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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 20 Documents
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Search results for , issue "Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science" : 20 Documents clear
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%.
Comparative Analysis of Random Forest and LSTM Models for Customer Churn Prediction Based on Customer Satisfaction and Retention Gegeleso, Babajide; Ebiesuwa, Oluwaseun
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.v6i2.244

Abstract

Forecasting of Customer churn and prediction is important for sustaining long-term customer relationships and enhancing profitability in competitive markets. This study outlines the comparison of the performance of Random Forest (RF) and Long Short-Term Memory (LSTM) models in predicting customer churn using a dataset of 2,850 customers. The dataset comprises of behavioral, transactional, and satisfaction metrics. Key evaluation metrics include accuracy, precision, recall, F1-score, and AUC-ROC. The result clearly shows that while Random Forest offers strong baseline performance with interpretable results, LSTM captures temporal patterns very effectively and performs better in identifying subtle churn indicators, especially in sequential customer satisfaction data. The result of metrics evaluated shows LSTM has an Accuracy of 88.6%,Precision of 85.3%,Recall of 82.5%,F1-score of 83.9% and AUC-ROC of 0.92 while Random Forest has Accuracy of 85.2%,Precision of 81.5%,Recall of 77.0%,F1- Score of 79.2% and AUC-ROC of 0.89. This shows the preference of LSTM for rapidly changing and large volume dataset over RF excellence in less complicated and sparse dataset
Enhanced NER Tagging Model using Relative Positional Encoding Transformer Model Achir, Jerome Aondongu; Abdulkarim, Muhammed; Abdullahi , Mohammed
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.v6i2.245

Abstract

Named Entity Recognition remains pivotal for structuring unstructured text, yet existing models face challenges with long-range dependencies, domain generalisation, and reliance on large, annotated datasets. To address these limitations, this paper introduces a hybrid architecture combining a transformer model enhanced with relative positional encoding and a rule-based refinement module. Relative positional encoding improves contextual understanding by capturing token relationships dynamically, while rule-based post-processing corrects inconsistencies in entity tagging. After being evaluated on the Groningen Meaning Bank and Wikipedia Location datasets, the proposed model achieves state-of-the-art performance, with validation accuracies of 98.91% for Wikipedia and 98.50% for GMB with rule-based refinement, surpassing existing benchmark research of 94.0%. The relative positional encoding contributes 34.42% to the attention mechanism’s magnitude, underscoring its efficacy in modelling token interactions. Results demonstrate that integrating transformer-based architectures with rule-based corrections significantly enhances entity classification accuracy, particularly in complex and morphologically diverse contexts. This work highlights the potential of hybrid approaches to optimise sequence labelling tasks across domains.
Integration of Yolov8 And Instance Segmentation in The Chinese Sign Language (CSL) Recognition System Wijaya, Mikel Ega; Handayani, Anik Nur
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.v6i2.247

Abstract

This research aims to develop an advanced recognition system for Chinese Sign Language (CSL) by integrating YOLOv8 and instance segmentation techniques. Communication through sign language is essential for the deaf community, and although CSL has been standardized in China, recognizing complex hand movements remains a significant challenge. YOLOv8 is employed for real-time object detection, while instance segmentation is used to provide more detailed analysis of hand gestures. This integration seeks to improve hand gesture recognition under varying lighting and background conditions, which is crucial for more effective communication between the deaf community and the wider society. The study evaluates the system’s performance using common metrics such as Mean Average Precision (mAP), precision, recall, and F1-score. The findings indicate that the non-segmentation model performs better than the segmentation model in terms of precision, recall, and mAP, especially when trained with a larger dataset ratio. The non-segmentation model provides faster and more accurate detection, while the segmentation model, despite using the same amount of data, shows potential for more detailed recognition of gestures. Although the segmentation model shows improvements in the F1-score with more detailed accuracy, the non-segmentation model remains superior in overall detection speed and accuracy. This research highlights the importance of integrating YOLOv8 and instance segmentation for improving CSL recognition, with better results on the non-segmentation model for more effective communication for the deaf
Performance Analysis of Random Forest and Naive Bayes Methods for Classifying Tomato Leaf Disease Datasets Ananda, Rima; Lilis Nur Hayati; Irawati
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.v6i2.252

Abstract

Tomato productivity is often disrupted by diseases affecting tomato plants, such as early blight and late blight, which can significantly reduce crop yields. Early detection of these diseases is crucial to prevent greater losses. This study compares two machine learning-based classification methods, namely Random Forest and Naïve Bayes, in identifying diseases on tomato leaves. The dataset used consists of 1,255 images obtained from Kaggle, with the data divided into two classes: early blight with 627 images and late blight with 628 images, which then underwent preprocessing and data splitting with three ratio scenarios (70:30, 80:20, and 90:10) for training and testing. This study shows that it only achieved an accuracy of 76.98%, while the Random Forest method had the highest accuracy of 92.86% in the 90:10 data ratio scenario. Thus, the Random Forest method proves to be more effective in classifying tomato leaf diseases compared to Naïve Bayes. The implementation of this model can help farmers detect diseases more quickly and accurately, thereby increasing agricultural productivity.
Optimization of Nglegena Javanese Script Recognition With Machine Learning Based on Zoning And Normalization of Feature Extraction Graciello, Manuel Tanbica; Handayani, Anik Nur; Wibawa, Aji Prasetya
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.v6i2.256

Abstract

Machine learning offers promising solutions for the recognition of handwritten Javanese Nglegena script, which is crucial for preserving Indonesia's cultural heritage. This study explores the application of several supervised learning algorithms-K-Nearest Neighbors (KNN), Naïve Bayes, Decision Tree, and Random Forest-for classifying handwritten images of Nglegena Javanese script. Feature extraction is performed using a zoning technique, where each character image is divided into multiple zones (16, 25, 36, and 64) to capture local details. The extracted features are further processed using normalization methods, including Min-Max, Z-Score, and Binary normalization, to ensure uniform data distribution. The dataset, consisting of 600 images representing Javanese Nglegena characters, is split into training and testing sets using various ratios. Experimental results show that the combination of Naïve Bayes classification, 36-zone feature extraction, and Min-Max or Z-Score normalization achieves the highest accuracy of 65%. These findings demonstrate that optimizing zoning and normalization can significantly enhance the accuracy of machine learning models for Javanese script recognition. The research contributes to developing Optical Character Recognition (OCR) technology for Javanese script, supporting the digital preservation of Indonesia's historical and cultural heritage.

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