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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 6 Documents
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Search results for , issue "Vol. 4 No. 6 (2024): Oktober 2024" : 6 Documents clear
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
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.
Sistem Pakar Diagnosis Mood Disorder Pada Anak Menggunakan Pendekatan Dempster-Shafer Theory Priyangan, Donny Muda; Herdiansah, Arief; Mulyana, Iwan; Nurhayati
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.v4i6.374

Abstract

Mood disorders in children are a serious mental health issue that can have long-term impacts on their emotional, social, and academic development. In Indonesia, the limited availability of mental health professionals, especially in remote areas, hinders the process of fast and accurate diagnosis. Manual diagnosis of mood disorders in children often faces challenges in terms of time, resources, and professional expertise, creating a need for an effective solution to support medical practitioners. This research aims to develop a web-based expert system to detect mood disorders in children using the Dempster-Shafer Theory (DST) approach. DST is chosen as the primary method due to its ability to process ambiguous or incomplete information, enabling the integration of multiple pieces of evidence to generate accurate decisions. The system allows users to perform diagnoses based on input symptoms, accompanied by analysis results and follow-up recommendations. The expert system is developed as a web-based platform to optimize accessibility, allowing users to easily carry out the diagnosis process without time and location constraints. Evaluation of the system shows an accuracy rate of 92.5%, validating the effectiveness of DST in mood disorder diagnosis. This research contributes to supporting early detection of children's mental health issues and facilitates the identification of mood disorders based on the symptoms experienced.
Perancangan Pengendali Mesin Teabags Otomatis Pembuat Teh Kelor (Moringa Oleifera) Menggunakan Pengendali PLC (Programmable Logic Controller) Mitsubishi FX3U 24MR Ananda Nur Hidayat; Paniran; Budi Dermawan
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.v4i6.375

Abstract

Moringa plant (Moringa oleifera) is known as “The Miracle Plant” due to its nutrient content and extensive health benefits. This research discusses the design and implementation of an automation system for the production of Moringa (Moringa oleifera) tea bags using a Mitsubishi FX3U 24MR Programmable Logic Controller (PLC). Moringa is a tropical food crop with high nutritional value and therapeutic benefits. With the increasing demand for tea bags, an efficient production system is needed to increase productivity and reduce costs. This research designs an automatic teabags machine that utilizes PLC to control various components in the production process, including grinding machine, mixer machine, and oven machine. The research method includes designing PLC hardware and software schematics, testing components, and analyzing test result data. The results show that the PLC system is effective in controlling the production process, improving efficiency and product quality, and reducing the involvement of human labor. The conclusion of this research is that the use of PLC in the moringa teabag production automation system offers an efficient solution, the speed of the ac motor can be adjusted through giving the setting value of PWM which affects the motor terminal voltage (Vt), the greater the PWM setting value, the higher the number of motor rotations (rpm). Vice versa, the smaller the PWM setting value, the lower the number of motor revolutions (rpm).

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