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All Journal JSAI (Journal Scientific and Applied Informatics) bit-Tech
ZA, Makmun
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Computer Science & IT

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Analisis Perbandingan Sistem Bengkel Berbasis Dekstop dan Web Menggunakan Metode User Acceptance Testing Affandi, Muhammad Surya; Setiyanto, Rudi; ZA, Makmun
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8416

Abstract

This study aims to compare desktop-based and web-based workshop information systems at PT. Motoreko Mobilindo using the User Acceptance Testing (UAT) method. The evaluation was conducted based on five key indicators: ease of use, access speed, reliability, security, and user satisfaction. Data were collected through questionnaires filled out by active users who had experience with both systems. The results show that the desktop system excels in terms of access speed (96%), reliability (97%), and security (99%). Meanwhile, the web system outperforms in ease of use (97%) and user satisfaction (96%). These findings indicate that although the desktop system has more stable technical performance, the web system is more widely accepted by users due to its convenience and flexibility. This study recommends a hybrid approach as a strategic solution, by maintaining the technical strengths of the desktop system while integrating the accessibility benefits of the web system. Thus, the company can develop a balanced, adaptive information system that aligns with the needs of digital transformation.
Sentiment Classification of Customer Reviews in the Fast-Food Industry Using the Naïve Bayes Algorithm Rukmana, Diding; Putri, Aliya Namira; Karim, Abdul; ZA, Makmun
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2766

Abstract

In the digital era, online reviews have become a significant source of information, influencing consumer perceptions and purchasing decisions, particularly in the fast-food industry. This research focuses on classifying customer sentiment towards A&W restaurants based on online reviews using the Naïve Bayes algorithm. The objective of this study is to analyze customer feedback to understand their perceptions of A&W’s services and products. The research follows the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, which involves six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Data was collected from Google Reviews of the A&W Palem Semi branch, consisting of 200 customer reviews, which were preprocessed to remove irrelevant content and prepare the data for analysis. The Naïve Bayes algorithm was applied to classify the sentiments into three categories: positive, negative, and neutral. The model achieved an overall accuracy of 83%. However, the results revealed a significant class imbalance, with most reviews labeled as neutral. While the model performed well in identifying neutral sentiment (precision 0.89, recall 0.97, F1-score 0.93), it failed to classify positive and negative sentiments accurately, as both achieved precision, recall, and F1-scores of 0.00. This demonstrates that the data imbalance severely impacted the model’s ability to detect minority sentiment classes. The research concludes that while Naïve Bayes offers useful insights into customer sentiment, improvements are necessary, including applying data balancing techniques or exploring alternative algorithms such as SVM or Random Forest to enhance classification performance across all sentiment categories.
Co-Authors Abdul Karim Affandi, Muhammad Surya Putri, Aliya Namira Rukmana, Diding Setiyanto, Rudi