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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic JurTI (JURNAL TEKNOLOGI INFORMASI) Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Jurnal Dharmabakti Nagri International Journal of Information Technology and Computer Science Applications (IJITCSA) Seminar Nasional Inovasi Teknologi AMMA : Jurnal Pengabdian Masyarakat Jurnal Riset Informatika dan Teknologi Informasi (JRITI)
Ajif Yunizar Pratama Yusuf
Fakultas Ilmu Komputer; Universitas Bhayangkara Jakarta Raya
Religion Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing Social Sciences Other

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Sentiment Analysis of On-Demand Ride-Hailing Systems using Support Vector Machine and Naïve Bayes Bhagaskara Farhan Wiguna; Herlawati Herlawati; Ajif Yunizar Pratama Yusuf
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 11 No. 2 (2023): September 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i2.7384

Abstract

Gojek is one of Indonesia's most popular online transportation, founded in 2010. The Gojek application has been downloaded one hundred forty-two million times with more than two million drivers and four hundred thousand partners in food delivery services. Due to the increasing use of the Gojek application and the importance of knowing user views about the services provided by the application. In this research, the sentiment analysis is using Support Vector Machine and the Naïve Bayes method to classify positive sentiment and negative sentiment. The target label focus on positive and negative labels to aims avoid the bias that exists in neutrally labeled reviews on the Gojek Application. The research process includes data collection, pre-processing the data, weighting with Term Frequency-Invers Document Frequency, Support Vector Machine, and Naïve Bayes training by dividing the data into 90% training data and 10% testing data and then evaluating the results using a confusion matrix. The results of testing using the Support Vector Machine algorithm resulted in 90% accuracy, 94% recall, 91% precision, and 94% f1-score, therefore the Naïve Bayes algorithm produces 77% accuracy, 96% recall, 77% precision, and 85% f1-score.
Co-Authors Adi Muhajirin Aditya Putra Prastyo Agus Setyawan Ahmad Dedi Jubaedi Ardhiyanto, Ozzi Beno Aditya Sanusi Bhagaskara Farhan Wiguna Dr. Tb. Ai Munandar, S.Kom., MT Endang Retnoningsih Fata Nidaul Khasanah Gedhe Hilman Wakhid Harsiti Harsiti Herlawati Herlawati Herlawati Herlawati Illa Nur’aini Khairunnisa Fadhilla Ramdhania Muhammad Ganang Martyana Muhammad Salam Asyidqi Ozzi ardhiyanto Prima Dina Atika Purnomo, Rakhmat RAFIKA SARI Reghita Suryani Putri Renilda Filiandini Salam Asyidqi, Muhammad Syifa Rahmadhani Tb Ai Munandar Tyastuti Sri Lestari