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All Journal Jurnal Sains dan Teknologi Jurnal Ilmiah KOMPUTASI Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Sebatik Jurnal Ilmiah Media Sisfo EXPLORE JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal Teknologi Dan Sistem Informasi Bisnis Indonesian Journal of Electrical Engineering and Computer Science Majalah Ilmiah UPI YPTK Journal of Computer Scine and Information Technology Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) EXPLORE Jurnal Komtekinfo
Musli Yanto
Universitas Putra Indonesia YPTK Padang
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences

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Public Sentiment Analysis of Train Services Based on Twitter Opinions Using K-Menas and SVM Methods Dina Selvia; Sumijan; Musli Yanto
Jurnal KomtekInfo Vol. 13 No. 1 (2026): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v13i1.677

Abstract

The development of social media, particularly Twitter, has become a primary means for the public to express opinions, criticisms, and complaints regarding train services, ranging from delays, facility comfort, to ticket policies. The large number of opinions appearing in short, non-standard characters, and containing slang and emoticons makes manual analysis ineffective, resulting in service providers not optimally utilizing valuable information from the public. This study aims to analyze public opinion sentiment on Twitter regarding train services to systematically and structuredly determine public perceptions. The methods used in this study are K-Means Clustering and Support Vector Machine (SVM). K-Means is used to group public opinion based on similarities in language patterns and sentiments to obtain initial labels, while SVM is used to classify opinions into positive and negative sentiments more accurately. The research data comes from the Twitter platform and is obtained through a crawling technique. The maximum limit of tweets retrieved is set at 2005 tweets. The results show that the K-Means method is able to assist the initial labeling process of sentiment data, while the SVM algorithm can classify public opinion with an accuracy level of 99.02%. The combination of clustering and classification methods has proven effective in processing large-scale, unstructured opinion data. Based on the research results, it can be concluded that the sentiment analysis approach using K-Means and Support Vector Machines can provide an objective picture of public perception of train service quality. The results of this analysis are expected to be used by service providers as evaluation material and a basis for decision-making to improve service quality to the public
Co-Authors - Khairiazaz - Yuhandri Darma Syahrullah Ekajaya Dewi Eka Putri Dewi Eka Putri Dhio Saputra Dina Selvia Dodi Guswandi Eka Naufaldi Novri Eka Praja Wiyata Mandala Febri Hadi hadi syahputra Irzal Arief Wisky Julsapargi Nursam Liga Mayola M. Hafizh M. Hafizh Muhammad Hafizh Nursam, Julsapargi Putri Melati Putri Melati Rahma Yanti Randy Permana Rini - Sovia Rini Sovia Rini Sovia Rini Sovia Rini Sovia - Safitri, Wifra Saputra, Dhio SIGIT SANJAYA, SIGIT Sitti Rizki Mulyani Sumijan Syafri Arlis Widya Nursanty Yogi Wiyandra Yuhandri Yuhandri Yuhandri Yuhandri, Yuhandri Yulasmi