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Bulletin of Computer Science Research
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27743659     DOI : -
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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 56 Documents
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Search results for , issue "Vol. 5 No. 4 (2025): June 2025" : 56 Documents clear
Penerapan Information Gain Untuk Seleksi Fitur Pada Klasifikasi Jenis Kelamin Tulang Tengkorak Menggunakan Backpropagation Khair, Nada Tsawaabul; Afrianty, Iis; Syafria, Fadhilah; Budianita, Elvia; Gusti, Siska Kurnia
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.v5i4.637

Abstract

Forensic anthropology and skull analysis play a crucial role in the biological identification of individuals, including sex determination. This study aims to improve the accuracy of gender classification based on skull structure by combining the Information Gain feature selection method with the Backpropagation algorithm. The dataset used is the craniometric data compiled by William W. Howells, consisting of 2,524 samples with 85 measurement features. The preprocessing stage includes data selection, data cleaning, and normalization. Feature selection was conducted using the Information Gain method with three threshold values: 0.01, 0.05, and 0.1, resulting in 79, 46, and 38 selected features, respectively. The model was evaluated using the K-Fold Cross Validation method with K=10 and K=20. The highest accuracy of 93.91% was achieved at the 0.01 threshold using the Backpropagation architecture [79:119:1], a learning rate of 0.01, and K=20. These results demonstrate that feature selection using Information Gain enhances the performance of the Backpropagation model by eliminating irrelevant features and minimizing the risk of overfitting.
Klasifikasi Citra Sampah Botol Plastik Jenis HDPE dan PET Menggunakan Algoritma YOLOv7 Purwasih, Opita; Widhiarso, Wijang; Muhammad Rizky Pribadi
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.v5i4.654

Abstract

The classification of plastic bottle waste, particularly High Density Polyethylene (HDPE) and Polyethylene Terephthalate (PET), remains a challenge in recycling processes due to their similar visual characteristics. Misclassification can lead to a decline in recycled material quality and economic losses in the waste management industry. This research aims to develop an automated image-based classification system to distinguish between HDPE and PET plastic waste using the You Only Look Once version 7 (YOLOv7) object detection algorithm. The dataset consists of plastic bottle images in various physical conditions, annotated with bounding boxes to support model training. The data were split into 70% for training, 20% for validation, and 10% for testing. The best performance was achieved with a batch size of 16 and 100 training epochs, resulting in a precision of 93.9%, recall of 91.6%, and a mean Average Precision (mAP@0.5) of 96.5%. The model demonstrated the ability to accurately classify both types of plastic bottles, even when objects were deformed. These results suggest that the YOLOv7 algorithm is highly capable for implementation in image-based waste classification systems, enhancing sorting efficiency and supporting more sustainable plastic waste management practices.
Analisis Kinerja Random Forest Dalam Deteksi Gejala Alergi Rongga Mulut Berbasis Warna Gusi Gea, Juli Hartati; Agustinus Rudatyo Himamunanto; Haeny Budiati
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.v5i4.657

Abstract

Early detection of allergies in the oral cavity remains challenging due to the subjective nature of visual assessment and limited access to diagnostic facilities. This study proposes a novel approach using the Random Forest algorithm to classify the severity of allergic symptoms based on gum color analysis from digital images. A total of 2,742 gum images were clinically categorized using the Modified Gingival Index (MGI) into mild, moderate, and severe conditions. Preprocessing included conversion to HSV color space and adaptive segmentation using red thresholds on the hue channel (0–10 and 160–180), saturation > 50, and value > 40. Statistical features, including mean, standard deviation, skewness, kurtosis, and entropy, were extracted and normalized using Z-Score. Six parameter combinations were tested with an 80:20 train-test split. The optimal configuration with n_estimators=80, max_depth=9, and min_samples_leaf=2 achieved an accuracy of 95.81%. The highest performance was achieved in the mild class with precision and recall of 98.91%, and stable results in the moderate (93.80%) and severe (94.74%) classes, with only a 0.94% difference. Cross-validation evaluation demonstrated excellent model stability, with an average accuracy of 95.30% and a standard deviation of 0.67%, indicating consistent performance across data subsets. Feature importance analysis showed the dominance of the hue and saturation channels, particularly kurtosis and mean saturation. This study demonstrates that a Random Forest-based allergy detection system using gum color is highly accurate and effective as a non-invasive screening tool in dental and oral health, especially in resource-limited settings, with the potential to improve early screening access in primary healthcare facilities.
Deteksi Penyakit Tanaman Padi (Oryza Sativa L.) Menggunakan Support Vector Machine (SVM) Dan Random Forest Pada Citra Daun Gulo, Bintang Karmila; Agustinus Rudatyo Himamunanto; Jatmika
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.v5i4.660

Abstract

Rice (Oryza sativa L.) is a major food crop that is susceptible to disease attacks, which can reduce farmers' productivity and yields. This study aims to develop a digital image-based rice leaf disease classification system using the Support Vector Machine (SVM) and Random Forest algorithms. The dataset consists of three disease classes (Blast, Blight, and Tungro), which are processed through pre-processing stages such as resizing, normalization, and augmentation. Feature extraction is performed using HSV histograms, RGB average values, and Gray Level Co-occurrence Matrix (GLCM) to obtain color and texture characteristics. The data is then divided with a ratio of 80:20 for model training and testing. The evaluation results show that Random Forest provides the best performance with an accuracy of 97.73%, precision and recall values ??above 0.94, and an average F1 score of 0.98. This study shows that a machine learning-based image classification approach can be an effective solution for early detection of diseases in rice plants.
Penggunaan Convolutional Neural Network NASNetLarge Dalam Klasifikasi Citra Daging Babi dan Sapi Aqilah, M Alfandri; Jasril; Sanjaya, Suwanto; Insani, Fitri
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.v5i4.666

Abstract

The adulteration of beef with pork is a serious issue in Indonesia, particularly for Muslim consumers who are required to consume halal products. According to a Kompas (2020) report, a case of meat adulteration involving 100 kilograms of mixed meat sold as beef was discovered in Tangerang City. This practice not only violates religious laws but also poses threats to public health and consumer trust. To address this challenge, this study adopts a deep learning approach using NASNetLarge for the classification of pork, beef, and mixed meat images. Unlike previous research that utilized EfficientNet-B2 and achieved an accuracy of 98.23%, this study’s NASNetLarge approach produced a comparably competitive accuracy of 98.03%. The dataset used consists of 1,932 images sourced from the Kaggle platform, which were processed through preprocessing and augmentation stages. The data were then split into two distribution scenarios: the entire dataset and a balanced class dataset with 90:10 and 80:20 ratios. Evaluation results show that the best parameter combination was achieved in the first scenario with a 90:10 ratio using augmented images, a learning rate of 0.001, 128 dense units, and the Adam optimizer. The model achieved the highest accuracy of 98.03%, with a precision of 98.63%, recall of 98.40%, and an F1-score of 98.50%. These results indicate that NASNetLarge is effective in accurately and consistently classifying meat images. Image augmentation significantly improved model performance, and the 90:10 data ratio yielded more optimal results compared to 80:20. These findings have the potential to support food surveillance efforts by enabling rapid and accurate detection of meat adulteration.
Perbandingan Akurasi Arsitektur EfficientNet-B0, VGG16, dan Inception V3 Dalam Deteksi Tumor Ginjal Pada Citra CT-Scan Muhammad Fahri; Yanto, Febi; Syafria, Fadhilah; Abdillah, Rahmad
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.v5i4.670

Abstract

Kidney dysfunction can trigger the development of various diseases, including kidney tumors. Early detection of kidney tumors is very important to increase the effectiveness of treatment and the chances of patient recovery. The use of deep learning technology in medical image classification has become a promising approach, especially in detecting abnormalities in the kidney organ through CT-Scan images. This study compares the performance of three Convolutional Neural Network (CNN) architectures, namely EfficientNet-B0, Inception-V3, and VGG16, in detecting kidney tumors. The dataset used was obtained from the kaggle website, namely CT-scan images with normal and tumor classes and divided by a ratio of training  data and test data of 80:20. The hyperparameter used is Stochastic Gradient Descent (SGD) with a learning rate of 0.001 and 0.0001. The evaluation was carried out using a confusion matrix with metrics of accuracy, precision, recall, and F1-score . According to the test outcomes, the VGG16 model configured with a 0.001 learning rate achieved the highest classification performance, recording 99.46% accuracy, precision, recall, and F1-score.
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.
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.

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