Implementasi Algoritma Genetika Pada Perancangan Aplikasi Android Untuk Memprediksi Buta Warna

Meirina Suci Ridha, Hani Harafani

Abstract


Color blindness is one of the decreasing diseases which is very difficult to determine its deterioration, whether a family member will suffer from color blindness or not, especially with the prediction of color blindness which has been using manual methods by calculating the inheritance formula. Genetic Algorithms have advantages over other traditional optimization algorithms. To implement a computerized method in predicting color blindness that can be used by many people, it is necessary to have a user-friendly operating system like the Android operating system. The test results show that the implementation of genetic algorithms in applications to predict color blindness produces more optimal predictions and their application to Android makes the application a user friendly application.

 

Keywords: Heredity, Color Blindness, Genetic Algorithms, Android Application, Color Blindness Predictions


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References


Aditiawarman, Nasution, H., & Tursina. (2017). Sistem Pakar Pendeteksi Penyakit Mata Berbasis Android. Jurnal Sistem Dan Teknologi Informasi (JUSTIN), 1(2), 57–61. Retrieved from jurnal.untan.ac.id/index.php/justin/article/view/18695

Andriani, A. (2013). Sistem prediksi penyakit diabetes berbasis decision tree. Jurnal Bianglala Informatika, I(1), 1–10.

Darmawan, H. D., Yuniarti, D., & Nasution, N. (2017). Klasifikasi Lama Masa Studi Mahasiswa Menggunakan Perbandingan Metode Algoritma C . 45 dan Algoritma Classification and Regression Tree Comparison of C4 . 5 Algorithm and Classification and Regression Tree Algorithm In The Classification of Study Period o. Jurnal Eksponensial, 8(2), 151–160.

Ervan, D. S., & Mulyanto, E. (2015). DETEKSI RISIKO PENDERITA BUTA WARNA MENURUN. Techno.COM, 14(2), 145–150.

Fatmawati. (2016). PERBANDINGAN ALGORITMA KLASIFIKASI DATA MINING MODEL C4 . 5 DAN NAIVE BAYES UNTUK PREDIKSI PENYAKIT DIABETES. Jurnal Techno Nusa Mandiri, XIII(1), 50–59.

Harafani, H. (2018). OPTIMASI ALGORITMA GENETIKA PADA K-NN UNTUK MEMPREDIKSI KECENDERUNGAN “BLOG POSTING.” Jurnal Pendidikan Teknologi Dan Kejuruan, 15(1). https://doi.org/10.23887/jptk-undiksha.v15i1.12873

Harafani, H., & Wahono, R. S. (2015). Optimasi Parameter pada Support Vector Machine Berbasis Algoritma Genetika untuk Estimasi Kebakaran Hutan. Journal of Intelligent Systems, 1(2).

Kemala, V., Irawan, B., & Nasrun, M. (2015). Rancang Bangun Aplikasi Sistem Pakar Untuk Diagnosis Penyakit Kulit Dan Kelamin Berbasis Smartphone Android. Program Studi Sistem Komputer, Fakultas Elektro Dan Komunikasi, Institut Teknologi Telkom Bandung, 2(2), 3568–3574.

Mujahidin, A., & Pribadi, D. (2017). Penerapan Algoritma C4 . 5 Untuk Diagnosa Penyakit Pneumonia Pada Anak Balita Berbasis Mobile. Jurnal Swabumi, 5(2), 155–161.

Praningki, T., & Budi, I. (2017). Sistem Prediksi Penyakit Kanker Serviks Menggunakan CART , Naive Bayes , dan k-NN. Citec Journal, 4(2), 83–93.

Puteri, N. A., Maharani, W., & Suliiyo, M. D. (2013). PREDIKSI PENYAKIT JANTUNG DENGAN ALGORITMA CLASSIFICATION AND REGRESSION TREE. Tugas Akhir.




DOI: https://doi.org/10.31294/jtk.v5i1.4697

Copyright (c) 2019 Meirina Suci Ridha, Hani Harafani

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ISSN: 2442-2436 (print), and 2550-0120


 dipublikasikan oleh LPPM Universitas Bina Sarana Informatika Jakarta

Jl. Kramat Raya No.98, Kwitang, Kec. Senen, Kota Jakarta Pusat, DKI Jakarta 10450
Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License