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All Journal Prosiding Semnastek Indonesian Journal on Software Engineering (IJSE) Jurnal Teknik Komputer AMIK BSI Paradigma Bina Insani ICT Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Jurnal Mantik JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Infortech JPM: JURNAL PENGABDIAN MASYARAKAT Journal of Informatics Management and Information Technology KLIK: Kajian Ilmiah Informatika dan Komputer Reputasi: Jurnal Rekayasa Perangkat Lunak IMTechno: Journal of Industrial Management and Technology JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Administrasi Kantor Info Abdi Cendekia Journal of Accounting Information System Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Komtika (Komputasi dan Informatika)
Titik Misriati
Universitas Bina Sarana Informatika
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Klasifikasi Sentimen Terhadap Kebijakan Tapera Menggunakan Komparasi Machine Learning dan SMOTE Leidiyana, Henny; Misriati, Titik; Aryanti, Riska
Jurnal Komtika (Komputasi dan Informatika) Vol 8 No 2 (2024)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v8i2.12595

Abstract

The Indonesian government's Public Housing Savings Program (Tapera) aims to help low- and middle-income persons get housing financing. Although the initiative strives to satisfy housing requirements, the public has responded in a variety of ways, as evidenced by social media posts. The goal of this study is to examine public sentiment towards the Tapera policy using YouTube comment data to provide an overview of popular perspective. This study combines sentiment analysis with machine learning algorithms, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multinomial Naïve Bayes (NB), and Decision Tree. The data is divided into three scenarios, namely 70% training data and 30% test data, 80% training data and 20% test data, and 90% training data and 10% test data. Data balancing is also performed with SMOTE. The performance evaluation is based on each algorithm's accuracy, precision, recall, and F1 Score values. The results showed that the SVM algorithm performed the best in all circumstances, with the greatest accuracy of 88% and an F1 score of 85%. The multinomial Naïve Bayes algorithm ranked second with steady accuracy, whereas KNN and Decision Tree had poorer performance. This suggests that SVM is the most effective method for processing sentiment data regarding Tapera policy.
Co-Authors Agustiani, Sarifah Aldian Mauluda Amanda Subhi Pertiwi Arie Kurniawan Arifin, Yosep Tajul Asriyani Sagiyanto ASRIYANI SAGIYANTO, ASRIYANI Eka Dyah Setyaningsih Fachri, Muhamad Fachry Abda El Rahman Handayani Meilisa Hari Sugiarto - UBSI Haryani Henny Leidiyana Ida Zuniarti Ida Zuniarti, Ida Instianti Elyana Instianti Elyana Istiqomah, Amelia Maria, Vivi Martenia, Rina Mohammad Afdhol Rizal Muhammad Arif Sulistiyo Nadhifah, Rifda Nadhifah, Rifda Norma Yunita Nurvi Oktiani Oktaviyani Oktaviyani Rahayu Ningsih Rahayu Ningsih Rahmat Hidayat Rahmat Hidayat Rahmat Hidayat Rahmawati Rahmawati Ramadhani, Arya Rina Martenia Riska Aryanti SALMAN ALFARIZI Sari Hartini, Sari Siti Khotimatul Wildah Siti Marlina Siti Marlina, Siti Siti Nurajizah Sri Ariyati Widiarina . Widiarina, Widiarina Wina Yusnaeni Yoseph Tajul Arifin