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All Journal Prosiding KOMMIT Jurnal Simetris Prosiding SNATIF Jurnal Transformatika JAIS (Journal of Applied Intelligent System) Journal of Information System Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Devotion: Journal of Research and Community Service Jurnal Pengabdian Literasi Digital Indonesia
Fikri Budiman
Universitas Muria Kudus
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

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Journal : Building of Informatics, Technology and Science

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Analisa Optimasi Grid Search pada Algoritma Random Forest dan Decision Tree untuk Klasifikasi Stunting Rahmayani, Ririt Sheila Tina; Budiman, Fikri
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6128

Abstract

Stunting is a serious problem that is of global concern because of its significant impact on the health and growth of children under five. This condition occurs due to long-term malnutrition. In Indonesia, nutritional problems are still common, including stunting which affects children's growth and development. In this regard, data mining has an important role in facing this challenge. Therefore, the aim of this research is to optimize stunting classification using Decision Tree and Random Forest algorithms optimized with Grid Search. This optimization was carried out to increase the accuracy of the two algorithms and identify algorithms that are superior in determining stunting. The dataset used consists of 10,000 toddler data with important attributes related to health conditions. The analysis results show that the initial Decision Tree model has an accuracy of 70.2%. After optimization using Grid Search, the accuracy of the Decision Tree model increased significantly to 82.8%. Meanwhile, the initial Random Forest model achieved an accuracy of 77.9%, and after optimization with Grid Search, its accuracy increased even higher compared to Decision Tree, namely 84.1%. This increase reflects the effectiveness of optimization in increasing the model's ability to classify stunting more accurately. This research provides important insights into the effectiveness of both algorithms in identifying stunting and emphasizes the importance of optimization to improve classification accuracy, which can support appropriate interventions for the well-being of future generations.
Segmentasi Pelanggan Kartu Kredit Menggunakan Metode Klustering: Analisis dan Profiling Arifudin, Agus; Budiman, Fikri
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6879

Abstract

The use of credit cards in Indonesia has increased significantly, creating complex challenges for financial institutions in understanding user behavior and meeting their needs. This growth poses a higher risk of fraud, customer dissatisfaction due to unmet expectations, and financial instability for both consumers and banks. These issues highlight the urgency of conducting research to segment customers based on their usage behavior. The analyzed dataset includes information from 8,950 credit card users, covering transaction frequency, account balance, and transaction types. This study aims to segment customers using K-Means, DBSCAN, and Hierarchical Clustering algorithms. K-Means groups customers with similar behavioral patterns, DBSCAN identifies irregular clusters and outliers, while Hierarchical Clustering provides insights into relationships between clusters. The analysis results reveal four main segments, each with unique characteristics. For instance, the active user segment exhibits high transaction frequency and large balances, whereas new users demonstrate lower transaction frequency. These findings offer valuable insights for financial institutions to enhance their services and product offerings. By understanding the characteristics of each segment, financial institutions can tailor their marketing strategies and products to improve customer satisfaction and loyalty
Co-Authors A.A. Ketut Agung Cahyawan W Abi Senoprabowo Agus Winarno, Agus Arief Soeleman, M Arifudin, Agus Asih Rohmani, Asih Awaludin, Yoga Mahendra Bao Sumantoro, Yeremia Joen Candra Irawan Edi Sugiarto Fahmi Amiq Fudhlatina, Dina Hastuti, Nanda Tri Kamarudin, Fatkhurridlo Pranoto Karis Widyatmoko Kurniawan, Defri Mahendra, Syafrie Naufal Moch Arief Soeleman, Moch Arief Mulyanto, Edy Muslih Muslih MY. Teguh Sulistyono Noor Hasyim Novi Hendriyanto, Novi Rahmawan P, Elkaf Rahmayani, Ririt Sheila Tina S Nurhendratno Sri Winarno Supriyono Asfawi Susanto, Al Birr Karim Yuventius Tyas Catur Pramudi Zaenal Arifin