Comparison Support Vector Machine and Random Forest Algorithms in Detect Diabetes

Habib  Alrasyid; Ahmad  Homaidi; Zaehol  Fatah

Habib Alrasyid Sistem Informasi, Fakultas Sains & Teknologi Universitas Ibrahimy, Indonesia
Ahmad Homaidi Teknologi Informasi, Fakultas Sains & Teknologi Universitas Ibrahimy, Indonesia
Zaehol Fatah Sistem Informasi, Fakultas Sains & Teknologi Universitas Ibrahimy, Indonesia

Abstrak

One of the diseases that are very concerning and numerous cases that occur all over the world because the impact is very significant is diabetes. Diabetes sufferers experience disorders in metabolism that identify hyperglycemia caused by no the inability of the pancreas to secrete insulin, which has an impact on death Because No functioning of other body organs. Data states in 2019 that, 433 million people were diagnosed with diabetes, and the Number is predicted to increase until peaking in 2045 will be 700 million people. This matter needs to be anticipated as soon as possible, perhaps by society, with several characteristics that occur in patients. Data on diabetes sufferers can processed with data mining that utilizes machine learning to detect diabetes. Study This will compare the Support Vector Machine and Random Forest algorithms to find accurate results. Researchers use the KDD (Knowledge Discovery in Database) model in several stages, such as data selection, preprocessing, Transformation, and Evaluation. The dataset used was sourced from the kaggel.com website; there were 768, consisting of 500 negative and 268 positive for diabetes. The SVM algorithm with a linear kernel produces a mark accuracy of 77%, Precision of 75%, Recall of 51%, and F1 score of 61%. For the Random Forest algorithm with n_estimators =100, random_state =42, results mark Accuracy 75%, precision 69%, recall 55% and F1 score 61%. The process and results state that more SVM algorithms are suitable for detecting diabetes. Models made using the Python programming language will be implemented with stremlit so you can use Web-based.

Referensi

Amal, I., Pamungkas, E. W., Kom, S., Kom, M., & Ph, D. (n.d.). APLIKASI PENDETEKSI BERITA PALSU BAHASA INDONESIA MENGGUNAKAN FRAMEWORK FLASK DAN STREAMLIT SERTA ALGORITMA MACHINE LEARNING Teknik Informatika ,( 2019…. )1–18.
Apriyani, H. (2020). Perbandingan Metode Naïve Bayes Dan Support Vector Machine Dalam Klasifikasi Penyakit Diabetes Melitus. 1(3), 133–143.
Bingga, I. A. (2021). Kaitan kualitas tidur dengan diabetes melitus tipe 2.
Fikri, M., Syahbani, N., & Ramadhan, N. G. (2023). Klasifikasi Gerakan Yoga dengan Model Convolutional Neural Network Menggunakan Framework Streamlit. 7, 509–519. https://doi.org/10.30865/mib.v7i1.5520
Health, P. (2022). 1 . Improving Care and Promoting Health in Populations : Standards of Medical Care in Diabetes — 2022. 45(January), 8–16.
Ikhromr, F. N., Sugiyarto, I., Faddillah, U., Sudarsono, B., Mandiri, U. N., Bina, U., & Informatika, S. (2023). Implementasi data mining untuk memprediksi penyakit diabetes menggunakan algoritma naives bayes dan k-nearest neighbor implementation of data mining to predict diabetes disease using naives bayes and k-nearest neighbor algorithms. 6, 416–428.
Kurniawan, M. A., & Falentina, A. T. (n.d.). Analisis Big Data dan Official Statistics dalam Melakukan Nowcasting Pertumbuhan Ekonomi Indonesia Sebelum dan Selama Pandemi. 19, 521–532.
Maulida, A. (2020). Penerapan Metode Klasifikasi K-Nearest Neigbor pada Dataset Penderita Penyakit Diabetes. 1(2), 29–33.
Pamuji, F. Y., & Ramadhan, V. P. (2021). Jurnal Teknologi dan Manajemen Informatika Komparasi Algoritma Random Forest Dan Decision Tree Untuk Memprediksi Keberhasilan Immunotheraphy. 7(1), 46–50.
Puspitasari, A. M., Ratnawati, D. E., & Widodo, A. W. (2018). Klasifikasi Penyakit Gigi Dan Mulut Menggunakan Metode Support Vector Machine. 2(2), 802–810.
Putry, N. M., Sari, B. N., Kom, M., Informatika, T., & Karawang, U. S. (2022). KOMPARASI ALGORITMA KNN DAN NAÏVE BAYES UNTUK KLASIFIKASI DIAGNOSIS PENYAKIT DIABETES MELITUS. 10(1).
Rahayu, P. T., & Qurrota, A. (2022). Jurnal Smart Teknologi Perbandingan Algoritma K-Nearest Neighbor Dan Gaussian Naïve Bayes Pada Klsifikai Penyakit Diabetes Melitus Comparison Of K-Nears Neighbor And Gaussian Naïve Bayes Algorithm On The Classification Of Diabetes Mellitus Jurnal Smart Teknologi. 3(4), 366–373.
Sistem, R., Pasien, D., & Diabetes, P. (2021). JURNAL RESTI Perbandingan Support Vector Machine dan Modified Balanced Random. 1(10), 393–399.
Suryati, E., & Aldino, A. A. (2023). Analisis Sentimen Transportasi Online Menggunakan Ekstraksi Fitur Model Word2vec Text Embedding Dan Algoritma Support Vector Machine ( SVM ). 4(1), 96–106.