DOI : https://doi.org/10.62527/ijasce.5.2.151

Analysis of Eye Disease Classification by Comparison of the Random Forest Method and K-Nearest Neighbor Method

Dwiny Meidelfi (1), Hendrick (2), Fanni Sukma (3), Srintika Yunni Kharisma (4)
(1) (Scopus ID: 57202991476); Politeknik Negeri Padang, Sumatera Barat
(2) Department of Electrical Engineering, Politeknik Negeri Padang, West Sumatera, Indonesia
(3) Department of InformationTechnology, Politeknik Negeri Padang, West Sumatera, Indonesia
(4) Department of InformationTechnology, Politeknik Negeri Padang, West Sumatera, Indonesia
Fulltext View | Download
How to cite (IJASEIT) :
Meidelfi, D., Hendrick, Sukma, F., & Kharisma, S. Y. (2023). Analysis of Eye Disease Classification by Comparison of the Random Forest Method and K-Nearest Neighbor Method. International Journal of Advanced Science Computing and Engineering, 5(2), 136–145. https://doi.org/10.62527/ijasce.5.2.151
Citation Format :
Eye disease is a serious issue all over the world, and image-based classification systems play an important role in the early detection and management of eye disease. This research compares the performance between Random Forest (RF) and K-Nearest Neighbor (KNN) classification models in identifying eye disorders using image datasets divided into four classes: "normal," "glaucoma," "cataract," and "diabetic retinopathy."   The dataset is converted into a feature vector and then divided into training data and test data subsets. The analysis results show that the RF model achieved an accuracy level of 80%, whereas the KNN model achieved 70%. Based on these findings, it is possible to conclude that the RF model outperforms the other models in categorizing the types of eye illnesses in the dataset. A Python-based website was also built utilizing the Flask framework to build an interactive and real-time eye illness diagnosis system. Users can upload photos of their retinas to this website and quickly receive eye disease detection results. The adoption of this technology has a tremendous impact, making eye disease detection solutions more accessible. Furthermore, this solution plays an important role in the early detection and effective management of eye health cases.

S. N. Sari, B. S. Ginting, and N. Novriyenni, “Design of a walking aid for the blind using Arduino-based Fuzzy Logic,†JTIK (Jurnal Tek. Inform. Kaputama), vol. 6, no. 2, pp. 528–543, 2022.

D. A. Dharmawan, “Retinal Vessel Segmentation to Support Foveal Avascular Zone Detection,†Telemat. J. Inform. dan Teknol. Inf., vol. 20, no. 1, pp. 41–50, 2023.

A. U. Detty, I. Artini, and V. R. Yulian, “Characteristics of Risk Factors for Cataract Sufferers,†J. Ilm. Kesehat. Sandi Husada, vol. 10, no. 1, pp. 12–17, 2021.

M. A. K. Faadhil, P. R. A. Sangging, and R. Himayani, “Relationship between Glaucoma and Hypertension,†Med. Prof. J. Lampung, vol. 13, no. 4.1, pp. 36–41, 2023.

Y. Triyani, “Classification of diabetic retinopathy on fundus images based on deep learning,†ABEC Indones., vol. 9, pp. 1007–1018, 2021.

N. I. Widiastuti, E. Rainarli, and K. E. Dewi, “Peringkasan dan Support Vector Machine pada Klasifikasi Dokumen,†J. Infotel, vol. 9, no. 4, pp. 416–421, 2017.

G. Mercy, “Analisis Penyakit Disleksia Pada Anak Balita Akibat Kurangnya Asupan Vitamin A,†2019.

D. A. Budi, “Perancangan Sistem Login Pada Aplikasi Berbasis GUI Menggunakan QTDesigner Python,†J. SIMADA (Sistem Inf. dan Manaj. Basis Data), vol. 4, no. 2, pp. 92–100, 2021.

P. A. Nugroho, I. Fenriana, and R. Arijanto, “Implementasi Deep Learning Menggunakan Convolutional Neural Network (Cnn) Pada Ekspresi Manusia,†Algor, vol. 2, no. 1, pp. 12–20, 2020.

A. Putri and R. M. Awangga, Membangun Frontend dan Backend Packages dengan Golang" Studi Kasus Sistem Administrasi". Penerbit Buku Pedia, 2023.

Y. T. Widayati, Y. Prihati, and S. Widjaja, “Analisis Dan Komparasi Algoritma Na Ve Bayes Dan C4. 5 Untuk Klasifikasi Loyalitas Pelanggan Mnc Play Kota Semarang,†J. Transform., vol. 18, no. 2, pp. 161–172, 2021.

M. R. A. Nasution and M. Hayaty, “Perbandingan Akurasi dan Waktu Proses Algoritma K-NN dan SVM dalam Analisis Sentimen Twitter,†J. Inf., vol. 6, no. 2, pp. 226–235, 2019.

P. Rosyani, S. Saprudin, and R. Amalia, “Klasifikasi Citra Menggunakan Metode Random Forest dan Sequential Minimal Optimization (SMO),†J. Sist. dan Teknol. Inf., vol. 9, no. 2, p. 132, 2021, doi: 10.26418/justin.v9i2.44120.

V. W. Siburian and I. E. Mulyana, “Prediksi Harga Ponsel Menggunakan Metode Random Forest,†in Annual Research Seminar (ARS), 2019, vol. 4, no. 1, pp. 144–147.

Z. Nurfadilla, “Implementasi Data Mining untuk Memprediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Random Forest,†AGENTS J. Artif. Intell. Data Sci., vol. 2, no. 2, pp. 35–42, 2022.

C. Kurniawan and H. Irsyad, “Perbandingan Metode K-Nearest Neighbor Dan Naïve Bayes untuk Klasifikasi Gender Berdasarkan Mata,†J. Algoritm., vol. 2, no. 2, pp. 82–91, 2022.

D. Prasetyawan and R. Gatra, “Algoritma K-nearest neighbor untuk memprediksi prestasi mahasiswa berdasarkan latar Belakang pendidikan dan ekonomi,†JISKA (Jurnal Inform. Sunan Kalijaga), vol. 7, no. 1, pp. 56–67, 2022.

A. M. Zuhdi, E. Utami, and S. Raharjo, “Analisis sentiment twitter terhadap capres Indonesia 2019 dengan metode K-NN,†J. Inf. J. Penelit. dan Pengabdi. Masy., vol. 5, no. 2, pp. 1–7, 2019.

D. Cahyanti, A. Rahmayani, and S. A. Husniar, “Analisis performa metode Knn pada Dataset pasien pengidap Kanker Payudara,†Indones. J. Data Sci., vol. 1, no. 2, pp. 39–43, 2020.

I. I. Sholikhah, A. T. J. Harjanta, and K. Latifah, “Machine Learning Untuk Deteksi Berita Hoax Menggunakan BERT,†in Prosiding Seminar Nasional Informatika, 2023, vol. 1, no. 1, pp. 524–531.