ANALISA PERBANDINGAN ALGORITMA NAÏVE BAYES, K-NEAREST NEIGHBOR (KNN) DAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) UNTUK KLASIFIKASI KEMISKINAN DI JAWA BARAT

ANUNG, AKBAR (2022) ANALISA PERBANDINGAN ALGORITMA NAÏVE BAYES, K-NEAREST NEIGHBOR (KNN) DAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) UNTUK KLASIFIKASI KEMISKINAN DI JAWA BARAT. S1 thesis, Universitas Mercu Buana.

Preview	Text (HAL COVER) 01 Cover.pdf Download (541kB) | Preview
Preview	Text (ABSTRAK) 02 Abstrak.pdf Download (23kB) | Preview
	Text (BAB 1) 03 Bab 1.pdf Restricted to Registered users only Download (47kB)
	Text (BAB 2) 04 Bab 2.pdf Restricted to Registered users only Download (28kB)
	Text (BAB 3) 05 Bab 3.pdf Restricted to Registered users only Download (46kB)
	Text (BAB 4) 06 Bab 4.pdf Restricted to Registered users only Download (30kB)
	Text (BAB 5) 07 Bab 5.pdf Restricted to Registered users only Download (120kB)
	Text (BAB 6) 08 Bab 6.pdf Restricted to Registered users only Download (125kB)
	Text (DAFTAR PUSTAKA) 09 Daftar Pustaka.pdf Restricted to Registered users only Download (86kB)
	Text (LAMPIRAN) 10 Lampiran.pdf Restricted to Registered users only Download (255kB)

Abstract

Poverty is a condition in which a human being is seen as economically incapable of meeting his needs. Based on data from the Central Statistics Agency (BPS), the poor population in West Java is one of the highest when compared to other provinces in Indonesia. Even in March 2021, BPS recorded that poverty in West Java had increased by 8.4%, which is around 4.2 million people. This study aims to compare Machine Learning algorithms in classifying poverty in West Java. By using the Machine Learning method, we can find models or functions that can describe and distinguish data classes or concepts. This study will use the Naïve Bayes, K-Nearest Neighbor, and Support Vector Machine methods to find the best accuracy in classifying poverty in West Java. Of the three methods used, Naïve Bayes produces the highest average accuracy value with a value of 92,56% from five experiments carried out. In this study, we found the advantages of Naïve Bayes in classifying poverty compared to the K-Nearest Neighbor and Support Vector Machine methods. Key words: Classification, Proverty, Naïve Bayes, KNN, SVM Kemiskinan merupakan keadaan dimana seorang manusia dipandang tidak mampu dari ekonomi untuk memenuhi kebutuhannya. Berdasarkan data dari Badan Pusat Statistik (BPS), penduduk miskin di Jawa Barat ini menjadi salah satu yang tertinggi bila dibandingkan dengan provinsi lainnya yang ada di Indonesia. Bahkan pada Maret 2021, BPS mencatat kemiskinan di Jawa Barat mengalami kenaikan sebesar 8,4% yaitu sekitar 4,2 juta jiwa. Pada penelitian ini bertujuan untuk membandingkan algoritma Machine Learning dalam mengklasifikasikan kemiskinan di Jawa Barat. Dengan menggunakan metode Machine Learning kita dapat menemukan model atau fungsi yang dapat menggambarkan dan membedakan kelas data atau konsep. Penelitian ini akan menggunakan metode Naïve Bayes, K-Nearest Neighbor, dan Support Vector Machine untuk mecari akurasi terbaik dalam mengklasifikasikan kemiskinan di Jawa Barat. Dari ketiga metode yang digunakan, Naïve Bayes menghasilkan nilai rata-rata akurasi tertinggi dengan nilai 92,56% dari lima kali percobaan yang dilakukan. Pada penelitian ini didapatkan kelebihan Naïve Bayes dalam mengklasifikasikan kemiskinan dibandingkan dengan metode K-Nearest Neighbor, dan Support Vector Machine. Kata kunci: Klasifikasi, Kemiskinan, SVM, K-NN, Naïve Bayes

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 22 054
NIM/NIDN Creators:	41518010144
Uncontrolled Keywords:	Klasifikasi, Kemiskinan, SVM, K-NN, Naïve Bayes
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 005 Computer Programmming, Programs, Data/Pemprograman Komputer, Program, Data 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 005 Computer Programmming, Programs, Data/Pemprograman Komputer, Program, Data > 005.1 Programming/Pemrograman 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 005 Computer Programmming, Programs, Data/Pemprograman Komputer, Program, Data > 005.1 Programming/Pemrograman > 005.12 Software System Analysis and Design/Sistem Analisa dan Desain Perangkat Lunak
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	LUTHFIAH RAISYA ARDANI
Date Deposited:	16 Sep 2022 09:04
Last Modified:	19 Sep 2022 03:17
URI:	http://repository.mercubuana.ac.id/id/eprint/69177

Actions (login required)

View Item