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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 171 Documents
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Classification Of Organic And Inorganic Waste Using Resnet50 Qinantha, I Kadek Mahesa Chandra; Indrawan, I Gusti Agung; Putra, I Putu Satria Udyana; Aristamy, I Gusti Ayu Agung Mas; Willdahlia, Ayu Gede
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.v6i2.267

Abstract

Waste generation, particularly from organic and inorganic sources, has become a growing environmental issue, especially in culturally unique regions like Bali where traditional offerings contribute to organic waste volumes. Despite regulations such as Gianyar Regency Regulation No. 76 of 2023 mandating source-level separation, on-ground implementation remains inconsistent due to low public awareness and operational limitations. This study addresses the challenge by developing an automated image-based classification system using the ResNet50 deep learning architecture to distinguish between organic and inorganic waste. A total of 200 images were collected 100 per class using smartphone cameras, and the dataset was expanded to 1,400 images through geometric data augmentation techniques such as rotation, flipping, and zooming. Images were resized to 224x224 pixels and evaluated using K-Fold Cross Validation to ensure model stability. The model was trained using transfer learning and tested under two conditions with and without augmentation while optimizing hyperparameters such as learning rates (0.0001 and 0.00001) and optimizers (Adam and SGD). The results demonstrate that augmentation significantly enhanced model performance, with the augmented model achieving an average accuracy of 99.25%, precision of 99.32%, recall of 99.25%, and F1-score of 99.25%, compared to 89.88% accuracy in the non-augmented model. These findings confirm that ResNet50, when combined with geometric augmentation and proper preprocessing, offers a robust, accurate, and scalable solution for waste classification tasks. This research contributes to the advancement of AI-driven environmental technologies and offers a potential framework for smart waste management systems, with future directions including real-time deployment, multi-class classification, and expansion to more diverse and real-world datasets.
Comparation Analysis of Otsu Method for Image Braille Segmentation : Python Approaches Wicaksana, Ardi Anugerah; 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.v6i2.268

Abstract

Braille plays a crucial role in supporting literacy for individuals with visual impairments. However, converting Braille documents into digital text remains a technical challenge, particularly in accurately segmenting Braille dots from scanned images. This study aims to evaluate and compare the effectiveness of several classical image segmentation techniques—namely Otsu, Otsu Inverse, Otsu Morphology, and Otsu Inverse Morphology—in enhancing Braille image pre-processing. The methods were tested using a set of Braille image datasets and evaluated based on six quantitative image quality metrics: Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Mean Absolute Error (MAE), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), and Edge Similarity Index (ESSIM). The results show that the Otsu Morphology method achieved the highest PSNR (27.6798) and SSIM (0.5548), indicating superior image fidelity and structural preservation, while the standard Otsu method yielded the lowest MSE (113.3485).These findings demonstrate that applying morphological operations in combination with thresholding significantly enhances the segmentation quality of Braille images, supporting better accuracy in subsequent recognition tasks. This approach offers a practical and efficient alternative to deep learning models, particularly for resource-constrained systems such as portable Braille readers.
YOLOv8 Implementation on British Sign Language System with Edge Detection Extraction Romadlon, Muhammad Rizqi; Anik Nur Handayani
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.v6i2.276

Abstract

This study presents the development and implementation of a deep learning-based system for recognizing static hand gestures in British Sign Language (BSL). The system utilizes the YOLOv8 model in conjunction with edge detection extraction techniques. The objective of this study is to enhance the accuracy of recognition and facilitate communication for individuals with hearing impairments. The dataset was obtained from Kaggle and comprises images of various BSL hand signs captured against a uniform green background under consistent lighting conditions. The preprocessing steps entailed resizing the images to 640 640 pixels, implementing pixel normalization, filtering out low-quality images, and employing data augmentation techniques such as horizontal flipping, rotation, shear, and brightness adjustments to enhance robustness. Edge detection was implemented to accentuate the contours of the hand, thereby facilitating more precise gesture identification. Manual annotation was performed to generate both bounding boxes and segmentation masks, allowing for the training of two model variants: The first is YOLOv8 (non-segmentation), and the second is YOLOv8-seg (segmentation). Both models underwent training over a period of 100 epochs, employing the Adam optimizer and binary cross-entropy loss. The training-to-testing data splits utilized were 50:50, 60:40, 70:30, and 80:20. The evaluation metrics employed included mAP@50, precision, recall, and F1-score. The YOLOv8-seg model with an 80:20 split demonstrated the optimal performance, exhibiting a precision of 0.974, a recall of 0.968, and mAP@50 of 0.979. These metrics signify the model's capacity for robust detection and localization. Despite requiring greater computational resources, the segmentation model offers enhanced contour recognition, rendering it well-suited for high-precision applications. However, the generalizability of the model is constrained due to the employment of static gestures and controlled backgrounds. In the future, researchers should consider incorporating dynamic gestures, varied backgrounds, and uncontrolled lighting to enhance real-world performance.
Classification of Lontara Script Using K-NN Algorithm, Decision Tree, and Random Forest Based on Hu Moments and Canny Segmentation Septiani, Berlian; Hasanuddin, Tasrif; Astuti, Wistiani
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.v6i2.281

Abstract

Lontara script is a traditional writing system of the Bugis-Makassar people in South Sulawesi, used to write the Bugis, Makassar, and Mandar languages. This system is based on an abugida, in which each letter represents a consonant with an inherent vowel. It was once used to record history, customary law, and literature, but its use has declined due to the influence of the Latin alphabet. Today, the Lontara script is preserved through education and digitization as part of the cultural heritage of the Indonesian archipelago. In this article, the researchers attempt to use a dataset of handwritten Lontara Bugis-Makassar characters. The process begins with the collection of character datasets, which are then processed through Canny segmentation and Hu Moment feature extraction to obtain a representation of the shape that is invariant to rotation and scale. The processed data was divided into training and testing data, then classified using the K-NN, Decision Tree, and Random Forest algorithms. The results showed that the KNN algorithm with 6 neighbors achieved the highest accuracy, precision, and recall of 98%. The Decision Tree algorithm achieved an accuracy of 96.67%, precision of 96.22%, recall of 95.33%, and an F1-score of 95.98%. Meanwhile, Random Forest showed an accuracy of 96.67%, precision of 96.34%, recall of 96%, and an F1-score of 95.98%.
Deep Learning-Based Blood Cell Image Classification Using ResNet18 Architecture Edyson Tarigan, Thomas; Prasetyo, Agung Budi; Susanti, Emy
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.v6i2.300

Abstract

The classification of white blood cells (WBC) plays a critical role in haematological diagnostics, yet manual examination remains a labour-intensive and subjective process. In response to this challenge, this study investigates the application of deep learning, specifically the ResNet18 convolutional neural network architecture, for the automated classification of blood cell images into four classes: eosinophils, lymphocytes, monocytes, and neutrophils. The dataset used comprises microscopic images annotated by cell type and is divided into training and validation sets with an 80:20 ratio. Standard pre-processing techniques such as image normalization and augmentation were applied to enhance model robustness and generalization. The model was fine-tuned using transfer learning with pre-trained weights from ImageNet and optimized using the Adam optimizer. Performance was evaluated through a comprehensive set of metrics including accuracy, precision, recall, F1-score, mean squared error (MSE), and root mean squared error (RMSE). The best model achieved a validation accuracy of 86.89%, with macro-averaged precision, recall, and F1-score of 0.8738, 0.8690, and 0.8688, respectively. Lymphocyte classification yielded the highest F1-score (0.9515), while eosinophils posed the greatest classification challenge, as evidenced by lower precision and higher misclassification rates in the confusion matrix. Error-based evaluation further supported the model’s consistency, with an MSE of 0.7125 and RMSE of 0.8441. These results confirm that ResNet18 is capable of learning discriminative features in complex haematological imagery, providing an efficient and reliable alternative to manual analysis. The findings suggest potential for practical implementation in clinical workflows and pave the way for further research involving multi-model ensembles or cell segmentation pre-processing for improved precision
A Comparative Study of Public Opinion on Indonesian Police: Examining Cases in the Aftermath of the Kanjuruhan Football Disaster Purnawansyah, Purnawansyah; Raja, Roesman Ridwan; Darwis, Herdianti
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.v6i2.235

Abstract

This research explores public sentiment towards the Indonesian police using sentiment analysis and machine learning techniques. The study addresses the challenge of understanding public opinion based on social media comments related to significant police cases. The aim is to compare reported satisfaction levels with actual public sentiment. Utilizing the Indonesian RoBERTa base IndoLEM sentiment classifier, comments were analyzed and preprocessed. The classification was conducted using Random Forest (RF) and Complement Naive Bayes (CNB) models, incorporating unigram and bi-gram features. Oversampling techniques were applied to handle data imbalance. The best-performing model, Random Forest with bi-gram features, achieved high evaluation scores, including a precision of 0.91 and accuracy of 0.91. The findings reveal significant insights into public opinion, contributing to improved law enforcement strategies and public trust.
Performance Analysis of Convolutional Neural Networks and Naive Bayes Methods for Disease Classification in Tomato Plant Leaves Nadya Salsabilah; Irawati; Lilis Nur Hayati
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.255

Abstract

Tomatoes are one of the most widely cultivated and consumed crops, but they are highly susceptible to disease attacks. The main diseases that often attack tomato plants are early blight and late blight. This study compares two machine learning-based classification methods, namely Convolutional Neural Network (CNN) and Naïve Bayes, in detecting tomato leaf diseases. The dataset used consists of 1,255 images obtained from Kaggle, which have been processed and divided into three data ratio scenarios (70:30, 80:20, and 90:10) for training and testing. The results showed that CNN is superior to Naïve Bayes, with the highest accuracy reaching 83.01%, while Naïve Bayes only achieved 34%. With better stability and accuracy, CNN has the potential to help farmers detect diseases more quickly and increase agricultural productivity
Classifying Honors Class Eligibility Using SVM Khulfani Hendrawan; Ika Arfiani
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.261

Abstract

Introduction: The honors class program aims to group outstanding students, but an objective data-based classification system is not yet available. This can result in high-potential students going undetected due to the selection process relying on self-registration and causing a lack of student interest. Method: This research uses the Support Vector Machine (SVM) algorithm to classify the eligibility of students to participate in the honors program based on academic and non-academic data. The dataset consists of 453 entries with an imbalanced class distribution, which was then balanced using the SMOTE technique. The model was trained using GridSearchCV to find the optimal parameters and compared with four types of SVM kernels: linear, polynomial, sigmoid, and radial basis function (RBF). Result: The RBF kernel achieved the best performance with an accuracy of 84%, precision of 0.77, recall of 0.84, and an F1-score of 0.79. However, it was found that the precision in the minority class (high-achieving students) is still lower than in the majority class. Conclusion: The SVM model, particularly with the RBF kernel, has proven effective in automating the classification of students for the honors program. However, further improvements are needed to enhance performance on the minority class to make the selection system fairer and more accurate
Automated Waste Image Classification with Weighted Scoring Using MobileNetV2 on the OLSAM Platform Kartika Nur Anggraeni; Arin Yuli Astuti; Ismail Abdurrozzaq Zulkarnain
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.295

Abstract

This study presents the development of an automated waste image classification system for the OLSAM platform to enhance community participation in waste management. The objective is to integrate a lightweight CNN-based classifier with a weighted point calculation mechanism for five waste categories. A dataset of 1,500 images was used, split into 80% training, 10% validation, and 10% testing. The MobileNetV2 architecture was applied to perform image classification, while a weighted reward mechanism assigned points based on the detected waste type and its weight. The model achieved its best performance at epoch 65, reaching an accuracy of 96.67% and a weighted F1-score of 0.97. These results indicate that combining CNN-based recognition with a weighted point system effectively supports user engagement and promotes sustainable waste-sorting behavior within community waste management systems.
Classification of Employee Attendance Categories Using the Gradient Boosted Trees Algorithm Mutia Safitri; Sudin Saepudin; Carti Irawan; Mupaat
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.301

Abstract

Employee attendance is a crucial factor in human resource management as it affects productivity and operational efficiency. However, the recording and analysis of employee attendance often encounter challenges, particularly in terms of the accuracy and effectiveness of the systems used. This study aims to develop an employee attendance classification model using the Gradient Boosted Trees algorithm to improve the accuracy of grouping attendance categories such as Present, Permission, Sick, Leave, and Absent into attendance level categories: High, Medium, and Low. The research method includes collecting employee attendance data throughout the year 2024. The model evaluation is carried out using metrics such as accuracy, precision, recall, and the confusion matrix. The results indicate that the developed model achieves an accuracy of 100.00%, with a mean precision of 100.00% and a mean recall of 100.00%.

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