KHAIRUNNISA, NIKA RULLYTA (2023) KOMPARASI ALGORITMA NA�VE BAYES DAN K-NEAREST NEIGHBOR UNTUK KLASIFIKASI PRIORITAS KEBUTUHAN PENGANGGURAN DI SURABAYA. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
Indonesia is a developing country that is striving to address the issue of poverty. One of the factors contributing to poverty is high unemployment rates. The province of East Java has the highest number of people living in poverty, with around 4.6 million individuals as of March 2021. Surabaya, a city in East Java, has the highest Tingkat Pengangguran Terbuka (TPT) with a percentage of 9.68% in 2021. In this study, the researcher conducted a classification using the Naïve Bayes and K-Nearest Neighbor algorithms to identify the priority needs between jobs or assistance among the unemployed individuals in Surabaya. The high accuracy rates achieved through the implementation of Naïve Bayes and KNearest Neighbor algorithms demonstrate their effectiveness in determining the priority needs of the unemployed individuals in Surabaya. The accuracy rates of the implemented algorithms were 95% (0.954) for Naïve Bayes and 97% (0.968) for K-Nearest Neighbor. It is worth noting that the K-Nearest Neighbor algorithm outperformed the Naïve Bayes algorithm in classifying the unemployment data in Surabaya. However, both algorithms exhibited good accuracy and did not show significant differences in performance. Keywords : Unemployed, Priority Needs, Classification, Naïve Bayes, K-Nearest Neighbor Indonesia merupakan salah satu negara berkembang yang sedang berusaha mengatasi masalah kemiskinan. Salah satu faktor penyebab kemiskinan adalah tingkat pengangguran yang tinggi. Jawa Timur adalah provinsi dengan jumlah penduduk miskin terbanyak, yaitu sekitar 4,6 juta jiwa pada bulan Maret 2021 dengan Tingkat Pengangguran Terbuka (TPT) tertinggi di Surabaya berdasarkan kota yang memiliki persentase sebesar 9,68% pada tahun 2021. Dalam penelitian ini, peneliti melakukan klasifikasi menggunakan algoritma Naïve Bayes dan KNearest Neighbor untuk mengidentifikasi kebutuhan prioritas dalam pekerjaan atau bantuan bagi para pengangguran di Surabaya. Tingkat akurasi dari algoritma yang digunakan nantinya akan menunjukkan efektivitas dalam menentukan kebutuhan prioritas. Hasil implementasi kedua algoritma tersebut menunjukkan akurasi sebesar 95% (0,954) untuk Naïve Bayes dan 97% (0,968) untuk K-Nearest Neighbor. Algoritma K-Nearest Neighbor memberikan performa lebih baik daripada algoritma Naïve Bayes dalam mengklasifikasi data pengangguran di Surabaya. Namun, keduanya memberikan akurasi yang baik dan tidak memiliki perbedaan yang signifikan. Kata Kunci : Pengangguran, Kebutuhan Prioritas, Klasifikasi, Naïve Bayes, K-Nearest Neighbor
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