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All Journal Jurnal Transformatika Jurnal Informatika Upgris Jurnal Sains dan Informatika Jurnal Pengembangan Rekayasa dan Teknologi Jurnal Ilmiah Sinus Building of Informatics, Technology and Science Joined Journal (Journal of Informatics Education Journal of Computer System and Informatics (JoSYC) JITU : Journal Informatic Technology And Communication TIN: TERAPAN INFORMATIKA NUSANTARA Information System Journal (INFOS) Arcitech: Journal of Computer Science and Artificial Intelligence Adopsi Teknologi dan Sistem Informasi Journal of Data Insights Tematik The Indonesian Journal of Computer Science Jurnal DIMASTIK
Setiaji, Galet Guntoro
Universitas Semarang
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Transportation Veterinary Other

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Comparison of Decision Tree and Random Forest Performance for Sentiment Analysis of Public Service App Reviews: Perbandingan Kinerja Decision Tree dan Forest Performance untuk Analisis Sentimen Ulasan Aplikasi Layanan Publik Aditya Rizky Purnama; Galet Guntoro Setiaji; Ahmad Rifa'i
Journal of Data Insights Vol 4 No 1 (2026): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v4i1.1112

Abstract

The Info BMKG application is a government-developed digital public service platform designed to provide real-time weather, seismic, and climate information to Indonesian citizens. The substantial volume of user reviews accumulated on the Google Play Store holds significant potential as a service evaluation resource; however, the limitations of manual review processes necessitate an efficient computational approach. This study proposes a machine learning-based sentiment analysis framework to classify user reviews of the Info BMKG application, while systematically comparing the performance of two algorithms Decision Tree and Random Forest using a dataset of 10,000 reviews collected via web scraping. The data underwent text preprocessing, rating-based sentiment labeling, and TF-IDF feature extraction, followed by evaluation using accuracy, precision, recall, F1-score, cross-validation, and computational time metrics. Experimental results demonstrate that Random Forest achieved 81% accuracy with a 77% F1-score, outperforming Decision Tree which attained 78% accuracy and 75% F1-score. In terms of efficiency, Decision Tree exhibited faster testing time (0.114 seconds) compared to Random Forest (0.201 seconds), while Random Forest proved more efficient in training time (7.347 seconds versus 12.421 seconds). These findings confirm that Random Forest represents the more optimal algorithm for sentiment classification tasks involving public service application user reviews.
Sentiment Analysis of FlyGaruda Review Using Support Vector Machine and Naive Bayes Algorithm: Analisis Sentimen Ulasan FlyGaruda Menggunakan Algoritma Support Vector Machine dan Naive Bayes Gibran Masta Pangestu Baskoro; Galet Guntoro Setiaji; Ahmad Rifa’i
Journal of Data Insights Vol 4 No 1 (2026): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v4i1.1117

Abstract

FlyGaruda is an official digital application owned by Garuda Indonesia that provides ticket booking and online check-in services for users. This study analyzed the sentiment of reviews on the Google Play Store by comparing the performance of Support Vector Machine and Multinomial Naive Bayes. The methods used include scraping, text preprocessing, extraction of the Term Frequency-Inverse Document Frequency (TF-IDF) feature, and evaluation using the Confusion Matrix. The dataset used totaled 4,790 reviews with positive, negative, and neutral categories. The results showed that both models obtained an accuracy of 82.25%. However, the Support Vector Machine produces a weighted precision of 77.66% and an F1-Score of 78.91%, better at handling data imbalances. Meanwhile, Multinomial Naive Bayes excels in computing efficiency with a training time of 0.08 seconds compared to 90.60 seconds on the Support Vector Machine. In conclusion, although it is slower, the Support Vector Machine provides more consistent and accurate classification performance. This research contributes to the development of a machine learning-based opinion analysis system to improve the quality of aviation digital services in a sustainable manner. These findings can serve as a reference in the selection of the best algorithms between accuracy and computational speed in large text data and support data-driven decision-making in the modern air transportation industry in the current era of global sustainable digital transformation
IMPLEMENTASI METODE SCRUM PADA APLIKASI WEB MANAJEMEN PEMBAYARAN KOS BERBASIS LARAVEL DAN FILAMENT Rio Eko Saputro; Galet Guntoro Setiaji; Ahmad Rifa'i
Information System Journal Vol. 8 No. 02 (2025): Information System Journal (INFOS)
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/infosjournal.2025v8i02.2283

Abstract

Penelitian ini mengajukan solusi atas kendala operasional dalam manajemen kos konvensional melalui perancangan sebuah aplikasi web terintegrasi yang memusatkan fungsi pembayaran dan penanganan keluhan. Proses pengembangan mengadopsi metodologi Scrum dengan tumpukan teknologi modern: framework Laravel sebagai fondasi, toolkit Filament untuk akselerasi pembangunan antarmuka, dan library Filament Shield untuk implementasi kontrol akses berbasis peran. Hasil akhir penelitian adalah platform fungsional dengan arsitektur dua peran: peran Admin difasilitasi dengan dasbor analitik dan kontrol manajerial penuh, sementara peran User Kos memperoleh antarmuka yang terfokus untuk menyederhanakan interaksi pembayaran dan keluhan melalui widget informatif. Secara konklusif, sistem yang dikembangkan ini mampu meningkatkan transparansi manajerial dan kenyamanan penghuni, sekaligus memvalidasi kombinasi metodologi dan teknologi yang dipilih sebagai model implementasi yang praktis untuk mendukung digitalisasi sektor properti skala kecil.
Co-Authors Aditya Rizky Purnama Ahmad Rifa'i Ahmad Rifa'i Ahmad Rifa'i Ahmad Rifai Ahmad Rifa’i Ahmad Rifa’i April Firman Daru Astrid Novita Putri, Astrid Badroe Zaman Basworo Ardi Pramono Bernandika Reyhan Groovytala Djoko Handoko Gibran Masta Pangestu Baskoro Gunata, Krida Pandu Hafiq Ibnu Wardana Hakim, Adya Abdu Azizul Handini Arga Damar Rani Hanif, Mohammad Burhan Joko Suntoro Khoirudin Khoirudin, Khoirudin Mohammad Burhan Hanif Mohammad Burhan Hanif Muhammad Dedi Setiawan Muhammad Rizal Nurtriana Hidayati Pecaro, R Immanuel Giovanni Italiano Rastri Prathivi Retno Utami, Tri Rifa i, Ahmad Rifa'i, Ahmad Rio Eko Saputro Saifur Rohman Cholil Saputra, Satria Nugraha Setiarso, Galih Siti Asmiatun, Siti Surono Surono Syahrul Adi Saputra Tirta Jurista Kumkamdhani Tirta Kumkamdani Veithzal Rivai Zainal Vensy Vydia Whisnumurti Adhiwibowo Wicaksana, Dinar Anggit Widiyanto, Max Teja Ajie Cipta