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Edi Sutoyo
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Indonesian Scientific Journal (Jurnal Ilmiah Indonesia) Jl. Pasar Atas No 3, Kompleks Setramas Kota Cimahi, Bandung
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INDONESIA
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 137 Documents
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Classification of Crystallization Images of Pharmaceutical Raw Materials Using Convolutional Neural Network Algorithm Yudhana, Anton; Reski, Julia Mega
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1440

Abstract

The rapid advancement of artificial intelligence (AI) has opened new opportunities for automation in the pharmaceutical industry, particularly in the classification of raw drug materials. Manual classification methods are time-consuming and prone to human error, highlighting the need for reliable automated solutions. This study applied a deep learning approach for classifying crystallization images of pharmaceutical raw materials using a Convolutional Neural Network (CNN). A dataset of 300 crystallization images of Nicotinamide and Ferulic Acid was obtained through hot-stage microscopy, preprocessed with normalization, resizing, and augmentation, and divided into training, validation, and testing subsets. The CNN model was trained for 10 epochs and evaluated using a confusion matrix and standard performance metrics (accuracy, precision, recall, and F1-score). The model achieved perfect recall for Ferulic Acid and 90% recall with 100% precision for Nicotinamide, resulting in an overall accuracy of 95%. While these results are promising, the relatively small dataset may limit generalization, and further validation with larger or external datasets is required. The findings indicate that CNN-based methods hold strong potential for automating crystallization classification, improving pharmaceutical quality control, and reducing reliance on manual assessment, in line with recent advances in medical and pharmaceutical image analysis.
Analysis Of A Deep Learning Algorithm For Fracture Detection In X-Ray Images Zebada, Alana Mulya; Pamungkas, Endang Wahyu
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1451

Abstract

Identifying bone fractures in X-ray images is a complex task that requires special expertise from radiologists and can be time-consuming in clinical workflows. Deep learning offers a significant automated diagnostic solution to improve accuracy and efficiency. This study aims to analyze the performance of three Convolutional Neural Network (CNN) architectures namely, ResNet50, DenseNet169, and EfficientNet-B3 and specifically compare the performance of models trained using augmented data with that of models trained without augmentation. The research method utilizes a local dataset, which is divided equally between the fractured and non-fractured classes. Preprocessing techniques such as Contrast Limited Adaptive Histogram Equalization (CLAHE) were applied, and the models were evaluated on a separate test set (hold-out test set). Model evaluation was conducted using accuracy, precision, recall, F1-score, ROC-AUC metrics, as well as analysis through confusion matrix, classification report, sensitivity, specificity, and calibration curve to assess overall performance. The experimental results show that the application of data augmentation consistently improves the accuracy and robustness of all three models. In the augmentation scenario, EfficientNet-B3 showed the best performance, achieving an accuracy of 93.33%. This study concludes that the combination of the EfficientNet-B3 architecture with the data augmentation strategy is the most optimal and recommended approach for developing a reliable automatic detection system on local X-ray image datasets.
Geospatial Model Optimization for Mapping Social Vulnerability to Natural Disasters Using Fuzzy Geographically Weighted Clustering and Flower Pollination Algorithm Istiawan, Deden; Wulandari, Ratri; Ustyannie, Windyaning
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1452

Abstract

This study analyzes social vulnerability to natural disasters in Indonesia through a geospatial optimization model integrating Fuzzy Geographically Weighted Clustering (FGWC) with the Flower Pollination Algorithm (FPA). The hybrid FGWC–FPA enhances clustering accuracy by optimizing spatial parameters and addressing the limitations of index-based and non-spatial methods. The model tested two to four clusters, with the optimal configuration producing four distinct vulnerability groups. Cluster 1 (114 districts) exhibits high poverty, weak infrastructure, and low literacy; Cluster 2 (79 districts) reflects demographic pressure and gender-related inequality; Cluster 3 (87 districts) shows low education and poor disaster preparedness; while Cluster 4 (234 districts) represents health- and age-related vulnerability. A comparison with the 2024 Indonesian Disaster Risk Index (IRBI) shows strong spatial consistency, especially in high-risk regions such as Papua, Maluku, and Sulawesi. The FGWC–FPA model provides finer spatial granularity, allowing the identification of region-specific social issues not captured by deterministic index approaches. The findings validate national disaster risk patterns and offer complementary insights for implementing the National Disaster Management Master Plan (RIPB) 2020–2044, supporting regional prioritization, resource allocation, and capacity-building strategies.
Developing a Delphi Validated Instrument for Assessing Digital Forensics Readiness Based on COBIT 2019 Rochmadi, Tri; Fadlil, Abdul; Riadi, Imam
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1453

Abstract

The increasing complexcity of cyber threats has reinforced the need for robust digital forensic readiness in higher education institutions. However, existing frameworks often lack integration between forensic capabilities and IT governance practices. Objective: This study aims to develop and validate a new instrument to assess digital forensic readiness based on the COBIT 2019 framework. Methods: A three-round Delphi process was conducted with seven digital forensics and IT governance experts to develop and validate a new instrument comprising forty proposed indicators across six domains. Result : The instrument achieved full context, with  I-CVI values increasing from 0.60 to 0.99 and IQR values reaching  1.00 across all items. Implications: The validated instrument integrates governance and forensic principles, providing a standardized tool for institutional self-assessment and policy development, while contributing methodologically through the use of a structured Delphi validation process.
A Performance Enhancement Strategy for Sentiment Classification Models On Political Social Media Using Hyperparameter Tuning And Boosting Chan, Andi Supriadi; Husna, Meryatul; Putra, Pandu Pratama
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1455

Abstract

This study aims to develop an optimized machine learning-based sentiment classification model for election-related issues. A dataset comprising 10,001 entries was collected from the social media platform X and manually labeled into three sentiment classes: positive, negative, and neutral. The preprocessing stage involved text cleaning, stemming, and feature transformation using the Term Frequency-Inverse Document Frequency (TF-IDF) method. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was employed. Three baseline classification algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB)—were initially evaluated to establish a performance benchmark. Model development proceeded by applying hyperparameter optimization using the Optuna framework and further enhancing the models via boosting with Extreme Gradient Boosting (XGBoost). Experimental results revealed that the combination of SVM with Optuna and XGBoost achieved the best performance, reaching 97% accuracy, precision, recall, and F1-score across all classes. In contrast, the KNN and GNB models experienced a notable decline in performance following hyperparameter tuning, although partial recovery was observed when combined with boosting. These findings suggest that hyperparameter tuning and boosting are not universally effective across all classifiers, yet their synergistic application significantly enhances performance in SVM-based models. This study highlights the importance of model-specific optimization strategies in building robust sentiment analysis systems, particularly for handling unbalanced public opinion data in social media contexts.
Sentiment Analysis of Tokopedia Customer Reviews Using BiLSTM and IndoBERT with Comparative Analysis of Preprocessing and Labeling Methods Anadra, Rahmi; Wijayanto, Hari; Sadik, Kusman
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1458

Abstract

This study addresses key challenges in Indonesian sentiment analysis related to preprocessing, labeling strategies, and class imbalance. It compares the performance of BiLSTM and IndoBERT using user reviews collected from Tokopedia. The dataset was manually and automatically labeled, then processed under three preprocessing schemes. Both models were trained with tuned hyperparameters and imbalance-handling techniques and evaluated through twenty rounds of stratified five-fold cross-validation. Performance was assessed using balanced accuracy and F1-score. IndoBERT achieved the highest results, with balanced accuracy up to 0.85 and F1-scores up to 0.83, while BiLSTM reached balanced accuracy up to 0.78 and F1-scores up to 0.76. Applying class weight and focal loss improved model performance by approximately 2% to 11% over the baseline. BiLSTM demonstrated greater training efficiency, requiring only 1 to 2.5 minutes per epoch, compared with IndoBERT’s 2.6 to 3.6 minutes. Although manual labeling remained superior in capturing contextual nuance and emotional cues, GPT-based labeling showed strong agreement with the human annotations. A four-way ANOVA revealed that all main factors and several interactions significantly influenced classification outcomes. Overall, BiLSTM provides faster training efficiency, whereas IndoBERT delivers higher predictive accuracy.
XGBoost Model Optimization Using PCA for Classification of Cyber Attacks on The Internet of Things Ramadan, Afrijal Rizqi; Hariyadi, Mokhamad Amin; Almais, Agung Teguh Wibowo
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The rapid expansion of the Internet of Things (IoT) ecosystem has increased its susceptibility to cyberattacks, creating a critical need for reliable Intrusion Detection Systems (IDS). However, IDS performance is often hindered by severe class imbalance, high-dimensional features, and similarities among attack behaviors. This study proposes an optimized XGBoost model enhanced with the Synthetic Minority Over-sampling Technique (SMOTE) and Principal Component Analysis (PCA) to address these challenges. A systematic grid-search procedure was employed to ensure transparency, reproducibility, and optimal hyperparameter selection. The original imbalance ratio of approximately 1:27 was successfully normalized to nearly 1:1 through SMOTE. The Gotham dataset used in this study consists of roughly 350,000 IoT traffic records across eight attack categories. Five data-splitting scenarios (50:50 to 90:10) were evaluated using stratified hold-out validation supported by k-fold cross-validation. The optimized model achieved 99.68% accuracy, while extremely high AUC values approaching 1.0 were carefully validated to eliminate potential data leakage. Naive Bayes, Logistic Regression, Support Vector Machine, and Deep Neural Network were included as baseline comparisons. The results demonstrate that combining SMOTE and PCA significantly improves model stability and generalization on imbalanced IoT traffic, confirming the effectiveness of the proposed XGBSP method.

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