IMPLEMENTASI ALGORITMA NAÏVE BAYES UNTUK KLASIFIKASI PENERIMA BANTUAN BPJS DI RANTAUPRAPAT LABUHANBATU

TIARA POHAN, NPM 2109100081 (2025) IMPLEMENTASI ALGORITMA NAÏVE BAYES UNTUK KLASIFIKASI PENERIMA BANTUAN BPJS DI RANTAUPRAPAT LABUHANBATU. Skripsi thesis, Universitas Labuhanbatu.

	Text COVER.pdf Download (1MB)
	Text BAB I.pdf Download (228kB)
	Text BAB II.pdf Download (409kB)
	Text BAB III.pdf Download (354kB)
	Text BAB IV.pdf Download (1MB)
	Text BAB V.pdf Download (137kB)
	Text DAFTAR PUSTAKA.pdf Download (171kB)

Abstract

Penelitian ini membahas implementasi algoritma Naïve Bayes untuk klasifikasi penerima bantuan BPJS di Rantauprapat, Labuhanbatu dengan memanfaatkan atribut sosial-ekonomi masyarakat seperti usia, pekerjaan, pendapatan, jumlah tanggungan, dan status rumah untuk membangun model klasifikasi yang objektif dan akurat. Dasar teori penelitian ini mengacu pada konsep data mining, khususnya metode klasifikasi berbasis probabilitas, di mana Naïve Bayes dipilih karena sederhana, efisien, serta terbukti memberikan hasil yang baik dalam kasus data sosial. Analisis dilakukan dengan mengolah data penerima bantuan yang dibagi menjadi data training dan data testing, kemudian digunakan untuk melatih serta menguji model Naïve Bayes agar tingkat akurasi dapat diukur. Hasil klasifikasi menunjukkan bahwa sistem mampu memprediksi penerima bantuan dengan tingkat akurasi sebesar 98,72%, dengan nilai True Positive (TP) sebanyak 77, True Negative (TN) sebanyak 21, serta kesalahan klasifikasi yang sangat rendah yaitu 1 data False Positive (FP) dan 1 data False Negative (FN). Dengan hasil tersebut, dapat disimpulkan bahwa algoritma Naïve Bayes mampu memberikan performa tinggi dan efektif dalam klasifikasi penerima bantuan BPJS di Rantauprapat, sehingga dapat dijadikan sebagai solusi pendukung keputusan untuk memastikan penyaluran bantuan lebih tepat sasaran dan adil. Kata Kunci: Naïve Bayes, Data Mining, Klasifikasi, BPJS, Rantauprapat ================================================================================================ This study discusses the implementation of the Naïve Bayes algorithm for the classification of BPJS assistance recipients in Rantauprapat, Labuhanbatu by utilizing the socio-economic attributes of the community such as age, occupation, income, number of dependents, and housing status to build an objective and accurate classification model. The theoretical basis of this research refers to the concept of data mining, specifically probability-based classification methods, where Naïve Bayes was chosen because it is simple, efficient, and proven to provide good results in the case of social data. The analysis was carried out by processing the data of assistance recipients which were divided into training data and testing data, then used to train and test the Naïve Bayes model so that the level of accuracy could be measured. The classification results showed that the system was able to predict assistance recipients with an accuracy level of 98.72%, with a True Positive (TP) value of 77, True Negative (TN) of 21, and a very low classification error of 1 False Positive (FP) data and 1 False Negative (FN) data. With these results, it can be concluded that the Naïve Bayes algorithm is able to provide high and effective performance in classifying BPJS aid recipients in Rantauprapat, so that it can be used as a decision support solution to ensure that aid distribution is more targeted and fair. Keywords: Naïve Bayes, Data Mining, Classification, BPJS, Rantauprapat

Item Type:	Thesis (Skripsi)
Uncontrolled Keywords:	Naïve Bayes, Data Mining, Klasifikasi, BPJS, Rantauprapat=============Naïve Bayes, Data Mining, Classification, BPJS, Rantauprapat
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases
Divisions:	Fakultas Sains Dan Teknologi > Sistem Informasi
Depositing User:	Unnamed user with email repository@ulb.ac.id
Date Deposited:	21 Oct 2025 06:59
Last Modified:	21 Oct 2025 06:59
URI:	http://repository.ulb.ac.id/id/eprint/1827

Actions (login required)

View Item