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Edi Sutoyo
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International Journal of Advances in Data and Information Systems
Published by Indonesian Scientific Journal
ISSN : -     EISSN : 27213056     DOI : https://doi.org/10.25008/ijadis
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Advances in Data and Information Systems (IJADIS) (e-ISSN: 2721-3056) is a peer-reviewed journal in the field of data science and information system that is published twice a year; scheduled in April and October. The journal is published for those who wish to share information about their research and innovations and for those who want to know the latest results in the field of Data Science and Information System. The Journal is published by the Indonesian Scientific Journal. Accepted paper will be available online (free access), and there will be no publication fee. The author will get their own personal copy of the paperwork. IJADIS welcomes all topics that are relevant to data science, and information system. The listed topics of interest are as follows: Data clustering and classifications Statistical model in data science Artificial intelligence and machine learning in data science Data visualization Data mining Data intelligence Business intelligence and data warehousing Cloud computing for Big Data Data processing and analytics in IoT Tools and applications in data science Vision and future directions of data science Computational Linguistics Text Classification Language resources Information retrieval Information extraction Information security Machine translation Sentiment analysis Semantics Summarization Speech processing Mathematical linguistics NLP applications Information Science Cryptography and steganography Digital Forensic Social media and social network Crowdsourcing Computational intelligence Collective intelligence Graph theory and computation Network science Modeling and simulation Parallel and distributed computing High-performance computing Information architecture
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 177 Documents
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A Proof of Concept for Blockchain-Based Microcredential Verification in Higher Education Fadlan, Muhammad; Noviyantono, Endyk; Muhammad, Muhammad
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1516

Abstract

The risk of counterfeiting and inefficient verification processes poses challenges for the validation of additional microcredential in higher education institutions, where most verification mechanisms remain manual and depend on the issuing institution. To address this issue, a blockchain-based Proof of Concept will be developed to facilitate the verification process of these microcredential. This Research uses an experimental approach by building and testing a prototype system on a limited scale. The proposed solution utilizes blockchain technology with a hash-based verification approach, where only the digital representation of the microcredential is recorded in a smart contract deployed on an Ethereum based blockchain. The testing process for the developed prototype was conducted using microcredential datasets, both original and modified microcredential, as simulations of forgery. The results indicate that the developed blockchain-based system prototype can distinguish genuine from counterfeit microcredential. These findings demonstrate that blockchain-based verification mechanisms have the potential to reduce reliance on manual verification processes. However, this Research is still at the proof of concept stage on a limited scale, and its implementation in a real world environment still requires further testing.
Assesing Digital Evidence Availability in Discord Phishing using ISO/IEC 27037 and Anti-Forensics Analysis Yudhana, Anton; Rivai, Zulki Yanto; Riadi, Imam
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1518

Abstract

Phishing incidents on modern communication platforms pose significant challenges for digital forensics investigations, particularly regarding the availability and preservation of digital evidence. This research aims to evaluate the availability of digital evidence in Discord-based phishing cases by applying the ISO/IEC 27037 framework and interpreting the results from an anti-forensics perspective. This research uses a digital forensics case analysis approach on the victim’s mobile device by following the stages of identification, collection, acquisition, and preservation. The results show that of the five types of digital evidence identified, only 40% can be fully preserved, while 20% are partially preserved, and 40% cannot be preserved. The quantitative evaluation produced an average digital evidence availability score of 0.5, indicating that only half of the expected digital evidence could be retained even though the entire forensics procedure had been systematically applied. These findings confirm that the limitations in the availability of digital evidence are influenced not only by the investigation process but also by the technical characteristics of digital artifacts and the system mechanisms inherent to the Discord platform.
A Multilevel Image Processing Approach for Minangkabau Ornament Detection Using CLAHE, Multi Threshold Otsu, and CNN Wahyuni, Suci; Wiyandra, Yogi; Yenila, Firna
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1520

Abstract

Traditional Minangkabau ornaments such as pucuak rabuang, itiak pulang patang, kaluak paku, and rabuang sanjo represent a form of visual cultural heritage with high aesthetic and philosophical value. However, the digital documentation and preservation of these ornaments still face significant challenges, particularly due to variations in media, fine surface textures, uneven illumination, and complex image backgrounds. These conditions complicate the separation of ornament motifs from the background and consequently affect the accuracy of identification and classification processes. This study aims to develop an image processing approach for the detection and identification of Minangkabau ornaments through image quality enhancement and multi-level segmentation. The proposed method begins with a preprocessing stage that includes motif area cropping, image size normalization, noise reduction through filtering, contrast stretching, and Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance local contrast. Subsequently, segmentation is performed using the Multi-Threshold Otsu method to divide the image into multiple intensity classes, enabling a more detailed separation of ornament structures. The segmentation results are evaluated using morphological analysis and further tested using a Convolutional Neural Network (CNN) to assess classification performance. Experiments were conducted on a dataset of 1,024 images, with a training–testing split of 70% and 30%, respectively. The experimental results demonstrate that the proposed approach produces representative motif segmentation and achieves a classification accuracy of 99.67%. These findings indicate that the integration of systematic preprocessing, multi-threshold segmentation, and CNN-based classification is effective in supporting the digital preservation of Minangkabau ornaments.
Intelligent Decision Support System Based on Deep Learning with the Whale Optimization Algorithm for Oral Cancer Aldo, Dasril; Paramadini, Adanti Wido
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1521

Abstract

To build an accurate and reliable clinical decision support system, this study seeks to create a classification system using deep learning as a better approach in the analysis of oral cancer histopathological images. The dataset used consisted of 10,002 images, of which the two more balanced classes were normal oral and oral squamous cell carcinoma. Some pre-trained deep learning architectures are taken as baseline models and then optimized using the Whale Optimization Algorithm to obtain the best hyperparameter configuration. Performance evaluation was carried out on test data using accuracy, precision, recall, F1-score, confusion matrix, and operational efficiency metrics as well as evaluation of a trust-based decision support system with the same mechanism as the reject option system. The models are better optimized and all models show improved performance. From the results of the experiments, the model that was best optimized with an F1-score and an accuracy of 98.73%, and also showed the best performance, was the EfficientNet B3 model. This is accompanied by a stable training process and adequate generalization. Therefore, the model shows results with adequate performance in the coverage range of 0.60 - 0.90 and still provides a reasonable inference time for use in the clinic. These results show that this model has high potential to be integrated with clinical decision support systems. Therefore, this model can be used as a diagnostic tool in clinics that is more accurate and ensures consistency in each clinical practice and can also build a better diagnostic decision support system.
Application of EfficientNetV2-S Architecture with Focal Loss to Overcome Class Imbalances in Skin Cancer Classification Wati, Marfungah; Thobirin, Aris; Surono, Sugiyarto
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1524

Abstract

Imbalanced class distributions in skin lesion image datasets can reduce the effectiveness of multiclass classification models. This research proposes a classification model based on the EfficientNetV2-S architecture with the application of two-stage training and loss functions that emphasize learning in classes with limited data. The models were trained using on-the-fly image augmentation and evaluated to assess generalization capabilities to the test data. In the initial stage, the model is trained by freezing the backbone and only updating the classifier layer. Next, fine-tuning was carried out on part of the backbone layer to adjust the representation of features to the image characteristics of the skin lesion. Evaluation is conducted through multiple training times with different random initializations to ensure consistency of results. The test results showed that the model experienced an improvement in performance after the fine-tuning process, with an accuracy of about 88% as well as an increase in F1-score values in some classes. Overall, the results indicate that the proposed approach may help improve classification performance when dealing with imbalanced skin cancer image data.
Android Malware Detection with Hybrid Feature Selection and Bayesian Optimization Fadhillah, Muhammad Alif; Saputro, Setyo Wahyu; Muliadi, Muliadi; Faisal, Mohammad Reza; Nugroho, Radityo Adi
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1526

Abstract

The increasing dimensionality of Android application features poses significant challenges for accurate and efficient malware detection. This study proposes a hybrid feature selection framework that combines Minimum Redundancy Maximum Relevance (mRMR) and correlation filtering to optimize classification performance on the Drebin-215 dataset. A selected configuration of 175 features with a correlation threshold of 0.7 was evaluated using five classifiers: LSTM, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), and XGBoost. The experimental results show that dimensionality reduction improves classification stability and overall predictive performance. SVM exhibits the most notable improvement, with accuracy increasing from 63.05% without feature selection to 98.57% after applying the proposed framework. LSTM achieves 98.57% accuracy with an AUC of 99.86%, while Random Forest, KNN, and XGBoost consistently achieve accuracy above 97%. In addition to performance enhancement, the hybrid feature selection approach substantially improves computational efficiency. SVM training time decreases from 770.75 seconds to 155.88 seconds, and testing time is reduced from 15.581 seconds to 0.3824 seconds. KNN testing time also decreases from 1.623 seconds to 0.4595 seconds..
Machine Learning Algorithm for Questionnaire-Based Student Learning Style Classification Yuliansyah, Herman; Lestari, Agung Tri; Yudhana, Anton
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1536

Abstract

Identifying students’ learning styles is an important factor in supporting adaptive and data-driven learning. However, conventional methods based on manual questionnaires still have limitations in terms of efficiency and accuracy for data processing. This study presents a comparative analysis of machine learning algorithms to classify student learning styles based on questionnaire data. The dataset used consists of 1,170 student data with three learning style classes, namely visual, auditory, and kinesthetic. The four supervised learning algorithms used are Naïve Bayes, Decision Tree, Random Forest, and K-Nearest Neighbors. Model performance evaluation was conducted using 5-fold (80:20) and 10-fold (90:10) cross-validation with accuracy, precision, recall, and F1-score metrics. The results of the experiment show that the Naïve Bayes algorithm has the most optimal and stable performance with the highest accuracy value of 90.60% in both validation scenarios. These findings indicate that machine learning-based classification approaches, particularly Naïve Bayes, are effective for identifying student learning styles and have the potential to support the development of adaptive and personalized learning systems.

Page 18 of 18 | Total Record : 177

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