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Bulletin of Computer Science Research
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27743659     DOI : -
Core Subject : Science,
Computer Science & IT
Bulletin of Computer Science Research covers the whole spectrum of Computer Science, which includes, but is not limited to : • Artificial Immune Systems, Ant Colonies, and Swarm Intelligence • Bayesian Networks and Probabilistic Reasoning • Biologically Inspired Intelligence • Brain-Computer Interfacing • Business Intelligence • Chaos theory and intelligent control systems • Clustering and Data Analysis • Complex Systems and Applications • Computational Intelligence and Soft Computing • Distributed Intelligent Systems • Database Management and Information Retrieval • Evolutionary computation and DNA/cellular/molecular computing • Expert Systems • Fault detection, Fault analysis, and Diagnostics • Fusion of Neural Networks and Fuzzy Systems • Green and Renewable Energy Systems • Human Interface, Human-Computer Interaction, Human Information Processing • Hybrid and Distributed Algorithms • High-Performance Computing • Information storage, security, integrity, privacy, and trust • Image and Speech Signal Processing • Knowledge-Based Systems, Knowledge Networks • Knowledge discovery and ontology engineering • Machine Learning, Reinforcement Learning • Networked Control Systems • Neural Networks and Applications • Natural Language Processing • Optimization and Decision Making • Pattern Classification, Recognition, speech recognition, and synthesis • Robotic Intelligence • Rough sets and granular computing • Robustness Analysis • Self-Organizing Systems • Social Intelligence • Soft computing in P2P, Grid, Cloud and Internet Computing Technologies • Support Vector Machines • Ubiquitous, grid and high-performance computing • Virtual Reality in Engineering Applications • Web and mobile Intelligence, and Big Data • Cryptography • Model and Simulation • Image Processing
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 329 Documents
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Pemanfaatan Pengenalan Citra Kematengan Jengkol Untuk Saran Masakan Menggunakan Metode Algoritma Deep Learning Sanjaya, Handika; Kurniawan, Rudi; Rusdiyanto
Bulletin of Computer Science Research Vol. 4 No. 4 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i4.358

Abstract

Currently, technological development is progressing rapidly. One of the areas is in artificial intelligence and computer vision technology. One of the branches of science studied in artificial intelligence and computer vision technology is deep learning, which focuses on the use of artificial neural networks that learn about classification and object detection directly through images and videos. With the advent of deep learning, researchers are focusing on developing a jengkol (dogfruit) ripeness detection system using deep learning methods. This research uses 1500 jengkol images as a dataset and uses Roboflow for labeling. The results of the labeling will be divided into 3 types of classes: young, medium, and old jengkol. The YOLOV5 algorithm is used for training the jengkol dataset. The next stage is testing, where the approaches used are confusion matrix, classification report, Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), as well as the F1-Score value. The purpose of this testing is to see the precision results and identify the optimal accuracy on data from the trained model that can be achieved by the system being tested or evaluated, which involves compiling a classification report into three categories: young, medium, and old, based on visual characteristics that can be detected by the deep learning algorithm. From the real-time testing and evaluation results obtained in this study, the accuracy value is 80%.
Implementasi dan Pengujian Menggunakan Metode BlackBox Testing Pada Sistem Informasi Tracer Study Zen, Muhammad; Irwan; Hafni; Ananda, M. Dea Putra
Bulletin of Computer Science Research Vol. 4 No. 4 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i4.359

Abstract

The research is aimed at implementing and testing the information systems of the Tracer Study at Panca Development University using the BlackBox Testing method. The BlackBox test method is chosen to evaluate the functionality of the system without having to know the internal details of that system. Previous research results showed that BlackBox testing was effective in identifying functional errors without requiring in-depth knowledge of the internal structure of the system. In the context of information system development, the use of testing methods such as BlackBox Testing becomes essential to ensure that the developed system functions properly according to the needs of the user. Implementation of this method on the Tracer Study information system is expected to improve the quality of the system and ensure that the system can provide accurate and useful information to the user. Thus, the research contributes to the development of the information system Tracer Studies in the higher education environment, in particular Panca Budi Development University, with a testing approach that focuses on the external functionality of the systems. The results of the implementation and testing using the BlackBox Testing method are expected to improve the quality and reliability of the Tracer Study information system that can support better decision-making in the future.
Implementation of Entropy and Additive Ratio Assessment Methods in Determining the Best Warehouse Location Waqas Arshad, Muhammad; Setiawansyah; Mesran
Bulletin of Computer Science Research Vol. 4 No. 4 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i4.360

Abstract

The main problems in determining warehouse location include high operational costs, inadequate accessibility and infrastructure, and strict and complex local regulations. In addition, environmental risks and natural disasters, as well as economic fluctuations and market dynamics also add to the challenge of choosing an optimal location. The purpose of this study is to apply the Entropy and ARAS methods in evaluating the potential of optimal warehouse locations, thereby providing clear and structured guidance for decision-makers in choosing the optimal warehouse location to meet the company's operational and strategic needs. The implementation of the entropy and ARAS methods in determining the best warehouse location involves a systematic approach in evaluating several criteria that are important for optimal decision-making. The Entropy method helps in objectively assessing the importance of each criterion by quantifying the uncertainty or variation present in the data. The ARAS method complements this by allowing comparative analysis of alternative warehouse locations based on their performance against ideal criteria, taking into account both quantitative and qualitative aspects. Thus, both methods provide a solid framework for selecting warehouse locations that not only meet logistics requirements efficiently but are also in line with strategic business objectives, ensuring a well-informed decision-making process in supply chain management and logistics. The results of the ranking of the best warehouse location selection using the entropy and ARAS methods show AB Location as the first rank with a value of 0.9781, DD Location as the second place with a value of 0.8362, and IP Location as the third place with a value of 0.8143.
Klasifikasi Citra Penyakit Daun Anggur Menggunakan Radial Basis Function Neural Networks Moh. Erkamim; Yanuardi, Yanuardi; Mohammad Imam Shalahudin; Arisantoso, Arisantoso
Bulletin of Computer Science Research Vol. 4 No. 5 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i5.324

Abstract

Grapes are one of the important horticultural commodities in Indonesia, but their productivity is often disrupted by leaf diseases that affect both the quality and quantity of the harvest. Manual disease identification remains time-consuming, requires specialized expertise, and often results in inconsistent diagnoses. Challenges such as fatigue, varying levels of experience, and visual differences in leaf diseases hinder the ability to perform fast and accurate diagnosis. Therefore, this study focuses on developing an automatic grape leaf disease classification model using the Radial Basis Function Neural Networks (RBFNN) algorithm. The model utilizes color feature extraction through Mean Color to detect changes in the color distribution of infected leaves, as well as GLCM (Gray Level Co-occurrence Matrix) to analyze texture patterns that serve as disease indicators. The classification process is conducted by the RBFNN algorithm, which calculates the distance between inputs and neuron centers using radial basis functions in the hidden layer. The results of this study show that the model is capable of classifying grape leaf diseases with an overall accuracy of 92.5%, indicating that the model is highly effective in detecting and classifying both healthy and infected leaves with minimal errors.
Pemanfaatan Algoritma C4.5 untuk Mendukung Pemilihan Konsentrasi Studi yang Tepat di Teknik Informatika Desyanti; Rudi Faisal
Bulletin of Computer Science Research Vol. 4 No. 6 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i6.345

Abstract

Choosing the right study concentration in the Informatics Engineering Study Program is crucial in supporting the success of the student's learning process and career. Currently, when choosing a contrast for the information engineering study program at Campus This research uses the C4.5 algorithm to build a decision tree to help students determine study concentration based on variables such as gender, high school major, course grades, and interests and talents. This method begins with the Knowledge Discovery in Databases (KDD) process which includes data selection, data cleaning, and transformation using a Likert scale. Data from 74 fifth semester students were analyzed to produce relevant decision rules. The implementation results show that the C4.5 algorithm is able to provide high accuracy in determining the appropriate study concentration. This system is expected to be a decision support tool for students and educational institutions in the majoring process
Sistem Informasi Penjualan Kartu dan Voucher Internet Berbasis Web Menggunakan Metode Waterfall Muhammad Yusuf; Barany Fachri
Bulletin of Computer Science Research Vol. 4 No. 5 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i5.364

Abstract

Currently, card and internet voucher sales at The One Ponsel Store are still done manually. This causes recording errors such as inaccurate stock and missed transactions, and results in long customer waiting times when the store is busy. Another disadvantage of this manual method is the difficulty of tracking sales and stock data in real-time, making it difficult for store managers to make quick and accurate decisions, which results in lost sales potential and operational efficiency. To overcome this problem, the application of web-based information technology is the right solution. The proposed card and voucher sales information system is designed to automate the entire business process, enabling more accurate inventory management, more efficient sales recording, and faster reporting. The Waterfall method was chosen because it provides a systematic framework in the analysis, design, implementation, and testing of the system so that it meets user needs. The results of system testing with Lighthouse showed high performance with scores: Performance 92, Accessibility 88, Best Practices 96, and SEO 90, indicating that the resulting information system is reliable and effective. This system significantly improves the efficiency of stock and sales management at The One Ponsel Store, reduces manual errors, and speeds up the customer service process, providing a positive impact on operations and customer satisfaction.
Pengembangan Sistem Informasi Geografis Pemetaan Tower Base Transceiver (BTS) Berbasis Web Menggunakan Metode Waterfall Muhammad Rivaldi; Khairul Abdi; Imam Adlin Sinaga
Bulletin of Computer Science Research Vol. 4 No. 5 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i5.365

Abstract

Tower Base Transfer Receiver or BTS plays an important role in providing more stable internet services to users. BTS Tower is used as the main access point so that devices can connect to each other and communicate between the same network. Medan Communication and Information Agency as the agency responsible for government affairs in the field of technology and informatics plays an important role in ensuring that BTS Tower operations can run smoothly. One of its tasks is to carry out monitoring, evaluation and reporting activities on information technology infrastructure such as BTS Tower. In carrying out its operational activities, Medan Communication and Information Agency stores data related to BTS Tower in the form of physical documents and digital files such as Microsoft Word & Excel. This will later cause the process of retrieving data related to BTS Tower to be slower because data related to BTS Tower is stored separately. In addition, technicians are often confused because of the large number of BTS Towers spread across various locations. The process of identifying and navigating to the Tower location often takes a long time which causes the maintenance process to be hampered. Therefore, a system was created that can be used as integrated storage that facilitates the process of archiving BTS Tower operational documents. The system that was built also provides a digital map that presents information about the distribution location of BTS Towers that are under the supervision of Medan Communication and Information Agency. The digital map can be used by field technicians for the identification and navigation process to the BTS Tower location, making it easier to maintain the BTS Tower. The system development in this study uses the Software Development Life Cycle Waterfall model. The system is built using the CodeIgniter Framework with the PHP programming language and the MySql database. The creation of digital maps uses the help of Leaflet Library javascript, Google Maps API and also Open StreatMap. The results of this study are Web-Gis which can facilitate the process of archiving documents and viewing data on the distribution of BTS Tower locations under the supervision of the Medan Kominfo service. The test results using the Black-Box testing method show that the features and functions of the system can run properly.
Penentuan Bibit Kelapa Sawit Unggul dengan Metode ARAS dan TOPSIS Yessica Siagian; Mulyani, Neni
Bulletin of Computer Science Research Vol. 4 No. 6 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i6.367

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
Analisis Sentimen Platform X Mengenai Pro Kontra Rekrutmen Guru Melalui Marketplace Menggunakan Metode Naïve Bayes Yusuf Ramadhan Nasution; Aidil Halim Lubis; Tengku Fira Eliza
Bulletin of Computer Science Research Vol. 4 No. 6 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i6.368

Abstract

Minister of Education, Culture, Research and Technology, Nadiem Anwar Makarim has discussed new breakthroughs regarding the teacher recruitment system through the marketplace. On May 24, 2024 since this policy was first presented, the teacher marketplace system has raised pros and cons in society. One of the social media that is busy discussing this topic is social media X. This research aims to conduct sentiment analysis of the opinions of Social Media X users regarding the teacher marketplace. Sentiment analysis was carried out by analyzing 640 opinion data. The data is classified using the Naive Bayes Method. The test results show that there are two classes in the test, namely positive and negative. This shows that platform X users who provide opinions are more pro towards this policy. Then, the data is divided, namely 90% training data and 10% test data. Based on the analysis that has been carried out, an accuracy value of 74%, precision 80%, recall 35%, and f-1 score 48% were obtained.
Model Klasifikasi Risiko Stunting Pada Balita Menggunakan Algoritma CatBoost Classifier Pahlevi, Omar; Wulandari, Dewi Ayu Nur; Rahayu , Luci Kanti; Leidiyana, Henny; Handrianto, Yopi
Bulletin of Computer Science Research Vol. 4 No. 6 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i6.373

Abstract

Stunting is a significant health issue in Indonesia, affecting the growth and development of young children and influenced by various complex risk factors such as nutrition, environment, and access to healthcare services. The manual process of identifying stunting risks often requires considerable time, resources, and specialized expertise from medical professionals. This study aims to develop a stunting risk classification model for young children using machine learning through the CatBoost Classifier algorithm. This algorithm was chosen for its advantages in handling categorical variables without requiring complex encoding processes and its ability to manage imbalanced data, ultimately improving prediction accuracy. In the conducted case study, the model's prediction updates were illustrated by increasing the initial prediction from 0.25 to 0.27 after accounting for residual corrections in the first iteration, with a learning rate of 0.1. This process demonstrates CatBoost's iterative mechanism for improving model predictions through gradual updates. Evaluation results showed that the developed model achieved an accuracy of 98.47% and a ROC-AUC score of 1.00 for several classes, indicating a high capability in accurately classifying stunting risks. These findings suggest that the CatBoost algorithm is effective for stunting risk classification, capable of handling data complexity, and expected to contribute significantly to supporting stunting prevention efforts through improved early detection.

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