DOI: https://doi.org/10.21456/vol13iss2pp127-134

Komparasi Algoritme Random Forest dan XGBoosting dalam Klasifikasi Performa UMKM

*Moh Erkamim orcid  -  Universitas Tunas Pembangunan, Indonesia
Suswadi Suswadi  -  Universitas Tunas Pembangunan Surakarta, Indonesia
Muhammad Zidni Subarkah  -  Universitas Sebelas Maret, Indonesia
Erni Widarti  -  Universitas Tunas Pembangunan Surakarta, Indonesia
Open Access Copyright (c) 2023 JSINBIS (Jurnal Sistem Informasi Bisnis)

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Abstract
The Covid-19 pandemic has greatly impacted the whole world, especially Indonesia. Various policies have been implemented starting from the implementation of lockdowns, restrictions on large-scale economic activities, and bans from leaving the region. The economic sector is a sector that has been affected quite a lot, one of which is Micro, Small, and Medium Enterprises (MSMEs). As a result of the Covid-19 pandemic, many MSMEs have suffered losses, so many investors have started to consider investing in MSMEs. Therefore, MSMEs need to know their business performance through potential analysis and financial reports to deal with the economic crisis during a pandemic. This study compares two algorithms namely Random Forest and XGBoosting in classifying the good or bad performance of MSME financial conditions. The performance of the developed algorithm will be improved using hyperparameter tuning to obtain the best parameter combination for each algorithm. In this study, the Random Forest algorithm has an accuracy value of 0.944 and an f1-score of 0.944, while the XGBoosting algorithm has an accuracy value of 0.944 and an f1-score of 0.950. Based on the model with the best evaluation metric, six important features are obtained: the 2021 profit and loss variable, 2020 cash, 2020 liabilities, 2020 capital, 2021 sales, and 2021 liabilities.
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Keywords: Klasifikasi; Algoritma; Random Forest; XGBoosting; UMKM; Keuangan

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  1. Dewi, D. S., & Tobing, T. N. W. (2021). Optimalisasi Penyelenggaran Pelayanan Publik Dalam Masa Perubahan Melawan Covid-19 Di Indonesia. Journal of Information System, Applied, Management, Accounting and Research, 5(1), 210. https://doi.org/10.52362/jisamar.v5i1.362
  2. Haumahu, J. P., Permana, S. D. H., & Yaddarabullah, Y. (2021). Fake news classification for Indonesian news using Extreme Gradient Boosting (XGBoost). IOP Conference Series: Materials Science and Engineering, 1098(5), 052081. https://doi.org/10.1088/1757-899X/1098/5/052081
  3. Ismawanti, R. (2021). Dampak Manajemen Perubahan Lingkungan Kerja Masa Pandemi Covid-19 Terhadap Pegawai PT Telkom Indonesia Tbk DIVREG 3 Jawa Barat. Kebijakan: Jurnal Ilmu Administrasi, 12(1), 57-62. https://doi.org/10.23969/kebijakan.v12i1.3468
  4. Mardiana, D., & Darmalaksana, W. (2020). Relevansi syahid ma'nawi dengan peristiwa pandemic covid-19. Jurnal Perspektif, 4(1), 12-20
  5. Maxwell, A. E., Warner, T. A., & Fang, F. (2018). Implementation of machine-learning classification in remote sensing: An applied review. International Journal of Remote Sensing, 39(9), 2784-2817
  6. https://doi.org/10.1080/01431161.2018.1433343
  7. Miftahusalam, A., Nuraini, A. F., Khoirunisa, A. A., & Pratiwi, H. (2022). Perbandingan Algoritma Random Forest, Naïve Bayes, dan Support Vector Machine Pada Analisis Sentimen Twitter Mengenai Opini Masyarakat Terhadap Penghapusan Tenaga Honorer. Seminar Nasional Official Statistics, 2022(1), 563-572. https://doi.org/10.34123/semnasoffstat.v2022i1.1410
  8. Mursianto, G. A., Falih, I. M., Irfan, M., Sakinah, T., & Prasvita, D. S. (2021). Perbandingan Metode Klasifikasi Random Forest dan XGBoost Serta Implementasi Teknik SMOTE pada Kasus Prediksi Hujan. Senamika, 2(2), 41-50
  9. Nalini, S. N. L. (2021). Dampak Dampak covid-19 terhadap Usaha MIkro, Kecil dan Menengah. Jesya (Jurnal Ekonomi & Ekonomi Syariah), 4(1), https://doi.org/10.36778/jesya.v4i1.278
  10. Rosita, R., Zailani, A. U., Hanun, N. L., Maxwell, A. E., Warner, T. A., Fang, F., Kotsiantis, S. B., Zaharakis, I. D., Pintelas, P. E., Haumahu, J. P., Permana, S. D. H., Yaddarabullah, Y., Salamah, U., Ramayanti, D., Sheridan, R. P., Wang, W. M., Liaw, A., Ma, J., Gifford, E. M., … He, T. (2020). Supervised Classification of Indonesian Text DocumentUsing Extreme Gradient Boosting (XGBoost). International Journal of Remote Sensing, 56(5), 79-84
  11. Salamah, U., & Ramayanti, D. (2018). Supervised Classification of Indonesian Text DocumentUsing Extreme Gradient Boosting ( XGBoost ). 5(5), 79-84
  12. Saragih, G. S., Rustam, Z., Bustaman, examiner A., & Sarwinda, examiner D. (2018). Prediksi kebangkrutan bank dengan menggunakan random forest = Predict bank failures using random forest
  13. Septianingrum, F., Jaman, J. H., & Enri, U. (2021). Analisis Sentimen Pada Isu Vaksin Covid-19 di Indonesia dengan Metode Naive Bayes Classifier. Jurnal Media Informatika Budidarma, 5(4), 1431. https://doi.org/10.30865/mib.v5i4.3260
  14. Sheridan, R. P., Wang, W. M., Liaw, A., Ma, J., & Gifford, E. M. (2016). Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships. Journal of Chemical Information and Modeling, 56(12), 2353-2360. https://doi.org/10.1021/acs.jcim.6b00591
  15. Siswanti, T. (2020). Analisis Pengaruh Faktor Internal Dan Eksternal Terhadap Kinerja Usaha Mikro Kecil Dan Menengah (Umkm). Jurnal Bisnis &
  16. Akuntansi Unsurya, 5(2), 61-76. https://doi.org/10.35968/jbau.v5i2.430
  17. Suci, Y. R., Tinggi, S., & Ekonomi, I. (2017). Perkembangan UMKM (Usaha Mikro Kecil Menengah) di Indonesia. Jurnal Ilmiah Fakultasi Ekonomi, 6(1), 51-58
  18. Thaha, A. F. (2020). Dampak Covid-19 Terhadap UMKM Di Indonesia [The Impact of Covid-19 on MSMEs in Indonesia]. Jurnal Brand, 2(1), 148-153
  19. Widiyani. (2020). Latar Belakang Virus Corona, Perkembangan hingga Isu Terkini. detikNews
  20. Zailani, A. U., & Hanun, N. L. (2020). Penerapan Algoritma Klasifikasi Random Forest Untuk Penentuan Kelayakan Pemberian Kredit Di Koperasi Mitra Sejahtera. Infotech: Journal of Technology Information, 6(1), 7-14. https://doi.org/10.37365/jti.v6i1.61

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