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Bulletin of Data Science
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 28079493     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering
Bulletin of Data Science journal publishes manuscripts within the fields of: 1. Soft Computing, 2. Experts System, 3. Decision Support System, 4. Cryptography, 5. Big Data, 6. Data Mining, 7. Artificial Inteligence, and etc
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 59 Documents
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Penerapan Jaringan Syaraf Tiruan Untuk Identifikasi Citra Iris Mata Menggunakan Algoritma Delta Rule Hasibuan, Putry Hetty
Bulletin of Data Science Vol 4 No 1 (2024): October 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletinds.v4i1.6414

Abstract

The development of technology today has greatly influenced the development of science, one of which is in the recognition of iris patterns. When compared to fingerprints, the iris has the advantage of being protected by the eyelids and is more stable as the human age increases. The iris in human vision functions to regulate the size of the pupil and regulate the amount of light entering the eye. If observed more deeply the iris has unique characteristics of each individual. so that the iris can be used as a biometric mark for identification. Artificial Neural Network (ANN) is a tool to solve problems, especially in the field and iris pattern recognition. In general, Artificial Neural Network has a working principle that mimics the human neural network system, weighs the actions to be taken, and makes decisions like humans. Iris recognition can be used as an alternative if the introduction of fingerprints as a biometric identity fails. in this study, iris recognition uses the Dela Rule algorithm. The Delta Rule algorithm has the advantage of being able to check errors during the learning process. This will certainly make the Delta Rule algorithm have a high level of accuracy in iris pattern recognition.
Penggunaan Jaringan Saraf Tiruan untuk Memperkirakan Tenaga Kerja Berdasarkan Kategori Industri Ariani, Dhini; Saragih, Farah Yusni; Asyifah, Hazha Hikmah; Nasution, Alisa Putri Amanda; Alkhairi, Putrama
Bulletin of Data Science Vol 4 No 1 (2024): October 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletinds.v4i1.6433

Abstract

Industrial growth can affect labor mobility both geographically and in terms of professional qualifications, large industries have a strategic role as creators of added value and important job providers in the Region. As an important part of industrial production, it cannot be separated from the demand for labor, but if viewed macro, it can be concluded that the quality of work determines or greatly influences the results of labor productivity itself. The industrial sector plays a significant role in economic growth, because it absorbs labor. Labor growth is much greater than the availability of jobs, thus causing other new problems, namely high unemployment. This study uses the Backpropagation Method to classify special patterns, which reduces the error rate by adjusting the weight based on the difference between output and the desired target. The results of this study are predictions of the level of truth of the Number of Large and Medium Industrial Workers according to Industry Group. Using 5 models, namely 10-10-1, 10-45-1, 10-45-10-1, 10-75-10-1, and 10-100-75-1. From 5 architecture models, 1 best model is produced, namely the 10-75-10-1 model with an accuracy rate of 70% and the smallest epoch with a total of 383.
Peningkatan Layanan Customer Service Melalui Chatbot Menerapkan Algoritma Text Mining dan TF-IDF Atmaja, Fajar Surya
Bulletin of Data Science Vol 4 No 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletinds.v4i2.6416

Abstract

Dr. Hospital Pirngadi is a regional general hospital owned by the government and is a type B hospital located in the Medan City area, North Sumatra. Apart from that, Dr. Pirngadi is also a referral hospital for the Medan and surrounding areas. As a regional general hospital, Pirngadi Regional Hospital also plays a role in providing health services for the people of Medan city and its surroundings, services provided by customer service at Dr. Pirngadi Medan City, such as registration and information on patients who wish to register for either inpatient or outpatient care, information regarding doctor's practice schedules, facility service information, patient guarantor cooperation, bad management, and visitor information. Customer service is not yet optimal for patients and visitors, such as limited information provided, lack of accessibility and clarity of information, lack of coordination between various hospital departments. To overcome this problem, customer service can utilize artificial intelligence technology to improve customer service. This research provides a solution by building a system in the form of a chatbot, this chatbot system will become an information medium for patients and visitors. The chatbot development process uses a text mining algorithm for text processing and TF-IDF to give weight to each document available in the database. The system provides responses based on the highest level of similarity, with text mining and TF-IDF algorithms, chatbots can provide precise and accurate information on questions asked by patients and visitors. The final result of this research is a chatbot that can be used by patients and visitors to find out available information. The existence of a chatbot can make it easier for patients and visitors to get information about the services available at Dr. RSUD. Pirngadi, Medan City.
Analisis Perbandingan Metode Certainty Factor Dan Teorema Bayes Untuk Mendiagnosa Penyakit Paru-Paru Anak Sitinjak, Trifave
Bulletin of Data Science Vol 4 No 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletinds.v4i2.6417

Abstract

Expert systems are one of the fields of artificial intelligence engineering that is quite in demand because of its application in various fields both in the fields of science and business which have proven to be very helpful in making decisions and are very broad in application. One of the applications of the expert system is in the field of medicine which is used to diagnose a disease. Lung disease is a disease that attacks the respiratory system in humans whose incidence rate is quite wide and can attack various ages. infect the respiratory tract and cause the patient to experience shortness of breath. Lung diseases that are often found in everyday life are pneumonia, tuberculosis, bronchitis, common cough accompanied by fever. Diagnosing diseases with an expert system, it is necessary to apply methods to solve the problem. The method applied is the certainty factor method, the Bayes theorem method. The use of these two methods will be compared to measure the level of expert confidence and the level of probability of symptoms that are likely to suffer from lung disease so that the most appropriate and best method for diagnosis can be known.
Implementasi Metode OCRA Dan ROC Rekomendasi E-Commerce Terbaik Dalam Peningkatan UMKM Al-Adawiyah, Robiah; Halawa, Advent
Bulletin of Data Science Vol 4 No 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletinds.v4i2.6434

Abstract

This research aims to implement the OCRA (Ordering and Ranking with Criteria) and ROC (Rank Order Centroid) methods in providing the best e-commerce recommendations to improve the performance of MSMEs (Micro, Small and Medium Enterprises). The OCRA method is used to assess various relevant criteria, such as cost, ease of use, and market reach, while ROC is used to determine the final ranking of each e-commerce platform. The research results show that Bukalapak received the highest final preference score of 0.2324, making it the main choice for MSMEs. It is hoped that these findings can help MSMEs choose the most suitable e-commerce platform, so that they can increase their competitiveness and business growth. This research makes a significant contribution to the development of digitalization strategies for MSMEs in Indonesia
Multi-Criteria Decision Support System for Best Warehouse Performance Selection Using Combined Compromise Solution Method Wang, Junhai; Setiawansyah, Setiawansyah; Isnain, Auliya Rahman
Bulletin of Data Science Vol 4 No 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletinds.v4i2.7196

Abstract

Selecting the best performing warehouse is a strategic step in supporting the efficiency of the supply chain and distribution of goods. This research aims to design a multi-criterion-based decision support system in evaluating and determining the best warehouse using the Combined Compromise Solution (CoCoSo) method. This method was chosen for its ability to combine the strength of weighted average approaches and relative compromises between alternatives, resulting in more balanced and objective decisions. This research involves eight warehouse alternatives that are assessed based on a number of relevant performance criteria. The process starts from problem identification, determination of criteria, data collection, normalization, weighting, to the application of the CoCoSo method. The final results showed that Warehouse C obtained the highest score of 4.8155, followed by Warehouse E and Warehouse A, indicating that the three warehouses had the best performance. These findings are expected to be a reference in strategic decision-making related to warehousing management as well as the basis for the development of a data-based performance evaluation system.
Penerapan Metode Operational Competitiveness Rating Analysis (OCRA) dalan Penentuan Kelayakan Penerima Bantuan Langsung Tunai Mesran, M.Kom, Mesran; Boangmanalu, Mei Mariana; Rohayani, Hetty
Bulletin of Data Science Vol 4 No 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletinds.v4i2.7197

Abstract

Bantuan Langsung Tunai (BLT) is a financial assistance program provided by the government to people in need. The main purpose of the BLT program is to support the economy of underprivileged families. In the selection process of BLT recipients, there are several criteria that must be considered, such as the condition of the house, the completeness of the files, the number of dependents, income, and the size of the land owned. To ensure that the determination of BLT recipient eligibility is done objectively and accurately, a decision support system is needed. One method that can be used is Operational Competitiveness Rating Analysis (OCRA). Through this method, it is expected that more accurate results can be obtained in determining who is entitled to receive BLT. The results of the research using the OCRA method showed that Jimmi Nababan was the most deserving BLT recipient candidate with the first rank and a score of 1,297. Thus, this decision support system is expected to be an effective solution in determining BLT recipients who really need this assistance.
Model Klasifikasi Kenaikan Pangkat Pegawai Negeri Sipil Menggunakan Decision Tree C4.5 Kamaruddin, Kamaruddin; Wijaya, Suman; Mashud, Mashud; Aisa, Sitti
Bulletin of Data Science Vol 5 No 1 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletinds.v5i1.9644

Abstract

The promotion process of Civil Servants (PNS) is a critical component of human resource management aimed at improving performance and professionalism. However, in practice, the process often faces challenges such as subjective assessments and inefficiencies in data processing. This study aims to develop a classification model for determining the eligibility of regular promotions for civil servants using the Decision Tree C4.5 algorithm based on historical personnel data. The dataset consists of 6,193 records obtained from the Regional Personnel and Human Resource Development Agency of Pangkajene and Kepulauan Regency. The attributes used include years of service, performance scores, and employment type. The research stages involve data preprocessing, feature selection, model construction, and evaluation using confusion matrix, accuracy, precision, recall, and F1-score. The results indicate that the proposed model achieves an accuracy of 95.4%, with precision of 87.6%, recall of 83.7%, and F1-score of 85.6%. Additionally, the model generates interpretable decision rules that support decision-making processes. Therefore, the application of data mining using the C4.5 algorithm is proven effective in enhancing objectivity, transparency, and efficiency in civil servant promotion decision-making
Optimalisasi Strategi Penjualan Sparepart Menggunakan Association Rule Berbasis Algoritma Apriori Amali, Amali; Widodo, Edy
Bulletin of Data Science Vol 5 No 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletinds.v5i2.9903

Abstract

The development of information technology encourages companies to utilize sales transaction data as a strategic source of information in business decision-making. However, the increasing amount of transaction data is often not optimally utilized to identify consumer purchasing patterns. This study aims to analyze consumer purchasing patterns in spare parts sales transactions using association rules based on the Apriori algorithm to support the optimization of sales strategies and inventory management. The research method used is a quantitative approach consisting of data collection, data preprocessing, transaction data transformation, frequent itemset generation, and association rule formation. The data used in this study consisted of 350 spare parts sales transactions processed using the Apriori algorithm with a minimum support value of 20% and a minimum confidence value of 70%. The results showed that the products Front Bumper and Brake Pads had the strongest association relationship with a confidence value of 76% and support value of 23%. In addition, the relationship between Radiator and Side Mirror products showed a confidence value of 71%. The study proves that the Apriori algorithm is effective in identifying relationships between products and can assist companies in determining promotional strategies, inventory management, and data-driven business decision-making to improve spare parts sales

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