KLASIFIKASI CHRONIC KIDNEY DISEASE (CKD) MENGGUNAKAN TOOLS WEKA, RAPIDMINER, DAN ORANGE DATA MINING: ANALISIS PERBANDINGAN KINERJA

Agus Wantoro

doi:10.23960/jitet.v14i1.8951

Authors

Agus Wantoro Universitas Aisyah Pringsewu

DOI:

https://doi.org/10.23960/jitet.v14i1.8951

Abstract Views: 70 File Views: 69

Keywords:

Chronic Kidney Disease, Machine Learning, Klassifikasi, Analisis Perbandingan

Abstract

Chronic Kidney Disease (CKD) merupakan salah satu penyakit tidak menular dengan tingkat prevalensi dan mortalitas yang terus meningkat secara global. Deteksi dini CKD sangat penting untuk mencegah komplikasi dan memperpanjang harapan hidup pasien. Penelitian ini bertujuan untuk membandingkan performa algoritma klasifikasi yang diterapkan pada dua platform data mining populer, yaitu WEKA, RapidMiner, dan Orange dalam menganalisis dataset penyakit ginjal kronis dari UCI Machine Learning (ML) Repository. Lima algoritma klasifikasi digunakan dalam eksperimen, yaitu Naive Bayes, Support Vector Machine (SVM), Random Forest, k-NN, dan Logistic Regression dengan skema validasi silang 10-fold. Kinerja model dievaluasi berdasarkan Confusion Matrix berupa nilai accuracy, precision, dan recall. Hasil menunjukkan bahwa terdapat perbedaan performa antar algoritma pada masing-masing tools. Pada tools WEKA, algoritma Random Forest menunjukkan performa terbaik dengan akurasi 99.81% dan algoritma k-NN menunjukkan performa terburuk. Pada tools RapidMiner, algoritma k-NN justru menampilkan nilai terbaik dengan nilai akurasi 99.5%, sedangkan Niave Bayes menyusul di bawahnya. Pada tools Orange algoritma SVM dan Random Forest memiliki performa terbaik dengan nilai 99.8% dan algoritma terburuk k-NN. Secara umum tools WEKA memiliki kinerja yang lebih baik, disusul Orange, dan RapidMiner. Namun, setiap platform memiliki keunggulan masing-masing. Ketiga tools memiliki potensi yang besar dalam pengembangan sistem pendukung keputusan berbasis ML untuk diagnosis CKD

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