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All Journal Eduvest - Journal of Universal Studies Business and Economic Publication Business and Accounting Education Journal
Lukito Angga Prasakti
Universitas Komputer Indonesia
Aerospace Engineering Computer Science & IT Economics, Econometrics & Finance Health Professions Neuroscience Social Sciences

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Development of a Machine Learning Model for Estimating GRDP at Constant Prices (PDRB ADHK) for Regencies and Cities in West Java Lukito Angga Prasakti; Isniar Budiarti
Eduvest - Journal of Universal Studies Vol. 6 No. 4 (2026): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i4.53015

Abstract

Gross Regional Domestic Product (GRDP) at constant prices (ADHK) is a key indicator for measuring real economic growth at the regional level. However, estimating GRDP at the regency/city level in Indonesia still faces challenges related to limited real-time data availability, publication delays, and reliance on conventional statistical methods that are often unable to capture complex and nonlinear relationships. This research aims to develop and compare several machine learning models in estimating ADHK GRDP for 27 regencies/cities in West Java Province using data from 2010–2024. The study employs a quantitative explanatory approach with panel data consisting of 405 observations obtained from the West Java Open Data portal. Feature engineering was conducted by incorporating historical growth rates, temporal variables, and regional encoding to capture temporal dynamics and spatial heterogeneity. Four predictive models were developed, namely linear regression, Random Forest, Gradient Boosting, and Support Vector Regression (SVR), and were evaluated using RMSE, MAE, MAPE, and R² metrics with cross-validation. The results indicate that ensemble-based models outperform traditional methods, with Gradient Boosting demonstrating the best performance by achieving the lowest error values and the highest explanatory power. Random Forest also shows strong predictive capability, while linear regression yields the lowest accuracy. These findings highlight the superiority of machine learning, particularly tree-based ensemble methods, in modeling complex regional economic data. The study contributes to the limited literature on regency/city-level GRDP estimation in Indonesia and suggests that machine learning can serve as a reliable tool for supporting data-driven policy formulation.
Analysis of the Development and Business Opportunities of Digital Business in Indonesia in the Last Five Years Lukito Angga Prasakti; Eddy Soeryanto Soegoto; Rahma Wahdiniwaty; Adam Mukharil Bachtiar
Eduvest - Journal of Universal Studies Vol. 6 No. 4 (2026): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i4.53071

Abstract

Indonesia's digital economy has shown rapid growth over the past five years. The e-Conomy SEA 2024 report noted that the gross merchandise value (GMV) of the digital economy increased from US$27 billion in 2018 to US$90 billion in 2024, with projections of reaching US$200–360 billion by 2030. The largest contribution comes from the e-commerce sector, which reached US$65 billion in 2024. Meanwhile, the adoption of digital payments and fintech is increasing rapidly; Bank Indonesia reported that electronic money transactions increased from 47.2 trillion rupiah in 2018 to 594.2 trillion rupiah in 2024. This article is designed to map trends, analyze opportunities, and link digital business developments in Indonesia to government policies, technological developments, consumer behavior, and the startup and MSME ecosystems. The research will employ a systematic literature review approach and secondary data analysis from government reports, scientific journals, and industry surveys. In addition to examining e-commerce and fintech, the study will also examine the edtech subsector—which is projected to have a market value of US$3.23 billion in 2024 with a predicted annual growth of 11.79% through and healthtech, with transaction value projected to increase from US$16 billion in 2023 to US$34 billion in 2027. Challenges such as digital infrastructure inequality, talent shortages, data privacy and cybersecurity regulations, and funding gaps will also be part of the analysis. This research is expected to provide a comprehensive mapping and strategic recommendations for the government, business actors, and researchers to strengthen Indonesia's digital business ecosystem.
“Don’t Make the Same Mistake!”: Peer-to-Peer Knowledge Sharing among SME Owners in Digital Business Communities Asep Koswara; Tarso Tarso; Lukito Angga Prasakti; Rahma Wahdiniwaty; Deden Abdul Wahab Sya'roni
Business and Economic Publication Vol. 4 No. 1 (2026): Business and Economic Publication
Publisher : Fakultas Ekonomi Universitas KH. A. Wahab Hasbullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32764/bep.v4i1.1766

Abstract

Digital social media environments have increasingly become informal spaces where entrepreneurs exchange experiences, operational reflections, and business survival strategies through peer interaction. However, previous studies have predominantly emphasized formal entrepreneurial education, institutional mentoring, or structured online communities, while organically emerging peer-to-peer entrepreneurial learning within public social media discussions remains underexplored. This study aims to examine how entrepreneurial learning is socially constructed among SME owners through interaction within a public Facebook Reel discussion. The study employed a qualitative descriptive content analysis approach using comments extracted through web scraping. From 789 initial comments, 312 comments were purposively selected and analyzed based on their experiential relevance. The findings reveal several dominant entrepreneurial learning themes, including financial discipline, employee management, strategic business growth, operational competence, and customer orientation. The study further demonstrates that digital communities function not only as communication platforms but also as informal entrepreneurial learning ecosystems that strengthen adaptive awareness, experiential reflection, and peer-based business learning among SME owners.
Pengambilan Keputusan Berbasis Data untuk Kinerja Pemasaran TikTok Shop: Bukti dari Kategori Produk Teratas di Indonesia Asep Koswara; Tarso; Lukito Angga Prasakti; Agus Riyanto; Herman Surdianto Soegoto
Business and Accounting Education Journal Vol. 7 No. 2 (2026): Business and Accounting Education Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/baej.v7i2.49356

Abstract

TikTok Shop menjadi salah satu platform social commerce dengan pertumbuhan tercepat di Indonesia yang menghasilkan jutaan interaksi pengguna dan impresi produk setiap bulannya. Penelitian ini menganalisis hubungan antara metrik performa pemasaran dan popularitas produk menggunakan pendekatan business analytics kuantitatif. Penelitian menggunakan metode statistik deskriptif dan analisis korelasi dengan dukungan teknik pengolahan data berbasis Python. Dataset penelitian terdiri dari 20 kategori produk teratas TikTok Shop Indonesia selama periode Maret 2026. Hasil penelitian menunjukkan bahwa shares memiliki korelasi positif terkuat terhadap popularitas produk (r = 0.669), diikuti impressions (r = 0.538) dan advertising cost (r = 0.453). Selain itu, kategori “Cases & Stickers” memiliki conversion rate tertinggi sebesar 78.44% dan cost per acquisition terendah sebesar 0.07 USD, sedangkan produk beauty menghasilkan engagement tertinggi dengan lebih dari 215.000 likes. Temuan penelitian menunjukkan bahwa perilaku berbagi konten oleh pengguna dan tingkat eksposur produk memiliki hubungan yang lebih kuat dengan popularitas produk dibandingkan metrik click efficiency maupun conversion. Penelitian ini berkontribusi terhadap pengembangan literatur digital marketing dan social commerce melalui penerapan business analytics komputasional untuk mendukung pengambilan keputusan strategis berbasis data pada ekosistem perdagangan digital berbasis platform.
Co-Authors Adam Mukharil Bachtiar Agus Riyanto Asep Koswara Deden Abdul Wahab Sya'roni Eddy Soeryanto Soegoto Herman Surdianto Soegoto Isniar Budiarti Rahma Wahdiniwaty Tarso Tarso Tarso