PERBANDINGAN, ALGORITMA C.45, NAÏVE BAYES DAN K-NEAREST NEIGHBOR UNTUK KLASIFIKASI PENERIMA BANTUAN SOSIAL TUNAI (BST) (Studi Kasus : Kecamatan Tebet Jakarta Selatan)

AZILLATIN, QONITA (2023) PERBANDINGAN, ALGORITMA C.45, NAÏVE BAYES DAN K-NEAREST NEIGHBOR UNTUK KLASIFIKASI PENERIMA BANTUAN SOSIAL TUNAI (BST) (Studi Kasus : Kecamatan Tebet Jakarta Selatan). S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Cash Social Assistance (BST) is one of the government's assistance in the form of cash to the poor affected by covid-19. BST recipients are viewed from an integrated database id (BDT) which is a data set of unable people and elderly cards. In the receipt of BST the government also gives authority to the Social Service to determine the participants who are entitled to receive BST. In this case, researchers took a case study from one of the villages in DKI Jakarta Province, namely Bukit Duri Village, Tebet District, South Jakarta where many underprivileged residents could not receive BST because there was no data in the BDT. This is because the data is not updated, data selection is still manually, uneven dissemination of information and determination of decisions that are not accurate so that BST recipient participants are not on target. From the average accuracy that can be seen that the average accuracy value of the K-NN algorithm is 79.74%. Key words: Classification, C.45, Naïve Bayes, K-NN, BST. Bantuan Sosial Tunai (BST) merupakan salah satu bantuan dari pemerintah berupa uang tunai kepada masyarakat miskin yang terkena dampak dari covid – 19. Penerima BST dilihat dari id basis data terpadu (BDT) yang merupakan kumpulan data orang tidak mampu dan kartu lansia. Dalam penerimaan BST pemerintah juga memberikan kewenangan terhadap Dinas Social untuk menentukan peserta yang berhak menerima BST. Dalam hal ini peneliti mengambil studi kasus dari salah satu kelurahan yang ada di Provinsi DKI Jakarta yaitu Kelurahan Bukit Duri Kecamatan Tebet Jakarta Selatan dimana banyak warga yang kurang mampu yang tidak bisa menerima BST karena tidak ada data di BDT tersebut. Hal ini dikarenakan data tidak di update, penyeleksian data masih secara manual ,penyebaran informasi yang tidak merata dan penentuan keputusan yang kurang akurat sehingga peserta penerima BST tidak tepat sasaran. Dari rata – rata accuracy yang ada dapat dilihat bahwa nilai rata rata accuracy algoritma K-NN adalah 79,74%, sedangkan algoritma C.45 nilai rata rata accuracy 76,66% dan untuk algoritma naïve bayes nilai rata – rata accuracy sebesar 75,48%. Kata kunci: Klasifikasi, BST, C.45. Naïve Bayes, K-NN

Item Type: Thesis (S1)
NIM/NIDN Creators: 41518010088
Uncontrolled Keywords: Klasifikasi, BST, C.45. Naïve Bayes, K-NN
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
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 > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika
000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 020 Library and Information Sciences/Perpustakaan dan Ilmu Informasi > 025 Operations, Archives, Information Centers/Operasional Perpustakaan, Arsip dan Pusat Informasi, Pelayanan dan Pengelolaan Perpustakaan > 025.3 Bibliographic Analysis and Control/Bibliografi Analisis dan Kontrol Perpustakaan > 025.34 Cataloging, Classification, Indexing of Special Materials/Pengatalogan, Klasifikasi, Pengindeksan Bahan Tertentu
Divisions: Fakultas Ilmu Komputer > Informatika
Depositing User: ADELINA HASNA SETIAWATI
Date Deposited: 05 Dec 2023 06:55
Last Modified: 05 Dec 2023 06:55
URI: http://repository.mercubuana.ac.id/id/eprint/84585

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