Algoritma K-Nearest Neighbor Untuk Menentukan Kelayakan Keluarga Penerima Bantuan Pangan Non Tunai (Studi Kasus : Kelurahan Karya Jaya)

Yani, Sastri; Jumeilah, Fithri Selva; Kadafi, Muhamad

doi:10.51519/journalita.volume1.isssue2.year2020.page75-87

Cited by 2 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Pada penelitian Sastri Yani et al yang menganalisis penerimaan Bantuan Pangan Non Tunai (BPNT) di Kelurahan Karya Jaya, Palembang menggunakan algoritma K-nearest neighbor (K-NN) dikarenakan penyaluran bantuan yang dilakukan oleh pemerintah kurang maksimal. Hasil dari analisis tersebut adalah dari 511 data dapat diprediksi bahwa tingkat akurasi dari penerimaan bantuan yang sesuai kriteria pada penggunaan algoritma K-NN ini adalah 75,79% [11]. Pada penelitian lainnya, menggunakan perbandingan algoritma decision tree C4.5 dan algoritma decision tree C4.5 berbasis Particle Swarm Optimization (PSO) tanpa pruning.…”

Section: Pendahuluanunclassified

Analisis Pemberian Bantuan UMKM Menggunakan Algoritma K-NN dan C4.5

Suseno,

Amin,

Mahardika

2023

JoSYC

View full text Add to dashboard Cite

The Covid-19 pandemic has had an impact on the economies of countries in the world, including Indonesia. The low trade balance, public consumption and the implementation of large-scale social restrictions (PSBB) have caused business actors to suffer losses and even go out of business, especially Micro, Small and Medium Enterprises (MSMEs). Assistance to MSMEs has been provided by the government to maintain the functioning of the economy in the micro environment. However, the provision of this assistance needs to be carried out further analysis because there are reports that the assistance is not on target, causing the budget spent to be ineffective. In this research, an analysis will be carried out on the provision of assistance from the government to MSMEs, especially in Pekalongan district, using data mining techniques, especially classification. The algorithms used are K-Nearest Neighbor (K-NN) and C.45 which are then compared to determine the highest level of effectiveness in recommendations for providing MSME assistance in the District. Pekalongan. The data used was 312 MSMEs and after going through the data preprocessing process we got accurate data, namely 279. The data was divided into 2, namely training data of 200 data and testing data of 79 data. The results of this research, the K-NN algorithm obtained an accuracy level of 94.94%, precision 94.73% and recall 94.73%, while the C.45 algorithm obtained an accuracy level of 86.08%, precision 87.21% and recall 88.3%. Based on the results of this research, it can be concluded that the use of data mining techniques with the K-NN and C4.5 algorithms has a high level of accuracy for recommendations for assistance to MSMEs, however for the best results you can use the K-NN algorithm which has an accuracy level of 94.94%.

show abstract

Section: Pendahuluanunclassified

Analisis Pemberian Bantuan UMKM Menggunakan Algoritma K-NN dan C4.5

Suseno,

Amin,

Mahardika

2023

JoSYC

View full text Add to dashboard Cite

show abstract

“…Berbagai cara penanggulangan kemiskinan telah dilakukan oleh pemerintah salah satunya adalah pemberian bantuan untuk masyarakat kurang mampu. Diharapkan melalui program pemberian bantuan untuk masyarakat kurang mampu dapat mengatasi permasalahan yang dialami setiap warga kurang mampu khususnya permasalahan ekonomi [3].…”

unclassified

Analisa Data Mining Dalam Memprediksi Masyarakat Kurang Mampu Menggunakan Metode K-Nearest Neighbor

Nurdin

2024

JITET

View full text Add to dashboard Cite

Poverty is one of the fundamental issues that is center of attention of the government in a country. One important aspect to support the poverty reduction strategi is the availability of accurate and targeted poverty data. One of the main problems that often hinders the success of these government programs is the availability of appropriate data on the targeting of the poor. This study aims to design an application than can predict the poor using the K-Nearest Neighbor Algorithm with the five main indicators being the type of work, number of dependents, age income and condition of the household head of the family. This prediction provides data on poor families that are suitable for receiving various assistance from the government. The data used for predictions are sample data from Pegasing District. In this study, the K-NN Algorithm was analyzed which was developed based on the web. The working principle of K-Nearest Neighbor is to find the shortest distance between the evaluated data and training data. The results of the evaluation using the confusion matrix obtained the resulting accuracy for 216 training data with 93 testing data with a ratio of 70:30 and five attributes used produced an accuracy of 86,02%, Recall 61,90%, Precision 72,22%, and F1-Score 66,04%.

show abstract

Algoritma K-Nearest Neighbor Untuk Menentukan Kelayakan Keluarga Penerima Bantuan Pangan Non Tunai (Studi Kasus : Kelurahan Karya Jaya)

Cited by 2 publications

References 4 publications

Analisis Pemberian Bantuan UMKM Menggunakan Algoritma K-NN dan C4.5

Analisis Pemberian Bantuan UMKM Menggunakan Algoritma K-NN dan C4.5

Analisa Data Mining Dalam Memprediksi Masyarakat Kurang Mampu Menggunakan Metode K-Nearest Neighbor

Contact Info

Product

Resources

About