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All Journal Jurnal Elektronika dan Teknik Informatika Terapan Repeater: Publikasi Teknik Informatika dan Jaringan
Ricardus Mba Dala Pati
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Computer Science & IT

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Pengembangan Pendidikan Kesehatan Tentang Pengenalan Sumber Penyakit Dan Penanganannya Kepada Masyarakat Menggunakan Website dengan metode Agile Muhammad Fauzi; Muhamad Ihsan Hasanudin; Muhammad Sadam Ramadhoni; Ricardus Mba Dala Pati
Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK ) Vol. 2 No. 2 (2024): Juni: Jurnal Elektronika dan Teknik Informatika Terapan (JENTIK)
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jentik.v2i2.671

Abstract

This study aims to develop health education regarding disease identification and management for the community using a website. The development process incorporates the principles of health promotion and utilizes online platforms to disseminate information effectively. Through this initiative, the community gains access to comprehensive resources on disease identification, preventive measures, and appropriate management strategies. The website serves as a user-friendly tool to enhance health literacy and empower individuals to make informed decisions about their well-being.
Analisis Sentimen pada Ulasan Aplikasi JakLingko Menggunakan Metode Naïve Bayes Ricardus Mba Dala Pati; Eka Kusuma Pratama; Tuslaela Tuslaela
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 3 No. 4 (2025): Oktober: Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v3i4.638

Abstract

JakLingko is a digital-based public transportation integration system developed to facilitate access to various transportation modes in Jakarta. Along with the increasing number of users, reviews on the JakLingko application reflect user experiences and perceptions. This study aims to analyze the sentiment of user reviews on the Google Play Store using the Naïve Bayes method. Data collection was conducted through web scraping, resulting in 3,260 reviews. The data were preprocessed, sentiment-labeled, and classified using Orange Data Mining. The research applied a quantitative experimental approach with a machine learning framework. The classification results showed that neutral sentiment dominated user reviews, followed by negative and positive sentiments. The Naïve Bayes model achieved 100% accuracy based on the confusion matrix and other evaluation metrics such as precision, recall, and F1-score. The findings highlight that Naïve Bayes can be a reliable approach for analyzing public opinion and serve as a reference for evaluating and improving digital service applications.
Co-Authors Eka Kusuma Pratama Muhamad Ihsan Hasanudin Muhammad Fauzi Muhammad Sadam Ramadhoni Tuslaela