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All Journal Jupiter Sistemasi: Jurnal Sistem Informasi METIK JURNAL Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Pengabdian Masyarakat Bumi Raflesia Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) Jurnal Digital Teknologi Informasi Jurnal Algoritma Malcom: Indonesian Journal of Machine Learning and Computer Science The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika
Sanjaya, M. Rudi
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Comparison of SVM and Naive Bayes Algorithms in Sentiment Analysis of User Reviews on Bukalapak Alghifari, M Yasir; Sanjaya, M. Rudi; Dwi Rosa Indah; Ruskan, Endang Lestari
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/dqhpkb12

Abstract

Indonesia’s rapid e-commerce growth has produced a vast volume of user reviews, yet their use for insight extraction remains limited—particularly for the Bukalapak platform. This study compares the performance of Naïve Bayes and Support Vector Machine for sentiment classification on 10,000 Bukalapak reviews. The workflow includes text preprocessing (cleaning, case folding, tokenization, stopword removal, and stemming) and feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF; max_features = 10,000). Evaluation employs 10-fold cross-validation with accuracy, precision, recall, and F1-score, complemented by a paired t-test for significance. Results show SVM outperforming NB (accuracy 84.48% vs. 83.96%; F1 0.8253 vs. 0.8205) with better consistency (standard deviation ±1.08% vs. ±1.24%). The t-test confirms a significant difference (p = 0.019), with SVM’s advantage most evident for the negative class (precision 0.80 vs. 0.78). Both models underperform on the neutral class due to severe class imbalance. These findings provide empirical evidence for algorithm selection in Indonesian e-commerce sentiment analysis and open avenues for future research using deep learning and class-imbalance handling techniques.
Optimization of Sentiment Analysis on Tokopedia User Reviews Using Gridsearchcv and Smote with Machine Learning Algorithms Imran, Athallah Yasyfi; Sanjaya, M. Rudi; Bayu Wijaya Putra; Gabriel Ekoputra Hartono Cahyadi
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/5ax8km80

Abstract

Understanding user sentiment from e-commerce reviews is essential for platform improvement and business strategy. This study compares three machine learning algorithms—Logistic Regression, Random Forest, and XGBoost—for sentiment classification of Indonesian-language Tokopedia reviews. A dataset of 6,822 user reviews was preprocessed through tokenization, stopword removal, and TF-IDF vectorization. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied to the training set. Models were evaluated using accuracy, precision, recall, and F1-score. Results demonstrate that Random Forest achieved the highest accuracy at 86.86%, followed by Logistic Regression at 84.86%, and XGBoost at 82.60%. The application of SMOTE significantly improved classification performance across all models, particularly for minority sentiment classes. These findings indicate that tree-based ensemble methods, especially Random Forest, are effective for sentiment analysis in imbalanced e-commerce datasets. This research provides practical insights for e-commerce platforms to implement automated sentiment monitoring systems, enabling faster response to customer feedback and targeted service improvements. However, the study is limited to Tokopedia reviews and may not generalize to other platforms or languages. Future work should explore deep learning approaches and cross-platform validation to enhance model robustness.
Penerapan artificial intelligence media desain website pembelajaran inovatif Sanjaya, M. Rudi; Ruskan, Endang Lestari; Indah, Dwi Rosa; Putra, Bayu Wijaya; Afif, Hasnan; Seprina, Iin; Faiq, Al Iksan; Wijayanto, Muhammad Ravi; Imran, Athallah Yasyfi; Danendra, Muhammad Archi Daffa; Rachmad, M. Ichsan Farel
Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) Vol. 7 No. 1 (2026)
Publisher : Universitas Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/jp2m.v7i1.24377

Abstract

Program Kreativitas Mahasiswa (PKM) ini bertujuan untuk meningkatkan kompetensi digital guru melalui penerapan teknologi Artificial Intelligence (AI) dalam desain website sekolah dan pengembangan media pembelajaran inovatif di SMA Negeri 10 Palembang.  Kegiatan ini dilatarbelakangi oleh kebutuhan guru untuk beradaptasi dengan era pembelajaran digital yang menuntut keahlian, kreativitas, efisiensi, dan interaktivitas tinggi. Metode pengabdian kepada masyarakat menggunakan pendampingan, pelatihan, praktik, diskusi. Melalui pelatihan berbasis praktik, guru dibimbing menggunakan AI dalam pembuatan desain website sekolah yang dinamis serta pengembangan media pembelajaran interaktif seperti pembuatan media pembelajaran aplikasi Gamma, ChatGPT, Wix Studio, Web Flow.  Hasil kegiatan di ukur dan di evauasi menggunakan test pre test dan post test dimana hasil tersebut menunjukkan peningkatan kemampuan guru dalam mengintegrasikan teknologi AI (ChatGPT, Gamma, Wix Studio, Web Flow) pada proses pembelajaran inovatif, kreatif, kolaboratif, dan berorientasi teknologi di  SMA Negeri 10 Palembang. sekolah SMA N 10 Palembang . Program ini berkontribusi nyata dalam mendorong transformasi digital pendidikan serta memperkuat peran guru di SMA Negeri 10 Palembang sebagai inovator dalam lingkungan belajar yang modern dan adaptif yang berbasis teknologi digital.
Penerapan Metode Smart pada Sistem Pendukung Keputusan Pemilihan Ustadz Terbaik Rumah Tahfidz Al-Firdaus Rachmad, Muhammad Ichsan Farel; Sanjaya, M. Rudi; Wijaya Putra, Bayu; Indah, Dwi Rosa
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.3089

Abstract

This study aims to develop a decision support system for selecting the best ustadz by applying the SMART (Simple Multi Attribute Rating Technique) method at Rumah Tahfidz Al-Firdaus Banyuasin. The institution faces challenges due to the absence of a formal procedure for objectively and measurably identifying outstanding instructors. The SMART method is employed because it enables transparent decision-making through systematic calculations based on predefined weights and parameters. The research adopts the Waterfall development model, which includes requirement analysis, design, implementation, testing, and maintenance phases. The evaluation parameters include attendance level, frequency of lateness, professional behavior, teaching techniques, team collaboration, and guidance in memorizing the Qur’an. This study contributes to the development of decision support systems in tahfidz institutions by adapting the SMART method to assessment criteria specific to Qur’anic learning. The findings show that the application developed is capable of assisting management in objectively measuring and determining the best ustadz, while also simplifying the performance evaluation process. Black-box testing confirms that all system features operate in accordance with the initial design, and the SMART calculation output successfully generates an automatic ranking of the top instructors. The presence of this system makes the assessment process more efficient, accurate, and sustainable, thereby supporting the improvement of teaching quality in Qur’an-based educational institutions.
Implementasi Algoritma K-Nearest Neighbors dalam Analisis Sentimen Ulasan Aplikasi Bank Aladin: Implementation of the K-Nearest Neighbors Algorithm for Sentiment Analysis on Aladin Bank Application Reviews Putri, Taniya Raisha Dwiva; Sanjaya, M. Rudi; Firdaus, MGS Afriyan; Indah, Dwi Rosa
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2633

Abstract

Digitalisasi layanan Aladin Bank Syariah melahirkan variasi ulasan di Google Play Store yang mencakup aspek kepraktisan hingga permasalahan teknis aplikasi. Guna memahami polaritas opini tersebut, penelitian ini melakukan evaluasi sentimen pengguna dengan memanfaatkan metode klasifikasi K-Nearest Neighbors (KNN). Proses analisis melibatkan pengelompokan ulasan ke dalam kelas positif, netral, dan negatif menggunakan 80% data hasil scraping, yang diproses melalui pembatasan 6.000 fitur pembobotan TF-IDF. Pengujian model mencatatkan tingkat akurasi sebesar 86,71%, di mana algoritma menunjukkan keunggulan signifikan dalam mengidentifikasi sentimen positif (F1-score 0,93) dan berkinerja cukup baik pada sentimen negatif (F1-score 0,68). Walaupun pengenalan terhadap kelas netral masih belum maksimal (F1-score 0.08), perolehan nilai rata-rata keseluruhan adalah 0.86, yang mengindikasikan efektivitas model secara umum. Temuan riset ini dapat dioptimalkan oleh manajemen bank sebagai landasan evaluasi untuk menyempurnakan kualitas layanan perbankan digital mereka.
Analisis Ulasan Sentimen pada Aplikasi Mobile BSB Menggunakan Algoritma Convolutional Neural Networks dan Support Vector Machine: Sentiment Review Analysis on the BSB Mobile Application Using Convolutional Neural Networks and Support Vector Machine Algorithm Faza, Muhamamad; Sanjaya, M. Rudi; Ibrahim, Ali; Syahbani, M. Husni
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2637

Abstract

Peningkatan jumlah ulasan yang diberikan pengguna pada platform digital seperti Google Play Store menghasilkan berbagai opini yang mencerminkan kepuasan maupun ketidakpuasan terhadap layanan aplikasi tersebut. Analisis sentimen diperlukan guna mengategorikan opini pengguna dalam ulasan dari data teks menjadi informasi penting. Penelitian ini menggunakan pendekatan metode Convolutional Neural Network (CNN) serta algoritma pendekatan Support Vector Machine (SVM) dalam rangka melakukan pengelompokan data sentimen pada ulasan aplikasi BSB Mobile. Tahapan proses penelitian mencakup tahapan pengumpulan data ulasan, pra-pemrosesan teks, pembentukan algoritma CNN beserta SVM, serta penilaian performa memanfaatkan parameter evaluasi, yaitu tingkat akurasi, presisi, recall, dan F1-Score. Merujuk pada temuan tersebut, dapat disimpulkan dari analisis bahwa arsitektur CNN menghasilkan tingkat akurasi yang menyentuh angka 80,10%, sedangkan model SVM mencapai akurasi 80,70%. Berdasarkan hasil evaluasi, model SVM sedikit lebih unggul dibandingkan dengan CNN dalam mengklasifikasikan ulasan sentimen. Temuan ini menunjukkan bahwa metode machine learning, khususnya SVM, dapat menjadi alternatif yang efektif dalam menganalisis opini pengguna terhadap aplikasi BSB Mobile.
Co-Authors Afif, Hasnan Alghifari, M Yasir Ali Ibrahim Allsela Meiriza, Allsela Alzaini, Akbar Aprillia, Nazwa Bayu Wijaya Putra Budiman S, M. Arif Danendra, Muhammad Archi Daffa Dwi Rosa Indah Endang Lestari Ruskan Endang Lestari Ruskan Faiq, Al Iksan Fathoni - Faza, Muhamamad Gabriel Ekoputra Hartono Cahyadi Gumay, Naretha Kawadha Pasemah Hardini Novianti Haristina Putri, Maulida Imran, Athallah Yasyfi Indah Dwi Rosa Ismail, Ahmad Arrijal Maula, Nurly Izzatul Mgs Afriyan Firdaus Nugroho, Doni Tri Putri, Taniya Raisha Dwiva Rachmad, M. Ichsan Farel Rachmad, Muhammad Ichsan Farel Rezqe, Beriadi Agung Nur Saputra, Ariansyah Seprina, Iin Syahbani, M. Husni Wijaya Putra, Bayu Wijayanto, Muhammad Ravi Wiwik Handayani Yadi Utama Yanto, Dimas Hadi