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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 19 Documents
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Search results for , issue "vol 7 no 2 (2026)" : 19 Documents clear
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.
Analisis Sentimen Masyarakat Terhadap Program Gratis Pol Di TikTok Menggunakan Algoritma Naive bayes Nandhita Helda Widayani; Eka Arriyanti; Yulindawati
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.v7i2.2701

Abstract

The Gratis Pol Program is an educational initiative in East Kalimantan that has garnered public attention and sparked a wide range of reactions on social media, particularly TikTok. The characteristic use of informal language, abbreviations, and colloquial expressions in TikTok comments poses a challenge for sentiment analysis, necessitating a method capable of systematically classifying public opinion. This study aims to analyze public sentiment toward the Gratis Pol Program based on TikTok user comments using the Naive Bayes algorithm. The research was conducted through the stages of text preprocessing, sentiment labeling using a lexicon-based approach, feature representation using TF-IDF, and the classification process using the Naive Bayes algorithm. The research data was obtained from 13 selected TikTok videos with a total of 1,528 comments, divided into 80% training data and 20% test data. The results show that positive sentiment dominates with 722 comments, followed by 496 neutral comments and 310 negative comments. The classification model achieved an accuracy of 56%, with a macro average F1-score of 0.44 and a weighted average F1-score of 0.48. This study contributes to understanding public perception of the Gratis Pol Program and demonstrates the application of the Naive Bayes algorithm in analyzing the sentiment of social media comments that possess certain characteristics.
Implementasi Klasifikasi Teks Menggunakan Algoritma Naïve Bayes pada Sistem Pengarsipan Surat Masuk Nur Afifah; Ita Arfyanti; Yunita
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.v7i2.2713

Abstract

Incoming mail management is an important part of higher education administration because it is related to document storage, classification, and retrieval in a fast and accurate manner. However, the incoming mail archiving process at the General Administration and Finance Bureau (BAUK) of STMIK Widya Cipta Dharma is still carried out manually, making document grouping inefficient, slowing down archive retrieval, and potentially causing inconsistencies in determining mail categories. This study aims to implement the Naïve Bayes algorithm in a web-based incoming mail archiving system to support automatic mail classification. The system was developed using Laravel and Livewire as the main application, and Flask as the classification service. The dataset consisted of 79 incoming mail documents divided into four categories: requests, invitations, notifications, and reports. The preprocessing stage included case folding, text cleaning, tokenizing, stopword removal, and stemming. The results show that the system is able to automatically classify incoming mail and present detailed classification processes through training reports and classification results. Based on testing on 16 incoming mail documents, the model achieved an accuracy of 75.00%, an average precision of 63.89%, and an average recall of 72.22%. These results indicate that the Naïve Bayes algorithm is sufficiently effective in supporting a more structured and efficient incoming mail archiving process.
Optimasi Penentuan Sales Ececutive Terbaik Menggunakan Metode MOORA Pada Dealer Mitsubishi Tenggarong Sepriana Ose Gunawan; Muhammad Ibnu Sa’ad; Muhammad Nur Madani
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.v7i2.2714

Abstract

Penentuan sales executive terbaik merupakan salah satu upaya penting dalam meningkatkan kinerja dan daya saing perusahaan, khususnya pada dealer otomotif, karena berpengaruh langsung terhadap pencapaian target penjualan dan kualitas pelayanan pelanggan. Permasalahan yang sering terjadi adalah proses penilaian yang masih bersifat subjektif dan belum menggunakan metode yang terstruktur, sehingga hasilnya kurang objektif dan konsisten. Penelitian ini bertujuan untuk membangun sistem pendukung keputusan dalam menentukan sales executive terbaik dengan menggunakan metode Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) sebagai solusi berbasis pengambilan keputusan multikriteria. Data yang digunakan merupakan data internal dealer Mitsubishi yang mencakup beberapa alternatif sales executive dengan kriteria penilaian berupa pencapaian target penjualan, kedisiplinan, kemampuan komunikasi, dan kualitas pelayanan. Proses pengolahan data dilakukan melalui tahap pemilihan data, pra-pemrosesan data, penentuan kriteria pembobotan, implementasi metode, hingga perangkingan. Hasil penelitian menunjukkan bahwa metode MOORA mampu menghasilkan nilai preferensi dan urutan peringkat sales executive secara objektif dan sistematis. Alternatif dengan nilai tertinggi ditetapkan sebagai sales executive terbaik. Dengan demikian, sistem yang dibangun dapat membantu pihak manajemen dalam mengambil keputusan yang lebih efektif, efisien, dan transparan serta mendukung evaluasi kinerja berbasis data yang lebih optimal.
Implementasi Robot Mobile Dual Mode Berbasis Arduino Uno sebagai Media Pembelajaran Ekstrakulikuler Robotik Nazil Fikri Hidayatullah; Abdul Halim; Rudianto Rudianto; Mochammad Darip
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.v7i2.2715

Abstract

The advancement of robotics technology offers significant opportunities to enhance practice-based learning in vocational education. However, the implementation of robotics learning at the vocational high school (SMK) level still faces limitations in interactive learning media and low student engagement. This study aims to develop and evaluate a dual-mode mobile robot based on Arduino Uno as a learning medium for robotics extracurricular activities at SMK Negeri 11 Kabupaten Tangerang. The research method employs a Research and Development (R&D) approach with a prototyping model, including problem identification, system design, implementation, testing, and evaluation stages. The developed system integrates two operational modes: an automatic mode (obstacle avoider) and a manual control mode using Bluetooth communication. The results indicate that the system operates stably and responsively, with an average response time of less than one second and acceptable sensor accuracy. Learning evaluation through questionnaires shows an improvement in student interest and understanding, with scores above 78%. This study contributes to the development of interactive and practical robotics-based learning media, which enhances student engagement in the learning process.

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