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INDONESIA
Indonesian Journal of Data and Science
Published by yocto brain
ISSN : -     EISSN : 27159930     DOI : -
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics
IJODAS provides online media to publish scientific articles from research in the field of Data Science, Data Mining, Data Communication, Data Security and Data Representation
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 135 Documents
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Comparison of ResNet-50 and DenseNet-121 Architectures in Classifying Diabetic Retinopathy Yoga Pramana Putra, I Putu Gede; Ni Wayan Jeri Kusuma Dewi; Putu Surya Wedra Lesmana; I Gede Totok Suryawan; Putu Satria Udyana Putra
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.232

Abstract

Introduction: Diabetic Retinopathy (DR) is a vision-threatening complication of diabetes that requires early and accurate diagnosis. Deep learning offers promising solutions for automating DR classification from retinal images. This study compares the performance of two convolutional neural network (CNN) architectures—ResNet-50 and DenseNet-121—for classifying DR severity levels. Methods: A dataset of 2,000 pre-processed and augmented retinal images was used, categorized into four classes: normal, mild, moderate, and severe. Both models were trained using two approaches: standard train-test split and Stratified K-Fold Cross Validation (k=5). Data augmentation techniques such as flipping, rotation, zooming, and translation were applied to enhance model generalization. The models were trained using the Adam optimizer with a learning rate of 0.001, dropout of 0.2, and learning rate adjustment via ReduceLROnPlateau. Performance was evaluated using accuracy, precision, recall, and F1-score. Results: ResNet-50 outperformed DenseNet-121 across all evaluation metrics. Without K-Fold, ResNet-50 achieved 84% accuracy compared to DenseNet-121’s 80%; with K-Fold, ResNet-50 scored 83% and DenseNet-121 81%. ResNet-50 also demonstrated better balance in class-wise classification, with higher recall and F1-score, especially for moderate and severe DR classes. Confusion matrices confirmed fewer misclassifications with ResNet-50. Conclusions: ResNet-50 provides superior accuracy and robustness in classifying DR severity levels compared to DenseNet-121. While K-Fold Cross Validation enhances model stability, it slightly reduces overall accuracy. These findings support the use of ResNet-50 in developing reliable deep learning-based screening tools for early DR detection in clinical practice
Performance Comparasion of DenseNet-121 and MobileNetV2 for Cacao Fruit Disease Image Classification Ariawan, Kadek Rizki; Ekayana, Anak Agung Gde; Indrawan, I Putu Yoga; Winatha, Komang Redy; Setiawan , I Nyoman Anom Fajaraditya
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.233

Abstract

Introduction: Cacao fruit diseases significantly impact cocoa yield and quality in Indonesia. Early detection is critical, yet traditional methods are often time-consuming and error-prone due to the visual similarity of disease symptoms. This study aims to compare the performance of two Convolutional Neural Network (CNN) architectures—DenseNet-121 and MobileNetV2—for automated image classification of cacao fruit diseases. Methods: The dataset comprises 8000 images augmented from 2000 original photos taken in cocoa plantations in Bali, categorized into four classes: fruit rot, fruit-sucking pests, pod borers, and healthy fruits. Both CNN models were trained using the Adam optimizer, a learning rate of 0.001, and a dropout rate of 0.4. The input images were resized to 224×224 pixels. Evaluation metrics included accuracy, precision, recall, and F1-score. Results: DenseNet-121 outperformed MobileNetV2 across all metrics. DenseNet-121 achieved an accuracy of 94.50%, precision of 94.75%, recall of 94.25%, and F1-score of 94.50%. In comparison, MobileNetV2 reached an accuracy of 93.88%. Although MobileNetV2 offered faster training time and lower model complexity, DenseNet-121 demonstrated superior feature extraction and stability, supported by its deeper architecture and greater parameter capacity. Conclusions: DenseNet-121 is more effective than MobileNetV2 in classifying cacao fruit diseases, providing higher accuracy and robustness. Despite requiring more computational resources, it is better suited for developing a web-based cocoa disease detection tool to assist farmers in timely and accurate disease identification.
Implementation of Ensemble Deep Learning for Brain MRI Classification in Tumor Detection Syam, Rahmat Fuadi
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.236

Abstract

Introduction: Brain tumor detection from MRI images is critical for early diagnosis and treatment planning. While individual deep learning models have shown high accuracy in medical image classification, combining multiple models can potentially enhance performance. This study aims to develop an ensemble deep learning framework using ResNet18 and DenseNet121 to improve the accuracy of brain tumor classification. Methods: A dataset of 7,023 brain MRI images categorized into four classes—glioma, meningioma, no tumor, and pituitary tumor—was used. Pre-processing included resizing to 224×224 pixels, normalization, and augmentation (random flipping and rotation). ResNet18 and DenseNet121 models were fine-tuned separately using the Adam optimizer with a learning rate of 0.001. The ensemble method was implemented by averaging the softmax outputs of both models to generate final predictions. Results: When evaluated individually, ResNet18 and DenseNet121 achieved validation accuracies of 97.72% and 97.79%, respectively. The ensemble model significantly outperformed both, reaching a validation accuracy of 99.36%. This result demonstrates that integrating both architectures effectively reduces misclassification and enhances overall robustness. Confusion matrix analysis confirmed high classification accuracy across all four tumor categories. Conclusions: The proposed ensemble deep learning approach successfully leverages the strengths of ResNet18 and DenseNet121, achieving superior classification accuracy for brain tumor detection in MRI images. This method holds promise as a reliable tool in clinical diagnostic workflows. Future research should focus on integrating additional architectures, advanced augmentation strategies, and hyperparameter optimization to further improve performance
Optimizing Javanese Numeral Recognition Using YOLOv8 Technology: An Approach for Digital Preservation of Cultural Heritage Syafie, Lukman; Azis, Huzain; Admojo, Fadhila Tangguh
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.239

Abstract

Introduction: The preservation of Javanese script as part of Indonesia’s cultural heritage is increasingly urgent in the digital era, especially due to declining literacy among younger generations. This study aims to explore the effectiveness of YOLOv8, an advanced object detection algorithm, for recognizing handwritten Javanese numerals to support efforts in cultural digitization and education. Methods: A dataset of 2,790 handwritten Javanese numerals (0–9) was collected from 93 respondents. Each numeral was manually annotated using bounding boxes via the MakeSense.ai platform. The YOLOv8 model was trained using 80% of the data and validated on the remaining 20%. Training was performed in the PyTorch framework with data augmentation techniques to increase robustness. Model performance was evaluated using precision, recall, F1-score, and mean Average Precision (mAP), along with visualization through confidence curves and confusion matrices. Results: The model achieved a high validation precision of 88.3%, recall of 89.1%, and mAP of 0.88 at IoU 0.90. F1-score peaked at a confidence threshold of 0.89, while certain numerals like 'six' and 'nine' achieved near-perfect detection. Visualizations confirmed the model’s ability to accurately classify and localize characters in both training and unseen data. Minor misclassifications occurred between visually similar numerals. Conclusions: YOLOv8 demonstrates high effectiveness in recognizing handwritten Javanese numerals and holds significant potential for digital heritage preservation. Future work should focus on expanding the dataset, improving generalization under varied conditions, and integrating this model into educational tools and augmented reality applications for interactive learning.
Evaluating Machine Learning Approaches: A Comparative Study of Random Forest and Neural Networks in Grade Classification Sivakumar, Subitha; Venkataraman, Sivakumar
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.240

Abstract

Introduction: Accurate grade classification in education is essential for early intervention and performance assessment. This study presents a comparative analysis of Random Forest and Neural Networks in classifying student grades using a dataset of 2,392 high school students. The aim is to evaluate both models’ predictive performance and interpretability in an educational data mining context. Methods: The dataset, containing academic and demographic features, was pre-processed by handling missing values, encoding categorical variables, and scaling numerical features. Grades were categorized into five classes: A, B, C, D, and F. Both models were implemented using Python and evaluated with metrics including accuracy, precision, recall, and F1-score. Hyperparameter tuning was performed via Grid Search with cross-validation to optimize performance. Results: The Random Forest model achieved a baseline accuracy of 70.2%, outperforming Neural Networks at 69.1%. After tuning, Random Forest improved to 71.45% accuracy, while Neural Networks reached 70.49%. Both models demonstrated strong precision and recall in identifying failing students (class F), with F1-scores of 0.90 and 0.89, respectively. However, classification of mid-range grades (A to D) remained challenging due to class overlap. Feature importance analysis highlighted interpretability advantages in the Random Forest model. Conclusions: Both models are effective for grade classification, with Random Forest offering slightly better accuracy and interpretability. Neural Networks, while slightly less accurate, capture nonlinear relationships effectively post-tuning. The results suggest that model selection should be guided by context-specific needs, balancing performance with transparency. Future work may include ensemble techniques and expanded feature sets to improve classification robustness.
Design and Build an Automatic Spraying System for Shallot Plants using Soil Moisture and Air Temperature Sensors with the Fuzzy Method Abdul Rachman Manga'; Dedy Atmajaya; Amaliah Faradibah
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.213

Abstract

Agriculture utilizes biological resources to produce food, industrial raw materials, energy sources, and manage the environment. This sector plays a strategic role in national economic development. This research aims to design an automatic spraying system for shallot plants based on soil moisture using soil moisture sensors. This system utilizes soil moisture sensors to detect the water content in the soil as well as soil moisture sensors to measure the air humidity around the plants. Data from both sensors are processed by the microcontroller to regulate the timing and duration of the spraying. The prototype of this system was built using soil moisture sensors, soil moisture sensors, microcontrollers, water pumps, solenoid valves, and other supporting components. Testing was conducted in the field with red onion plants as the test subjects. The results show that the system is capable of functioning effectively in watering plants based on soil moisture levels. The sensor works accurately in measuring water content, while the microcontroller successfully controls the spraying optimally. The implementation of this system has proven to increase water usage efficiency and support better growth of red onion plants. Thus, this automatic spraying system offers an environmentally friendly and efficient solution for irrigation based on soil moisture and soil moisture sensors.
Performance Comparison of MobileNet and EfficientNet Architectures in Automatic Classification of Bacterial Colonies Wahyudi, I Putu Alfin Teguh; Sudipa, I Gede Iwan; Libraeni, Luh Gede Bevi; Radhitya, Made Leo; Asana, I Made Dwi Putra
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.218

Abstract

Bacterial colony classification is crucial in microbiology but remains labor-intensive and time-consuming when performed manually. Deep learning, particularly Convolutional Neural Networks (CNNs), enables automated classification, improving accuracy and efficiency. This study compares MobileNetV2 and EfficientNet-B0 for bacterial colony classification, evaluating the impact of data augmentation on model performance. Using the Neurosys AGAR dataset, preprocessing techniques such as histogram equalization, gamma correction, and Gaussian blur were applied, while data augmentation (rotation, noise addition, luminosity adjustments) improved model generalization. The dataset was split (80% training, 20% testing), and models were trained with learning rates (0.0001, 0.001) and epochs (100, 150, 200). Results show EfficientNet-B0 outperforms MobileNetV2, achieving higher validation accuracy and stability, with optimal performance at 150–200 epochs and a lower learning rate (0.0001). Data augmentation significantly improved accuracy and reduced overfitting. While MobileNetV2 remains a lightweight alternative, its performance is heavily reliant on augmentation. These findings highlight EfficientNet-B0 as the superior model, supporting the automation of microbiological diagnostics. Future research should explore hybrid CNN architectures, Vision Transformers (ViTs), and real-time implementation for improved classification efficiency.
Yoga Posture Recognition and Classification Using YOLOv5 Maqbullah, Afwatul; Handayani, Anik Nur; Kurniawan, Wendy Cahya
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.228

Abstract

Yoga, a centuries-old health practice from India, has gained global recognition for its benefits to physical, mental, and emotional well-being. However, incorrect execution of yoga poses can lead to injuries or diminished results. This research develops an automated system for recognizing and classifying yoga postures using YOLOv5, a state-of-the-art deep learning algorithm. YOLOv5, part of the YOLO (You Only Look Once) series, is designed for real-time object detection and offers enhanced performance through features like anchor-free detection and adaptive training strategies. The study collects a dataset of 1,000 images across 20 yoga pose categories, followed by manual annotation and training using transfer learning. Validation results show strong performance, achieving an accuracy of 90% with precision and recall scores of 0.942 and 0.941, respectively, and mAP@50 and mAP@50-95 values of 0.976 and 0.866. Despite challenges with certain poses showing lower accuracy due to variations in posture and dataset limitations, the model demonstrates robustness in detecting and classifying yoga postures effectively. This system has potential applications in artificial intelligence-driven yoga education, enabling practitioners to train independently with real-time feedback
Assessing Machine Learning Techniques for Cryptographic Attack Detection: A Systematic Review and Meta-Analysis Akwaronwu, Bright; Akwaronwu, Innocent U.; Adeniyi, Oluwabamise J.; Abiodun, Ayodeji G.
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.234

Abstract

The detection of cryptographic attacks is a vital aspect of maintaining cybersecurity, especially as digital infrastructures become increasingly intricate and susceptible to sophisticated threats. This systematic review aims to examine and compare a range of machine learning approaches applied to cryptographic attack detection, focusing on their performance in terms of detection rates, efficiency, and overall effectiveness. A comprehensive review and meta-analysis were conducted, focusing on existing research that utilized machine learning models for identifying cryptographic attacks. The models included in the review were Naïve Bayes, C4.5, Random Forest, Decision Tree, K-Means, and Particle Swarm Optimization (PSO) combined with Neural Networks. Studies were selected based on their relevance to cryptographic security, with particular attention paid to performance metrics like classification accuracy, precision, recall, and area under the curve (AUC). The findings indicated that the C4.5 decision tree model achieved a high classification rate of 98.8%, while both Random Forest and Decision Tree models performed with an accuracy of 99.9%, making them highly suitable for real-time attack detection. Additionally, the PSO + Neural Network model showed enhanced detection precision, illustrating the value of integrating optimization techniques with machine learning models. The use of machine learning, especially with ensemble methods such as Random Forest and Decision Trees, proves to be highly effective for cryptographic attack detection. The study underscores the necessity for customized machine learning solutions in cybersecurity, balancing both high accuracy and operational efficiency. Further research should focus on the real-world deployment of hybrid models to confirm their practical effectiveness.
Performance Comparison of MobileNetV2 and NASNetMobile Architectures in Soybean Leaf Disease Classification I Gede Rian Lanang Oka; Anak Agung Gede Bagus Ariana; Wayan Sauri Peradhayana; Ni Luh Wiwik Sri Rahayu Ginantra; I Ketut Sutarwiyasa
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.243

Abstract

Soybean is one of the essential commodities in Indonesia, commonly used as a raw material for tofu and tempeh, making it highly sought after. However, soybean production has decreased by up to 30% due to disease attacks, necessitating preventive measures. This study aims to compare two Convolutional Neural Network (CNN) architectures, MobileNetV2 and NASNetMobile, in classifying soybean leaf diseases. The models were trained using a leaf image dataset collected directly from agricultural fields and categorized into five classes. The dataset underwent augmentation to increase its size, resulting in a total of 6,000 images, which were then split with an 80:10:10 ratio. The models were trained using the Adam optimizer with a learning rate of 0.001, optimized using ReduceLROnPlateau, and a dropout rate of 0.2 to prevent overfitting. Evaluation results using a confusion matrix indicated that MobileNetV2 performed better with an accuracy of 96.67%, precision of 96.70%, recall of 96.67%, and an F1-score of 96.68%, compared to NASNetMobile, which achieved an accuracy of 86.33%, precision of 86.91%, recall of 86.33%, and an F1-score of 86.40%.

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