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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Simetris Jurnal Informatika dan Teknik Elektro Terapan JIKO (Jurnal Informatika dan Komputer) Jurnal Bina Praja JUTIK : Jurnal Teknologi Informasi dan Komputer JURNAL ILMIAH INFORMATIKA JURNAL PENDIDIKAN TAMBUSAI JSI (Jurnal sistem Informasi) Universitas Suryadarma JURNAL TEKNOLOGI INFORMASI Journal of Innovation and Future Technology (IFTECH) J-3P (Jurnal Pembangunan Pemberdayaan Pemerintahan) Jurnal Syntax Imperatif : Jurnal Ilmu Sosial dan Pendidikan Jurnal Pendidikan dan Teknologi Indonesia Prosiding Seminar Nasional Pengabdian Kepada Masyarakat EXPLORE Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Ilmiah Administrasi Pemerintahan Daerah Knowbase : International Journal of Knowledge in Database Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Data Sciences Indonesia (DSI) Jurnal Media Birokrasi Innovative: Journal Of Social Science Research RENATA Jurnal Pengabdian Masyarakat Kita Semua Majapahit Journal of Islamic Finance dan Management Majapahit Journal of Islamic Finance dan Management Jurnal Kecerdasan Buatan dan Teknologi Informasi Jurnal Pengabdian Masyarakat Ekonomi dan Bisnis Digital Jurnal Teknologi dan Komunikasi Pemerintahan Explore Jurnal Studi Pemerintahan MAYARA: Jurnal Pengabdian Masyarakat Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Journal of Social Growth and Development Studies
Mohammad Rezza Fahlevvi
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Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Physics Public Health Social Sciences Other

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Journal : Knowbase : International Journal of Knowledge in Database

 1 
Sentiment Analysis And Topic Modeling on User Reviews of Online Tutoring Applications Using Support Vector Machine and Latent Dirichlet Allocation Mohammad Rezza Fahlevvi
Knowbase : International Journal of Knowledge in Database Vol 2, No 2 (2022): December 2022
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v2i2.5906

Abstract

Ruangguru is an online non-formal education application in Indonesia. There are several appealing features that encourage students to study online. The app's release on the Google Play Store will assist app developers in receiving feedback through the review feature.Users submit various topics and comments about Ruangguru in the review feature of Ruangguru, making it difficult to manually identify public sentiments and topics of conversation. Opinions submitted by users on the review feature are interesting to research further. This study aims to classify user opinions into positive and negative classes and model topics in both classes. Topic modeling aims to find out the topics that are often discussed in each class. The stages of this study include data collection, data cleaning, data transformation, and data classification with the Support Vector Machine method and the Latent Dirichlet Allocation method for topic modeling. The results of topic modeling with the LDA method in each positive and negative class can be seen from the coherence value. Namely, the higher the coherence value of a topic, the easier the topic is interpreted by humans. The testing process in this study used Confusion Matrix and ROUGE. The results of model performance testing using the Confusion Matrix are shown with accuracy, precision, recall, and f-measure values of 0.9, 0.9, 0.9, and 0.89, respectively. The results of model performance testing using ROUGE resulted in the highest recall, precision, and f-measure of 1, 0.84, and 0.91. The highest coherence value is found in the 20th topic, with a value of 0.318. Using the Support Vector Machine and Latent Dirichlet Allocation algorithms are considered adequate for sentiment analysis and topic modeling for the Ruangguru application.
Sentiment Analysis And Topic Modeling on User Reviews of Online Tutoring Applications Using Support Vector Machine and Latent Dirichlet Allocation Fahlevvi, Mohammad Rezza
Knowbase : International Journal of Knowledge in Database Vol. 2 No. 2 (2022): December 2022
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v2i2.5906

Abstract

Ruangguru is an online non-formal education application in Indonesia. There are several appealing features that encourage students to study online. The app's release on the Google Play Store will assist app developers in receiving feedback through the review feature.Users submit various topics and comments about Ruangguru in the review feature of Ruangguru, making it difficult to manually identify public sentiments and topics of conversation. Opinions submitted by users on the review feature are interesting to research further. This study aims to classify user opinions into positive and negative classes and model topics in both classes. Topic modeling aims to find out the topics that are often discussed in each class. The stages of this study include data collection, data cleaning, data transformation, and data classification with the Support Vector Machine method and the Latent Dirichlet Allocation method for topic modeling. The results of topic modeling with the LDA method in each positive and negative class can be seen from the coherence value. Namely, the higher the coherence value of a topic, the easier the topic is interpreted by humans. The testing process in this study used Confusion Matrix and ROUGE. The results of model performance testing using the Confusion Matrix are shown with accuracy, precision, recall, and f-measure values of 0.9, 0.9, 0.9, and 0.89, respectively. The results of model performance testing using ROUGE resulted in the highest recall, precision, and f-measure of 1, 0.84, and 0.91. The highest coherence value is found in the 20th topic, with a value of 0.318. Using the Support Vector Machine and Latent Dirichlet Allocation algorithms are considered adequate for sentiment analysis and topic modeling for the Ruangguru application.
Sentiment Analysis And Topic Modeling on User Reviews of Online Tutoring Applications Using Support Vector Machine and Latent Dirichlet Allocation Fahlevvi, Mohammad Rezza
Knowbase : International Journal of Knowledge in Database Vol. 2 No. 2 (2022): December 2022
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v2i2.5906

Abstract

Ruangguru is an online non-formal education application in Indonesia. There are several appealing features that encourage students to study online. The app's release on the Google Play Store will assist app developers in receiving feedback through the review feature.Users submit various topics and comments about Ruangguru in the review feature of Ruangguru, making it difficult to manually identify public sentiments and topics of conversation. Opinions submitted by users on the review feature are interesting to research further. This study aims to classify user opinions into positive and negative classes and model topics in both classes. Topic modeling aims to find out the topics that are often discussed in each class. The stages of this study include data collection, data cleaning, data transformation, and data classification with the Support Vector Machine method and the Latent Dirichlet Allocation method for topic modeling. The results of topic modeling with the LDA method in each positive and negative class can be seen from the coherence value. Namely, the higher the coherence value of a topic, the easier the topic is interpreted by humans. The testing process in this study used Confusion Matrix and ROUGE. The results of model performance testing using the Confusion Matrix are shown with accuracy, precision, recall, and f-measure values of 0.9, 0.9, 0.9, and 0.89, respectively. The results of model performance testing using ROUGE resulted in the highest recall, precision, and f-measure of 1, 0.84, and 0.91. The highest coherence value is found in the 20th topic, with a value of 0.318. Using the Support Vector Machine and Latent Dirichlet Allocation algorithms are considered adequate for sentiment analysis and topic modeling for the Ruangguru application.
Co-Authors Abrory, Yudhistira Abubakar, Asti Astari Ade Setiawan, Farid Adiguna Ahlul Bai’at Ahmad Ali Zanki Ahmad Fikri Rahman Aji Bayu Ramadhan Akbar, Mohammad Irham Alifkah, Muhammad Haerul Alwan Syarofi Anugerah, Muhammad Wahyu Anugrah, Lalu Reza Apriansya, Ari Aprina Putri Sanda Ari Apriyansa Ariandi, Wahyu Arif Abdul Rahman Sakhi Arya Pangestu Azhari SN Bahar, Ana Aulia Basnella, Rindu Buding, Asri Devid, Devid Diana Romauli Tanggo Ledeng Mandala Dimas, Muhamad Diminaka Tebai Dytihana, Zahra Aqilah Eduardus Julio Bastian Matutina Faisal Akbar Faisal Akbar Nasution Fajar Nurmansyah Falah, Rifki Zainal Frezy Albertus Silaban Haiqal, Rif’at Dwiki Hardiansyah, Richo Hengki Andika Putra Ihcsan, Muhammad Ikra Novar, Rizqi Irham Akbar, Mohammad Joshua Faitri Ick Kastella, Tzarina Aaliyah Muzdalifah Kelana, Muhammad Azanil Kodrat Alkauzar Alda Kusuma, Kadek Agus Prasdya Indra Lalang Pratama Akhmad Putra Lubis, Ahmad Rifki M.Y Divan Wanimbo Madiya, Rehuelli Mandala, Diana Romauli Tanggo Ledeng Manik, Irvan Alvharel Ginting Maulana, Achmat Miyosi Nur Fajri Muaafii, Daffa Ammar Muhamad Dimas Muhammad Athaillah Akbar Iskandar Muhammad Reza Pahlevi Munawwar, Rais Fakhri Nurhikmah Dhita Sari Pandega, Laksmana Rajwa Pangemanan, Kristina Carlen Parhusip, Reynaldo Joy Christian Pratama, Ilham Pratama, Risnu Pratiwi, Annisa Dhea Purwowicaksono, Rizki Putra, Dhita Satria Aprilliana Putri, Nur Azizah Heriyani Putrie Saridewi Ranang Zulfikram Ratih Latifah Anggraini Rezkiadi, Faqih Septiya RIZQI, IKRA NOVAR Rompas, Veronica Rozali Ilham Safitri, Khairunnisa Dian Salsabillah, Wanda Thalia Sekira, Selvi Septiandi Harhari, Nurfadhilah Sinaga, Eimanisura Tambi, Noordeyana Tebai, Norlince G Theresia Farica De Ganza Titis Sari Putri Titis Sari Putri Tyas Wijaya, Luna Ayuning ULAYYA, AIRA SYATRA Wahyu Ariandi Wenty, Zahrati Widiya Yuska Zahra Aqilah Dytihana Zahrati, Wenty Zikri Hadi Prawira