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All Journal Urania Jurnal Ilmiah Daur Bahan Bakar Nuklir Majalah Ilmiah Bijak JTIM : Jurnal Teknologi Informasi dan Multimedia Jurnal Sains dan Teknologi Journal Sensi: Strategic of Education in Information System Jurnal Sistem Komputer & Kecerdasan Buatan Infotech: Journal of Technology Information Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Abdi Masyarakat Humanis PASCIDEV: Pasundan Social Science Development Jabdimas: Jurnal Pengabdian Masyarakat E-Amal: Jurnal Pengabdian Kepada Masyarakat
Taswanda Taryo
Center for Development of Research Reactor Technology-BATAN, Kawasan Puspiptek, Gedung 31, Serpong 15310, Tangerang, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology Mathematics Physics Social Sciences

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Journal : Infotech: Journal of Technology Information

 1 
ANALISIS SENTIMEN OPINI PENGGUNA JASA PENGIRIMAN JNE MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER DAN K-NEAREST NEIGHBORS Halimatussadiah, Siti; Tukiyat, Tukiyat; Taryo, Taswanda
Infotech: Journal of Technology Information Vol 11, No 1 (2025): JUNI
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i1.358

Abstract

JNE's high-quality service will provide optimal satisfaction to users, ensuring they feel valued and have a reliable and efficient delivery experience. To provide optimal service, this research explores in-depth user sentiment analysis of freight forwarding applications in Indonesia. The purpose of the study is to analyze user sentiment towards the My JNE app, which is one of the leading freight forwarding apps in Indonesia. This research uses user review data from Google Play Store collected from 2018 to 2024. The review sentiment is categorized into positive, neutral, and negative using the VADER analysis tool. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool specifically designed to detect sentiment in social media text. After the data reduction process, neutral sentiment classes were removed to focus the analysis on two main categories: positive and negative. Of the total 5,000 review samples analyzed, it was found that 35.78% belonged to the positive category and 64.21% to the negative category. The classification methods used in this study are Naïve Bayes and K-Nearest Neighbors (KNN). The analysis results show that the Naïve Bayes model has an accuracy of 81.64%, while K-Nearest Neighbors (KNN) has an accuracy of 76.25%. This accuracy test confirms that the KNN model is more effective in classifying user sentiment compared to Naïve Bayes. The results of this study provide important insights into user perceptions of the My JNE application, which can be used as a basis for improving service quality in the future. This research suggests that My JNE focus on improving features that often receive negative reviews to increase user satisfaction.
ANALISIS DAN PENGEMBANGAN SISTEM MONITORING RADIOAKTIF ALAMIAH RADON MENGGUNAKAN DETEKTOR SINTILASI BERBASIS WEB SECARA REAL TIME Saepudin, Asep; Madinah, Dzahra Al; Makhsun, Makhsun; Taryo, Taswanda
Infotech: Journal of Technology Information Vol 11, No 1 (2025): JUNI
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i1.384

Abstract

Monitoring radon gas concentrations in various environments, such as residential areas, buildings, caves, and mining sites, is crucial to minimizing health risks associated with radon exposure exceeding the 100 Bq/m³ threshold set by the World Health Organization (WHO). Additionally, anomalies in radon concentration in fault zones are often considered precursors to seismic activity. Therefore, this study develops a real-time Internet of Things (IoT)-based radon gas monitoring system using a cost-effective approach. The system utilizes a ZnS(Ag)-based scintillation detector combined with a Photo Multiplier Tube (PMT) model H10492-001 (Hamamatsu, Japan). Calibration results at the Geological Resource Research Center – BRIN Bandung indicate that the detector has an average efficiency of 79.8%. The cloud-based monitoring system is developed using PHP 8.0 and MySQL 10.5, with performance evaluation conducted through an API using the GET method via the cURL application. Testing with various intervals and iterations shows that the system achieves 99% data reception and recording efficiency compared to the data sent by the test device. Performance testing using Chrome DevTools indicates a response time ranging from 32–140 ms, demonstrating that the system responds quickly and efficiently handles user requests. The system includes an early warning mechanism that activates when sensor data exceeds a predefined threshold, featuring a red indicator on the dashboard, an alarm sound, and automatic notifications to a Telegram bot. Responsiveness testing confirms that the dashboard display adapts optimally to various screen sizes, ensuring accessibility across multiple devices. From a cybersecurity perspective, the system implements HTTPS protocols and has received an A rating from www.ssllabs.com. It also employs BCrypt encryption with a 184-bit hash length for password protection.
Co-Authors Achmad Hindasyah, Achmad Adhyaksa Waskita, Arya Agung Budi Susanto Agustian, Rizki B., Hamsinah Baharuddin, Hamsinah Budi Susanto, Agung Geni Rina Sunaryo Halimatussadiah, Siti Hamsinah Hamsinah, Hamsinah Hilman, Amirul iman, Fajar nur Madinah, Dzahra Al Makhsun, Makhsun Mardiyanto Mardiyanto Meniek Rachmawati Nafiah, Nafiah Ridwan, Ridwan Rizaldi, Devan rusilowati, umi Saepudin, Asep Salsabilah, Zafira Sarwani Sarwani, Sarwani Sekar Putri, Utari Sholihin, Dwi Rahmad Sunardi, Nardi Sunarsi, Denok Syarifudin Syarifudin Teguh Prasetyo Tukiyat Tukiyat, Tukiyat Ubaidillah Ubaidillah