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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 436 Documents
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Pengelompokan Untuk Penjualan Obat Dengan Menggunakan Algoritma K-Means Holwati; Edi Widodo; 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.v4i3.848

Abstract

Drug grouping is an arrangement that adjusts to the flow of placement or drug layout is more suitable for standard processes. Utilization of existing data through the clustering method approach can be applied to analyze in grouping drug data on data availability and inventory in warehouses so as to provide knowledge and information. The clustering method is processed using the K-Means algorithm where the results also show a new knowledge, namely the grouping of drug data based on 2 clusters. Cluster 1 is a high need category with availability of 71 out of 100 availability categories based on the amount of drug data tested, then cluster 2 is a drug category with moderate or low availability, namely 29 out of 100 availability categories based on the number of drug data tested. Tests using Rapid Miner tools can also produce similar insights, namely each cluster has cluster group members according to manual calculations such as Cluster_0 in Rapid Miner has 72 cluster members representing the Medium cluster, Cluster_1 has 72 cluster group members as high cluster representations, and Cluster_2 has 3 cluster members corresponding to low representation.
Pemetaan Tingkat Kriminalitas di Indonesia: Analisis Spasial dengan Pendekatan SIG pada Tingkat Provinsi Ronal Watrianthos; Sudi Suryadi; Kusmanto; Samsir Samsir
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.v4i3.861

Abstract

Indonesia memiliki Indeks Pembangunan Manusia yang rendah, yang menunjukkan bahwa masih ada pekerjaan yang harus dilakukan untuk meningkatkan kualitas hidup dan kesehatan masyarakatnya. Selain itu, Indonesia menghadapi banyak masalah sosioekonomi, termasuk populasi yang berlebihan, kemiskinan, tingkat pengangguran yang tinggi, dan sistem pendidikan yang buruk. Masalah-masalah ini dapat memengaruhi masyarakat, termasuk meningkatkan kejahatan. Banyak indikator yang umum digunakan dalam bidang statistik kriminal untuk mengukur kejahatan dari perspektif yang luas dan untuk menilai tingkat keparahannya. Tujuan dari penelitian ini adalah untuk menggambarkan distribusi tingkat kejahatan secara keseluruhan di antara provinsi-provinsi Indonesia, dengan penekanan khusus pada Sumatra dan Jawa. Studi ini menggunakan data dari Badan Pusat Statistik dari tahun 2010 hingga 2020 tentang jumlah kejahatan yang dilaporkan oleh petugas polisi regional. Objek yang diamati di masing-masing provinsi dikelompokkan ke dalam kelompok yang saling terkait menggunakan teknik pembelajaran tidak terbimbing dengan algoritma klasifikasi K-Means. Hasil menunjukkan bahwa antara tahun 2010 dan 2020, provinsi Bengkulu, Kepulauan Bangka Belitung, dan Banten memiliki tingkat kejahatan terendah dibandingkan provinsi lain di Sumatra dan Jawa. Hasil ini menunjukkan bahwa ketiga provinsi ini mungkin memiliki kemampuan yang lebih baik untuk mengatasi masalah sosioekonomi yang diketahui berkontribusi pada kejahatan.
Distribusi Spasial Unmet Need Pelayanan Kesehatan dengan Algoritma K-Means untuk Pemetaan Provinsi di Indonesia Kusmanto; Samsir Samsir; Ronal Watrianthos; Sudi Suryadi
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.v4i3.862

Abstract

Pemetaan spasial terhadap kebutuhan pelayanan kesehatan yang belum terpenuhi (unmet need) penting dilakukan untuk mengenali wilayah yang memerlukan prioritas intervensi guna meningkatkan akses dan kualitas layanan kesehatan. Penelitian ini bertujuan memetakan tingkat unmet need pelayanan kesehatan di 34 provinsi Indonesia tahun 2015-2022 dengan algoritma klasterisasi K-Means. Data unmet need dianalisis dan dievaluasi menggunakan Indeks Davies-Bouldin untuk menentukan jumlah klaster optimal. Hasil analisis menunjukkan 3 klaster provinsi optimal berdasarkan tingkat unmet need. Klaster 1 (DKI Jakarta, Bali, Papua) memiliki rata-rata unmet need terendah 2,47%. Klaster 2 (sebagian provinsi di Jawa dan Kalimantan) memiliki rata-rata unmet need sedang 5,46%. Klaster 3 (sebagian besar provinsi di luar Jawa) merupakan kelompok dengan unmet need tertinggi rata-rata 7,35%. Secara spasial, provinsi di luar Jawa cenderung berada di klaster dengan unmet need tinggi, sejalan dengan tantangan aksesibilitas pelayanan kesehatan. Hasil pemetaan K-Means ini dapat menjadi acuan dalam merumuskan rekomendasi peningkatan akses dan kualitas layanan kesehatan di provinsi-provinsi prioritas berdasarkan tingkat unmet need.
The Expert System of Determining the Type of Malaria by using Dempster-Shafer Method Ronal Maruli Marusaha; Dian Noviandri; Andre Hasudungan Lubis
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.v4i3.887

Abstract

Malaria is the most dominant disease in Asia and Africa and may become a life-threatening disease for it suffers. The types of malaria such as Plasmodium Vivax, Plasmodium Ovale, Plasmodium Malariae, and Plasmodium Falciparum are mostly infected people around the world. These types of malaria have certain symptoms that drives difficulties for some patients to confirm which malaria that their infected. A clinical testing and medical diagnostic assessments may be performed to determine the types of malaria, but utilizing a system also brings some benefits for rural areas which lack of medical facilities. The study develops a system by implementing the Dempster Shafer method to determine types of malaria. We collected the knowledge from the experts including 18 possible symptoms along with the density value. This paper present 5 cases of sufferers and provide the system result with the possibilities of malaria types. The result pointed out a various percentage of malaria types that may infected to the patients.
Perbandingan Algoritma Extreme Learning Machine dan Multilayer Perceptron Dalam Prediksi Mahasiswa Drop Out Muhammad Ibnu Saad Saad
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.v4i3.890

Abstract

Determined by the university concerned. The high number of drop out students at tertiary institutions can be minimized by policies from tertiary institutions to direct and prevent students from dropping out that detecting at-risk students in the early stages of education is very important to do to keep students from dropping out. The purpose of this study is to classify and compare the Extreme Learning Machine and Multilater Perceptron algorithms in predicting student drop out. This study uses two algorithms, namely Extreme Learning Machine and Multilater Perceptron which are feedforward artificial neural network learning methods. The data used is 110 data according to the number of students from class 2012 to 2018. The data is taken from the Doctor of Education Management academic information system. In this case how to predict student drop out using the variables Gender, Working Status, Family Status, Age, Semester 3 GPA, Comprehensive Examination, Dissertation Progress, and Publications. The results of the Extreme Learning Machine classification based on a ratio of 80:20 get an accuracy of 95% with a hidden layer of 20 and a Mean Squared Error value of 0.369. Whereas the Multilater Perceptron with the same ratio gets 91% accuracy. From the two models used, it shows that the two artificial neural network algorithms can produce good performance in predicting drop out students.
Memprediksi Kualitas Produk Inspeksi Dalam Meminimalisasi Resiko Produk Ng Meggunakan Algoritma Regresi Linier Dini Rahayu; Aris Gunaryati
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.v4i3.894

Abstract

Predicting the product is a form of analyzing data, predicting the product is an important factor that determines the smooth running of the product. Utilization of product and defect data can be used in carrying out the process of data mining and modeling stages to predict the number of product defects at one time. The application of the simple Linear Regression algorithm equation model can be implemented where the results also provide a new insight for the prediction needs of the number of product defects. The simple Linear Regression equation model after comparison with actual calculation results (observations) and also with the Rapid Miner application in general shows similar results. Evaluation and testing of the RMSE value was also obtained when evaluating the applied linear regression model, with an RMSE value of 0.984 with a standard deviation of +- 0.0
Rancang Bangun Aplikasi Sistem Pendukung Keputusan Kenaikan Jabatan Pegawai Dengan Menggunakan Metode Multi Criteria Decision Making (MCDM) Riandy Putratama Nasution; Supiyandi; Muhammad Amin
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.v4i3.923

Abstract

The system for increasing employee positions at the Dinas Perkebunan dan Peternakan of North Sumatra Province has still been carried out using a computer, but the system used is only for inputting data and is not integrated, so it does not rule out the possibility that decision making is carried out subjectively by only looking at it from one side. In certain aspects, of the promotion process, employees who only look at their daily habits at work are not effective. Seeing the existing problems, a decision support system is needed that can help solve problems in decision-making by adjusting the criteria and weights that have been determined by the agency. The method used in decision-making is a multi-criteria decision-making method, with this method we can find out employee data included in the weights and criteria for promotion at the Dinas Perkebunan dan Peternakan of North Sumatra Province. There are problems with the Department of Agriculture and Animal Husbandry of North Sumatra Province requiring a Decision Support System in determining employee promotions. The system development method using the waterfall model is a systematic and sequential software development that will be built using a programming language. The final result of this research is the creation of a Decision Support System for Employee Promotion at Dinas Perkebunan dan Peternakan of North Sumatra Province based on integrated co-opterization can determine promotions for employees who are worth considering and have not been selected to choose the best one in the completeness of the position file. After the decisions obtained in the decision support system application, the system will display the results of the decisions that have been taken.
Prediksi Penjualan Menggunakan Exponential Smoothing sebagai Pendukung Business Intelligence UMKM Kuliner  Ahmad Sonitoro; Afriza Meigi Zukhruf; Muhammad Azkia Maksalmyna; Muchamad Rizky Bagus Saputra; David Febrian
Bulletin of Information Technology (BIT) Vol 7 No 3: September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i3.3062

Abstract

Micro, Small, and Medium Enterprises (MSMEs) in the culinary sector face challenges in sales management due to unpredictable demand fluctuations. This study aims to apply the Exponential Smoothing method as part of a time series approach to predict daily sales of fried chicken and catfish pecel, and integrate the results into a Business Intelligence (BI) framework to support operational and strategic decision-making. Primary data were collected through unstructured interviews with the business owner during Januari-December 2025, with a total of 313 operating days. Modeling used alpha (α) = 0.3 selected through trial and error to obtain the best accuracy. Model evaluation used MAPE, MAE, and RMSE metrics. Results showed MAPE values of 19.33% for fried chicken (good accuracy) and 21.12% for catfish pecel (reasonably acceptable). MAE values were 3.58 and 3.25 portions/day respectively, while RMSE values were 4.42 and 4.09. Sales forecasts for January 2026 projected 23 portions/day for fried chicken and 19 portions/day for catfish pecel. Prediction results were visualized in a Business Intelligence dashboard using Microsoft Excel, producing data-driven managerial recommendations for production planning and inventory management.
Klasifikasi Jenis Ayam Menggunakan Ensemble Voting CNN (Mobilenetv2 Dan Efficientnet-B0) Berbasis Citra Sudanto; Joni Karman; Satrianansyah; Budi Santoso
Bulletin of Information Technology (BIT) Vol 7 No 3: September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i3.3067

Abstract

Digital image-based chicken species classification plays a crucial role in supporting the identification process in the livestock sector. However, the similarity in visual characteristics between chicken species makes the manual classification process inefficient and error-prone. This study aims to develop a chicken species classification model using the Ensemble Soft Voting method by combining two Convolutional Neural Network (CNN) architectures, namely MobileNetV2 and EfficientNet-B0. The dataset used consists of 1,500 images from five chicken classes: Bangkok Chicken, Broiler Chicken, Free-Range Chicken, Bantam Chicken, and Laying Hen. The research stages include preprocessing, data augmentation, transfer learning, model training, and combining prediction probabilities using the Soft Voting method. Evaluation is carried out using Accuracy, Precision, Recall, F1-Score, and Confusion Matrix metrics. The results showed that MobileNetV2 achieved an accuracy of 97.18%, EfficientNet-B0 achieved 96.48%, while the Ensemble Soft Voting method produced the best performance with an accuracy of 97.89%, a precision of 97.96%, a recall of 97.89%, and an F1-score of 97.88%. These results indicate that the Ensemble Soft Voting method is able to improve classification accuracy and stability compared to using a single model, so it has the potential to be applied as a solution for digital image-based chicken species identification.
Explainable Machine Learning for Post-Disaster Stunting Risk Prediction to Support Emergency Nutrition Decision-Making Sri Wahyuni; Wiwin Handoko; Hadijah; Kana Saputra S; Sybil Auzi; Evelyn Keisha Silalahi
Bulletin of Information Technology (BIT) Vol 7 No 3: September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i3.3069

Abstract

Stunting in children under five is a chronic nutritional disorder whose risk increases significantly in post-disaster settings due to disruptions in food supply chains, damage to health infrastructure, and deteriorating sanitation conditions. Delays in identifying children at high risk of stunting in evacuation centers prevent targeted, timely, and efficient emergency nutrition interventions. This study proposes a post-disaster stunting risk prediction system based on Prepared Machine Learning (XML), which integrates a predictive model based on ensemble learning with the SHapley Additive exPlanations (SHAP) interpretability framework to support transparent and evidence-based emergency nutrition intervention decision-making. The model is built with Explainable Artificial Intelligence (XAI) to make the stunting prediction system transparent and understandable. SHAP is used as the primary method to explain the contribution of each factor to stunting risk prediction. Data were processed using anthropometric and sociodemographic datasets of children aged 0–59 months, consisting of victim data collected from the three provinces of Aceh, North Sumatra, and West Sumatra, data from the Center for Disaster Management (CDC), and data and information from the Indonesian National Disaster Management Agency (BNPB). Twelve input features were used, including height-for-age (HAZ) z-score, weight-for-age (WAZ) z-score, exclusive breastfeeding, frequency of food intake, immunization status, household sanitation conditions, family income level, and duration of displacement. Model performance was assessed using Area Under the Receiver Operating Characteristic Curve (AUC-ROC), F1-Score, precision, and recall, with stratified tenfold cross-validation to ensure model generalizability. SHAP-based interpretability analysis identified the five features with the highest predictive contribution as HAZ z-score, duration of displacement, frequency of food intake, exclusive breastfeeding, and sanitation conditions, which consistently align with established stunting risk factors in the nutrition science literature. The system then interfaces into a web-based decision support interface that displays SHAP power plots per child and population-level least squares plots, enabling field nutrition workers without a data science background to understand the rationale behind each risk prediction. This study provides an explainable and operationally applicable machine learning framework for the Ministry of Health, BNPB, and National humanitarian agencies as a transparent, auditable, and accountable decision-support instrument for emergency nutrition interventions in post-disaster situations.

Page 32 of 44 | Total Record : 436

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