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Bulletin of Information Technology (BIT)
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27220524     DOI : 10.47065/bit.v2i3.106
Core Subject : Science,
Computer Science & IT
Jurnal Bulletin of Information Technology (BIT) memuat tentang artikel hasil penelitian dan kajian konseptual bidang teknik informatika, ilmu komputer dan sistem informasi. Topik utama yang diterbitkan mencakup:berisi kajian ilmiah informatika tentang : Sistem Pendukung Keputusan Sistem Pakar Sistem Informasi, Kriptografi Pemodelan dan Simulasi Jaringan Komputer Komputasi Pengolahan Citra Dan lain-lain (topik lainnya yang berhubungan dengan teknologi informasi)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 267 Documents
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Penerapan Data Mining Menggunakan Metode Cluster K-Means Untuk Pengelompokkan Fasilitas Sekolah Muhammad, Faisal; Suharmanto; Janu Ilham Saputo; Wiranti Sri Utami
Bulletin of Information Technology (BIT) Vol 6 No 4 (2025): Desember 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i1.2340

Abstract

School Facilities are facilities provided by schools or universities to support activities and can be utilized by students, teachers, students and staff within the scope of a particular education. In order to create good teaching and learning activities (KBM) and support the development process and achievements, good schools or universities must have classroom facilities, laboratories, libraries, canteens, places of worship and fields. By applying data mining and utilizing the data sources obtained and the application of the K-Means cluster method, information related to school facilities can be drawn. The number of clusters obtained is 2 clusters with the number of squares according to the cluster of 76.0%.
Penerapan Algoritma K-Means dan Apriori dalam Manajemen Stok UMKM Toko Sembako Berbasis Analisis BCG Matrix Tasril, Virdyra; Olivian, Daffa; Hasmajaya Simarmata, Randy
Bulletin of Information Technology (BIT) Vol 6 No 4 (2025): Desember 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i4.2375

Abstract

This study aims to analyze purchasing patterns at Toko Sembako HAS in Medan City, Medan Polonia District, using a Hybrid Data Mining approach that combines K-Means and Apriori algorithms. The dataset consists of 75,294 items sold over a 7-month period. The research workflow began with problem identification, literature review, data collection, and pre-processing, followed by algorithm implementation to produce product clustering and association patterns. Data normalization was performed using the Min-Max method to align the scales of Quantity and Profit, ensuring accurate K-Means clustering. The K-Means clustering combined with BCG Matrix categorized products into Stars, Cash Cows, Question Marks, and Dogs. Products such as Indomie and Mie Sedap were classified as Stars with high sales volume and medium-high profitability, while Minyak Curah and Beras were Cash Cows with moderate sales volume but the highest profitability. The Apriori algorithm revealed hidden purchasing patterns, with the highest Lift Ratio of 1.48 observed for the pair Pampers S and Mie Sedap, indicating a strong correlation within the young family segment. The hybrid approach provides strategic insights: K-Means supports inventory management and product segmentation, while Apriori guides marketing strategies such as product bundling and store layout. However, combinations of Cash-Cows and Question Marks yielded Lift Ratios below 1, indicating insignificant associations. The results demonstrate that this hybrid approach enhances understanding of consumer behavior and supports data-driven decisions to optimize sales and profitability.
Analisis Dan Prediksi Hasil Pertandingan Dota 2 Menggunakan Fuzzy Tsukamoto Tan, Muhammad Arief Adidharma; Yulindawati; Fahmi, Muhammad
Bulletin of Information Technology (BIT) Vol 6 No 4 (2025): Desember 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i4.2382

Abstract

Predicting the outcome of a Dota 2 match is a complex problem because it is influenced by many dynamic variables that change at each stage of the game. This study aims to analyze and predict the probability of winning a Dota 2 match using the Fuzzy Tsukamoto method based on three main variables: Hero Win Rate, Number of Kills, and Tower Destroyed. The fuzzy model was constructed using triangular and trapezoidal membership functions, with variable weights adjusted for the early game, mid game, and late game. Test results show that in the early game, the Hero Win Rate variable has the most dominant influence on the probability of winning, with a weight of 0.7. In the mid game, the number of kills and tower destruction begin to have a significant impact, while in the late game, towers and kills become the primary determinants of the probability of winning. The proposed system is able to generate different percentages of the probability of winning at each stage of the game and logically reflect the dynamics of the Dota 2 game. Based on these results, the Fuzzy Tsukamoto method is considered capable of handling uncertainty in Dota 2 match prediction and provides more flexible results than deterministic approaches, although it still depends on the quality of the dataset and the fuzzy rules used.
Sistem Pendukung Keputusan Pemilihan Staf Keamanan Terbaik Hotel Menggunakan Metode SMARTER Surizar Rahmi Danur; Nirwan Sinuhaji; Alyiza Dwi Ningtyas; Donny Sanjaya
Bulletin of Information Technology (BIT) Vol 6 No 4 (2025): Desember 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i4.2423

Abstract

The hotel industry provides accommodations that focus on guest comfort and safety. One crucial aspect of maintaining hotel security is selecting qualified security staff. However, the hotel faces challenges due to a lack of prior security staff selection. This results in difficulties in establishing clear selection criteria, resulting in an inefficient and unprofessional selection process. To address this issue, a decision support system (DSS) is required. This system will assist in data management, value calculation, and the generation of informed decisions. With a DSS, decision-making becomes easier, faster, and more accurate. This will increase efficiency, professionalism, and reliability in the security staff selection process. Furthermore, the best security staff will be given incentives for a period as a reward for the quality of their work. Implementing a DSS allows the selection of the best security staff based on predetermined criteria. The SMARTER (Simple Multi-Attribute Rating Technique Exploiting Ranks) method can be used as a tool in a DSS to select the best security staff. Using the SMARTER method can solve the problem of selecting the best security staff. Aripin, with a score of 0.87, was selected as the best security staff. This will help the hotel maintain guest trust, provide a sense of security and comfort, and build a positive reputation. As a result, it will become more professional in managing hotel security and providing excellent guest service.
Implementasi Metode ROC dan WP Dalam Sistem Pendukung Keputusan Terhadap Calon Penerima Pinjaman Koperasi Suhada, Karya; Hendrik, Dede; Andriyana; Isnandar, Evi; Dauni, Popon
Bulletin of Information Technology (BIT) Vol 6 No 4 (2025): Desember 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i4.2446

Abstract

Cooperatives are financial institutions that play a vital role in the economy, especially in developing countries. They provide various financial services to their members, including loans with relatively lower interest rates compared to commercial financial institutions. This study aims to develop and implement a Decision Support System (DSS) using the Rank Order Centroid (ROC) and Weighted Product (WP) methods for selecting cooperative loan recipients. Cooperatives often face challenges in determining eligible loan recipients to minimize default risk. The ROC method is used to objectively determine the criteria weights, while the WP method integrates these weights with the performance values of each candidate. By combining these two methods, it is expected to produce more accurate and fair decisions. The study was conducted in Asahan with data collected from various official sources. The criteria used include age, monthly income, employment status, credit history, and income stability. The results show that the combination of ROC and WP methods can improve the accuracy and efficiency of the cooperative loan recipient selection process and minimize the risk of default. This study contributes significantly to the field of DSS and can serve as a reference for developing decision-making methods in other fields requiring multi-criteria analysis. The findings also can be used by cooperatives to enhance the loan granting process, ensuring financial health, and member welfare. The implementation results indicate that the selected cooperative loan recipient is alternative A6, Fitriani Sari, with a score of 0.1257.
Penentuan Prioritas Bantuan Sosial Dengan Metode Combined Compromise Solution (CoCoSo) Darmansyah, Darmansyah; Yanitasari, Yessy; Yudiana, Yudiana; Nugraha, Agus; Suryana, Nana
Bulletin of Information Technology (BIT) Vol 6 No 4 (2025): Desember 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i4.2447

Abstract

Penyaluran bantuan sosial yang tepat sasaran merupakan kunci dalam mendukung kesejahteraan masyarakat, terutama di tengah keterbatasan sumber daya. Penelitian ini bertujuan untuk menentukan prioritas penerima bantuan sosial dengan menggunakan metode Combined Compromise Solution (CoCoSo), sebuah metode pengambilan keputusan multi-kriteria yang mampu menghasilkan solusi kompromi optimal dengan mempertimbangkan berbagai kriteria secara seimbang. Metode CoCoSo digunakan untuk mengevaluasi dan mengkombinasikan nilai dari setiap alternatif penerima bantuan berdasarkan kriteria yang telah ditentukan, sehingga menghasilkan peringkat prioritas yang objektif dan efisien. Penerapan metode ini diharapkan dapat membantu dalam proses seleksi penerima bantuan sosial yang lebih transparan dan tepat sasaran, terutama dalam kondisi di mana terdapat konflik atau perbedaan kepentingan antar kriteria. Hasil penelitian menunjukkan bahwa metode CoCoSo efektif dalam memberikan rekomendasi prioritas penerima bantuan sosial dengan solusi yang seimbang dan dapat diandalkan.
Penentuan Bibit Kelapa Sawit Unggul Dengan Metode ARAS Dan TOPSIS Nur Alam, Sitti; Yesputra, Rolly; Zikra Syah, Arridha; Parini; Ernawati, Andi
Bulletin of Information Technology (BIT) Vol 6 No 4 (2025): Desember 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i4.2213

Abstract

The Industrial Era 4.0 opens up great opportunities to increase production, efficiency and sustainability of the palm oil industry. The problem faced by farmers is that farmers are often hampered by limited knowledge and lack of guidance in choosing plant seeds. Because seeds are an important factor in supporting satisfactory results. This research was carried out to help farmers who have difficulty in choosing oil palm seeds which could become a problem for farmers in the future. day. This research uses the ARAS and TOPSIS methods to evaluate seeds based on criteria that have been identified and analyzed, to assess 10 types of superior seeds based on 5 criteria: oil potential, pest resistance, seed price, productive planting period, and maintenance costs. It is hoped that this research can help oil palm farmers increase their productivity and profits, as well as support the sustainability of the palm oil industry in the Industry 4.0 era. The ARAS and TOPSIS methods have proven to be effective in helping farmers choose superior oil palm seeds. From the results of research conducted using the ARAS and TOPSIS methods, VIM 1 seeds were recommended as the best choice based on the points obtained.
Implementing Mobile-based AI in Household Waste Type and Condition Classification Suwarno, Suwarno; Lie, Joen; Siahaan, Mangapul
Bulletin of Information Technology (BIT) Vol 7 No 1: Maret 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i1.2504

Abstract

Urbanization and population growth have significantly increased waste generation, creating challenges for effective waste management and recycling. Improper waste sorting and management often results to unrecyclable waste contaminating recycling streams or recyclable waste ending up in landfill. This research presents a mobile-based waste classification application that integrates YOLOv11n for real-time object detection, and uses TensorFlow Lite with a Flutter-based user interface. The model was trained on a dataset of 4,410 images, which combines self-gathered images and images from Kaggle dataset. The images are then augmented to 10,936 images covering 23 waste classes, including organic, inorganic, hazardous, and residual types, with their recyclability conditions. The application allows users to detect objects using their phone camera, to identify their classification and condition, as well as receive actionable 3R (Reduce, Reuse, Recycle) recommendations. Evaluation results show a precision of 0.5963, recall of 0.60563, mAP@0.5 of 0.62246, and mAP@0.5:0.95 of 0.5279, indicating decent classification despite challenges posed by visually similar objects and variable backgrounds. Overall, the system demonstrates the feasibility of deploying a lightweight AI model on mobile devices in hopes of supporting proper waste segregation, increase user awareness, and potentially reduce contamination in recycling streams through practical waste classification.
Performance Analysis of XGBoost in Handling Missing Data on the Telco Customer Churn Dataset atsauri, muhammad riki; Dalimunthe, Aulia Rahman; Syahputra, Nugroho
Bulletin of Information Technology (BIT) Vol 7 No 1: Maret 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i1.2524

Abstract

This study analyzes the performance of Extreme Gradient Boosting (XGBoost) algorithm in handling missing data for telecommunications customer churn prediction. The research objective is to compare the effectiveness of various missing data imputation techniques (mean, k-NN, and MICE) on XGBoost performance using the IBM Telco Customer Churn dataset. The research methodology includes data preprocessing, implementation of imputation techniques, XGBoost model training, and evaluation using accuracy, precision, recall, and F1-score metrics. The results show that MICE imputation technique provides the best performance improvement with 81.24% accuracy, 69.80% precision, 58.40% recall, and 63.60% F1-score, compared to XGBoost without imputation achieving 79.43% accuracy. These findings demonstrate that explicit missing data handling can enhance XGBoost's predictive capability in identifying potential churning customers. The practical implications of this research provide guidance for telecommunications industry in optimizing customer retention strategies through more accurate churn prediction
Pemodelan Biaya Sewa pada Data Pendidikan Internasional Menggunakan Pendekatan Machine Learning dan CRISP-DM Nababan, Arif; Lumban Gaol, Rezeki; Rahmadhani, Fauziah
Bulletin of Information Technology (BIT) Vol 7 No 1: Maret 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i1.2557

Abstract

Advances in machine learning drive its application in analyzing complex educational data. In international education, housing rent (Rent_USD) is a critical cost-of-living component showing significant variation across regions. These variations are influenced by geography, local economics, and educational environments, requiring systematic data modeling. This study aims to model Rent_USD using the CRISP-DM framework: Business Understanding, Data Understanding, Data Preparation, Modeling, and Evaluation. Three algorithms were employed: Decision Tree as the baseline, Random Forest as a comparison, and XGBoost as the primary model. To enhance performance, hyperparameter tuning was conducted via GridSearchCV. Model evaluation utilized Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R2). The experimental results demonstrate that the XGBoost algorithm delivers the most superior performance, achieving the lowest RMSE of 93.27 USD and an R2 of 0.96. This performance outperforms Random Forest (RMSE: 114.87, R2: 0.94) and Decision Tree (RMSE: 157.16, R2: 0.89). Furthermore, feature importance analysis revealed crucial findings: the Living Cost Index and Tuition Fee are the most dominant factors influencing Rent_USD variations, contributing 58.32% and 32.94% respectively. This research provides an empirical overview of machine learning applications in modeling international education costs and serves as a vital reference for future studies regarding educational data management and predictive analytics in global student mobility.

Page 26 of 27 | Total Record : 267

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