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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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Analisis Penjualan Frozen Food Yang Paling Banyak Diminati Menggunakan Metode K-Means Dwi Nopriyani; Hetty Rohayani; Zulfikri Akbar
Bulletin of Computer Science Research Vol. 5 No. 1 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i1.445

Abstract

Today's interest in frozen foods is very high, as frozen foods are easy to process and they can last long to store. So many households are buying frozen food for daily necessities. The purpose of this study is to know how much the purchase rate of products consumers often buy so that goods stock providers can focus more on the products that are most in demand, by using the K-Means method and by using the RapidMiner app. Where the results of the study can provide information to the business owner Frozen Food what production should be increased so that the business owner can increase the sales of Frozen Food.
Penerapan Algoritma Naive Bayes Untuk Sistem Klasifikasi Status Gizi Bayi Balita Abas, Mohamad Ilyas; Lamusu, Rizal; Pranata, Widya Eka; Syahrial, Syahrial; Ibrahim, Irawan; Hasyim, Wahyudin; Kiayi, Verliana
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.508

Abstract

Infants and toddlers are in a critical period of rapid growth and development, often referred to as the "golden age." During this stage, regular nutritional assessments are essential to monitor health status and detect potential nutritional problems early. This study aims to classify the nutritional status of infants and toddlers using the Naïve Bayes algorithm, a probabilistic classification method based on Bayes' theorem with a strong assumption of attribute independence. The main attributes used in the classification system include age, weight, and height. The dataset consists of 700 records of infants and toddlers collected from previous observations. The results show that the Naïve Bayes algorithm can be effectively implemented for nutritional status classification, achieving a system accuracy of 88.14%. This indicates that the method performs well and has the potential to be utilized in decision support systems for child health monitoring.
Klasifikasi Penyakit Jamur Pada Tanaman Tomat dengan Algoritma SVM Sri Rahayu, Eka; Anugrah Ade Purnama, Oktaviana; Zakaria, Hadi; Rosyani, Perani
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.515

Abstract

Diseases in tomato plants, such as mosaic virus and yellow leaf curl virus, can significantly reduce crop yields. Therefore, early detection based on artificial intelligence (AI) presents a strategic solution to improve the efficiency of plant disease identification. This study aims to develop and evaluate a classification model using Support Vector Machine (SVM) for the automatic and accurate detection of tomato leaf diseases. SVM is selected as the primary classification method due to its ability to handle high-dimensional data with better computational efficiency compared to Convolutional Neural Network (CNN) and Random Forest. The dataset used is the PlantVillage Tomato Leaf Dataset from Kaggle, consisting of 600 images categorized into three classes: healthy tomato leaves, leaves affected by mosaic virus, and leaves affected by yellow leaf curl virus. The research stages include data preprocessing such as image normalization, dataset splitting (80% training, 20% testing), and undersampling to address class imbalance. The SVM model is trained using various kernels and evaluated using accuracy, precision, recall, and F1-score metrics. The results show that the SVM model achieves an accuracy of 98.33%, demonstrating its effectiveness in detecting tomato plant diseases. Therefore, this model can be implemented in smart agriculture systems to enhance early disease detection and assist farmers in optimizing crop yields.
Implementasi Algoritma Random Forest untuk Analisis Sentimen Ulasan Pengguna Aplikasi Merdeka Mengajar Jumaryadi, Yuwan; Meiyanti, Ruci; Fajriah, Riri; Mahsyar, Athiyyah Nisrina; Anggraeni, Puspita Sari
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.530

Abstract

Education plays a major role in determining the quality of human resources. The role of teachers is very important as educators who provide guidance and learning. As an effort to facilitate teachers to carry out their duties and responsibilities, especially in the Merdeka Mengajar curriculum, the Ministry of Education and Culture has developed an application called Merdeka Mengajar. However, there is no method to classify sentiment or opinions from comment data on the Merdeka Mengajar application user satisfaction survey on the Google Playstore, in order to determine the extent of user satisfaction with the Merdeka Mengajar application. This study aims to observe sentiment analysis regarding user opinions on the Merdeka Mengajar application on the Google Playstore using the Random Forest, SVM and Naïve Bayes algorithms using TF-IDF weighting for the classification process. This study uses secondary data derived from user reviews of the Merdeka Mengajar application and is classified using the Random Forest, SVM, and Naïve Bayes methods. The results of the classification show that the Random Forest algorithm is the best algorithm in predicting Merdeka Mengajar application user reviews compared to Naive Bayes and SVM.
Sistem Pendukung Keputusan Pemilihan Perangkat Umum Persmabimed Berbasis Web dengan Metode Profile Matching Setiawan, Abi; Reza Nur Afdal; Shabrina Prabudi; Debi Yandra Niskah
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.531

Abstract

Persmabimed is an organization under Universitas Negeri Medan that serves as a platform for students receiving the KIP-K scholarship and plays an important role in managing member activities and information. However, the selection process for general board members such as the chairman, secretary, and treasurer is still conducted manually and tends to be subjective, potentially leading to a mismatch between candidates and their assigned roles. This study aims to design and implement a web-based decision support system using the Profile Matching method to assist in the selection process of Persmabimed board members. The method is chosen for its ability to compare candidate profiles with ideal profiles based on GAP values and criterion weights. Data was collected through observation, interviews with organization administrators, and literature studies. The selection process involves calculating core and secondary factors, followed by ranking based on a weighted combination of hard and soft skills. The system’s results demonstrate that the Profile Matching method can produce objective and accurate decisions, selecting Irvan Affandi as chairman, Dinda Rizky Fadilah as secretary, and Kiki Ratna Sari as treasurer. The system was developed using PHP and Bootstrap to ensure accessibility and streamline the selection process. This research improves efficiency and fairness in organizational decision-making and can be applied to similar organizations in the future.
User Interface Design for Doctor Reservation Website using Design Thinking Method Puspa Diah Narendri, Azelia; Cahya Wardhana, Ariq
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.534

Abstract

In the current era of digital transformation, the rapid integration of healthcare services is crucial to meet patient expectations and improve service delivery. Klinik Putri faces challenges such as long queues and difficulties in booking doctor appointments, which negatively impact patient satisfaction. This study aims to design a user-friendly, web-based doctor reservation system using the Design Thinking methodology, which consists of five stages: empathize, define, ideate, prototype, and test. Usability testing was conducted using the System Usability Scale (SUS), a standardized tool for evaluating system usability. The results showed an average SUS score of 83, placing the system in the “acceptable” category, Grade B, and receiving an “excellent” rating according to the Adjective Rating scale. These findings demonstrate that the proposed website design effectively addresses user needs, enhances the user experience, and contributes to improving the efficiency of healthcare services at Klinik Putri.
The Certainty Factor Method in An Expert System for Tuberculosis Disease Diagnosis Kumara, Dimas Maulana Dwi; Linda Perdana Wanti; Purwanto, Riyadi
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.549

Abstract

Tuberculosis is an infection caused by acid-fast bacilli (AFB) and is an infectious disease that can attack anyone through the air. This disease is hazardous and chronic, with a high prevalence among individuals aged 15-35 years. The diagnosis of tuberculosis traditionally takes a long time because it involves an interview process by medical experts and testing sputum samples in the laboratory to determine whether the patient is positive or negative for this disease. This process is not only time-consuming but also requires significant resources. To overcome this problem and speed up the diagnosis process, a technology-based approach is needed, namely the Expert System with the certainty factor method. This method can handle uncertainty in medical diagnosis by providing a certainty value for each observed symptom. This article discusses in depth the application of the certainty factor method in an expert system to diagnose Tuberculosis. By using this method, the system can provide faster and more accurate diagnosis results in diagnosing tuberculosis with a confidence level of 94.6% and reduce the workload of medical personnel. The application of the certainty factor method allows the integration of various symptoms and relevant medical data to produce more precise and reliable diagnostic conclusions.
Evaluating End-to-End ASR for Qur'an Recitation Using Whispers in Low Resource Settings Abdullah Azzam; Ichsan Taufik; Aldy Rialdy Atmadja
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.561

Abstract

This study investigated the use of End-to-End Automatic Speech Recognition (E2E ASR) for Qur'an recitation under low resource conditions using the Whisper model. This study follows the CRISP-DM methodology, starting with defining the research gap and preparing a curated dataset of 200 verses from Juz 30. These verses were chosen because of their short and consistent structure, allowing for efficient experimentation. Audio and transcription pairs are verified and cleaned to ensure alignment and quality. The modeling was done using Whisper in Google Colaboratory, leveraging its pre-trained architecture to reduce training time and computing costs. Evaluations use the Character Error Rate (CER) metric to measure transcription accuracy. The results showed that Whisper achieved an average CER of 0.142, corresponding to a transcription accuracy of about 85%. However, the average processing time per father is 11 seconds, almost double the time it takes for a human readout. Although Whisper provides strong accuracy for Arabic transcription, its runtime efficiency remains a challenge in real-time applications. This research contributes reproducible channels, validated datasets, and performance benchmarks for future studies of the Qur'anic ASR under computational constraints.
Business Intelligence untuk Validasi Desain Karakter Berbasis Budaya pada Game Aventala: “The Lost Tribe” Damastuti, Fardani Annisa; Yoda, Sevtian Bintang; Revindasari, Fony; Kusdianta, Naufal Airlangga
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.563

Abstract

This study focuses on culture-driven character design for Aventala: The Lost Tribe by transforming Indonesian endemic animals and cultural elements into humanoid forms documented in a character sheet. The objective is to formulate and validate a culture-driven character design pipeline via: (i) a personification sheet that maps physiology–fantasy–psychology–sociology, (ii) scene-based moodboards to align tone and persona, and (iii) a user study employing a six-indicator 5-point Likert instrument (mythology, culture, fantasy, naming, traits, and sheet readability) analyzed in a Business Intelligence dashboard. The method combines narrative comprehension (DRTA), qualitative data curation, sheet construction, moodboard development, and an online survey with the target audience. Results show a moodboard satisfaction level of 85.24% and character acceptance ranging from 83% to 86%, indicating coherence across cultural representation, fantasy elements, naming, and traits. These findings suggest the proposed pipeline is effective for evidence-based design, and the personification sheet serves as a practical cross-team artifact to guide iteration decisions.
Prediksi Harga Emas Mengunakan Jaringan Saraf Tiruan Algoritma Backpropagation Yupita Sari; Andri Anto Tri Susilo; Lukman Sunardi
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.566

Abstract

Gold is a precious metal with high value that is often used as an investment commodity due to its stability and tendency to increase in price compared to other assets, such as stocks. In the global economy, gold is also an important part of international reserves in national banks. However, public awareness of the benefits of gold investment remains low. One solution to increase interest and understanding of gold investment is to predict gold prices using accurate forecasting techniques. Forecasting utilizes historical data that is analyzed to project future trends, making it an important component in strategic decision-making. This study uses the backpropagation algorithm in artificial neural networks to predict gold prices. This algorithm minimizes errors in the data training process, improves model accuracy, and provides better results in prediction classification. Additionally, this algorithm is efficient in processing large amounts of training data, resulting in a reliable prediction model. The study aims to evaluate the performance of the backpropagation algorithm in predicting gold prices, including comparing the accuracy and correlation of predictions with other algorithms. The results of the study are expected to contribute to the development of a more accurate gold price prediction model, support investment decision-making, and increase public understanding of the benefits of investing in gold. This study successfully developed an Artificial Neural Network (ANN) model to predict gold futures prices based on historical data, including features such as opening price, high, low, and trading volume. The model was trained using the Backpropagation algorithm to capture non-linear patterns in complex data. The research results encompass three main aspects: Data Preprocessing, where data was effectively processed, including converting values to numerical format and normalizing features to accelerate model convergence; Model Training, where the model was trained using 80% of the training data and tested with 20% of the testing data; Monitoring train loss and validation loss shows that the model is learning well, although there are indications of overfitting risk. Evaluation and Prediction: The model is able to predict gold prices with good accuracy on the test data. Evaluation metrics such as MAE (Mean Absolute Error) show that the prediction results are quite close to the actual values, although there is still room for improvement. Overall, this model demonstrates satisfactory performance in predicting short-term gold prices and can be used as a tool in gold price analysis based on historical data.

Page 22 of 33 | Total Record : 329

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