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All Journal JMK Jurnal Manajemen dan Kewirausahaan Jurnal Akutansi Manajemen Ekonomi Kewirausahaan (JAMEK)
Diroatmodjo, Indah Safira
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Economics, Econometrics & Finance

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Peranan Internet dalam Meningkatkan Pendapatan Wirausaha Informal Bidang Jasa Diroatmodjo, Indah Safira; Wardhana, Adhitya
Jurnal Akutansi Manajemen Ekonomi Kewirausahaan (JAMEK) Vol 5 No 3 (2025): Edisi September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jamek.v3i1.2158

Abstract

Despite employing more than half of the national workforce, informal entrepreneurs in the service sector continue to earn less than their counterparts in the formal sector. This study aims to analyze informal entrepreneurship in the service sector, which is indicated to have the potential to improve local economies. The study uses 52,349 observations and applies the Ordinary Least Squares (OLS) method. Data were drawn from the 2019 National Labor Force Survey (Sakernas) and regional minimum wage (UMR) data published by Statistics Indonesia (BPS). The variables used in this study include internet usage, age, education level, gender, place of residence, work duration, weekly working hours, and UMR. The results indicate that internet usage has a positive and significant effect on income, with an estimated contribution of 29.1 percent. Other factors such as age, education, urban residence, work duration, working hours, and minimum wage are also significant. These findings highlight the importance of accelerating digitalization as a strategy to enhance the welfare of informal service entrepreneurs.
Data-Driven Approach to Managing Best-Selling Beauty Categories: Price, Rating, Review, and Stock Diroatmodjo, Indah Safira; Samidi, Samidi
JMK (Jurnal Manajemen dan Kewirausahaan) Vol 10 No 3 (2025): September
Publisher : Universitas Islam Kadiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32503/jmk.v10i3.7810

Abstract

The beauty industry in Indonesia is experiencing rapid growth, particularly through e-commerce platforms like Tokopedia. Many businesses still rely on intuition for product management, including decisions related to stock and pricing. This study develops a machine learning-based classification model to identify beauty products with high sales potential on Tokopedia, considering factors such as price, rating, review count, and stock availability. Ten classification algorithms are applied, including Naive Bayes, SVM, K-Nearest Neighbors, Decision Tree, Random Forest, XGBoost, LightGBM, CatBoost, Extra Trees, and Multi-Layer Perceptron (MLP). The data is processed using Python on Google Colab. The results show that ensemble algorithms, particularly Random Forest, LightGBM, and Extra Trees, provide prediction accuracy above 91% and are highly effective in predicting best-selling products. Based on this model, businesses can optimize stock and pricing management to ensure that best-selling products are always available, thereby improving operational efficiency in a highly competitive market. This research offers a data-driven solution for more strategic and evidence-based product management on e-commerce platforms.
Co-Authors Adhitya Wardhana Samidi Samidi, Samidi