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All Journal Jurnal Informatika Journal of Data Science and Software Engineering
Andi Farmadi
Lambung Mangkurat University
Computer Science & IT

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Implementasi Metode Haralick dengan Random Forest Classifier untuk identifikasi Penyakit Kentang Pada Citra Daun Muhammad Syahriani Noor Basya Basya; Andi Farmadi; Dwi Kartini; Radityo Adi Nugroho; Rudy Herteno
Journal of Data Science and Software Engineering Vol 3 No 03 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Potato plants are one of the most widely grown food crops in the highlands of Indonesia. Besides being used as food, potatoes are now known to be used to fight free radicals, control blood sugar, and nourish the digestive system. Therefore, potatoes have good prospects for development. In connection with efforts to develop potatoes in Indonesia, there are obstacles, namely the attack of potato plants by disease. As for the disease in potato plants, one of the characteristics of knowing it is on the leaves. To identify the leaf image, the texture feature is an important feature to recognize the leaf from an image. This is because there are differences in texture between normal and diseased leaves. To perform image processing through texture features, one method that can be used is haralick. In this study, a system was created to identify the types of diseases present in potato leaves using the Haralick method with the Random Forest Classifier. The image used is 300 data consisting of 3 classes, namely Late Blight, Early Blight, and Health. In this study, the testing was carried out by dividing the training and testing data with a percentage of 70:30, 80:20, and 90:10. The highest accuracy value in this study was obtained by using a combination of 80:20 split data, which was 0.88. The 70:30 data split gets an accuracy of 0.85 and the 90:10 data split gets an accuracy of 0.87.
Random Forest Dengan Random Search Terhadap Ketidakseimbangan Kelas Pada Prediksi Gagal Jantung Muhammad Ali Abubakar; Muliadi Muliadi; Andi Farmadi; Rudy Herteno; Rahmat Ramadhani
Jurnal Informatika Vol 10, No 1 (2023): April 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v10i1.14531

Abstract

Prediksi keberlangsungan hidup pasien gagal jantung telah dilakukan pada penelitian untuk mencari tahu tentang kinerja, akurasi, presisi dan performa dari model prediksi ataupun metode yang digunakan dalam penelitian, dengan menggunakan dataset heart failure clinical records. Namun dataset ini memiliki permasalahan yaitu bersifat tidak seimbang yang dapat menurunkan kinerja model prediksi karena cenderung menghasilkan prediksi kelas mayoritas. Pada penelitian ini menggunakan pendekatan level algoritma untuk mengatasi ketidakseimbangan kelas yaitu teknik bagging dengan metode Random Forest lalu digabungkan dengan metode Hyper-Parameter Tuning agar kinerja yang dihasilkan menjadi lebih baik. Selanjutnya model dilatih dengan dataset dan dibandingkan dengan metode lain, hasilnya menunjukkan bahwa Random Forest dengan Random Search Hyper Parameter-Tuning mencapai nilai AUC sebesar 0,906 dan untuk model Random Forest tanpa Random Search memperoleh nilai AUC sebesar 0,866. Prediction of the survival of heart failure patients has been carried out in research to find out about the performance, accuracy, precision and performance of the prediction model or method used in the study, using the heart failure clinical records dataset. However, this dataset has a problem, namely being unbalanced which can reduce the performance of the prediction model because it tends to produce predictions for the majority class. This study uses an algorithm level approach to overcome class imbalance, namely the bagging technique with the Random Forest method and then combined with the Hyper-Parameter Tuning method so that the resulting performance is better. Then the model was trained with the dataset and compared with other methods, the results showed that the Random Forest with Random Search Hyper Parameter-Tuning achieved an AUC value of 0,906 and for the Random Forest model without Random Search the AUC value of 0,866 was obtained. 
Co-Authors Dwi Kartini, Dwi Muhammad Ali Abubakar Muhammad Syahriani Noor Basya Basya Muliadi Muliadi Radityo Adi Nugroho Rahmat Ramadhani Rudy Herteno Rudy Herteno