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All Journal Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Jurnal Algoritma
Hartantom, Budi
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Computer Science & IT Electrical & Electronics Engineering

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Evaluasi Keterlibatan Mahasiswa Dalam Lingkungan Pembelajaran Daring Menggunakan Natural Language Processing (NLP) dan Analisis Sentimen Hartantom, Budi; Yunita, Hilda Dwi; Fahurian, Fatimah; Dirayati, Fadhilah; Winarko, Triyugo; Marliana, Iin
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2154

Abstract

This research aims to evaluate student engagement in online learning environments using Natural Language Processing (NLP) and sentiment analysis. The research method involves text analysis of student interactions on a Learning Management System (LMS) platform, including discussion forums, comments, and messages. NLP techniques were used to identify patterns of student engagement, while sentiment analysis assessed the emotions contained in the interactions, including positive, negative, or neutral sentiments. The results show that student engagement can be effectively measured through this analysis, as well as providing an overview of engagement patterns and the factors that influence them. The findings are expected to be used to improve the quality of online learning.
Pengenalan Sinyal Otak Berbasis Machine Learning untuk Aktivasi Lampu Sen Otomatis pada Kendaraan Bermotor (Kasus Ibu-Ibu di Indonesia) Hartantom, Budi; Marliana, Iin; Pramono, Doni Eko Hendro
Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2025: SNESTIK V
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/p.snestik.2025.6901

Abstract

This study proposes an automatic turn signal activation system for motor vehicles based on brain signals using a machine learning approach, with a specific focus on rider behavior, particularly among Indonesian mothers. The system is designed to enhance driving safety by detecting brain signals using EEG devices and processing them through machine learning algorithms to identify the rider's intent to activate the turn signals. Data were collected from various rider groups, processed, and trained using machine learning models to ensure high classification accuracy. The test results indicate that this system effectively recognizes brain signal patterns and automates turn signal activation with adequate accuracy. The implementation of this system is expected to reduce the risk of accidents caused by riders' negligence in providing signals when turning.
Co-Authors Dirayati, Fadhilah Fatimah Fahurian Hendro Pramono, Doni Eko Hilda Dwi Yunita marliana, iin Triyugo Winarko, Triyugo