Analisis Pemberian Bantuan UMKM Menggunakan Algoritma K-NN dan C4.5
-
Akrim Teguh Suseno
*
Institut Teknologi dan Sains Nahdlatul Ulama Pekalongan, Pekalongan,
Indonesia
- M Al Amin Institut Teknologi dan Sains Nahdlatul Ulama Pekalongan, Pekalongan, Indonesia
- Fajar Mahardika Institut Teknologi dan Sains Nahdlatul Ulama Pekalongan, Pekalongan, Indonesia (*) Corresponding Author
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%.
Downloads
References
Anggara Cahya Putra and K. Dwi Hartomo, “Optimalisasi Penyaluran Bantuan Pemerintah Untuk UMKM Menggunakan Metode Fuzzy C-Means,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 3, pp. 474–482, Jun. 2021, doi: 10.29207/resti.v5i3.2980.
Windia Ambarsari, Erlin Khotijah, and Siti Sunarmintyastuti, “Pemodelan Reward Rule Game Streamer Indonesia Tingkat Amatir dengan Orange Data Mining,” Aug. 2019.
U. Kurniasih and A. T. Suseno, “Analisis Sentimen Terhadap Bantuan Subsidi Upah (BSU) pada Kenaikan Harga Bahan Bakar Minyak (BBM),” Jurnal Media Informatika Budidarma, vol. 6, no. 4, Oct. 2022.
L. B. Putong and K. E. Wahyudi, “Implementasi Program Bantuan Sosial Tunai Pada Umkm,” Jurnal Kebijakan Publik, vol. 13, no. 4, pp. 338–345, 2022.
A. Majid, D. D. Kurniawan, and K. N. Sigit, “Pengaruh Bantuan Presiden BLT UMKM Terhadap Produktivitas Usaha Mikro Kecil Dan Menengah Di Kabupaten Batang,” Jurnal Ilmu Manajemen Dan Akuntansi Terapan (JIMAT), vol. 12, no. 3, pp. 333–341, 2021.
H. C. Maharani and J. Jaeni, “Determinan kebijakan pemerintah sebuah solusi keberlangsungan usaha UMKM di tengah pandemi covid-19,” AKSES: Jurnal Ekonomi Dan Bisnis, vol. 16, no. 1, 2021.
D. T. S. Kumar, “Data mining based marketing decision support system using hybrid machine learning algorithm,” Journal of Artificial Intelligence and Capsule Networks, vol. 2, no. 3, pp. 185–193, 2020.
R. S. Asa, “Identifikasi Penyaluran Zakat Menggunakan Algoritma C4.5 (Studi Kasus Di Baznas Kabupaten Agam),” Jurnal Sains Dan Informatika, vol. 5, no. 1, 2019.
Ari Rohmawati, “PEMETAAN DAN REKOMENDASI PENINGKATAN KINERJA UMKM DI KABUPATEN PESAWARAN LAMPUNG MENGGUNAKAN ALGORITMA NAIVE BAYES DAN NORMALIZED RATING FREQUENCY(NRF),” Masters thesis, IIB Darmajaya, 2022.
A. Damuri, U. Riyanto, H. Rusdianto, and M. Aminudin, “Implementasi Data Mining dengan Algoritma Naïve Bayes Untuk Klasifikasi Kelayakan Penerima Bantuan Sembako,” JURIKOM (Jurnal Riset Komputer), vol. 8, no. 6, p. 219, Dec. 2021, doi: 10.30865/jurikom.v8i6.3655.
S. Yani, F. S. Jumeilah, and M. Kadafi, “Algoritma K-Nearest Neighbor Untuk Menentukan Kelayakan Keluarga Penerima Bantuan Pangan Non Tunai (Studi Kasus: Kelurahan Karya Jaya),” Journal of Information Technology Ampera, vol. 1, no. 2, pp. 75–87, 2020.
E. Ermawati, “Algoritma Klasifikasi C4.5 Berbasis Particle Swarm Optimization Untuk Prediksi Penerima Bantuan Pangan Non Tunai,” Sistemasi (jurnal sistem informasi), vol. 8, no. 3, 2019.
R. L. Hasanah et al., “KLASIFIKASI PENERIMA DANA BANTUAN DESA MENGGUNAKAN METODE KNN (K-NEAREST NEIGHBOR),” Jurnal TECHNO Nusa Mandiri, vol. 16, no. 1, p. 1, 2019, [Online]. Available: http://nusamandiri.ac.id/
S. Ulya, M. A. Soeleman, and F. Budiman, “Optimasi Parameter K Pada Algoritma K-NN Untuk Klasifikasi Prioritas Bantuan Pembangunan Desa,” Techno. Com, vol. 20, no. 1, pp. 83–96, 2021.
R. Rusbandi, F. A. Ma’ruf, A. Pratama, I. Sholihin, and A. Rinaldi, “Penerapan Model Prediksi Menggunakan Algoritma C. 45 Untuk Prediksi Kelulusan Siswa SMK Wahidin,” JURNAL DATA SCIENCE & INFORMATIKA, vol. 1, no. 1, pp. 16–20, 2021.
A. P. Wibawa, M. Guntur, A. Purnama, M. F. Akbar, and F. A. Dwiyanto, “Metode-metode Klasifikasi,” Prosiding Seminar Ilmu Komputer dan Teknologi Informasi, vol. 3, no. 1, 2018.
D. A. Nawangnugraeni, M. Z. Abdillah, and A. T. Suseno, “C4. 5 Algorithm Implementation For Public Sentyment Analysis Covid-19 Vaccine,” Digital Zone: Jurnal Teknologi Informasi dan Komunikasi, vol. 13, no. 2, pp. 151–160, 2022.
A. Saifur Rohman Cholil and T. Ardianita, “Prediksi Penyakit Demam Berdarah Di Puskesmas Ngemplak Simongan Menggunakan Algoritma C4.5,” Sistemasi, pp. 529–542, Jul. 2020.
I. Junaedi, N. Nuswantari, and V. Yasin, “Perancangan dan Implementasi Algoritma C4.5 Untuk Data Mining Analisis Tingkat Risiko Kematian Neonatum Pada Bayi,” JISICOM (Journal of Information System, Informatics and Computing), vol. 3, no. 1, pp. 29–44, 2019.
J. Smith and J. Doe, “Image Classification using Convolutional Neural Networks: An Experimental Study,” Int J Comput Vis, vol. 20, no. 5, 2020.
D. Lee and E. Chen, “Predicting Stock Market Trends with Machine Learning Algorithms,” IEEE Transactions on Financial Engineering, vol. 15, no. 3, 2021.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Analisis Pemberian Bantuan UMKM Menggunakan Algoritma K-NN dan C4.5
Pages: 248-256
Copyright (c) 2023 Akrim Teguh Suseno, M Al Amin, Fajar Mahardika
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
