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All Journal Prosiding KOMMIT Jurnal Simetris Prosiding SNATIF Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika JAIS (Journal of Applied Intelligent System) Journal of Information System Jurnal Informatika Upgris Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURIKOM (Jurnal Riset Komputer) 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 Jurnal Pengabdian kepada Masyarakat Nusantara Devotion: Journal of Research and Community Service Jurnal Pengabdian Literasi Digital Indonesia
Fikri Budiman
Universitas Muria Kudus
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management 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

 1 
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
Analisis Sentimen Ulasan Mobile JKN pada Playstore dengan Perbandingan Akurasi Algoritma Naïve Bayes dan SVM Pranata, Eka Arya; Budiman, Fikri; Kurniawan, Defri
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7334

Abstract

The facilities provided by BPJS Health by releasing the Mobile JKN application, with this application the administrative process that previously had to be done directly can be done online and more flexibly. This research aims to see the sentiment of the community towards the JKN Mobile application review by comparing the SVM and Naïve Bayes algorithms. As well as optimizing the Naïve Bayes algorithm by using grid search. Reviews are taken from Google play with the help of Google Play Scraper API, the dataset taken amounted to 7,000 reviews. The results of using Naïve Bayes with an accuracy value of 86%, after tuning optimization using Grid Search significantly increases the accuracy value of the Naïve Bayes algorithm to 91% and for the SVM algorithm has an accuracy value of 92%. From the trial, it was found that the SVM algorithm is still better than the Naïve Bayes algorithm even though it has been optimized, but by optimizing the accuracy value Naïve Bayes is closer to SVM performance. This research can provide insight into the comparison of the two algorithms in identifying JKN Mobile reviews and the need for optimization to improve the performance of algorithms in sentiment analysis, besides that this research also contributes to the improvement and development of the JKN Mobile application so that it is useful for the community.
Analisis Sentimen Pengguna X terhadap Kasus Korupsi Gula Tom Lembong Menggunakan Naïve Bayes, SVM, dan Random Forest Kuncoro, Aneira Vicentiya; Budiman, Fikri; Kurniawan, Defri
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8577

Abstract

The alleged sugar import corruption case involving Tom Lembong has become one of the most widely discussed public issues on social media, generating diverse reactions. This phenomenon illustrates how public opinion on legal issues is often influenced by perceptions of the public figures involved. This study aims to analyze public sentiment regarding the case on the social media platform X (formerly Twitter). The dataset consists of 1,802 tweets collected through a crawling process using the X API with the keyword “Tom Lembong.” The research stages include data cleaning, case folding, text normalization, tokenizing, stopword removal, stemming, sentiment labeling using a lexicon-based approach, and feature extraction with the Term Frequency–Inverse Document Frequency (TF-IDF) method. The prepared dataset was then tested using three classification algorithms: Naïve Bayes, Support Vector Machine (SVM), and Random Forest. The results show that the SVM algorithm achieved the highest accuracy (84%), followed by Random Forest (80%) and Naïve Bayes (76%). Based on the sentiment labeling results, positive sentiment dominated with 61%, while negative sentiment accounted for 39%. Although the analyzed issue concerns an alleged corruption case, the dominance of positive sentiment indicates that public opinion tends to focus on Tom Lembong’s personal image or public track record, which is viewed positively rather than on the substance of the legal allegations. These findings demonstrate the effectiveness of the SVM algorithm in analyzing high-dimensional text and provide insights into how public perception of legal issues can be influenced by image factors and the socio-political context on social media.
Co-Authors A.A. Ketut Agung Cahyawan W Abi Senoprabowo Achmad Achmad Agus Winarno, Agus Ajib Susanto Akhmadi Akhmadi Arief Soeleman, M Arifudin, Agus Asih Rohmani, Asih Awaludin, Yoga Mahendra Bao Sumantoro, Yeremia Joen Cahyani, Okta Nur Candra Irawan Edi Sugiarto Fahmi Amiq Fudhlatina, Dina Hastuti, Nanda Tri Janto, Dwi Kamarudin, Fatkhurridlo Pranoto Karis Widyatmoko Kuncoro, Aneira Vicentiya Kurniawan, Defri Kusumawati, Yupie Mahendra, Syafrie Naufal Marjuni, Aris Moch Arief Soeleman, Moch Arief Mulyanto, Edy Muslih Muslih MY. Teguh Sulistyono Noor Hasyim Novi Hendriyanto, Novi Pranata, Eka Arya Rahmawan P, Elkaf Rahmayani, Ririt Sheila Tina S Nurhendratno Saputra, Fahrizal Denta Sri Winarno SUDARYANTO SUDARYANTO Supriyono Asfawi Susanto, Al Birr Karim Yuventius Tyas Catur Pramudi Zaenal Arifin