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All Journal ILKOM Jurnal Ilmiah Indonesian Journal of Data and Science
Roesman Ridwan Raja
Kyushu Institute of Technology
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Comparing ECLAT and Decision Tree for Drug Therapy Recommendation Rules on Multi Label Clinical Data Muh Rayhan Fahreza Rayhan; Harlinda; Herdianti Darwis; Roesman Ridwan Raja
Indonesian Journal of Data and Science Vol. 7 No. 2 (2026): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i2.410

Abstract

Introduction: Accurate sorting of plastic waste using Resin Identification Codes (RICs) is essential for improving recycling quality. However, conventional deep learning approaches generally require large labeled datasets, which are difficult and costly to collect for small RIC symbols on plastic packaging. Method: This study employed a Few-Shot Learning approach based on Prototypical Networks using a 7-way 5-shot episodic training configuration. A self-collected dataset of 350 images covering seven RIC categories was used, and three backbone architectures, ConvNet4, ResNet-18, and EfficientNet-B2, were compared. An ablation study evaluated support-set augmentation, followed by supervised fine-tuning of the selected model. Results and Discussion: EfficientNet-B2 achieved the highest episodic accuracy of 93.36%, outperforming ResNet-18 at 88.71% and ConvNet4 at 54.14%. EfficientNet-B2 with light augmentation achieved 85.71% accuracy on the fixed 42-image test set. Most errors occurred between visually similar HDPE and PP symbols. Fine-tuning corrected five of six misclassifications, increasing test accuracy to 90.48% and the F1-score from 0.857 to 0.903. Conclusion: Prototypical Networks with an EfficientNet-B2 backbone and cosine distance provide an effective approach for RIC classification under limited-data conditions and offer a practical foundation for automated plastic-waste sorting systems.
Ensemble Association Analysis on Kalla Toyota’s Employee Data Using Apriori, FP-Growth, and ECLAT Ahmad Zaky; Lilis Nur Hayati; Herdianti Darwis; Roesman Ridwan Raja
ILKOM Jurnal Ilmiah Vol 18, No 2 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i2.2550.221-236

Abstract

The effective management of employee data plays an important role in supporting organizational decision-making, particularly in today’s data-driven business environment. This study examines the identification of association patterns within employee data at Kalla Toyota by applying a combined approach using the Apriori, ECLAT, and FP-Growth algorithms. The dataset includes information such as demographic characteristics, educational background, marital status, and job classification. Prior to analysis, the data were carefully preprocessed to improve consistency and ensure suitability for pattern discovery. Relevant variables were then selected using statistical measures, including Cramér’s V, Kendall’s Tau, and Chi-Square tests, to capture meaningful relationships among attributes. With a minimum support threshold set at 10%, the combined method produced 84 association rules considered significant. These patterns were further explored using visual tools such as network graphs and matrix plots to better understand the relationships between variables. The findings highlight notable connections among factors such as gender, generational groups, job roles, and marital status. These insights may assist the company in refining its human resource strategies, particularly in areas such as recruitment, employee development, and retention. This study shows that combining multiple association rule techniques can provide a more comprehensive understanding of employee data and support more informed decision-making
Co-Authors Ahmad Zaky Harlinda Herdianti Darwis lilis nurhayati Muh Rayhan Fahreza Rayhan