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All Journal Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen International Journal of Artificial Intelligence in Medical Issues
Tarigan, Thomas Edyson
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Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Engineering Health Professions Medicine & Pharmacology Public Health

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Performance Metrics of AdaBoost and Random Forest in Multi-Class Eye Disease Identification: An Imbalanced Dataset Approach Tarigan, Thomas Edyson; Susanti, Erma; Siami, M. Ikbal; Arfiani, Ika; Jiwa Permana, Agus Aan; Sunia Raharja, I Made
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 2 (2023): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v1i2.98

Abstract

This study presents a comprehensive evaluation of AdaBoost and Random Forest Classifier algorithms in the classification of eye diseases, focusing on a challenging scenario involving an imbalanced dataset. Eye diseases, particularly Cataract, Diabetic Retinopathy, Glaucoma, and Normal eye conditions, pose significant diagnostic challenges, and the advent of machine learning offers promising avenues for enhancing diagnostic accuracy. Our research utilizes a dataset preprocessed with Canny edge detection for image segmentation and Hu Moments for feature extraction, providing a robust foundation for the comparative analysis. The performance of the algorithms is assessed using a 5-fold cross-validation approach, with accuracy, precision, recall, and F1-score as the key metrics. The results indicate that the Random Forest Classifier outperforms AdaBoost across these metrics, albeit with moderate overall performance. This finding underscores the potential and limitations of using advanced machine learning techniques for medical image analysis, particularly in the context of imbalanced datasets. The study contributes to the field by providing insights into the effectiveness of different machine learning algorithms in handling the complexities of medical image classification. For future research, it recommends exploring a diverse range of image processing techniques, delving into other sophisticated machine learning models, and extending the study to encompass a wider array of eye diseases. These findings have practical implications in guiding the selection of machine learning tools for medical diagnostics and highlight the need for continuous improvement in automated systems for enhanced patient care.
Penggunaan Metode SAW dan AHP dalam Penilaian Kinerja Pegawai untuk Pemberian Penghargaan Sudarmanto, Sudarmanto; Subiyantoro, Cuk; Tarigan, Thomas Edyson; Sumiyatun, Sumiyatun
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 22 No 3 (2024): September 2024
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v22i3.147

Abstract

Pengelolaan kinerja pegawai adalah aspek kunci dalam menjaga produktivitas dan kualitas kerja dalam institusi, dalam hal ini institusi pendidikan seperti UTDI. Dalam lingkungan kerja yang sangat kompetitif dan dinamis, penting bagi institusi untuk mengidentifikasi, mengakui, dan memberikan penghargaan kepada pegawai yang telah memberikan kontribusi yang luar biasa terhadap pencapaian tujuan. Untuk mencapai hal ini, seringkali menggunakan metode penilaian kinerja pegawai. Metode menentukan pemberian penghargaan kepada pegawai yang memiliki kinerja luar biasa, digunakan pendekatan gabungan yang terdiri dari metode Simple Additive Weighting (SAW) dan Analytic Hierarchy Process (AHP). Metode SAW digunakan untuk menghitung skor kinerja pegawai dengan memberikan bobot pada kriteria penilaian. Bobot ini didasarkan pada perbandingan pentingnya masing-masing kriteria. Penilaian kinerja menggunakan metode AHP dan SAW memberikan hasil yang sistematis dan dapat dipertanggungjawabkan. Visualisasi yang disajikan membantu memperkuat pemahaman terhadap hubungan antar kriteria dan bagaimana kinerja pegawai dinilai secara keseluruhan. Metode ini sangat bermanfaat dalam konteks penilaian multi-kriteria di berbagai organisasi.
Ensemble Learning Using KNN and Decision Tree for Virus Infection Classification in Mouse Study Dataset Wahyu Murdiyanto, Aris; Tarigan, Thomas Edyson; Zein, Hamada
International Journal of Artificial Intelligence in Medical Issues Vol. 3 No. 1 (2025): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v3i1.359

Abstract

In this study, we propose an ensemble learning approach to classify viral infection presence in mice using the Mouse Viral Infection Study Dataset. The dataset includes two numerical features—volumes of two administered medications—and a binary label indicating viral presence. To improve prediction performance, we combined K-Nearest Neighbor (KNN) and Decision Tree (DT) classifiers within a soft voting ensemble framework. Standardization was applied as a preprocessing step to ensure fair feature contribution, especially for the distance-sensitive KNN. The ensemble model underwent hyperparameter optimization using GridSearchCV with 5-fold cross-validation to fine-tune the number of neighbors for KNN and depth-related parameters for DT. The experimental results demonstrated that the ensemble classifier achieved perfect performance, with 100% accuracy, precision, recall, and F1-score on the test set. The confusion matrix showed no misclassifications, and the Receiver Operating Characteristic (ROC) curve achieved an Area Under Curve (AUC) of 1.00, indicating excellent separability between classes. These results suggest that the proposed ensemble effectively leverages the strengths of both KNN and DT, making it suitable for biomedical classification tasks where interpretability and reliability are critical. Although the model performed exceptionally well, the simplicity of the dataset, including balanced classes and clear feature boundaries, may have contributed to the ideal performance. Thus, while the findings are promising, further validation is necessary using more complex or noisy datasets. This study contributes a practical, interpretable, and effective ensemble learning framework for binary classification problems in experimental virology, and opens pathways for further research in preclinical biomedical data analytics using hybrid classification systems.
Co-Authors Agus Aan Jiwa Permana Aris Wahyu Murdiyanto Cuk Subiyantoro, Cuk Erma Susanti Hamada Zein Ika Arfiani Siami, M. Ikbal Sudarmanto Sudarmanto, Sudarmanto Sumiyatun Sunia Raharja, I Made