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All Journal Conference on Innovation and Application of Science and Technology (CIASTECH) Informatika : Jurnal Informatika, Manajemen dan Komputer Systemic: Information System and Informatics Journal Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan JSAI (Journal Scientific and Applied Informatics) Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Jurnal Penelitian Inovatif Ilmu Komputer untuk Masyarakat Nusantara Journal of Computers and its Applications
Honainah Honainah
Universitsa Nurul Jadid
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences Other

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Penerapan Algoritma Naive Bayes dengan Pembobotan TF-IDF dan Pendekatan Lexicon Untuk Analisis Sentimen Opini Mahasiswa Terhadap Fasilitas Kampus Honainah
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9836

Abstract

This study aims to analyze student sentiment towards campus facilities at Nurul Jadid University using the NaiveBayes method. Data was collected through questionnaires and processed using Visual Studio Code software. The process stages included manual sentiment classification, data preprocessing, lexicon classification, TF-IDF weighting, and classification using the Naive Bayes method. The results of each step are presented in tables and graphs. The system was also implemented by creating a web application that allows users to enter new text/opinions and obtain sentiment classification results. The results of testing manual sentiment classification and lexicon with the Naive Bayes algorithm showed different levels of accuracy, with manual sentiment classification having an accuracy value of 75% and lexicon sentiment classification having an accuracy value of 85%. In conclusion, the lexicon approach with the Naive Bayes algorithm is superior to the manual approach because it is more objective, consistent, efficient, and easy to develop and is suitable for analyzing student opinions on campus facilities, thus providing material for consideration in campus policy.
Co-Authors Alifia Nur Afni Lismawati Amilia agustin Anani Asmani Andi Wijaya Anis Yusrotun Nadhiroh Arika Ramadhany Ato’illah Ato’illah Fahrial Faqih Romadhoni Khuzairi Mun’im Moh Jasri Nurul Imaniyah Putri Eka Aliyatul Himma Siti Romelah Tijaniyah Tijaniyah Wahab Sya’roni Yaqin, Moh. Ainol Yuda Asror Maulana