Optimizing Aspect-Based Sentiment Analysis for Kyai Langgeng Park Using PSO and SVM

  • Dio Raka Venda Saputra Universitas Muhammadiyah Magelang, Indonesia
  • Maimunah Maimunah Universitas Muhammadiyah Magelang, Indonesia
  • Endah Ratna Arumi Universitas Muhammadiyah Magelang, Indonesia
Keywords: Aspect-based Sentiment Analysis, PSO, SMOTE, SVM

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.

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References

B. Prasetyo, W. Hidayat, and N. Ngatno, “Pengaruh Fasilitas dan Electronic Word Of Mouth terhadap Keputusan Berkunjung Wisatawan di Objek Wisata Taman Kyai Langgeng Kota Magelang,” J. Ilmu Adm. Bisnis, vol. 11, no. 2, pp. 134–141, 2022, doi: 10.14710/jiab.2022.34132.

M. R. A. Yudianto, P. Sukmasetya, R. A. Hasani, and Maimunah, “Aspect-Based Sentiment Analysis of Borobudur Temple Reviews Use Support Vector Machine Algorithm,” E3S Web Conf., vol. 500, pp. 1–9, 2024, doi: 10.1051/e3sconf/202450001005.

R. Naquitasia, D. H. Fudholi, and L. Iswari, “Analisis Sentimen Berbasis Aspek pada Wisata Halal dengan Metode Deep Learning,” J. Teknoinfo, vol. 16, no. 2, p. 156, 2022, doi: 10.33365/jti.v16i2.1516.

A. R. Makhtum and M. Muhajir, “Sentiment Analysis of Omnibus Law Using Support Vector Machine (Svm) With Linear Kernel,” BAREKENG J. Ilmu Mat. dan Terap., vol. 17, no. 4, pp. 2197–2206, 2023, doi: 10.30598/barekengvol17iss4pp2197-2206.

J. Ipmawati, S. Saifulloh, and K. Kusnawi, “Analisis Sentimen Tempat Wisata Berdasarkan Ulasan pada Google Maps Menggunakan Algoritma Support Vector Machine,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 1, pp. 247–256, 2024, doi: 10.57152/malcom.v4i1.1066.

A. M. Ndapamuri, D. Manongga, and A. Iriani, “Analisis Sentimen Ulasan Aplikasi Tripadvisor Dengan Metode Support Vector Machine, K-Nearest Neighbor, Dan Naive Bayes,” INOVTEK Polbeng - Seri Inform., vol. 8, no. 1, p. 127, 2023, doi: 10.35314/isi.v8i1.3260.

I. W. B. Suryawan, N. W. Utami, and K. Q. Fredlina, “Analisis Sentimen Review Wisatawan pada Objek Wisata Ubud Menggunakan Algoritma Support Vector Machine,” J. Inform. Teknol. dan Sains, vol. 5, no. 1, pp. 133–140, 2023.

I. Maulana, W. Apriandari, and A. Pambudi, “Analisis Sentimen Berbasis Aspek Terhadap Ulasan Aplikasi Mypertamina Menggunakan Support Vector Machine,” IDEALIS Indones. J. Inf. Syst., vol. 6, no. 2, pp. 172–181, 2023, doi: 10.36080/idealis.v6i2.3022.

R. Nurhidayat and K. E. Dewi, “Penerapan Algoritma K-Nearest Neighbor Dan Fitur Ekstraksi N-Gram Dalam Analisis Sentimen Berbasis Aspek,” Komputa J. Ilm. Komput. dan Inform., vol. 12, no. 1, pp. 91–100, 2023, doi: 10.34010/komputa.v12i1.9458.

A. P. Giovani, A. Ardiansyah, T. Haryanti, L. Kurniawati, and W. Gata, “Analisis Sentimen Aplikasi Ruang Guru Di Twitter Menggunakan Algoritma Klasifikasi,” J. Teknoinfo, vol. 14, no. 2, p. 115, 2020, doi: 10.33365/jti.v14i2.679.

M. H. Wicaksono, M. D. Purbolaksono, and S. Al Faraby, “Perbandingan Algoritma Machine Learning untuk Analisis Sentimen Berbasis Aspek pada Review Female Daily,” eProceedings Eng., vol. 10, no. 3, pp. 3591–3600, 2023.

S. A. Pratomo, S. Al Faraby, and M. D. Purbolaksono, “Analisis Sentimen Pengaruh Kombinasi Ekstraksi Fitur TF-IDF dan Lexicon Pada Ulasan Film Menggunakan Metode KNN,” e-Proceeding Eng., vol. 8, no. 5, pp. 10116–10126, 2021.

D. A. Fatah, E. M. S. Rochman, W. Setiawan, A. R. Aulia, F. I. Kamil, and A. Su’ud, “Sentiment Analysis of Public Opinion Towards Tourism in Bangkalan Regency Using Naïve Bayes Method,” E3S Web Conf., vol. 499, pp. 1–8, 2024, doi: 10.1051/e3sconf/202449901016.

Muhammad Daffa Al Fahreza, Ardytha Luthfiarta, Muhammad Rafid, and Michael Indrawan, “Analisis Sentimen: Pengaruh Jam Kerja Terhadap Kesehatan Mental Generasi Z,” J. Appl. Comput. Sci. Technol., vol. 5, no. 1, pp. 16–25, 2024, doi: 10.52158/jacost.v5i1.715.

Y. A. Sir and A. H. H. Soepranoto, “Pendekatan Resampling Data Untuk Menangani Masalah Ketidakseimbangan Kelas,” J. Komput. dan Inform., vol. 10, no. 1, pp. 31–38, 2022, doi: 10.35508/jicon.v10i1.6554.

N. Sholihah, F. Fauzi Abdulloh, and M. Rahardi, “Optimasi Analisis Sentimen terhadap Kinerja Direktorat Jenderal Pajak Indonesia Melalui Teknik Oversampling dan Seleksi Fitur Particle Swarm Optimization,” Smart Comp Jurnalnya Orang Pint. Komput., vol. 12, no. 4, 2023, doi: 10.30591/smartcomp.v12i4.5814.

K. M. Ang et al., “A Modified Particle Swarm Optimization Algorithm for Optimizing Artificial Neural Network in Classification Tasks,” Processes, vol. 10, no. 12, pp. 1–35, 2022, doi: 10.3390/pr10122579.

M. Isnan, G. N. Elwirehardja, and B. Pardamean, “Sentiment Analysis for TikTok Review Using VADER Sentiment and SVM Model,” Procedia Comput. Sci., vol. 227, pp. 168–175, 2023, doi: 10.1016/j.procs.2023.10.514.

S. Rabbani, D. Safitri, N. Rahmadhani, A. A. F. Sani, and M. K. Anam, “Perbandingan Evaluasi Kernel SVM untuk Klasifikasi Sentimen dalam Analisis Kenaikan Harga BBM,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 3, no. 2, pp. 153–160, 2023, doi: 10.57152/malcom.v3i2.897.

A. H. Ali and M. Z. Abdullah, “A parallel grid optimization of SVM hyperparameter for big data classification using spark radoop,” Karbala Int. J. Mod. Sci., vol. 6, no. 1, 2020, doi: 10.33640/2405-609X.1270.

N. Hafidz and D. Yanti Liliana, “Klasifikasi Sentimen pada Twitter Terhadap WHO Terkait Covid-19 Menggunakan SVM, N-Gram, PSO,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 2, pp. 213–219, 2021, doi: 10.29207/resti.v5i2.2960.

Published
2024-12-31
Abstract views: 125 times
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How to Cite
Saputra, D., Maimunah, M., & Arumi, E. (2024). Optimizing Aspect-Based Sentiment Analysis for Kyai Langgeng Park Using PSO and SVM. Journal of Information Systems and Informatics, 6(4), 2856-2867. https://doi.org/10.51519/journalisi.v6i4.930
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