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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 335 Documents
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Analisis Faktor Penentu Profit Penjualan Mobil Menggunakan Algoritma Random Forest Muhamad Fahrul Rozi; Mukhammad Fakhir Rizal
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.v6i3.1004

Abstract

The automotive industry has significant changes in recent years that have directly affected vehicle sales profitability. The objective of this study is to analyze the factors influencing car sales profit using the USA Car Sales dataset for the 2018–2024 period. The approach employed is a quantitative method based on machine learning using the random forest algorithm, which was selected for its ability to handle complex data and identify important variables contributing to profit. The analysis was conducted through several stages, including data preprocessing, model training, performance evaluation, and result interpretation using feature importance techniques. These stages aim to obtain an accurate model while providing a comprehensive understanding of the influence of each variable on car sales profit. The results indicate that several factors have a significant impact on car sales profit, including car brand, year of sale, and the number of units purchased in a single transaction. Car brand reflects market preferences and consumer segmentation, while the year of sale represents market trends and changing conditions over time. In addition, the number of units sold per transaction plays an important role in increasing total profit. These findings provide strategic insights for automotive companies in formulating more effective, adaptive, and data-driven sales strategies.
Evaluasi Aplikasi Pembelajaran Berbasis Web Menggunakan Generative Artificial Intelligence dengan Metode ROUGE Rusmanto Rusmanto; Nuranisah Nuranisah
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.v6i3.1032

Abstract

This study aims to evaluate the functionality and answer quality of a web-based learning application that uses Generative Artificial Intelligence (GenAI) for the Pancasila and Civic Education (PPKN) course. The primary focus of this research lies in the system evaluation process, while the application development was carried out solely as a means of generating test data. The system was evaluated in two stages: functional testing using the black-box testing method and answer quality assessment using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) method. Black-box testing was conducted to ensure that all core system features operated according to specifications. The results of the black-box testing showed a 100% success rate across all test scenarios. Furthermore, answer quality evaluation was performed on 50 test data pairs consisting of GenAI-generated answers and reference texts (gold standards) prepared by PPKN lecturers using the ROUGE method. The evaluation results showed an average F1-score of 97% on the ROUGE-1, ROUGE-2, and ROUGE-L metrics. A total of 49 out of 50 answers were categorized as “Very Good” (? 0.75), while 1 answer was categorized as “Good.” These findings indicate that the application is capable of generating answers with a very high level of textual similarity to academic references. This study contributes to filling the gap in empirical evidence and provides a standardized evaluation benchmark for web-based GenAI applications in education, while also offering an evaluation approach that integrates system functional testing and ROUGE-based answer quality measurement. However, this evaluation is still limited to linguistic aspects based on n-grams and does not yet fully represent semantic depth.
Prediksi Kelulusan Mahasiswa Prodi Informatika dengan Algoritma Decision Tree (C4.5) dan Naïve Bayes Steven Gerrard; Ade Eviyanti; Hamzah Setiawan; Ika Ratna
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.v6i3.1035

Abstract

The primary parameter for measuring higher education quality, which also has a crucial impact on the accreditation process, is the percentage of students graduating on time. However, the reality on the ground shows that many students face obstacles in completing their studies within the ideal timeframe. Therefore, a data-driven strategy is needed to project students' chances of graduation early. This research aims to compare the performance of the Decision Tree (C4.5) and Naïve Bayes algorithms in classifying the potential for on-time graduation. The data utilized included 161 entries from the Informatics Study Program, class of 2022, at the University of Muhammadiyah Sidoarjo. The attributes analyzed were divided into academic and non-academic factors, including gender, first-semester social studies grades (IPS), GPA, PKMU (Community Service Program) graduation score and status, BQ and Ibadah scores, and accumulated SKEK points. The research process went through several phases: preprocessing, class labeling, model development, and performance evaluation through a confusion matrix and 5-fold cross-validation. The test was validated by separating the training and test data into ratios of 70:30, 80:20, and 90:10. Based on the test results, the C4.5 algorithm achieved a peak accuracy of 100% across all ratio scenarios, with an average cross-validation accuracy of 96.88%. Meanwhile, Naïve Bayes achieved a maximum accuracy of 94.13% with an average cross-validation of 93.00%. These findings indicate that the C4.5 algorithm has superior performance on this specific dataset. The output of this predictive model is expected to serve as an objective basis for institutions in establishing proactive academic policies.
Sistem Pemantau Siklus Haid Sebagai Media Manajemen Kesehatan Reproduksi Menggunakan Metode Forward Chaining dan Certainty Factor Rizki, Dimas Alva; Supriyono, Supriyono; Wibowo, Feri; Hamka, Muhammad
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.v6i3.1064

Abstract

The lack of understanding regarding the normal limits of physiological menstrual parameters leads to delayed detection of reproductive health disorders. Current conventional tracking applications generally focus on date prediction without analyzing accompanying symptoms. This research provides a technical contribution in the form of an expert system design for the early diagnosis of menstrual disorders based on four basic physiological variables: menstrual duration, cycle length, blood volume, and pain symptoms. The system is built using the Forward Chaining method to map the diagnostic inference flow, and the Certainty Factor (CF) to calculate the percentage of the expert's confidence level in the initial medical conclusion. Rule base validation was conducted with a general medical expert as a reference for early-stage screening (Amenorrhea, Oligomenorrhea, Polymenorrhea, Hypermenorrhea, Hypomenorrhea, Dysmenorrhea, and Normal). Black Box functionality testing shows that the system logic runs validly according to the static rule boundaries. Evaluation using the System Usability Scale (SUS) on 30 respondents resulted in a score of 83, indicating that the application has an excellent level of usability. As an early detection prototype, this system focuses on presenting diagnostic probabilities based on expert certainty, although continuous clinical validity testing using a Confusion Matrix remains necessary to measure medical accuracy comprehensively.
Identifikasi Tingkat Intensitas Opini dalam Analisis Sentimen Berbasis Aspek Menggunakan Enhanced Triplet Extraction Jimmy Richardo Chastelo B, Gabriel; Berutu, Sunneng Sandino; Budiati, Heani
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.v6i3.1074

Abstract

Conventional sentiment analysis often overlooks variations in the intensity of opinions within text reviews. This is due to the limitations of the Aspect-Based Sentiment Analysis (ABSA) approach, which is restricted to three main triplet components. This study aims to develop and expand the Aspect-Sentiment-Opinion Triplet Extraction (ASOTE) framework to extract entity relationships and sentiment polarity by integrating opinion intensity detection. This study implements the ABSA approach by expanding the triplet structure into four components: aspect, opinion, intensifier, and sentiment (Enhanced Triplet). Data was collected via web scraping of Twitter (X) comments related to the Free Nutritious Meals program, which served as a case study to test the model’s ability to analyze public sentiment. The data then undergoes pre-processing and BIO Tagging, and is classified using a fine-grained sentiment approach to capture the nuances of emotional intensity in greater detail. A Transformer-based model, namely IndoBERT, was used to understand the context and intensity of meaning in the Indonesian language. Evaluation results on the test data show that the model achieved an accuracy of 88% and an average F1-score of 0.88 in sentiment polarity classification between entities, indicating strong model performance. These results demonstrate that providing a framework that is more sensitive to the intensity of opinions when classifying the nuances of public sentiment is a highly effective solution. 

Page 34 of 34 | Total Record : 335

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