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All Journal International Journal of Electrical and Computer Engineering Jurnal Edukasi dan Penelitian Informatika (JEPIN) Scientific Journal of Informatics Jurnal Sains dan Informatika Infotekmesin Indonesian Journal of Electrical Engineering and Computer Science Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Jurnal Pengabdian Kepada Masyarakat (JPKM) Langit Biru
Maharrani, Ratih Hafsarah
Politeknik Negeri Cilacap
Aerospace Engineering Computer Science & IT Education Electrical & Electronics Engineering Engineering Mathematics Mechanical Engineering Transportation

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Journal : Scientific Journal of Informatics

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Coastal Sentiment Review Using Naïve Bayes with Feature Selection Genetic Algorithm Somantri, Oman; Maharrani, Ratih Hafsarah; Purwaningrum, Santi
Scientific Journal of Informatics Vol 10, No 3 (2023): August 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i3.43988

Abstract

Purpose: The tourism potential in the maritime sector can be Indonesia's mainstay at this time, especially in enjoying the charm of the natural beauty of the coast as people know Indonesia is an archipelagic country. The purpose of this study is to find the best model by applying the feature selection genetic algorithm (GA) and Information Gain (IG) to get the best Naïve Bayes (NB) model and the best features to produce the best level of sentiment classification accuracy.Methods: The stages of the research were carried out by going through the process of searching, pre-processing, analyzing research data using the Naïve Bayes model and optimizing genetic algorithms, validating data, and model evaluation.Result: The experimental results show that the best model is naïve Bayes based on information gain and the genetic algorithm yields an accuracy rate of 86.34%.Novelty: The main contribution to this research is proposing a new model of the best NB optimization model by applying an optimization algorithm in the search for feature selection to increase sentiment classification accuracy.
Co-Authors Abda'u, Prih Diantono Arif Wirawan Muhammad Ikhtiagung, Ganjar Ndaru Linda Perdana Wanti Lutfi Syafirullah Muhammad Nur Faiz, Muhammad Nur Novia Prasetyanti, Dwi Nur’Aini, Ummu Habibah Oman Somantri Prihantara, Andesita Purwaningrum, Santi Rahadi, Nur Wahyu Riyadi Purwanto Rostika Listyaningrum Santi Purwaningrum Sari, Laura Supriyono, Abdul Rohman Zaenurrohman, Zaenurrohman