Bulletin of Computer Science Research

Analisis Faktor Penentu Profit Penjualan Mobil Menggunakan Algoritma Random Forest

Muhamad Fahrul Rozi, Mukhammad Fakhir Rizal — Mon, 13 Apr 2026 00:00:00 +0700

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.

Prediksi Kelulusan Mahasiswa Prodi Informatika dengan Algoritma Decision Tree (C4.5) dan Naïve Bayes

Steven Gerrard, Ade Eviyanti, Hamzah Setiawan, Ika Ratna — Mon, 13 Apr 2026 00:00:00 +0700

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.

Evaluasi Aplikasi Pembelajaran Berbasis Web Menggunakan Generative Artificial Intelligence dengan Metode ROUGE

Rusmanto Rusmanto, Nuranisah Nuranisah — Mon, 13 Apr 2026 00:00:00 +0700

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.

Pengembangan Aplikasi E-Booking Konser K-Pop Berbasis QRIS dengan Pendekatan User-Centered Design untuk Optimalisasi Pengalaman dan Efisiensi Transaksi

Mon, 13 Apr 2026 00:00:00 +0700

The increase in the number of K-Pop concerts in Indonesia drives the need for a ticket e-booking system that is not only efficient but also capable of accommodating transaction characteristics with high demand levels and optimal integration of digital payment systems. The main issue with the current e-ticketing system lies in the platform's general nature (multi-event marketplace) and the suboptimal integration of user experience with digital payment systems like QRIS. This research aims to design and develop a mobile-based K-Pop concert e-booking application integrated with QRIS and empirically test its impact on transaction efficiency and user experience. The method used is Research and Development (R&D) with a System Development Life Cycle (SDLC) Waterfall model approach, complemented by a quasi-experimental design (posttest control group design). Testing was conducted on 60 respondents divided into experimental and control groups, with variables measured including transaction time, transaction success rate, usability using the System Usability Scale (SUS), and user satisfaction. The research results show that the K-Party application is capable of effectively and integratively supporting the e-booking process. Statistical analysis shows that the use of QRIS has a significant impact on transaction efficiency and user experience, as well as contributing to business performance improvements, including a 36.2% increase in revenue and a 5-10% increase in audience numbers. Thus, the developed system is not only technically feasible but also empirically proven to add value in the context of the digital entertainment industry.