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All Journal Journal Of Artificial Intelligence And Software Engineering JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Fathinah, Nadiva Azro
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Computer Science & IT

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Implementasi Certainty Factor dalam Sistem Pakar untuk Mendiagnosis Penyakit pada Kelapa Sawit Fathinah, Nadiva Azro; Suryani, Suryani; Desiani, Anita
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 3 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i3.11886

Abstract

Diseases in palm oil plants are one of the main causes of palm oil production not being maximized, and can even result in crop failure. Farmers need to know the symptoms that occur in oil palm plants in order to diagnose and overcome the diseases that infect the palm oil plants. A system for early detection of disease in palm oil plants is needed in order to prevent a decrease in productivity. An approach that can be used for early diagnosis is an expert system. Expert systems not only provide a diagnosis, but also offer an explanation of the type of disease as well as practical and accurate treatment recommendations. This research applies one of the methods of the certainty factor method to an expert system that combines several symptoms to determine how likely a diagnosis is. This expert system involves 22 symptoms to diagnose six diseases in palm oil plants. The accuracy rate obtained from the application of the expert system with the certainty factor method in diagnosing diseases of oil palm plants based on data from five users shows a result of 100%. This shows that the expert system with the certainty factor method is accurate and can be applied to early detection of diseases that attack palm oil plants.
Comparison of Adaptive Boosting and Categorical Boosting in Heart Attack Diagnosis Amran, Ali; Suryani, Suryani; Fathinah, Nadiva Azro; Desiani, Anita; Ramayanti, Indri
Journal of Artificial Intelligence and Software Engineering Vol 6, No 1 (2026): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i1.9051

Abstract

Heart disease is one of the leading causes of death worldwide, and therefore, accurate early detection methods are needed to help reduce mortality rates. One approach that can be applied is machine learning using classification techniques based on ensemble boosting algorithms. This study aims to compare the performance of two ensemble algorithms, namely Adaptive Boosting (AdaBoost) and Categorical Boosting (CatBoost), in classifying heart attack disease. The labels used in this study are positive and negative. The evaluation process was conducted using two testing techniques: percentage split with a ratio of 80% training data and 20% testing data, and 10-fold cross-validation. Model performance was evaluated based on accuracy, precision, and recall to comprehensively measure classification capability. The results show that in the percentage split method, CatBoost achieved the highest accuracy of 98.88%, while in k-fold cross-validation it reached 98.43%. Nevertheless, AdaBoost also demonstrated good performance, with all evaluation metrics exceeding 90%. Therefore, the best-performing model in this study is CatBoost with the k-fold cross-validation technique on the heart attack dataset.
Co-Authors Ali Amran Anita Desiani Ramayanti, Indri Suryani Suryani