Perbandingan Hasil Prediksi Diagnosis pada Indian Liver Patient Dataset (ILPD) dengan Teknik Supervised Learning Menggunakan Software Orange

Ira Rosianal Hikmah, Ray Novita Yasa

Abstract


The development of the volume of data every day has resulted in the need for data mining to obtain valuable and meaningful data. There are many data mining software that has been developed, both free and paid. One of the free data mining software is Orange. This software provides modeling, both supervised and unsupervised learning. Orange also provides model evaluation features, such as accuracy, precision, the time required for training and testing, specificity, and other evaluation measures. Therefore, Orange makes it easy for users to perform data mining. One of the users who need Orange is a user with a non-IT background, such as a health user who can make predictions for the diagnosis of a disease. Users do not need to focus on syntax to perform data mining. With Orange, healthcare users can easily and faster predict the diagnosis of the disease. This study uses Indian Liver Patient (ILPD) data from the UCI-Machine Learning Repository. The objective of the diagnosis is to determine whether the patient has a liver disorder or not. The methods that are used in this study are Decision Tree, Random Forest, SVM, Neural Network, Naïve Bayes, k-NN, and Logistic Regression. This study evaluates using a confusion matrix, accuracy level, precision level, training time, and testing time. The results show that the time required for training and testing is relatively short. With the data used, this study has proved that the four best methods based on accuracy are Logistic Regression, Neural Network, Random Forest, and Naïve Bayes.

 

 

Perkembangan volume data setiap hari mengakibatkan perlunya data mining untuk mendapatkan data berharga dan berguna. Terdapat banyak data mining software yang telah dikembangkan, baik gratis maupun berbayar. Salah satu data mining software yang gratis adalah Orange. Sofware ini menyediakan pemodelan, baik supervised maupun unsupervised learning. Orange juga menyediakan fitur evaluasi model, seperti akurasi, presisi, waktu yang dibutuhkan untuk training dan testing, spesifisitas, dan ukuran evaluasi lainnya. Oleh karena itu, dapat dikatakan bahwa Orange memudahkan pengguna untuk melakukan data mining. Salah satu pengguna yang membutuhkan Orange adalah pengguna dengan latar belakang non-IT, seperti pengguna bidang kesehatan yang dapat melakukan prediksi untuk diagnosis suatu penyakit. Pengguna tidak perlu berfokus pada sintaks untuk melakukan data mining. Dengan Orange, pengguna bidang kesehatan dapat memprediksi diagnosis suatu penyakit dengan lebih mudah dan lebih cepat. Penelitian ini menggunakan data Indian Liver Patient (ILPD) dari UCI-Machine Learning Repository. Targetnya adalah menentukan diagnosis pasien apakah memiliki ganguan hati atau tidak. Metode yang digunakan adalah Decision Tree, Random Forest, SVM, Neural Network, Naïve Bayes, k-NN, dan Regresi Logistik. Penelitian ini melakukan evaluasi dengan menggunakan confusion matrix, tingkat akurasi, tingkat presisi, waktu training, dan waktu testing. Hasil penelitian menunjukkan bahwa waktu yang dibutuhkan untuk training dan testing terbilang singkat. Dengan data yang digunakan, dalam penelitian ini diperoleh hasil pula empat metode terbaik berdasarkan tingkat akurasi adalah Regresi Logistik, Neural Network, Random Forest, dan Naïve Bayes.

Keywords


data mining; Orange; Supervised Learning; ILPD

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