Article Per Year (5 Year)
p-Index From 2021 - 2026
0.817
P-Index
This Author published in this journals
All Journal AKSES: Jurnal Ekonomi dan Bisnis Indonesian Journal Of Business And Economics Jurnal Ekonomi dan Teknik Informatika Jurnal Manajemen Dan Bisnis Ekonomi
Agus Fitri Yanto
Politeknik Sawunggalih Aji
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
Faktor-Faktor Penentu Keberhasilan UMKM di Era Digital: Pendekatan Pemodelan Prediktif Menggunakan Gradient Boosting Galuh Aditya; Siska Narulita; Agus Fitri Yanto; Andreas Tigor Oktaga
JURNAL MANAJEMEN DAN BISNIS EKONOMI Vol. 4 No. 3 (2026): Juli: JURNAL MANAJEMEN DAN BISNIS EKONOMI
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jmbe-itb.v4i3.4180

Abstract

This study aims to compare the performance of three boosting algorithms, namely XGBoost, LightGBM, and CatBoost, to predict the success of MSMEs. The data used consists of 250 entries with 13 attributes that include business actor characteristics, initial capital, industry experience, financial record-keeping, internet utilization, business planning, partnerships, and the target variable success. The pre-processing stage includes checking for missing values, standardizing numerical attributes, and splitting the data into 80% training data and 20% test data. The evaluation results show that XGBoost provides the best performance with an accuracy of 0.92, precision of 0.8333, recall of 0.8333, F1-score of 0.8333, and ROC-AUC of 0.9715. LightGBM has an accuracy of 0.88, while CatBoost achieves an accuracy of 0.90. The research results show that XGBoost has the best ability to classify successful and unsuccessful MSMEs. The feature importance results also show that the success of MSMEs is influenced by a combination of several key factors. This research emphasizes that boosting algorithms are effectively used as predictive models to support the analysis of MSME success.
Co-Authors Afifah Choirina Rusda Andreas Tigor Oktaga, Andreas Ariya Fitriastuti Danis Imam Bachtiar Dini Zanuar Wulandari Dona Evi Fitriana Dwi Irawati Endah Aisyiyah Ningrum Galuh Aditya Sari Asih Siska Narulita