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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) International Journal of Artificial Intelligence Research Simtek : Jurnal Sistem Informasi dan Teknik Komputer Prosiding Semhavok Building of Informatics, Technology and Science Journal of Information Systems and Informatics Zonasi: Jurnal Sistem Informasi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknologi Informatika dan Komputer ABSYARA: Jurnal Pengabdian Pada Masyarakat Jurnal Pengabdian kepada Masyarakat Bina Darma International Journal Software Engineering and Computer Science (IJSECS) Jurnal Bina Komputer Indonesian Journal of Advanced Research (IJAR) Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Pengabdian Masyarakat Inovasi Indonesia
Amalia, Rahayu
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Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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Sentimen Analisis Pengguna Jasa Layanan Kereta Api dengan Menggunakan Metode CNN (Convolutional Neural Network) Alfikri, Zidan; Muzakir, Ari; Purnamasari, Susan Dian; Amalia, Rahayu
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9423

Abstract

Train services are a popular mode of transportation in Indonesia, especially in the Greater Jakarta area. However, the quality of train services is often debated among users. This study aims to analyze the sentiment of train service users using the Convolutional Neural Network (CNN) method with a focus on the DAOP 1 Jakarta area. The data used are reviews or comments of train users taken from Indonesian Railways social media. The results of the study show that the CNN method can classify user sentiment analysis with accurate results or high accuracy. This sentiment analysis shows that train users in DAOP 1 Jakarta have positive sentiments towards aspects such as punctuality, service, comfort and safety. The results of this study can help the railway to understand user needs and complaints so that they can improve service quality with a final value of 89.29% accuracy, 88.73% precision, 90.00% recall, and 89.36% F1-score.
Co-Authors Aan Restu Mukti, Aan Restu Ade Putra Adryansyah, Muhammad Rizqi Hidayah Alfikri, Zidan Almutaharah, Nanda Anisah, Popy Ardiansyah, Ari Ari Muzakir Arrafi, Muhammad Adji Bintang, Muhammad Yuan Dasmen, Rahmat Novrianda Fatoni Febriansyah Febriansyah Fitriani, Endah Hadinata, Novri Ilman Zuhri Yadi Is, Nina Paramitha Jemakmun, Jemakmun Juliansyah, Rendy Juniar, Kanero Komalasari, Dinny Kurniawan Kurniawan Makmuri, M. Kumroni Maria Ulfa Megawaty Muhammad Sobri Nainggolan, Christin Evasari Novrianda, Rahmat Nurul Huda Nyimas Sopiah Oktarina, Tri Oktavian, Sakbanullah Dwi Paramytha, Nina Pindriansyah Purwaningtias, Fitri R, Syafira Dian Rahmadini, Aulia Nurul Rasmila, Rasmila Salsabilah, Engga Sari, Dhea Nadita Seprina, Iin Sunda Ariana, Sunda Supratman, Edi Susan Dian Purnamasari, Susan Dian Taqrim Ibadi Tata Sutabri Thirta, Pramudya Dhanu wardani, kiky rizky nova Wardhani, Kiky Rizki Nova Wijaya, Alex Yulianingsih, Evi