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All Journal Semantik JUITA : Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Ilmiah Matrik Sebatik Jurnal Nasional Komputasi dan Teknologi Informasi JURTEKSI Jurnal Teknologi Komputer dan Sistem Informasi Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Bantenese Jurnal Pengabdian Masyarakat Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) TIN: TERAPAN INFORMATIKA NUSANTARA TEPIAN Jurnal Sistem Informasi dan Sistem Komputer JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Bulletin of Information Technology (BIT) J-Intech (Journal of Information and Technology) DiJITAC : Digital Journal of Information Technology and Communication Informatika Wicida Ensiklopedia Research and Community Service Review Journal of Artificial Intelligence and Digital Business JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Amelia Yusnita
STMIK Widya Cipta Dharma
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Journal : Building of Informatics, Technology and Science

 1 
Prediksi Persediaan Bahan Baku Makanan Menerapkan Algoritma Apriori Data Mining Salmon, Salmon; Azahari, Azahari; Yusnita, Amelia
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2563

Abstract

The company's operational activities are inseparable from the supply of raw materials that must be met every day to meet consumer demand. The restaurant uses raw materials, namely vegetables, raw meat which includes beef and chicken, yellow noodles and soun noodles, and the main seasoning. Sales of food at this restaurant quite a lot in a day. This will produce sales data that will continue to grow every day, but this data is useless if it is not processed again to get the knowledge contained in the data. The Apriori algorithm is a method for finding patterns of relationships between one or more items from a dataset. Thus the pile of data that has been collected can produce a sales pattern, from which the customer's buying interest in food can be identified. From the results of research using a data sample of 18 items with a minimum of 20% Support and 50% Confidence, it produces 5 interesting rules with the highest Support reaching 33.33% and the highest Confidence reaching 100%.
Clustering the Economic Conditions of Various Countries From 2010-2023 by Conducting A Comparative Analysis of the K-Means and K-Medoids Algorithms Yunita, Yunita; Ekawati, Hanifah; Yusnita, Amelia
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.v7i1.7330

Abstract

Understanding the similarities and differences in economic conditions across countries is crucial for various stakeholders. This research investigates the global economic landscape by clustering countries based on their economic indicators, including GDP, inflation rate, unemployment rate, and economic growth, spanning the period of 2010 to 2023. This timeframe encompasses significant global economic events, making it pertinent for analysis. The study employs and compares two prominent clustering algorithms: K-Means and K-Medoids, to identify groups of countries exhibiting similar economic patterns. Utilizing secondary data from Kaggle encompassing 19 countries, the research assesses the ability of each algorithm to delineate meaningful economic clusters. The K-Means algorithm, with a determined optimal number of four clusters, demonstrated a reasonably good cluster separation and moderate internal cohesion, evidenced by a Silhouette Coefficient of 0.58 and a Davies-Bouldin Index of 0.63. In contrast, the K-Medoids algorithm yielded a distinct clustering structure with a lower Silhouette Coefficient (0.26) and a higher Davies-Bouldin Index (1.16), suggesting less distinct cluster separation and potential sensitivity to data characteristics. This comparative analysis provides insights into the applicability and performance of K-Means and K-Medoids in discerning global economic structures, contributing to a deeper understanding of the world economic map and the utility of clustering techniques in economic data analysis.
Classification of Diabetes Diseases Based on Medical Features Using Optimized Support Vector Machine Arfyanti, Ita; Yusnita, Amelia; Adytia, Pitrasacha
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.v7i3.8880

Abstract

Diabetes mellitus is a chronic disease caused by impaired glucose metabolism and has become a global health threat with a steadily increasing prevalence each year. According to WHO and IDF, the number of people living with diabetes is projected to reach 783 million by 2045. This condition demands the development of an accurate and efficient early detection system to support medical decision-making. This study aims to develop an optimized Support Vector Machine (SVM)-based classification model to enhance the accuracy and interpretability of diabetes prediction. The dataset used is the Pima Indians Diabetes Dataset, which consists of eight medical features such as glucose level, blood pressure, and body mass index (BMI). The research stages include data preprocessing, class balancing using the Synthetic Minority Over-sampling Technique (SMOTE), parameter optimization with GridSearchCV, and interpretability analysis through SHapley Additive exPlanations (SHAP). The results show that the optimized SVM model with the Radial Basis Function (RBF) kernel achieved an accuracy of 82%, with a significant improvement in the diabetes class recall value from 0.564 to 0.83 after optimization. The Area Under Curve (AUC) value of 0.871 indicates the model’s effectiveness in distinguishing between positive and negative classes. The SHAP analysis reveals that Glucose, Age, BMI, and Diabetes Pedigree Function are the most influential features in prediction. These findings emphasize that the combination of normalization, balancing, hyperparameter optimization, and interpretability produces a reliable and transparent SVM model. This model has strong potential for implementation in Clinical Decision Support Systems (CDSS) for accurate and explainable early diabetes detection.
Co-Authors Adrian, Nurul Ahmad Rofiq Hakim Alenya, Nia Andi Yushika Rangan Andi Yusika Rangan Astuty Nilasari Azahari, Azahari Basrie Dewi Rosita Ekawati Yulsilviana Ekawati, Hanifah Ekawati, Hanifah Elys Diawati Emon Azriadi Eri Wandana Fachry Abda El Rahman Fadri, Roy Febryanto, Febryanto Fery Setiawan Hafifah, A. Harianto, Kusno Haristyawan, Ivan Hariyadi Hariyadi Heny Pratiwi I Gede Adi Merta Ita Arfyanti Khoiri Saumahudi Kusnandar Kusnandar Kusnandar Lukman Hariri M. Irwan Ukkas Irwan Ukkas Ukkas Maulana Umar Mayasari, Reny Melano, M Erick Muhammad Awaludin Muhammad Ridhuan. S Nila Ratna Wati Pahrudin, Pajar Pajar Pahruddin Pangestu, Nathania Clarissa Patrianto, Wahyu Paul, Debora Pitrasacha Adytia Pratiwi , Heny Putri Wulandari Rahman, Amalia Raya, Dimas Renni Mayasari Roba, Al-Muchlis Syachrul Ramadani Rosiana Handini Salmon Salmon Siti Lailiyah Siti Qomariah Siti Qomariah Susanti, Agustina Nona Tabrani Rija’i Theana Dwi Aprillita Vilianty Rafida Yonatan, Kevin Yulindawati, Yulindawati Yulindawati, Yulindawati Yunita Yunita