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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 317 Documents
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Optimasi Algoritma K- Nearest Neighbor Berbasis Particle Swarm Optimization Untuk Meningkatkan Kebutuhan Barang Taofik Safrudin; Gatot Tri Pranoto; Wahyu Hadikristanto
Bulletin of Information Technology (BIT) Vol 4 No 3: September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i3.724

Abstract

Abstract− The application of the K-Nearest Neighbor algorithm can be implemented where the results also show a new insight, namely predicting the level of need. With a ratio of 90%:10%, where there are 50 data objects tested to predict the level of needs in 2 groups, namely low needs or high needs. The results of the model scenario show that there are 2 objects in the Low needs group and 1 object in the High needs group. In evaluating this model, it was obtained from 10 fold Cross Validation that the Accuracy value was 82%, then the Precision value was 87.50%, and the Recall value was 80%. By measuring the performance of the model with Cross Validation, the resulting accuracy has a standard value or standard deviation, which aims to see the distance between the average accuracy and the accuracy of each experiment. While the Test Results using PSO In the evaluation of this model, it is obtained from 10 fold Cross Validation the Accuracy value is 100%, then the Precision value is 100%, and the Recall value is 100%, the test results have increased significantly
Prediksi Penyakit Diabetes Untuk Pencegahan Dini Dengan Metode Regresi Linear Niko Suwaryo Niko; Arif Rahman; Dewi Marini Umi Atmaja; Amat Basri
Bulletin of Information Technology (BIT) Vol 4 No 3: September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i3.739

Abstract

Estimation is a method in which we can estimate the population value by using the sample value and which can model an equation to calculate the estimate i.e. a linear regression algorithm attempts to model the relationship between two variables by fitting a linear equation to observe the data. The application of a simple Linear Regression algorithm model can be implemented well and is able to provide a new insight for the need for predictions about the condition of diabetes data quality in controlling sugar levels in the body. Predictions of diabetes in the future can be known through the use of datasets using a prediction method approach through structured stages in analyzing the data used to produce an RSME value when evaluating a model of 0.000 +/- 0.000. Performance testing of the models and algorithms used in the evaluation can produce a picture that is relevant to the scenario being modeled. The RMSE value is obtained when evaluating the model performance of 0.000 +/- 0.000 through the RapidMiner Studio application.
Perancangan Game Sederhana Perancangan Game Sederhana Menggunakan Scratch Programming Sebagai Media Pembelajaran Visual Bagi Anak Usia Dini Yunus Yunus Anis; Artin Bayu Mukti; Sri Mulyani
Bulletin of Information Technology (BIT) Vol 4 No 3: September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i3.769

Abstract

This study aims to design a simple game using Scratch Programming as a visual programming language learning media for early childhood. This article describes the implementation stages of game design and the evaluation of learning media carried out. The literature review reviews the advantages of Scratch Programming as a learning medium for children and previous related research. In the implementation of game design, game concepts are designed using Scratch Programming, and simple game examples are generated. Evaluation of learning media is carried out to collect evaluation data from participants using relevant methods. The results of the evaluation of learning media and the discussion of research findings provide information about the effectiveness of game design using Scratch Programming. The results of this study indicate that designing simple games using Scratch Programming can be an interesting and effective learning medium for young children in learning visual programming languages. The implication of this research is further development in game design and the use of visual programming languages in early childhood education. Suggestions for further research are to involve more participants and explore more complex programming concepts.
Sistem Rekomendasi Penentuan Titik Usaha Kafe Menggunakan Data Spasial dan Algoritma Topsis Irfan; Amil A.Ilham; Imran Taufik; Dedi Suarna
Bulletin of Information Technology (BIT) Vol 4 No 3: September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i3.918

Abstract

− The cafe business is a business that has very promising opportunities because this cafe business has a very wide target market, namely not only coffee lovers, but also ordinary people, especially millennials such as students and university students. Sinja City is a city with good business potential, but the problem is that there is no system that can determine suitable business locations, especially determining café business locations. This research aims to develop a recommendation system that can help prospective cafe entrepreneurs determine the optimal location to open their cafe business. This system uses a spatial data-based approach and the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to produce appropriate recommendations [1]. The results of this research produce a list of recommendations for the best locations to open a cafe business. This recommendation system can help cafe entrepreneurs make more informed decisions and minimize the risks associated with choosing a business location. In addition, the use of spatial data and the TOPSIS method allows this system to produce recommendations that are more accurate and relevant to actual geographic conditions. Apart from that, the use of spatial data and the TOPSIS method allows this system to produce more accurate recommendations with a trial with 4 alternatives using the TOPSIS method. The highest ranking result is alternative 1 as a recommendation for determining the location of a cafe business location
Optimalisasi Random Forest dengan Penyelarasan Temporal untuk Identifikasi Faktor Determinan Stunting Nusa Tenggara Barat Lalu Mutawalli; Mohammad Taufan Asri Zaen; Ahmad Tantoni; Muhammad Fauzi Zulkarnaen
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2648

Abstract

Stunting remains a major public health issue in West Nusa Tenggara, with key challenges including low data integrity caused by reporting discontinuities, spatial heterogeneity, and statistical noise that obscure accurate determinant identification. These issues are particularly evident during the 2023–2024 reporting transition, where inconsistencies distort the relationship between community health worker performance and stunting prevalence. This study applies a Spatio-temporal approach incorporating temporal alignment, normalization, and Random Forest regression. The analysis includes Moderated Regression Analysis using Ordinary Least Squares, R², and Global Moran's I. The objective is to improve accuracy in identifying determinants of stunting reduction and evaluate community health worker effectiveness using integrated data. Results show consistent decline in stunting prevalence during 2018–2024, with significant reductions in Central of Lombok (-42.2%), West Lombok (-39.4%), and Bima City (-47.7%), while increases occurred in Bima Regency (+8.5%) and Mataram City (+33.6%). Model performance improves significantly after data alignment, with R² exceeding 0.70. Feature importance identifies cadre workload ratio and child weighing participation as dominant predictors (p = 0.073), indicating a discovery effect. Spatial analysis yields Global Moran's I of -0.0615, suggesting a dispersed pattern. Overall, integrating temporal data alignment with machine learning and spatial analysis enhances determinant identification and supports data-driven policy for stunting reduction in West Nusa Tenggara.
Pengelompokan Data Penjualan Produk Cetakan Pada Algoritma K-Means Dengan Bantuan Tool Orange Susliansyah; Muhammad Ridho Caroko; Heny Sumarno; Hendro Priyono; Linda Maulida
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2659

Abstract

The main problem faced is the large amount of unstructured sales data, making it difficult to perform manual analysis. With the application of the K-Means algorithm, sales data can be grouped into clusters representing products with high and low sales. The research process begins with the stages of problem identification, data collection, preprocessing, application of the K-Means algorithm, evaluation of clustering results, and then analysis and interpretation. Iteration results show that cluster C1 consists of a number of high-selling sales data, while cluster C2 encompasses the majority of low-selling sales data. Evaluation using the Davies-Bouldin Index (DBI) yields a value of 0.2818, indicating fairly good cluster quality, while the Silhouette Plot provides values of 0.082 for C1 and 0.276 for C2, indicating that cluster C2 is more stable compared to C1. Scatter Plot visualization shows the data distribution forming a slanted pattern from C1 to C2. The result of this research is that by using the K-Means algorithm, it can effectively cluster sales data of printed products, so it can be used as a basis for business decision-making related to marketing strategies, stock control, and product performance evaluation.
Analisis Penentuan Pengolahan Kopi Arabika Terbaik Dengan Metode ELECTRE Ester Arisawati; Rinawati; Erene Gernaria Sihombing; Frisma Handayanna Handayanna
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2660

Abstract

Arabica coffee is a premium commodity with high economic potential, while also offering distinctive taste and aroma that make it valuable in both national and international markets. In processing practice, choosing the right method becomes its own challenge, considering the various alternatives such as wet, dry, and semi-wet methods that must be evaluated based on quality, cost, and environmental impact. The main issue lies in how to determine the optimal processing method that can produce coffee beans of superior quality. This study uses the ELECTRE method, one of the multi-criteria decision-making approaches, to evaluate various Arabica coffee bean processing alternatives. The analysis process includes the preparation of a decision matrix, normalization, weighting of criteria (aroma, flavor, aftertaste, acidity, body, and balance), calculation of concordance and discordance matrices, as well as dominance analysis to obtain priority. The calculation results show that alternative 6 (A6) for Pulped Natural/Honey and A10 for Wet Hulling are the best choices for processing Arabica coffee beans. These findings provide practical solutions for farmers and coffee industry players in improving quality as well as the competitiveness of Indonesian Arabica coffee.
Clustering Status Gizi Menggunakan Algoritma K-Means Dengan Pendekatan CRISP-DM Delia Wulan Rahmadhani; Amelia Yusnita; Aisyah Fajriantini
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2668

Abstract

The nutritional status of toddlers is a key indicator for assessing public health levels. Nutritional problems such as undernutrition remain common and require effective analysis to identify data patterns quickly and accurately. This study aims to cluster the nutritional status of toddlers in the Bukuan Community Health Center (Puskesmas) Posyandu area using the K-Means algorithm with the CRISP-DM approach. The main challenge in this study is that the processing of nutritional status data is still done manually, making it less effective in quickly and accurately identifying patterns and risk groups. The dataset consists of 2,145 records of toddlers from 12 Posyandu, with primary attributes including weight, height, age, and gender. The research process was conducted through the CRISP-DM stages, which include business understanding, data understanding, data preparation, modeling, and evaluation, without deployment implementation since the study focused on data analysis. The clustering process was performed using the K-Means algorithm, with the optimal number of clusters determined via the Elbow method, resulting in three clusters. Model evaluation using the Silhouette Score yielded a value of 0.629, indicating that the clustering quality falls into the “good” category. The results show that data on toddlers can be grouped into three nutritional status categories: under-nutrition, adequate nutrition (normal), and over-nutrition, based on centroid values. The data distribution indicates that the adequate nutrition category dominates, though there remains a significant number of cases in the under-nutrition category. Thus, the application of the K-Means algorithm provides more structured and accurate information for identifying the nutritional status of toddlers and can serve as a basis for data-driven decision-making in public health programs.
Pemanfaatan Algoritma K-Medoids Clustering dalam Menentukan Pendapatan Bersih Komoditas Pertanian Faisal Muhammad; Wiranti Sri Utami; Muhammad Subali; Janu Ilham Saputo; Haryanto; Martinus Gawi Tiga
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2682

Abstract

Agricultural products are one of the sectors that have a major role in the Indonesian economy. Currently, Indonesia is the largest producer in the world that produces Palm Oil, Cloves, Cinnamon, Nutmeg, and many others. Abundant agricultural products can be applied to research using Data Mining techniques. Data Mining is a technique that applies statistical analysis and artificial intelligence in extracting useful information from a database. In this study the author will use the K-Medoids method, K-Medoids is one of the Data Mining techniques. Analysis of K-Medoids results uses the silhouette coefficient which is used to measure the distance between clusters. The objective value using K-Medoids cluster analysis on the dataset used is 5.742047 and 5.093438. After conducting cluster analysis with the silhouette coefficient, the best results obtained are 2 clusters from 12 data and 12 attributes.
Pengembangan Model Deteksi Autism Spectrum Disorder (ASD) Dengan Algoritma Facenet Vggface Dan Insightface Marsha Falen Fransisca; Lukman Sunardi; Harma Oktafia LW; Budi Santoso
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2697

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects communication, social interaction, and behavior. Conventional ASD diagnosis relies on clinical observation, which is time-consuming and subjective. Therefore, an automated approach using artificial intelligence is required to support early detection. This study proposes an ASD detection model based on facial image analysis using deep learning approaches, namely FaceNet, VGGFace2, and InsightFace as facial feature extraction methods. The dataset consists of 3,620 facial images categorized into ASD and non-ASD classes. The research process includes preprocessing, feature extraction, model training, and evaluation using accuracy, precision, recall, and F1-score metrics. The results indicate that all models achieved good classification performance, with FaceNet achieving the highest accuracy of 98%, followed by InsightFace with 96%, and VGGFace2 with 95%. These findings demonstrate that face embedding-based models provide superior feature extraction capabilities for ASD detection.

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