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All Journal Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer)
Fahmi, Mohammad Irfan
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Computer Science & IT Decision Sciences, Operations Research & Management

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ANALYSIS OF STUDENT SATISFACTION WITH STUDENT MANAGEMENT SERVICES IN THE INFORMATION SYSTEMS STUDY PROGRAM AT PRIMA INDONESIA UNIVERSITY USING THE SERVICE QUALITY (SERVQUAL) METHOD Wijaya, Malvin Luckianto; Sitompul, Daniel Ryan Hamonangan; Sinurat, Stiven Hamonangan; Rahmad, Julfikar; Fahmi, Mohammad Irfan; Indra, Evta
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 2 (2023): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i2.3446

Abstract

The purpose of this research is to analyze the level of student satisfaction with student management services in the Information Systems Study Program at Prima Indonesia University. The method used in this study is the Service Quality method to measure the level of student satisfaction. The results of the research show that the level of student satisfaction with student management services is 4.28 and the Servqual method has good quality in measuring student satisfaction.
ANALISIS SENTIMEN ULASAN APLIKASI MEDIA SOSIAL WHATSAPP DAN TELEGRAM BERDASARKAN ULASAN PLAY STORE MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE Ginting, Yudhi Aginta Pranata; Ndraha, Esterina; Fahmi, Mohammad Irfan
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.1979

Abstract

The rapid development of mobile-based information technology has increased the relevance of sentiment analysis on user-generated reviews. This study applies the Support Vector Machine (SVM) method combined with TF-IDF feature extraction to classify user sentiments from WhatsApp and Telegram reviews on the Play Store. A total of 4,400 Indonesian-language reviews (2,200 per application), collected via web scraping from different time periods in 2024, were processed through standard text preprocessing techniques and transformed using TF-IDF with 1–2 n-grams. The SVM model with a linear kernel was trained on 80% of the data and tested on 20% using accuracy, precision, recall, and F1-score metrics. Results show that Telegram reviews achieved higher accuracy (83%) and F1-score (0.83) compared to WhatsApp (68% accuracy, 0.73 F1-score). Sentiment analysis revealed a positive sentiment dominance in WhatsApp (~60%) and negative sentiment in Telegram (~52%). These findings suggest that Telegram reviews tend to have more concise and structured language, contributing to better classification performance. The study confirms the effectiveness of the SVM–TF-IDF approach and recommends further research using advanced models and embeddings to handle the complexity of informal review language.
Co-Authors Ginting, Yudhi Aginta Pranata Indra, Evta Ndraha, Esterina Rahmad, Julfikar Sinurat, Stiven Hamonangan Sitompul, Daniel Ryan Hamonangan Wijaya, Malvin Luckianto