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Journal : journal of computer system and informatics josyc

 1 
Analisis Pemberian Bantuan UMKM Menggunakan Algoritma K-NN dan C4.5 Akrim Teguh Suseno; M Al Amin; Fajar Mahardika
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4312

Abstract

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%.
Co-Authors Abdillah, Muhammad Labib Abdul Hakim Prima Yuniarto Abdul Razak Naufal Adam Prayogo Kuncoro Adelia Fitriani Adhitya Putra Arif Nugroho Adlan Nugroho Ahmad Zulfan Akmal Firmansyah Al’ Amin, M. Al’ Amin, M. Amin, M. Al' Anang Martoyo Andik Wijanarko, Andik Angga Maulana Purba Arie Aryanto Arsa Anggun Anggraeni Aurora Dwi Khatulistian Bambang Bima Nugraha Bambang Sumatri, Raden Bagus Cahya Vikasari Dede Yusuf Dhanar Intan Surya Saputra Dhanar Intan Surya Saputra Dhanar Intan Surya Saputra Dwi Novia Prasetyanti Fareza Maulana A.P Fazar Sidik Fazar Sidik Felista Argita Putri Guntur Subari Hadi Jayusman Hasan Basri Hendra Marcos Hidayatul Ichwan Hilmi Mubarok I Putu Dody Suarnatha Ihsan Alparisi Ilham Budi Trisetyo Inayatul Inayah Inayatur Robaniyyah Isep Saepudin Jayusman, Hadi Joko Purwanto Kabul Agus Purwanto Khalifah Brilianti Ramadhani Kholil Mustofa Kukuh Muhammad Kukuh Muhammad Laura Sari Luhtitisari Luhtitisari Lutfi Syafirullah M. Al ‘Amin M. Azwan Merani, Sania Galuh Miftahul Khoiri Muhammad Ghofinda Prasetia Muhammad Gilland Taffy Shafa Sheehan Muhammad Nur Faiz Muhammad Sandi Muhammad Yusril Ihzam Muhammad, Kukuh Muin, Muhammad Abdul Muin Nadjwa Naela Aziz Noviana Safira Nugroho, Adlan Nur Azizah Nur Ifant Ristanto Nur Moniroh Nur Muniroh Nur Muniroh Nurul Alfiah Nurul Badriah Pari, Riki Aldi Prasetyanti, Dwi Novia Puji Cahniya Sari Raden Bagus Bambang Sumantri Rahmawan Bagus Trianto Randi Afif Ratih Ratih Ratih Ratih Hafsarah Maharrani ratih ratih Ratih Ratih Riki Aldi Pari Riki Aldi Pari Riki Aldi Pari Riyadi Purwanto Riyadi Purwanto Riyadi Purwanto Rizki Ripai Rizki Ripai Rizki Ripai Rizqya Chandra Muji Pratama Rostika Listyaningrum Saepudin, Isep Salsa Bila Salsabila Mutiara Defa Samsuri Yahya Samsuri Yahya Satriawan Desmana Setiawan, Endri Sitaresmi Wahyu Handani, Sitaresmi Wahyu Sony Veri Shandy Suhenda Suhenda Suhenda, Suhenda Suryo Nugroho Suseno, Akrim Teguh Tri Stiyo Famuji Tundo, Tundo Widya Pratiwi Yahya, Samsuri