Article Per Year (5 Year)
p-Index From 2021 - 2026
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All Journal Arcitech: Journal of Computer Science and Artificial Intelligence Journal of Data Insights
Ahmad Rifa’i
Universitas Semarang
Computer Science & IT Mathematics

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Implementasi Sistem Informasi Kluster Penjualan Beras Menggunakan Algoritma K-Means Syahrul Adi Saputra; Galet Guntoro Setiaji; Ahmad Rifa’i
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 6 No. 1 (2026): June 2026
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v6i1.17056

Abstract

Manual processing of rice sales transaction data results in data being archived and cannot be used optimally in decision-making. Therefore, business owners find it difficult to manage stock in sales planning. The K-Means Clustering algorithm was implemented in a web-based information system to create a recommendation feature for the best-selling rice. UD Maju Mapan, located in Demak Regency, was the location for the sales transaction data collection process for the sales period from October 2025 to March 2026. Data was processed using the Min-Max Normalization method and the K-Means algorithm with 3 clusters: premium, standard, and economy. The grouping will automatically appear in the dashboard display of the web-based information system, providing information for decision-making. The results show that the standard cluster has the largest amount of data compared to the premium and economy clusters. A value of 0.5361 is the result of the evaluation process using the Davies Bouldin Index method, which can be interpreted as quite good cluster quality. The rice sales information system is capable of managing and determining sales strategies and stock procurement based on the results of real transaction data analysis.
Sentiment Analysis of FlyGaruda Review Using Support Vector Machine and Naive Bayes Algorithm: Analisis Sentimen Ulasan FlyGaruda Menggunakan Algoritma Support Vector Machine dan Naive Bayes Gibran Masta Pangestu Baskoro; Galet Guntoro Setiaji; Ahmad Rifa’i
Journal of Data Insights Vol 4 No 1 (2026): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v4i1.1117

Abstract

FlyGaruda is an official digital application owned by Garuda Indonesia that provides ticket booking and online check-in services for users. This study analyzed the sentiment of reviews on the Google Play Store by comparing the performance of Support Vector Machine and Multinomial Naive Bayes. The methods used include scraping, text preprocessing, extraction of the Term Frequency-Inverse Document Frequency (TF-IDF) feature, and evaluation using the Confusion Matrix. The dataset used totaled 4,790 reviews with positive, negative, and neutral categories. The results showed that both models obtained an accuracy of 82.25%. However, the Support Vector Machine produces a weighted precision of 77.66% and an F1-Score of 78.91%, better at handling data imbalances. Meanwhile, Multinomial Naive Bayes excels in computing efficiency with a training time of 0.08 seconds compared to 90.60 seconds on the Support Vector Machine. In conclusion, although it is slower, the Support Vector Machine provides more consistent and accurate classification performance. This research contributes to the development of a machine learning-based opinion analysis system to improve the quality of aviation digital services in a sustainable manner. These findings can serve as a reference in the selection of the best algorithms between accuracy and computational speed in large text data and support data-driven decision-making in the modern air transportation industry in the current era of global sustainable digital transformation
Co-Authors Gibran Masta Pangestu Baskoro Setiaji, Galet Guntoro Syahrul Adi Saputra