Article Per Year (5 Year)
p-Index From 2021 - 2026
1.829
P-Index
This Author published in this journals
All Journal Journal of Information Technology and Computer Science (JOINTECS) Jurnal INSYPRO (Information System and Processing) CogITo Smart Journal JURNAL INSTEK (Informatika Sains dan Teknologi) CCIT (Creative Communication and Innovative Technology) Journal Journal Software, Hardware and Information Technology Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat Agents: Journal of Artificial Intelligence and Data Science DIPANEGARA KOMPUTER TEKNOLOGI INFORMATIKA (DIPAKOMTI) DIPANEGARA KOMPUTER SISTEM INFORMASI (DIPAKOMSI) Journal Entrepreneurship Digital
Salman, Nur
Universitas DIPA Makassar
Arts Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
Tinjauan Sentimen Terhadap Ulasan Aplikasi Peminjaman Online dengan Metode Support Vector Machine (SVM) Ghani, ST. Aminah Dinayati; Nur Salman, Nur Salman; Farhan Wahyuta Kusuma, Farhan Wahyuta Kusuma
AGENTS: Journal of Artificial Intelligence and Data Science Vol 4 No 1 (2024): Vol 4 No 1 (2024): September - Februari
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/jagti.v4i1.74

Abstract

Online loans, abbreviated as "Pinjol," refer to the practice of lending money online through applications or websites without involving traditional financial institutions such as banks or other traditional creditors. Examples of applications in this field include AkuLaku and Kredivo. These apps operate in the e-commerce and credit provision sectors in Southeast Asia, including Indonesia. Despite their operational strengths, these applications have both advantages and disadvantages, leading to positive and negative reviews on platforms like the Play Store. The research aims to identify positive and negative reviews within these online loan applications that can influence users' decisions when choosing a particular app. SVM classification technique is employed to analyze positive and negative sentiments from these reviews. The accuracy results obtained after sentiment analysis for Kredivo are 81%, while for AkuLaku, it is 75%. A higher accuracy value indicates a better ability of the model to predict sentiments correctly. Visualization of impactful words based on word frequency is presented in the form of a Word Cloud. Therefore, based on the sentiment analysis conducted using the SVM model, the author suggests choosing the Kredivo app when selecting an online loan application, as the analysis indicates that Kredivo has better quality compared to AkuLaku.
Co-Authors Andi Irmayana Andika Andika Arini Fitramayanti Asmah Akhriana Cucut Susanto Donny Liklikwatil , Rudy Farhan Wahyuta Kusuma, Farhan Wahyuta Kusuma Fatmasari , Fatmasari Husain T Husain T I Gusti Made Aprianta Nugraha Kalvin Ryan Maria Nova Sarembona Muhammad Agus Muljanto Muhammad Nur Akbar Muhammad Salsabil Mustika Sari, Mustika Mustikasari Mustikasari Mustikasari Mustikasari Nasaruddin Nasaruddin Nurdin Nurdin Po Abas Sunarya Rauf, Abdul Rudy Donny Liklikwatil Siti Hajratul Aswa ST. Aminah Dinayati Ghani Surya Iin