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All Journal Sistemasi: Jurnal Sistem Informasi JURIKOM (Jurnal Riset Komputer) Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Information Systems and Informatics Jurnal Pengabdian Masyarakat Bumi Raflesia DEVOTE: Jurnal Pengabdian Masyarakat Global Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Journal of Computer Science and Information Technology Jurnal Pengabdian Masyarakat Ilmu Komputer
Yadi Utama
Universitas Sriwijaya
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Social Sciences Other

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Journal : journal of information systems and informatics

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Harnessing SVM for Sentiment Analysis: Insights from Gojek's Instagram Engagement Savero, Muhammad Juan; Ibrahim, Ali; Utama, Yadi; Lestari, Endang
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1041

Abstract

The development of digital technology has changed the transportation industry, including online services such as Gojek. Understanding customer sentiment is key in improving user experience and designing more effective business strategies. This research analyzes Gojek user sentiment on Instagram using Support Vector Machine (SVM). Data is obtained through web scraping, then processed through text cleaning, tokenization, common word removal, and stemming. Features were extracted using Term Frequency-Inverse Document Frequency (TF-IDF) before being classified with SVM. The results showed that the SVM model achieved 70.82% accuracy in classifying user sentiment. Most positive comments highlight the convenience and efficiency of the service, while negative comments are more related to high tariffs, application constraints, and less responsive customer service. These findings provide insights for Gojek to improve marketing strategies, optimize customer service, and adjust fare policies based on user feedback. In addition, this analysis can help in predicting real-time customer satisfaction trends through sentiment monitoring on social media. As a development step, this research recommends further exploration with deep learning and Aspect-Based Sentiment Analysis (ABSA) to improve accuracy and understand the service aspects that have the most influence on customer satisfaction.
Taxpayer Classification Using K-Means Clustering to Support CRM Strategy Development: Case Study of Prabumulih City Samsat Tammam, Bimmo Fathin; Ibrahim, Ali; Indah, Dwi Rosa; Oklilas, Ahmad Fali; Utama, Yadi
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1365

Abstract

Effective management of taxpayer data is crucial for enhancing compliance and optimizing regional revenue. This study addresses the limited use of data-driven taxpayer segmentation in local Samsat institutions by applying K-Means Clustering to support targeted Customer Relationship Management (CRM) strategies. A dataset of 3,999 motor vehicle taxpayer records from September 2025 was processed through feature selection, scaling, and clustering. The analysis identified three distinct taxpayer groups based on payment timeliness, compliance consistency, and vehicle age. Cluster validity was confirmed using the Davies-Bouldin Index, yielding a value of -41.327 for k = 3, supported by ANOVA for statistical significance. The findings highlight how clustering can reveal taxpayer behavior patterns, guiding personalized services and compliance programs. This study's novelty lies in integrating clustering outcomes with practical CRM strategies for public agencies, offering a data-driven approach to improve taxpayer engagement and regional revenue. However, the study is limited by its focus on a single-period dataset and vehicle-related attributes.
Co-Authors Abdiansah, Abdiansah Affandi, Azhar Kholiq Ahmad Fali Oklilas Ahmad Fali Oklilas Ahmad Rifai Al Farissi Ali Ibrahim Ali Ibrahim Allsela Meiriza, Allsela Alzaini, Akbar Anita Desiani Arhami, Muhammad Des Alwine Zayanti, Des Alwine Dhini Kurnia, Rizka Dwi Rosa Indah Endang Lestari Ermatita - Fathoni - Hardini Novianti Iman Saladin B. Azhar Irmeilyana Islamiansyah, Wira Kodri, Lay Mira Afrina Okllilas, Ahmad Fali Purwita Sari, Purwita Ramayanti, Indri Rezqe, Beriadi Agung Nur Ricy Firnando Rizka Dhini Kurnia Samsuryasi, Samsuryasi Sanjaya, M. Rudi Sasongko, Muhammad Aditya Savero, Muhammad Juan Septiana, Rika Septiana Tammam, Bimmo Fathin Thuraya, Zafira Wjaya, M.Aditya Yunus, Hedi Zahran, Ahmad Hafizh Zaini, Akbar Al Zhafiri, Muhammad Farisan