models across various resampling techniques, including Random Oversampling, SMOTE, and Near-Miss. Key findings reveal that LSTM consistently outperforms GRU in capturing complex emotional patterns. Specifically, the LSTM model combined with Random Oversampling emerged as the most robust configuration, achieving a Macro-F1 score of 71% and an accuracy of 73%. While Random Oversampling effectively enhanced minority class recognition without overfitting, SMOTE and Near-Miss introduced significant performance trade-offs. These results provide actionable insights for selecting optimal architectures and resampling strategies to mitigate imbalance-related biases in sequential classification tasks.

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