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All Journal Jurnal Tekno Kompak Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat Journal of Mathematics and Applied Statistics
Hading, Muhaimin
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Mathematics Other

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Sentiment Analysis of Bapenda South Sulawesi Mobile Application on Google Play Store Using Support Vector Machine Burhan, Muhammad Ikhwan; Ali, Andi Nurfadillah; Auliyah, A. Inayah; Hading, Muhaimin
Journal of Mathematics and Applied Statistics Vol. 2 No. 2 (2024): December 2024
Publisher : Yayasan Insan Literasi Cendekia (INLIC) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/mathstat.v2i2.244

Abstract

This study analyzes user sentiment toward the Bapenda Sulsel Mobile application, an e-government platform developed by the Regional Revenue Agency of South Sulawesi, Indonesia. The research aims to evaluate user feedback and identify areas for improvement to enhance user satisfaction. Using sentiment analysis, user reviews from Google Play Store were collected and classified into positive, negative, and neutral sentiments through the Support Vector Machine (SVM) algorithm. Preprocessing steps such as tokenization, stopword removal, and stemming were applied to prepare the data. Term Frequency-Inverse Document Frequency (TF-IDF) was used for feature extraction to enhance classification accuracy. The SVM model demonstrated an overall accuracy of 80%, achieving a high recall of 98% for positive reviews but only 40% for negative reviews, reflecting challenges in handling class imbalance. Results show that 72% of users expressed positive sentiment, praising the app’s functionality and ease of use. However, 28% of reviews were negative, citing issues like technical bugs and usability challenges The findings highlight the app’s strengths in delivering e-government services and its role in improving tax management. However, the significant proportion of negative feedback emphasizes the need for addressing user concerns. Recommendations include balancing the dataset, refining the SVM model, and prioritizing improvements based on user feedback. This study contributes to the broader understanding of applying sentiment analysis in evaluating e-government platforms and offers actionable insights for enhancing the user experience.
Comparative Analysis of Algorithms for Sensitive Outlier Protection in Privacy Preserving Data Mining Burhan, Muhammad Ikhwan; Ali, Andi Nurfadillah; Auliyah, A. Inayah; Hading, Muhaimin
Jurnal Tekno Kompak Vol 19, No 2 (2025): AGUSTUS (In Progress)
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jtk.v19i2.4754

Abstract

Data mining is a crucial method in the realm of Big Data for extracting valuable predictive insights from extensive datasets. In the contemporary digital landscape, a significant difficulty is preserving individual privacy during data mining, particularly in safeguarding sensitive outliers that may harbour personal information. Outliers are data points that markedly diverge from the overall trend and frequently encompass very specialised or sensitive information. This paper examines the comparative efficacy of various clustering algorithms employed in outlier detection, specifically PAM (Partitioning Around Medoids), CLARA (Clustering Large Applications), CLARANS (Clustering Large Applications Based on Randomised Search), and ECLARANS (Enhanced CLARANS). This study aims to evaluate the efficacy of each algorithm in identifying outliers and to examine the usefulness of the employed privacy protection strategy, specifically the Gaussian Perturbation Random method. This experiment utilises two health datasets: the Diabetes Dataset from the National Institute of Diabetes and Digestive and Kidney Diseases and the Wisconsin Breast Cancer Dataset. The two datasets were chosen because to their multivariate features, which exhibit adequate data variation for outlier detection. The study's results indicate that the CLARA algorithm effectively identified a superior quantity of outliers compared to the other algorithms, with the diabetes dataset exhibiting the greatest count of outliers (65 outliers). The CLARA algorithm shown superiority in identifying outliers within extensive datasets due to the utilisation of a sampling methodology. Conversely, the PAM, CLARANS, and ECLARANS algorithms identified a same quantity of outliers in both datasets. ECLARANS shown superior time efficiency on the diabetic dataset, but CLARA demonstrated the highest efficiency on the breast cancer dataset. The Gaussian Perturbation Random technique was employed for preserving the identified sensitive outliers. The findings indicate that this strategy effectively maintains privacy while ensuring detection accuracy is not compromised. This method provides a dependable means of safeguarding individual privacy in health data mining, a domain characterised by significant privacy concerns.
Implementasi Pembelajaran Berbasis Kecerdasan Buatan Di Upt Sd Negeri 16 Parepare Hading, Muhaimin; Radhiansyah, Radhiansyah; Noor, Nurul Chairunnisa; syahruddin , A. Syahrinaldy; Auliyah, A. Inayah; Ali, Andi Nurfadillah; Burhan, Muhammad Ikhwan; Irsan, Muhammad
Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat Vol. 6 No. 2 (2025): Volume 6, No 2 Juni 2025
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/atjpm.v6i2.6311

Abstract

Pemanfaatan teknologi dalam pendidikan dasar menjadi semakin krusial di tengah perkembangan era digital dan Revolusi Industri 4.0. Kegiatan pengabdian ini dilatarbelakangi oleh pentingnya peningkatan literasi teknologi di lingkungan sekolah dasar, khususnya dalam pemanfaatan kecerdasan buatan (Artificial Intelligence/AI) untuk mendukung proses belajar mengajar yang lebih interaktif dan adaptif. Program ini dilaksanakan di UPT SD Negeri 16 Parepare dengan tujuan utama untuk mengimplementasikan pendekatan pembelajaran berbasis AI. Metode pelaksanaan meliputi pelatihan intensif kepada guru mengenai konsep dan praktik penggunaan AI, penerapan langsung AI dalam kegiatan pembelajaran di kelas, serta evaluasi untuk mengukur dampak kegiatan. Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan keterampilan guru dalam mengintegrasikan AI ke dalam pembelajaran, serta meningkatnya minat dan partisipasi aktif siswa selama proses belajar. Kegiatan ini memberikan kontribusi positif dalam memperkenalkan transformasi digital di lingkungan sekolah dasar, serta berpotensi menjadi model replikasi untuk sekolah lainnya.
Co-Authors Andi Inayah Auliyah Andi Nurfadillah Ali Muhammad Ikhwan Burhan Muhammad Irsan Nurul Chairunnisa Noor Radhiansyah, Radhiansyah syahruddin , A. Syahrinaldy