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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Informatika Universitas Pamulang Jurnal Teknologi Sistem Informasi dan Aplikasi Journal on Education Journal of Artificial Intelligence and Innovative Applications (JOAIIA) Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Teknologi Sistem Informasi KOMMAS: Jurnal Pengabdian Kepada Masyarakat OKTAL : Jurnal Ilmu Komputer dan Sains Jurnal ilmiah teknologi informasi Asia
Junianto, Mochamad Bagoes Satria
Universitas Terbuka
Astronomy Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences

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Journal : Jurnal Teknologi Sistem Informasi dan Aplikasi

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Enhancing Usability Testing Through Sentiment Analysis: A Comparative Study Using SVM, Naive Bayes, Decision Trees and Random Forest Basri, Hasan; Junianto, Mochamad Bagoes Satria; Kusyadi, Irpan
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 4 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i4.45117

Abstract

In the digital age, mobile applications have become an integral part of everyday life, making usability testing an essential factor in ensuring a seamless user experience. Traditional usability testing methods often demand considerable resources, including time and cost, which calls for more efficient and automated alternatives. This study explores the use of sentiment analysis as an innovative approach to evaluate the usability of mobile applications. By analyzing user reviews from the Google Play Store, the research compares the effectiveness of four machine learning algorithms—Support Vector Machine (SVM), Naive Bayes, Decision Tree, and Random Forest—in classifying sentiment and evaluating usability. A dataset consisting of 2,000 reviews from a banking app was collected and labeled based on usability criteria, such as efficiency, user satisfaction, learnability, memorability, and error rates. The feature extraction process utilized Term Frequency-Inverse Document Frequency (TF-IDF) to enhance the relevance of the review texts for sentiment analysis. The findings reveal that Random Forest achieved the highest accuracy (68.15%) and demonstrated the best performance in terms of F1 Score, precision, and recall, although it had the longest processing time. In contrast, Naive Bayes, while the fastest, showed lower accuracy and F1 Score, making it suitable for applications with large datasets or limited processing time. Decision Tree and SVM offered a balanced trade-off between speed and accuracy. The study concludes that Random Forest is the preferred choice when high accuracy and prediction performance are crucial, despite its longer processing time. Meanwhile, Naive Bayes is more appropriate for scenarios demanding rapid data processing, and SVM and Decision Tree are recommended when a balance between speed and accuracy is needed.
Sistem Pendukung Keputusan Metode Simple Additive Weighting (SAW) Pemilihan Vendor Jasa Boga Terbaik pada Pusat Bisnis Universitas Terbuka Junianto, Mochamad Bagoes Satria; Basri, Hasan
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 4 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i4.45152

Abstract

The Open University Business Center has duties and functions that focus on the management and development of business units that support the operations and financial sustainability of the Open University. One of the policies handled is the selection of catering vendors used at every event in the Open University environment. Selecting the right catering vendor is a crucial factor in ensuring customer quality and satisfaction. There are 12 catering vendors who are partners of the Open University Business Center which are usually only assessed based on the presentation and quality of the food (Taste). In fact, besides these 2 criteria, there are still several other criteria that must be considered such as Punctuality, interest and vendor experience (administration). The SAW method was chosen because of its ability to carry out multi-criteria assessments simply and efficiently. Of the 12 catering vendors who collaborate with the Open University Business Center, a ranking will be given where the vendor with the highest score is considered most appropriate to the needs of the Open University Business Center. With this Decision Support System, it is hoped that the catering vendor selection process can be carried out objectively, transparently and faster so as to support better decision making at the Open University Business Center.
Co-Authors Abdoel Kadir Zaelani Agung Setia Budi Ali Djamhuri Derita Qurbani Entis Sutrisna Fajar Agung Nugroho Hasan Basri Hasan Basri Hendri Ardiansyah HENDRI ARDIANSYAH Irpan Kusyadi Mayang Anglingsari Putri, Mayang Anglingsari Minrohayati . Mochamad Iqsal Alfiansyah Monika Juwita Nardiono Nardiono Oky Gustian Resti Amalia Sartika Lina Mulani Syaeful Machfud Teti Desyani Trihapningsari, Denisha Unggul Utan Sufandi