Article Per Year (5 Year)
p-Index From 2020 - 2025
2.426
P-Index
This Author published in this journals
All Journal Jurnal Sains dan Teknologi Jurnal Teknologi Informasi dan Ilmu Komputer Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika E-Dimas: Jurnal Pengabdian kepada Masyarakat JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Infomedia Journal of Information Systems and Informatics Jurnal Pengabdian Dharma Bakti JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Universitas Muhammadiyah Yogyakarta Undergraduate Conference Proceeding SmartComp
Endah Ratna Arumi, Endah Ratna
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences

Published : 16 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Journal of Information Systems and Informatics

 1 
Optimizing Aspect-Based Sentiment Analysis for Kyai Langgeng Park Using PSO and SVM Saputra, Dio Raka Venda; Maimunah, Maimunah; Arumi, Endah Ratna
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.930

Abstract

This study aims to analyze aspect-based sentiment on Taman Kyai Langgeng tourism reviews, focusing on three main aspects: price, service, and facilities. This study combines Particle Swarm Optimization (PSO) method for feature selection and Synthetic Minority Over-sampling Technique (SMOTE) to handle data imbalance, which is a novel approach in aspect-based sentiment analysis. A total of 827 review data were retrieved from the Google Maps platform and manually labeled. This method resulted in significantly improved sentiment classification accuracy over the model without optimization. After the application of PSO and SMOTE, the model accuracy for the price aspect increased from 91.56% to 94.28%, the service aspect from 89.75% to 92.85%, and the facility aspect from 79.51% to 88.88%. The results of this study show that the combined PSO and SMOTE approach not only improves the accuracy, but also the consistency of sentiment classification on various aspects. These findings provide deep insights for tourism managers in identifying strengths and weaknesses based on visitor reviews.
Co-Authors Agus Setiawan Ardhin Primadewi Aulia Afifah Auliya Burhanuddin Dimas Sasongko Erlinda, Reza Ena Hasani, Rofi Abul Jalaluddin, Alif Maimunah, Maimunah Muhammad Resa Arif Yudianto Prawesty, Esty Purwono Hendradi Rojun, Afwiq Safin, Fatih Ali Sambodo Sriadi Pinilih Saputra, Dio Raka Venda Setiawan, Agus Setiya Nugroho Setyawan, Akhmad Fajar Sukmasetya, Pristi Uky Yudatama, Uky