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All Journal Jurnal CoreIT IT JOURNAL RESEARCH AND DEVELOPMENT INOVTEK Polbeng - Seri Informatika Jusikom : Jurnal Sistem Komputer Musirawas J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) Jurnal Teknologi Dan Sistem Informasi Bisnis Brahmana : Jurnal Penerapan Kecerdasan Buatan IJISTECH Journal of Applied Data Sciences J-SAKTI (Jurnal Sains Komputer dan Informatika) International Journal of Research in Vocational Studies (IJRVOCAS) J-COSCIS : Journal of Computer Science Community Service Internasional Journal of Data Science, Computer Science and Informatics Technology (InJODACSIT) SATIN - Sains dan Teknologi Informasi
Pandu Pratama Putra, Pandu Pratama
Universitas Lancang Kuning
Aerospace Engineering Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Journal : Journal of Applied Data Sciences

 1 
Optimizing Sentiment Analysis on Imbalanced Hotel Review Data Using SMOTE and Ensemble Machine Learning Techniques Putra, Pandu Pratama; Anam, M. Khairul; Chan, Andi Supriadi; Hadi, Abrar; Hendri, Nofri; Masnur, Alkadri
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.v6i2.618

Abstract

This research addresses the challenge of imbalanced sentiment classes in hotel review datasets obtained from Traveloka by integrating SMOTE (Synthetic Minority Oversampling Technique) with ensemble machine learning methods. The study aimed to enhance the classification of Positive, Negative, and Neutral sentiments in customer reviews. Data preprocessing techniques, including tokenization, stemming, and stopword removal, prepared the textual data for analysis. Various machine learning models—CART, KNN, Naive Bayes, and Random Forest—were evaluated individually and in ensemble configurations such as Bagging, Stacking, Soft Voting, and Hard Voting. The Stacking ensemble approach, utilizing Logistic Regression as a meta-classifier, demonstrated superior performance with an accuracy, precision, recall, and F1-score of 88%, outperforming Bagging (86%), Hard Voting (84%), and Soft Voting (81%). The findings highlight the effectiveness of SMOTE in balancing sentiment classes, particularly improving the classification of underrepresented Neutral and Negative categories. The novelty of this study lies in the comprehensive use of ensemble techniques combined with SMOTE, which significantly enhanced prediction stability and accuracy compared to previous approaches. These results provide valuable insights into leveraging advanced machine learning techniques for sentiment analysis, offering practical implications for improving customer experience and service quality in the hospitality industry.
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 Abrar Hadi Adi Rahmat afwan, zul Ahmad Zamsuri, Ahmad Alkadri Masnur Anam, M Khairul Andam Lukcyhasnita Andi Supriadi Chan, Andi Supriadi Anisya Anisya Arda Yunianta Bayu Febriadi Bayu Febriadi Bayu Febriadi Bayu Febriadi, Bayu Dafwen Toresa Didik Siswanto DWI ANA PERTIWI Edriyansyah Elva, Yesri Fana Wiza, Fana Febrizal Alfarasy Syam Febrizal Alfarasy Syam Firdaus, Muhammad Bambang Gabriel Ardi Hutagalung Gunawan, Chichi Rizka Guntoro, Guntoro Handrian Azhar hendri, nofri Ikhsan Ikhsan Karfindo, Karfindo Keumala Anggraini Lisnawita Lisnawita Loneli Costaner Lucky Lhaura Van FC, Lucky Lhaura Mahessya, Raja Ayu Malik, Rio Andika Muhamad Sadar, Muhamad Muhammad Syaifullah Nijal, Lasri Nori Sahrun Nuraini Panji Rachmat Setiawan Puspita Sari Rometdo Muzawi, Rometdo RR. Ella Evrita Hestiandari Sarjon Defit Siswanto, Didik Sularno Sularno Susi Handayani Sutejo Sutejo Sutejo, Aris Syam, Febrizal Alfarasy Teri Ade Putra Wahyu Joni Kurniawan, Wahyu Joni Yogi Ersan Fadrial Yogi Ersan Fadrial Yogi Yunefri Yogi Yunefri, Yogi