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All Journal International Journal of Advances in Applied Sciences Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Widya Cipta : Jurnal Sekretari dan Manajemen Applied Information System and Management JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) International Journal of Advances in Data and Information Systems Journal of Applied Data Sciences Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Jurnal Janitra Informatika dan Sistem Informasi LEBAH Journal of Information System and Education Development
Malik, Rio Andika
Universitas Perintis Indonesia
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Journal : Journal of Applied Data Sciences

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Enhancing the Performance of Machine Learning Algorithm for Intent Sentiment Analysis on Village Fund Topic Anam, M. Khairul; Putra, Pandu Pratama; Malik, Rio Andika; Karfindo, Karfindo; Putra, Teri Ade; Elva, Yesri; Mahessya, Raja Ayu; Firdaus, Muhammad Bambang; Ikhsan, Ikhsan; Gunawan, Chichi Rizka
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.637

Abstract

This study explores the implementation of Intent Sentiment Analysis on Twitter data related to the Village Fund program, leveraging Multinomial Naïve Bayes (MNB) and enhancing it with Synthetic Minority Over-sampling Technique (SMOTE) and XGBoost (XGB). The analysis categorizes tweets into six labels: Optimistic, Pessimistic, Advice, Satire, Appreciation, and No Intent. Initially, the MNB model achieved an accuracy of 67% on a 90:10 data split. By applying SMOTE, accuracy improved by 12%, reaching 89%. However, adding Chi-Square feature selection did not increase accuracy further. Incorporating XGB into the MNB+SMOTE model led to a 6% improvement, achieving a final accuracy of 95%. Comprehensive model evaluation revealed that the MNB+SMOTE+XGB model achieved 96% accuracy, 96% precision, 96% recall, and a 96% F1-score, with an AUC of 99%, categorizing it as excellent. These findings demonstrate that the combination of SMOTE for addressing class imbalance and XGBoost for boosting performance significantly enhances the MNB model's classification capabilities. The novelty lies in the integration of these techniques to improve intent sentiment classification for public opinion analysis on the Village Fund program. The results indicate that the majority of tweets labeled as "No Intent" reflect a lack of specific sentiment or actionable intent, providing valuable insights into public perception of the program.
Co-Authors Anam, M Khairul Carelsa, Hasnah Vithon Dwipa Junika Putra Dwipa Junika Putra Elsa Widia Elva, Yesri Faizal Riza Firdaus, Muhammad Bambang Gunawan, Chichi Rizka Ikhsan Ikhsan Karfindo, Karfindo Mahessya, Raja Ayu Marta Riri Frimadani Marta Riri Frimadani Marta Riri Frimadani Marta Riri Frimadani Muzaki, M.Nabil Natrio, Yeyep Pandu Pratama Putra, Pandu Pratama Putra, Dwipa Junika Rinawati Kasrin, Rinawati Sarjon Defit Sarjon Defit Sri Mona Octafia Teri Ade Putra Vicky Setia Gunawan Werifmayeni, Annisa Wilis Firmansyah Yuhandri Yuhandri Yuhandri Yuhandri, Yuhandri