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All Journal Jurnal Teknik Elektro InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JPEK (Jurnal Pendidikan Ekonomi dan Kewirausahaan) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal SASAK : Desain Visual dan Komunikasi Jurnal Bumigora Information Technology (BITe) Innovation in Research of Informatics (INNOVATICS) Informatics and Digital Expert (INDEX) Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Bakti Sekawan : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Pengabdian dan Inovasi Panthera : Jurnal Ilmiah Pendidikan Sains dan Terapan Valid Jurnal Pengabdian Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Muhammad Innuddin
Universitas Bumigora
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Mathematics Nursing Physics Social Sciences Other

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Optimizing Sentiment Analysis for Lombok Tourism Using SMOTE and Chi-Square with Machine Learning Hairani; Anggrawan, Anthony; Muhammad Ridho Akbar; Khasnur Hidjah; Muhammad Innuddin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6623

Abstract

Tourism is a vital economic sector for Lombok Island, which is renowned for its natural beauty and cultural richness as a top destination. The rapid growth of tourism in Lombok requires a deep understanding of tourists' perceptions and sentiments to ensure an optimal service quality. The sentiment analysis of online reviews is valuable for identifying service strengths and weaknesses and addressing tourists' needs more effectively. This not only enhances tourist satisfaction, but also aids in the design of more effective marketing strategies. However, text data analysis from online reviews presents unique challenges such as noise, class imbalance, and numerous features that may affect classification results. Therefore, this study aims to classify tourist sentiment toward Lombok tourism using machine learning methods combined with feature selection and oversampling techniques. This study focuses on optimizing sentiment analysis of tourism-related tweets using a combination of SMOTE oversampling and Chi-Square feature selection on improving classification performance without hyperparameter tuning. The study applies machine learning methods, such as SVM and Naïve Bayes, with feature selection and oversampling using Chi-Square and SMOTE. The dataset used was sentiment data regarding Lombok tourism obtained from Twitter in 2023, consisting of 940 instances divided into three classes: Negative, Neutral, and Positive. The research findings show that the use of SMOTE and Chi-Square can improve the accuracy of the SVM and Naive Bayes methods. Without optimization, the SVM method achieved an accuracy of 73.93% and a Naive Bayes of 67.02%. After optimization with SMOTE and Chi-Square, the accuracy increased for SVM by 90% and Naive Bayes by 84% to classify tourist sentiment towards Lombok tourism. The implications indicate that combining data balancing using SMOTE with feature selection via Chi-Square effectively improves the performance of sentiment classification models for tourist opinions on Lombok's tourism.
Improving Classification Performance on Imbalanced Stroke Datasets Using Oversampling Techniques Innuddin, Muhammad; Hairani; Jauhari, M. Thonthowi; Mardedi, Lalu Zazuli Azhar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6859

Abstract

Stroke is the second leading cause of death worldwide and a major factor in long-term disability. Although early detection based on machine learning continues to be developed, it still faces challenges in the form of data imbalance, which can reduce classification performance. This study aimed to evaluate the effectiveness of several oversampling techniques, such as SMOTE, Borderline-SMOTE, and SVM-SMOTE, in improving the performance of stroke classification models on imbalanced data. The methods used included the application of three oversampling techniques, namely SMOTE, Borderline-SMOTE, and SVM-SMOTE, to balance the data distribution. Furthermore, Random Forest and XGBoost algorithms were used as classification models to identify stroke cases. The results of this study show that the use of oversampling techniques significantly improves model performance, especially in MCC and AUC metrics, compared to models without oversampling. Borderline-SMOTE provides the best results, with the highest accuracy of 96.45% on Random Forest and 96.41% on XGBoost, as well as MCC and AUC values that are consistently superior to other techniques. Furthermore, this study highlights that the use of Borderline-SMOTE significantly enhances model performance, as evidenced by an increase in MCC of 87.51% and an AUC of 45.40% in Random Forest, along with an increase in MCC of 76.52% and an AUC of 41.81% in XGBoost. These findings confirm that Borderline-SMOTE is an effective approach for dealing with data imbalance and improving the model's ability to detect minority classes in stroke classification.
Co-Authors -, Ubaidi Admojo, Fadhila Tangguh Ahmad Fathoni Ahmad Zuli Amrullah Andi Sofyan Anas Anthony Anggrawan Dedi Aprianto Dedi Febry Rachman Dedy Febry Rachman Dedy Febry Rahman Dyah Susilowati Eka Sudarsana, I Putu Fauzan Natsir Gibran Satya Nugraha Hairani Hairani Hammad, Rifqi Hasbullah Hasbullah Hasbullah Hasbullah Hersanius Kurnia Peong Ida Putu Andika Irwan Darmawan Jauhari, M. Thonthowi Khasnur Hidjah Kurniadin Abd Latif Kurniawan Kurniawan Lilik Nurhayati Mardedi, Lalu Zazuli Azhar Mardedi, Lalu Zazuli Azhar Mokhammad Nurkholis Abdillah Mokhammad Nurkholis Abdillah Mokhammad Nurkholis Abdillah Mualfah, Desti Muhammad Arfa Muhammad Ridho Akbar Muhammad Sahdan Muhammad Zulkarnaen Haris Nazir Arifin, Mohammad Nurhayati, Lilik Ondi Asroni Pahrul Irfan Phyta Rahima Pio Pratama, I Wayan Puspa Dewi, Nindian Rahmawati, Lela Ramadhani, Nilam Ramadhanti Ramadhanti Ramadhanti, Ramadhanti Ria Rismayati Rifqi Hammad Samsul Hadi Slamet Risnanto Sri Anita Rachman Suratno, Danang Mulyadipa Suryati Sutarman Sutarman Syaiful Amri Wijaya, Frederico Indra Windiawati, Ai Wulan Wira Hendri Zilullah Nazir Hadi