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All Journal Teknika Sistemasi: Jurnal Sistem Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Inotera Jurnal Vokasi JINAV: Journal of Information and Visualization Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology Jurnal Tika jeti Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) TECHSI - Jurnal Teknik Informatika DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Dharmawangsa: International Journal of the Social Sciences, Education and Humanitis Jurnal Malikussaleh Mengabdi Gameology and Multimedia Expert Nawadeepa: Jurnal Pengabdian Masyarakat Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Nunsina, Nunsina
Universitas Malikussaleh
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Sentiment Analysis of Action Mobile Application Reviews Using Logistic Regression and Support Vector Machine Haniful Fikri; Nurdin; Nunsina
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 10 No. 1 (2026): Issues July 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v10i1.18024

Abstract

The digital transformation has driven PT. Bank Aceh Syariah to launch the Action Mobile application. Despite its benefits for customers, user reviews on the Google Play Store indicate varying perceptions due to differences in digital experiences. This study aims to analyze user sentiment toward the Action Mobile application while comparing the effectiveness of the Logistic Regression and Support Vector Machine (SVM) algorithms. A total of 3,000 clean review data were collected through web scraping techniques. The dataset exhibits an imbalanced distribution, dominated by 1,840 positive reviews (61.33%), followed by 821 negative reviews (27.37%), and 339 neutral reviews (11.30%). Model testing was conducted using the 10-Fold Cross Validation so that each data has the opportunity to become test data and the evaluation results become more objective, utilizing TF-IDF for word weighting. The evaluation results using a 3 × 3 multiclass confusion matrix based on a weighted average demonstrate that the Logistic Regression algorithm outperforms SVM across all testing metrics. The Logistic Regression model successfully achieved an Accuracy of 0.9023, Precision of 0.897, Recall of 0.9023,, and an F1-Score of 0.895. Meanwhile, the SVM model obtained an Accuracy of 0.9013, Precision of 0.8969, Recall of 0.9013, and an F1-Score of 0.898. This performance variance proves that the Logistic Regression architecture is more adaptive and optimal for this specific case study. The findings of this study are expected to serve as evaluation material for enhancing Action Mobile services.
K-Means Application to Rice Production with DBI Evaluation Badruttamam Badruttamam; Mukti Qamal; Nunsina Nunsina
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6510

Abstract

Rice production is a key indicator of regional food security, making it essential to analyze production patterns and identify differences in productivity across districts. This study aims to apply the K-Means clustering algorithm to classify rice production data in Bireuen Regency and evaluate the quality of the resulting clusters using the Davies–Bouldin Index (DBI). The study employed secondary data from 2020 to 2024 covering 17 districts, with four variables: cultivated area, harvested area, productivity, and total rice production. The K-Means algorithm was used to partition the data into three clusters representing high, medium, and low production levels based on similarities in their characteristics. The results indicate that the clustering process consistently classified the districts across the five-year observation period. Cluster quality evaluation using the Davies–Bouldin Index yielded values of 0.9147 in 2020, 0.8292 in 2021, 0.6119 in 2022, 0.8821 in 2023, and 0.8597 in 2024. The best clustering performance was achieved in 2022, as indicated by the lowest DBI value, reflecting a more compact and well-separated clustering structure. These findings provide valuable insights for supporting agricultural planning, improving rice production strategies, and informing policy decisions related to agricultural development in Bireuen Regency.
Clustering of Food Security Levels in North Aceh Using K-Medoids Tarisha Zhafira; Bustami Bustami; Nunsina Nunsina
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12881

Abstract

Food security is essential for ensuring food availability, accessibility, and utilization. This study applies a multidimensional indicator framework covering social, economic, and infrastructure aspects to cluster regions in North Aceh Regency, addressing limitations of previous studies that primarily focus on agricultural production indicators and lack policy-oriented analysis. The analysis uses 2023 data from 27 sub-districts at the village level, comprising 852 villages. The indicators include: (1) the ratio of agricultural land area to total population, (2) the ratio of food supply facilities and infrastructure to households, (3) the ratio of population with the lowest welfare status to total population, (4) the proportion of villages without adequate transportation access via land, water, or air, (5) the ratio of households without access to clean water, and (6) the ratio of population per health worker relative to population density. Data processing involves preprocessing, normalization, and K-Medoids clustering, evaluated using the Davies–Bouldin Index (DBI) and Silhouette Coefficient (SC). The results identify six clusters: highly food insecure (C1) with 63 villages, food insecure (C2) with 92 villages, moderately food insecure (C3) with 179 villages, moderately food secure (C4) with 42 villages, food secure (C5) with 49 villages, and highly food secure (C6) with 427 villages. Most villages fall within moderately food insecure to highly food secure categories, indicating disparities in food security distribution. The DBI value of 3.085 indicates moderate cluster compactness, while the SC value of 17.75% suggests weak separation between clusters. These findings provide policy recommendations for targeted and equitable food security interventions.
Co-Authors Ade Nahya Zahara Akbar, Andry Maulana Annisa Fitria Aqiilah Cahya Ningrum Ariyandi, Zulham Badruttamam Badruttamam Bustami Bustami Cut Fadhilah Cut Fadhilah Dahlan Abdullah Epa Yanti Eva Darnila Eva Darnila Eva Darnila Fadhilah, Cut Fadhlilah, Cut Fatimah Zuhra Fatimah Zuhra Fuadi, Wahyu Haniful Fikri Hasibuan, Fadilah Suryani Ichsan Kulbahri, Kulbahri Mahara, Sabda Maysura Muhammad Muaz Munauwar Mukti Qamal Munawwar Khalil, Munawwar Mustaqim Mutasar Nazimah Nazimah Nilahayati Nurdin Nurdin Nurdin Putri Raihatul Jannah Rahma Fitria, Rahma Rahmad Rahmad Razi, Ar Rifkial Iqwal Rini Meiyanti Rizal Tjut Adek Rosma Siregar Safrizal Safrizal Tarisha Zhafira Taufiq Taufiq Viki, Zakial Wahyu Fuadi Walad Hidayat Zahidi Pratama Zahratul Fitri Zahratul Fitri, Zahratul Zakial Viki Zara Yunizar Zuliani Zuliani