Perbandingan Algoritma K-Nearest Neighbor, Decision Tree, Dan Naive Bayes  Untuk Menentukan Kelayakan Pemberian Kredit

Muryono, Tupan Tri; Taufik, Ahmad; Irwansyah, Irwansyah

doi:10.37365/jti.v7i1.104

Cited by 7 publications

(9 citation statements)

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“…Algoritma ini sering digunakan dalam klasifikasi teks, seperti kategorisasi email, analisis sentimen, dan klasifikasi dokumen. Meskipun Naive Bayes memiliki asumsi yang cukup sederhana, dalam banyak kasus, algoritma ini memberikan hasil yang memadai dan mempunyai kelebihan dalam hal kecepatan dan efisiensi pada dataset besar (Muryono et al, 2021).…”

Section: Pendahuluanunclassified

Komparasi Algoritma Decision Tree, Naive Bayes Dan K-Nearest Neighbor Untuk Menentukan Kualitas Udara Di Provinsi Dki Jakarta

Irwansyah,

Wiranata,

2023

j.nanotechnol.inf.

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Air quality in DKI Jakarta Province refers to the state and cleanliness of the air in the area at a given time. The type and concentration of air pollutants are among the indicators used to assess air quality. DKI Jakarta's air pollution is severe, causing respiratory irritation, respiratory illnesses, and long-term health issues such as cardiovascular disease and lung cancer. Air pollution can also harm the environment by limiting visibility and harming ecosystems. The problem with the research is that no appropriate and relevant features for predicting air quality were used. The goal of this study is to identify and compare algorithms with the highest accuracy between decision trees. In determining air quality, naive Bayes and k-nearest neighbor are used. According to the findings of the K-5fold evaluation process performed with the RapidMiner tool, the accuracy of the Decision Tree algorithm was 95.89%, the accuracy of the Nave Bayes algorithm was 93.15%, and the accuracy of the K-NN algorithm was 91.78%. Based on these findings, the decision tree method has the greatest or best accuracy when compared to the Nave Bayes and K-NN algorithms.

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Section: Pendahuluanunclassified

Komparasi Algoritma Decision Tree, Naive Bayes Dan K-Nearest Neighbor Untuk Menentukan Kualitas Udara Di Provinsi Dki Jakarta

Irwansyah,

Wiranata,

2023

j.nanotechnol.inf.

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“…Permasalahan pada penelitian ini karena ketidaktahuan user ataupun pengguna semacam apaapa saja yang menjadi indikasi, kerusakan, pemicu, serta metode menanggulangi kendala ataupun pada kerusakan pada jaringan Local Area Network tersebut. Dengan adanya sistem pakar ini penulis ingin mencoba memecahkan masalah dalam pengambilan keputusan yang biasanya hanya bisa dipecahkan oleh seorang pakar (Muryono et al, 2021).…”

Section: Pendahuluanunclassified

Sistem Pakar Deteksi Kerusakan Jaringan Local Area Network (Lan) Menggunakan Metode Beckward Chaining Berbasis Web

et al. 2022

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The form of computer network connection can be via cable or wireless such as fiber optic, microwave, wireless, or satellite. One type of computer network that is often used to connect personal computers and workstations in an office or an organization, company or factory for the use of shared resources is a local area network. The purpose of this research is to analyze, design and create an application that can detect damage to Local Area Network (LAN) networks. The research method used is backward chaining. The results of this study are applications that can detect damage to local area networks using the web-based backward chaining method. With this expert system application, it can speed up and make it easier to detect damage to Local Area Network networks.

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“…Penelitian terkait juga dilakukan oleh (Muryono, et al, 2021) yang berjudul Perbandingan Algoritma K-Nearest Neighbor, Decision Tree, dan Naïve Bayes untuk Menentukan Kelayakan Pemberian Kredit. Tujuan dalam penelitian ini ialah untuk membandingkan hasil akurasi algoritma antara K-Nearest Neighbor (K-NN), Decision Tree, dan Naive Bayes yang mana hasil akurasi terbaiklah yang akan diimplementasikan untuk menentukan kelayakan pemberian kredit.…”

Section: Pendahuluanunclassified

Comparison of the K-Nearest Neighbor Method and the Naive Bayes Method in Classification of Eligibility for Lending

Septria

Asnawati

Fredricka

2022

j.komputer, j.informasi, j.teknologi

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The Kemala Aman Finance Bengkulu Cooperative also provides loan facilities for consumers, but not all loan applications will be approved. So far, the determination of the feasibility of applying for a loan is seen from several aspects including marital status, number of dependents, age, last education, occupation, monthly income, home ownership, collateral, number of loan applications, length of loan application. These aspects are analyzed manually by the survey team by filling out the form provided, and then the survey results are given to superiors to be followed up on whether the loan application is accepted or rejected. The loan eligibility application at the Kemala Aman Finance Bengkulu Cooperative is used to make it easier to determine the eligibility of prospective borrowers to be given loans based on marital status, number of dependents, age, last education, occupation, income, home ownership, collateral, number of loan applications, length of loan application. This application is made using the Visual Basic .Net programming language which can be accessed by the Kemala Aman Finance Bengkulu Cooperative Admin. Comparative analysis of the K-Nearest Neighbor method and the Naive Bayes method was carried out by looking at the level of accuracy by comparing the classification results of the two methods with the real data from the classification results obtained from the Kemala Aman Finance Cooperative Bengkulu. Based on the processing time, the KNN method is faster than the Naive Bayes method. Based on the level of accuracy, the KNN method has the highest level of accuracy compared to the Naive Bayes method. Based on the tests that have been carried out, the functionality of the loan eligibility application at the Kemala Aman Finance Bengkulu Cooperative runs as expected, and the application is able to display the results of the classification of loan eligibility through the KNN Method and the Naive Bayes Method.

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Perbandingan Algoritma K-Nearest Neighbor, Decision Tree, Dan Naive Bayes Untuk Menentukan Kelayakan Pemberian Kredit

Cited by 7 publications

References 0 publications

Komparasi Algoritma Decision Tree, Naive Bayes Dan K-Nearest Neighbor Untuk Menentukan Kualitas Udara Di Provinsi Dki Jakarta

Komparasi Algoritma Decision Tree, Naive Bayes Dan K-Nearest Neighbor Untuk Menentukan Kualitas Udara Di Provinsi Dki Jakarta

Sistem Pakar Deteksi Kerusakan Jaringan Local Area Network (Lan) Menggunakan Metode Beckward Chaining Berbasis Web

Comparison of the K-Nearest Neighbor Method and the Naive Bayes Method in Classification of Eligibility for Lending

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