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All Journal Techno.Com: Jurnal Teknologi Informasi Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Teknik Elektro Jurnal Media Wahana Ekonomika PREDIKSI Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Sistem dan Informatika InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL MANAJEMEN BISNIS Jurnal Literasiologi Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Tekno Kompak Jurnal Ilmiah Kebidanan Indonesia (Indonesian Midwifery Scientific Journal) Jurnal Teknologi Informasi dan Multimedia Jurnal Abdidas Jurnal SASAK : Desain Visual dan Komunikasi Jurnal Bumigora Information Technology (BITe) Jurnal Pengabdian UNDIKMA Journal of Electrical Engineering and Computer (JEECOM) Science in Information Technology Letters Warta Pengabdian Andalas: Jurnal Ilmiah Pengembangan Dan Penerapan Ipteks Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Jurnal Silva Samalas: Journal of Forestry and Plant Science Jurnal Elkasista ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal PRIMED:Primary Education Journal atau Jurnal Ke-SD An Journal of Engineering, Technology and Computing (JETCom) Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Journal of Management, Economic and Accounting (JMEA) Insand Comtech : Information Science and Computer Technology Journal International Journal of Engineering Continuity Valid Jurnal Pengabdian Journal of Economics and Management Scienties Jurnal Riset Rumpun Ilmu Pendidikan (JURRIPEN) Prosiding Seminar Nasional Unimus UPGRADE: Jurnal Pendidikan Teknologi Informasi Jurnal Ilmu Komputer dan Teknologi Informasi ABDIMASTEK Prosiding Seminar Nasional CORISINDO Media Pendidikan Matematika
Hairani Hairani
Universitas Bumigora, Mataram, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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A Comparative Analysis of Machine Learning Models Using PCA and Variance Threshold Approaches for Classifying Student Academic Success Djoko Rahardjo; Hairani Hairani; M. Thoriq Panca Mukti; M. Rizki
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 5 No. 2 (2026): September 2026 (In Press)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v5i2.6585

Abstract

Predicting student academic success is an important problem in higher education due to the high risk of delayed graduation or dropout. However, the prediction process often faces challenges, such as an imbalanced class distribution, which can reduce the classification model’s performance. This study aims to evaluate and compare the performance of Logistic Regression, Random Forest, and Gradient Boosting algorithms in classifying student academic status into Graduate, Enrolled, and Dropout categories. The research method begins with data balancing using the Synthetic Minority Oversampling Technique (SMOTE) applied to the training data, followed by a comparison of two dimensionality reduction techniques: feature selection with Variance Threshold (VT) and feature extraction with Principal Component Analysis (PCA). Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The results show that Random Forest without dimensionality reduction achieves the best performance, with an accuracy of 77.40% on SMOTE-balanced data. The application of SMOTE has also been shown to improve the balance of predictive capability across classes, as evidenced by higher F1-scores than with the original data, which is biased toward the majority class. Conversely, the use of PCA degrades model performance by reducing the information available to differentiate the classes. Furthermore, the Enrolled class is the most difficult to predict due to its high similarity in characteristics to the Graduate and Dropout classes. Based on these results, the combination of Random Forest and SMOTE, without dimensionality reduction, is the most effective approach for predicting students’ academic success. This model has the potential to be implemented as a component of an Early Warning System to support the identification of at-risk students and the implementation of earlier academic interventions.  
Optimizing Sentiment Analysis for Lombok Tourism Using SMOTE and Chi-Square with Machine Learning Hairani; Anthony Anggrawan; 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.
Enhancing Software Defect Prediction Performance using NR-Clustering SMOTE to Address Class Imbalance Hairani Hairani; Muhamad Masjun Efendi; Gede Yogi Pratama; Rahayun Amrullah Husaini; M.Khaerul Ihsan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i7558

Abstract

Software defect detection is important to prevent system failures and increased maintenance costs. However, the complexity of modern software makes manual testing inefficient, so machine learning approaches are used. The main challenge of this approach is data imbalance, where defective cases are far fewer, causing the model to overlook the minority class and reducing detection capability, even though accuracy appears high. This study aims to address class imbalance in software defect detection by applying the NR-Clustering SMOTE method to improve machine learning performance—the classification methods using Random Forest. NR-Clustering SMOTE not only oversamples the minority class but also incorporates a noise-reduction mechanism to remove minority data that may degrade classification performance. The results show that NR-Clustering SMOTE improves the performance of Random Forest compared with the original data, SMOTE, and NR-Modified SMOTE across all evaluation metrics, namely accuracy, recall, and F1-score. These findings indicate that integrating noise reduction and SMOTE-based data balancing using Manhattan distance within each cluster produces a more representative data distribution, thereby improving the model’s ability to classify software defect cases more accurately. Therefore, this study confirms that NR-Clustering SMOTE effectively improves Random Forest performance for software defect detection compared with existing approaches.
Analisis Sentimen Konsumen pada Rumah Makan di Mataram Menggunakan Algoritma K-Nearest Neighbor, Naïve Bayes, dan Support Vector Machine Ameylan Verina Tabun; Hairani Hairani; Dadang Priyanto
Jurnal Ilmu Komputer dan Teknologi Informasi Vol. 3 No. 2 (2026): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jikti.v3i2.2004

Abstract

Consumer reviews on digital platforms can be used to identify customer perceptions of food quality, service, price, and restaurant comfort. This study used 1,006 reviews from 18 restaurants in Mataram City collected through web scraping from TripAdvisor. After data cleaning and selection, 780 reviews were used as the final dataset. The study aimed to analyze consumer review sentiment and compare the performance of K-Nearest Neighbor (K-NN), Naïve Bayes (NB), and Support Vector Machine (SVM) algorithms in classifying positive, negative, and neutral sentiments. A quantitative approach with a comparative experimental method was employed. The research stages included data collection, preprocessing, sentiment labeling based on lexicon and rating, feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF), classification, and evaluation using a confusion matrix. Tests were conducted using 70:30, 80:20, and 90:10 data splits with accuracy, precision, recall, and F1-score as evaluation metrics. The results showed that SVM achieved the highest accuracy across all testing scenarios. The best performance was obtained with the 80:20 split, reaching 87.1% accuracy, 87.0% precision, 87.0% recall, and 86.0% F1-score.
Co-Authors Abdillah, Mokhammad Nurkholis Abdul Hadi Abdurraghib Segaf Suweleh Abdurraghib Segaf Suweleh Abu Tholib Adam, M. Awaludin Adawiyah, Rabi'atul Adrianto, Muhammad Subhan Dwi Afrig Aminuddin Ahmad Ahmad Ahmad Fathoni Ahmad Zuli Amrullah Aleeka Jasmine Amelia, Bengi Ameylan Verina Tabun Amin, Farda Milanda Andani, Nazwa Putri Andi Sofyan Anas Andi, Moh syaiful Andini, Nisha Anggarawan, Anthony Anthony Anggrawan Arfa, Muhammad Arifah Ulayya Ashadi, Diki Astuti, Ni Luh Budi Ayu Dasriani, Ni Gusti Bukran Bukran Candra, M. Ade Christine Eirene Christopher Michael Lauw Christopher Michael Lauw Dadang Priyanto Dedi Aprianto Dedy Febry Rachman Dedy Febry Rahman Dekki Widiatmoko Deny Jollyta Diah Ekawati Dian Syafitri Didik Dwi Prasetya Diki Ashadi Dirgantara, Bhintang Djoko Rahardjo Donny Kurniawan Dyah Susilowati Dyah Susilowaty ED. Yunisa Mega Pasha ED. Yunisa Mega Pasha Efendi, Muhamad Masjun Eka Setiawan, Rian Putra Ezra Azzahra Fahry, Fahry Fathorazi Nur Fajri Fatimatuzzahra Fatimatuzzahra Febriana, Annisa Dwi Fikrulia, Hidayah Jihan Firdaus, Adhitya Fitra Rizki Ramdhani Galih Hendro Martono Gede Yogi Pratama Gibran Satya Nugraha Gibran Satya Nugraha Guntara, Muhammad Gusti Ayu Diah Gita Kartika Santi, I Gustiya, Sherly Dwi Guterres, Juvinal Ximenes Hadi, M Fawazi Hammad, Rifqi Hartono Wijaya Haryono Haryono Hasanah, Maulida Hasbullah Hasbullah Herawati, Baiq Candra Heru Kurnianto Tjahjono Hery Widijanto Hidayati, Diana Huda, Dias Nabila Husain Husain I Gusti Agung Ayu Hari Triandini I Nyoman Switrayana Ida Putu Andika Ifnaldi Ifnaldi Iis Sopiah Suryani Ilham Saifuddin Indah Puji Lestari Indradewa, Rhian Irawan, Dudi Isviyanti, Isviyanti Janhasmadja, Mengas Jauhari, M. Thonthowi Jupriadi, Jupriadi Juvinal Ximenes Guterres Juvinal Ximenes Guterres Juvinal Ximenes Guterres Juvinal Ximenes Guterres Kandisa, Amelia Kasiyanto Kasiyanto Kasiyanto Kasiyanto, Kasiyanto Khairan marzuki Khairil Ihsan Khasnur Hidjah Khurniawan Eko Saputro Kurniadin Abd Latif Kurniawan Kurniawan Lalu Ganda Rady Putra Lalu Zazuli Azhar Mardedi Lestari, Jumiati Indah Lilik Nurhayati lnnuddin, Muhammad M. Ade Candra M. Rasyid Ridho M. Rizki M. Thoriq Panca Mukti M.Khaerul Ihsan M.Khaerul Ihsan Maariful Huda, Muhammad Malika, Riwayati Mamay Maulana Mamay Maulana Mardedi, Lalu Zazuli Azhar Mardedi, Lalu Zazuli Azhar Mayadi Mayadi Mayadi Mayadi Mayadi, Mayadi Mayasari, Astri Melati Rosanensi Mia Nisrina Anbar Fatin Michael Lauw, Christopher Miftahul Madani Mubarak, Ahmad Nazhif Mudawil Qulub Muhamad Azwar Muhamad Azwar, Muhamad Muhamad Reza Pahlevi Muhamad Reza Pahlevi Muhamad Wisnu Alfiansyah Muhammad Arfa Muhammad Fahmi Muhammad Ghifari, Muhammad Muhammad Innuddin Muhammad Maariful Huda Muhammad Ridho Akbar Muhammad Ridho Hansyah muhammad Syahbudi, muhammad Muhammad Tahir Muhammad Turmuzi Muhammad Zulfikri Muhammad Zulfikri Muhammad Zulkarnaen Haris Mujahid Mujahid Neny Sulistianingsih Ni Made Gita Gumangsari Nindya Alifia Khumaira Nisa, Rahayu Noor Akhmad Setiawan Novitasari Tsamrotul Fuadah Nur Intan Hayati Nur Intan Hayati Nurhayati, Lilik Nurul Azmi Nurvianti, Nurvianti Nuzululnisa, Bq Nadila Pahrul Irfan Pratama, Gede Yogi Putu Tisna Putra Qososyi, Sayidina Ahmadal Rahayun Amrullah Husaini Rahman, Mochamad Farhan Caesar Rahmawati, Lela Ramadhanti Ramadhanti Ramadhanti, Ramadhanti Rangga Wijaya Regandara, Ellysia Putri Rhomdani, Rohmad Wahid Rian Putra Eka Setiawan Rifqi Hammad Rio Riswanto Simanjuntak Riosatria, Riosatria Riwayati Malika Rizki Wahyudi Robo, Salahudin rokhim utomo Rosyda, Miftahurrahma RR. Ella Evrita Hestiandari Saifuddin Zuhri Saifuddin, Ilham Saifudin, Ilham Samsul Hadi Santoso, Heroe Shudiq, Wali Ja'far Soepriyanto, Harry Sofiansyah Fadli Soni Muhsinin Sri Farida Utami Sri Winarni Sofya Sri Winarni Sofya Sudi Prayitno Sukron, Moh Sutarman Sutarman Syahrir, Moch. tadianta m., Winardi aries Teguh Bharata Adji Tri Nur Jayanti Tri Nur Jayanti Triwijoyo, Bambang Krismono Triyanna Widiyaningtyas Umi Hanifah Vidiasari, Herlita Vidiasari, Viviana Herlita Vina Vitniawati Wahyuningsih, Rr. Sri Handari Wangiyana, I Gde Adi Suryawan Wening Asih Sutrisno Wening Asih Sutrisno Widhya Aligita Widhya Aligita Widiatmoko, Dekki Wira Hendri Wiyanto, Suko Ximenes Guterres, Juvinal Yuri Ariyanto Yuri Ariyanto Zilullah Nazir Hadi