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All Journal Jurnal technoscientia Jurnal Teknologi Jurnal Script Jurnal Jarkom PROSIDING SENATEK FAKULTAS TEKNIK UMP Panrita Abdi - Jurnal Pengabdian pada Masyarakat INTEGER: Journal of Information Technology Jurnal TAM (Technology Acceptance Model) JMM (Jurnal Masyarakat Mandiri) Dharma Bakti IJISCS (International Journal Of Information System and Computer Science) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal PkM: Pemberdayaan Masyarakat Prosiding SNAST Jurnal Sains dan Teknologi Prosiding Simposium Nasional Rekayasa Aplikasi Perancangan dan Industri Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Jurnal TAM (Technology Acceptance Model) IJISCS (International Journal of Information System and Computer Science)
Erfanti Fatkhiyah
Institut Sains & Teknologi AKPRIND Yogyakarta
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Journal : Jurnal TAM (Technology Acceptance Model)

 1 
SENTIMENT ANALYSIS FOR EXTRACTING STUDENT OPINION DATA ON HIGHER EDUCATION SERVICES USING THE NAIVE BAYES CLASSIFIER AND SUPPORT VECTOR MACHINE METHODS (CASE STUDY AKPRIND INSTITUTE OF SCIENCE AND TECHNOLOGY YOGYAKARTA) Uning Lestari; Tri Romadhani; Suraya Suraya; Erfanti Fatkhiyah
Jurnal TAM (Technology Acceptance Model) Vol 13, No 1 (2022): Jurnal TAM (Technology Acceptance Model)
Publisher : LPPM STMIK Pringsewu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/jurnaltam.v13i1.1220

Abstract

Opinions are ideas, opinions, or the results of someone's subjective thoughts in explaining or addressing something. IST AKPRIND Yogyakarta provides comment and suggestion box facilities in the learning evaluation questionnaire. Opinions that have been collected can be used to determine the sentiment of the campus community. This sentiment information can be used in future campus development. The development of a system that can analyze sentiment automatically is designed by comparing the Naive Bayes Classifier (NBC) method and the support vector machine (SVM) optimized by selecting the Information Gain (IG) feature. Prior opinion data needs to be prepared before being analyzed. Preprocessing (text preprocessing) used includes: cleanning, text folding, normalization, stemming, stopword removal, convert negation, and tokenization. The results of this study show that the SVM method produces higher accuracy than NBC. The accuracy test shows the highest accuracy of SVM reaches 99.09% while NBC is 96.56%. The application of IG did not significantly affect the accuracy of the analysis. GI greatly influenced the analysis duration of the SVM method, which could shorten the time by 195.71%.
Co-Authors -, Suraya Adina Sarmento Nunes Agradira Dwi Wahyuda Agus Suhardi Agus Suhardi, Agus Aldo Fiotama Josyaf Alfiandri - Amir Hamzah Andrianto - Andrianto . Andung Febi Prakoso Ardiansyah - Ariyana, Renna Yanwastika Arum Puspita Sari Asri Respti1 Azhar Munif Bima Gilang Pamukti Brilliani, Dhea Saintysta Catur Iswahyudi Catur Iswayudi Christina Anggreini Torar Cyrilla Indri Parwati Dadurrohman, Muhammad Iqbal Im Devi Iryanti Devi Iryanti, Devi Dian Anjarwati Dias Persada Dina Andayati Dina Mardiana Dody Pradana Edhy Sutanta (Jurusan Teknik Informatika IST AKPRIND Yogyakarta) Efendi, Endang Erma Susanti Evi Eltinah Fajar Febri Pratama Fikki Rian Irawan Galang Pratama Sukma Putra Hadi Prasetyo Suseno Haidar Ikram Ramadhan Haidar Ikram Ramadhan Hamdani . Harno Priyanto Indah Permata SarI Isa, Doddy Muhammad Isa, Doddy Muhammad Isworo Nugroho Jayanti, Laela Qodar Dwi Jefrianto Tafonao Joko Triyono Joko Triyono Joko Triyono Juewanto - Juliyanti, Nur Arifah Kartika Indayani Kurniawan, Widiharto Kusumaningsih, Rr. Yuliana Rachmawati Laksono Trisnantoro Lucio Almeida Da Costa Ludavikus Maturbongs Maria Ulfa Nofiani1 Mia Lusmiawati Muhammad Ardi Setiawan MUHAMMAD SHOLEH Muhammad Sholeh Muhammad Uwlinuha Muntaha Nega Naniek Widyastuti Niken Irawati Putri Nizar Izzuddin Yatim Fadlan Nizar Izzuddin Yatim Fadlan Nunes, Adina Sarmento Nurdiantoro Oktaviani Rahmita Putri Prita Haryani Pundha Kartika Putra, Galih Pradana Ramadhan, Renggana Surya Rifki Firdaus Kurniawan Rosalia Arum Kumalasanti Rosalina Elvideswita S. Sutysna Rr Yuliana Rachmawati Kusumaningsih Rr. Yuliana Rachmawati Rr. Yuliana Rachmawati RR. Yuliana Rachmawati Rr. Yuliana Rachmawati, Rr. Yuliana Rr.Yuliana Rachamawati, Rr.Yuliana Ryzka Rahmawati Sari, Indah Permata Sari1 Sholeh, Muhammad Siti Lestari Sukma Ageng Prihasmoro, Sukma Ageng Supariandi, Deddy Suraya Suraya - Suraya - Suraya - Suraya ., Suraya Suraya Suraya Suraya Suraya Suwanto Raharjo Taufik Ardiantoro Theresia Solot Diri Tri Romadhani Triyono Puji Pangestu Triyono, Joko Uning Lestari Uning Lestari Uning Lestari Utami, Annisaa Utomo, Hariyo Victor Motumona Waliadi, Julfikar Wibowo, Satrio Muslim Wirto Yoga Arjanggi Nofianto Yuliana Rachmawati