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All Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Biolokus: Jurnal Penelitian Pendidikan Biologi dan Biologi JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Minda Baharu AMALIAH: JURNAL PENGABDIAN KEPADA MASYARAKAT Building of Informatics, Technology and Science Jurnal Mantik Abdi Dosen : Jurnal Pengabdian Pada Masyarakat TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Jurnal Tekinkom (Teknik Informasi dan Komputer) U-NET Jurnal Teknik Informatika MEANS (Media Informasi Analisa dan Sistem) International Journal Of Science, Technology & Management (IJSTM) JPM: JURNAL PENGABDIAN MASYARAKAT Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Journal of Social Responsibility Projects by Higher Education Forum Jurnal Media Informatika Jurnal Minfo Polgan (JMP) Jurnal Sistem Informasi, Teknik Informatika dan Teknologi Pendidikan (JUSTIKPEN) Aptekmas : Jurnal Pengabdian Kepada Masyarakat Journal of Computer Science and Information Systems (JCoInS) IKA BINA EN PABOLO : PENGABDIAN KEPADA MASYARAKAT Journal of Student Development Information System (JoSDIS) Jurnal Pengabdian Masyarakat Gemilang (JPMG) Buletin Ilmiah Informatika Teknologi Jurnal Pengabdian Harapan Bangsa Jurnal Bima
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Journal : Building of Informatics, Technology and Science

 1 
Data Mining Dalam Clusterisasi Risiko Tinggi Obesitas Menggunakan Metode K-Means Clustering Hasby, Anzila; Bangun, Budianto; Masrizal, Masrizal
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.v7i1.7462

Abstract

Obesity is a condition of excess body fat due to an imbalance between calorie intake and expenditure. This problem has become a global epidemic, including in Indonesia, with serious impacts on physical, mental, and social health. Women are more susceptible to obesity due to biological factors and lifestyle choices, as evidenced by data from a community health centre where 76.6% of central obesity patients were women. This study developed an obesity risk segmentation model for women using the K-Means Clustering algorithm based on secondary data from Kaggle (n=898), incorporating variables such as age, family history, dietary patterns, physical activity levels, and mode of transportation used. The results of preprocessing and StandardScaler normalisation showed two optimal clusters (Silhouette Score: 0.267), where Cluster 1 (young age 24.53 years, family history of obesity 1.91, fast food consumption 1.84, low physical activity 2.71) has a higher risk compared to Cluster 0 (age 41.41 years with a healthier lifestyle), revealing a significant interaction between genetic factors and lifestyle as the main triggers. These findings provide a scientific basis for group-based interventions, such as targeted nutrition education programmes for the young population, while demonstrating the effectiveness of data mining approaches in public health for classifying the risk of non-communicable diseases.
Optimasi Prediksi Harga Sawit Menggunakan Teknik Stacking Algoritma Machine Learning dan Deep Learning dengan SMOTE Karim, Abdul; Bangun, Budianto; Prayetno, Sugeng; Afrendi, Mohammad
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.v7i1.7239

Abstract

The prediction of palm oil prices plays a strategic role in decision-making within the agribusiness sector, particularly in addressing market volatility and imbalanced historical data distribution. This study aims to optimize the accuracy of palm oil price prediction by applying a stacking approach that combines machine learning and deep learning algorithms, while integrating the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance issues. Three main models were employed in this study: Random Forest, Long Short-Term Memory (LSTM), and a model enhanced with SMOTE. The evaluation was conducted using accuracy, precision, recall, and F1-score metrics, supported by confusion matrix analysis. The results indicate that the model integrated with SMOTE outperforms the others, achieving an accuracy of 0.5447, precision of 0.5512, recall of 0.5447, and F1-score of 0.5462. This model also demonstrates a more balanced classification performance compared to the LSTM and Random Forest models. These findings confirm that the application of oversampling techniques such as SMOTE, when combined with appropriate algorithms, can significantly enhance predictive performance in imbalanced datasets. The study contributes to the development of predictive models for commodity prices based on historical data and opens opportunities for further exploration of more adaptive hybrid methods in future research.
Analisis Seleksi Penerimaan Karyawan Bank Menggunakan Metode Simple Additive Weighting (SAW) dan Pembobotan Rank Order Centroid (ROC) Naibaho, Sri Fitriani; Bangun, Budianto; Masrizal, Masrizal
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.v7i1.7360

Abstract

This study aims to improve the efficiency and effectiveness of the employee recruitment selection process at Bank BRI Aek Kanopan. The main problem faced is the lack of transparency and systematicity in decision making, so an objective approach is needed to determine the best candidate. As a solution, this study uses the Simple Additive Weighting (SAW) method, which allows structured weighting of criteria, such as last education, technical skills, work experience, communication skills, and age. The SAW method is applied to normalize data and compare candidates based on preference values ​​(Vi). This study involved 25 alternative candidates as evaluation objects, with the normalization and weighting process carried out based on predetermined criteria. The normalized data are presented in a table, with preference values ​​ranging from 0.667 to 0.793, which indicates the level of suitability of candidates to the selection criteria. The results of the study indicate that the SAW method is effective in determining the best candidates, increasing transparency, and facilitating decision making by management. Based on these findings, Bank BRI Aek Kanopan is advised to adopt the SAW method routinely, train staff involved in the selection process, and conduct periodic evaluations to maintain the effectiveness of this method. This research provides an important contribution in the development of human resource management, especially in improving the efficiency and accuracy of the employee recruitment selection process.
Analisis Faktor-Faktor yang Mempengaruhi Tingkat Kelulusan Siswa Menggunakan Algoritma KNN Sianipar, Vitasari; Irmayani, Deci; Bangun, Budianto
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.v7i1.7386

Abstract

Student graduation rates are influenced by various academic and non-academic factors, making it necessary to develop analytical methods to classify students based on their likelihood of graduation. This study applies the K-Nearest Neighbors (KNN) algorithm to analyze the factors affecting student graduation at SD Negeri 112269 Padang Lais. The KNN algorithm works by calculating the Euclidean distance between the tested student data and other student data, then determining the graduation status based on the majority of the K nearest neighbors. The results indicate that using K=5 produces highly accurate classifications with an accuracy rate of 100%, where students with the smallest distance to those who have graduated are more likely to pass. The contribution of this study is to demonstrate that the KNN method can serve as a decision-support tool for predicting student graduation and provide insights into the use of classification algorithms in educational decision-making. Future research can enhance the model by incorporating more diverse variables and testing it on larger datasets to improve prediction generalization.
Klasifikasi Jenis Bunga Iris Berdasarkan Fitur Morfologi Menggunakan Algoritma Naive Bayes Sari, Ely Novita; Irmayani, Deci; Bangun, Budianto
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.v7i1.7401

Abstract

This study aims to classify the types of Iris flowers based on morphological features using the Naive Bayes algorithm. Iris flowers consist of three types, namely Iris-Setosa, Iris-Versicolor, and Iris-Virginica, which can be distinguished based on the length and width of the petals as well as the length and width of the sepals. The dataset used in this research is the Iris dataset, which contains information on four morphological features from these three types of flowers. The Naive Bayes algorithm was chosen because of its advantages in performing probability-based classification in a simple, fast, and effective manner, especially for data with independent features. The research stages include data collection, feature extraction, splitting the data into training and testing sets, training the model using the Naive Bayes algorithm, and testing the model to evaluate classification accuracy. The results of the study show that the Naive Bayes model is able to classify the test data accurately, with the highest probability value obtained in the Iris-Versicolor class, with a value of P(Versicolor│X)=1. This indicates that the test data has the highest similarity to that species compared to the other two species. Thus, the Naive Bayes algorithm proves effective for classifying types of Iris flowers based on their morphological features.
Penentuan Pola Pada Dataset Penjualan Dalam Data Mining Menggunakan Metode Apriori Utami, Ulfa; Irmayani, Deci; Bangun, Budianto
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.v7i1.7498

Abstract

In everyday life and the business world, buying and selling activities play a central role. For companies, daily transaction data is not just a record, but an important asset that holds the potential to increase sales through analysis. The volume of sales data generated daily is enormous, making manual processing inefficient and prone to errors. The complexity of the number of products sold also makes it difficult to gain a comprehensive understanding of purchasing patterns. Dynamic changes in consumer preferences further complicate demand forecasting and may lead to inventory issues. This study aims to address these issues by analysing sales data to identify products that are frequently purchased together. This information will be utilised in designing more effective marketing strategies, such as cross-promotions or product bundling. Additionally, this data is useful for demand forecasting and optimising inventory management. The ultimate goal is to provide relevant product recommendations to customers and enhance their satisfaction. To achieve this objective, this study applies data mining techniques, specifically the Apriori Association method. Data from 15 types of items in 28 weekly transactions at TOKO BANGUNAN MAJU BERSAMA will be analysed as an initial sample to identify the most frequently purchased combinations of construction tools. The Apriori method will associate each item based on a minimum support value of 0.25 and a minimum confidence value of 0.80. The application of this method resulted in 4 rules from 3-item patterns with confidence values ranging from 0.88 to 0.89.
Co-Authors Abdillah, Ihsan Abdul Karim Abdul Karim Abdul Karim Afrendi, Mohammad Agus Susanto Ahmad Habibullah, Imam Akbar Ritonga, Ali Ali Akbar Ritonga Alpiansyah, Fredy Anjar, Agus Ardian, Aldi Aritonang, Putri Armaini, Indah Azwar Try Afandi Barasa, Kristina Budi Febriani Dahrul Aman Harahap Dandi Febrian Dandi Febrian Dandi Febrianto Deci Irmayani Delima Harahap, Risma Dwi Fitriani Dwi Fitriani Elysa Rohayani Hasibuan Febriani, Lisa Febrianto, Dandi Fitri Aini Nasution Guna Dharma, Aditya Halawa, Prianus Hanggi Kurniawan Harahap, Risma Delima Hari Mansah Harimansyah Harimansyah Harimansyah Harimansyah, Harimansyah Hary Syahputra Hasby, Anzila Hasibuan, Dilla Puspita Hutagalung, Charles Efendy Ibnu Rasyid Munthe Ibnu Rasyid Munthe Inez Chania Panjaitan Irmayani, Deci Irmayanti Irmayanti Irmayanti Irmayanti, Irmayanti Iwan Purnama Iwan Purnama Jati, Dewi Sekar Juwita Juwita, Juwita Karim, Abdul Karim Khailizah Khailizah Lubis, Rizky Ramadhan Hasan Masrizal Masrizal mawarni, Putri Sigit Mesran, Mesran Mila Nirmala Sari Hasibuan Muhammad Halmi Dar Muti'ah, Rahma Muwanti, Apri Naibaho, Sri Fitriani Nasution, Fitri Aini Nasution, Marnis Nona Oktari Novilda Elisabeth Mustamu Nurjannah Nurjannah Prayetno, Sugeng Prayetno, Sugeng Prayetno Purba, Mhd. Rafly Putra, Fasdiansyah Rahma Muti’ah Rahmadani Pane Rakhmi Khalida Ramadani Pane Rambe, Nurhayati Ridho Kurniawan Riskawati Riskawati Ritonga, Ali Akbar Ritonga, Mulkan Rohani Rohani Rohanita Hasibuan, Lily Safitri, Nina Sakinah Sakinah Sari, Ely Novita Sianipar, Vitasari Sihombing, Volvo Siregar, Ade Elvi Rizki Siregar, Sakinah Ubudiyah Sitorus, Sahat Parulian Sulaiman, Syahrol Suryadi, Sudi Sutrisno Dwi Raharjo Syafitri, Risma Syahputra, Rapian Syahrol Sulaiman Syahrol Sulaiman Ritonga Syahrol Sulaiman Ritonga Syaiful Zuhri Harahap Syaiful Zuhri Harahap Tegar, Tegar Tria Wulandari Utami, Ulfa Windo Windo Tan Yusmaidar Sepriani Zakaria Pratama