Article Per Year (5 Year)
p-Index From 2019 - 2024
3.137
P-Index
This Author published in this journals
All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) JURNAL MEDIA INFORMATIKA BUDIDARMA Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Computer Science and Informatics Engineering (J-Cosine) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Charity : Jurnal Pengabdian Masyarakat eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT)
Ade Romadhony
Universitas Telkom
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences Other

Published : 30 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Indonesian Journal on Computing (Indo-JC)

 1 
Sentiment Analysis of University Social Media Using Support Vector Machine and Logistic Regression Methods Fazainsyah Azka Wicaksono; Ade Romadhony; Hasmawati
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 2 (2022): August, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.2.638

Abstract

Social media has become one of the most powerful platforms for information sharing. Colleges and universities now have official social media profiles to convey information about the campus and boost its branding and popularity. Instagram is a popular social networking website among college students. It is important for a university to comprehend its performance from the community's perspective, whether positive, negative, or indifferent toward the university. One solution is to examine the university's social media sentiment to establish the public's perception of the university. In this study, we will conduct a sentiment analysis on university social media based on public opinion or comments for each post on the university's Instagram to identify whether the comments are “Positive,” “Negative,” or “Neutral.” To classify posts on university Instagram, we use two methods: Support Vector Machine and Logistic Regression. The results suggest combining the Support Vector Machine approach with the TF-IDF feature yields the best F1-Score performance. In contrast, Logistic Regression with the FastText feature produces the worst performance of all models and feature extraction employed.
Co-Authors Aditia Rafif Khoerulloh Agung Toto Wibowo Al Faraby, Said Ali Ridho Fauzi Rahman Ananda Affan Fattahila Ananda Wulandari Anisa Herdiani Arya Prima Al Aufar Bambang Pudjoatmodjo Bambang Pudjotatmodjo Bayu Tresna Ariesta Bedy Purnama Bhudi Jati Prio Utomo Bimmo Satryo Wicaksono Dade Nurjanah Dana Kusumo Dana S Kusumo Dana S Kusumo Dodi Wisaksono Sudiharto Donni Richasdy Ema Rachmawati Ema Rachmawati Farhan Ahmadi Javier Nur Fat’hah Noor Prawira Fat’hah Noor Prawira Fazainsyah Azka Wicaksono Fazmah Arif Yulianto Hamdy Nur Saidy Haryo Adi Nugroho Haryo Adi Nugroho Haryo Nugroho Hasmawat Hasmawat Hasmawati Hasmawati Hasmawati Hasmawati Hasmawati Hasmawati Hasmawati Kemas Rahmat S.W Kurniadi Ahmad Wijaya Lintani Afina Hajar Raudhoti Luh Putri Ayu Ningsih Mahmud Dwi Sulistiyo Moch. Arif Bijaksana Muhammad Arzaki Muhammad Arzaki Muhammad Aziz Pratama Muhammad Farrel Muhammad Rizki Ramadhan Setiawan Muhammad Taufik Wahdiat Muhammad Zaky Aonillah Nadine Azhalia Purbani Prawita, Fat’hah Noor Putu Harry Gunawan Rhesa Hermawan Ridea Valentini Peristiwari Siwabessy Rimba Whidiana Ciptasari Riska Junia Wulandari Said Faraby Selly Meliana Shabrina Retno Ningsih Siti Saadah