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All Journal Jurnal Bisnis dan Kewirausahaan Malcom: Indonesian Journal of Machine Learning and Computer Science
Daryani, Dian
Akademi Digital Bandung
Computer Science & IT Economics, Econometrics & Finance

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Sistem Hambatan Berlapis Digitalisasi UMKM Kuliner: Studi Kualitatif Berbasis Kerangka TOE Dafitri, Rahma; Daryani, Dian
Jurnal Bisnis dan Kewirausahaan Vol. 21 No. 3 (2025): JBK-Jurnal Bisnis dan Kewirausahaan
Publisher : Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/jbk.v21i3.153-164

Abstract

Digital transformation barriers in culinary micro, small, and medium enterprises (MSMEs) form a layered barrier system that mutually reinforces and hinders optimization. This qualitative exploratory study involving 10 culinary MSMEs in Bandung identifies five main dimensions of barriers with 42 sub-categories analyzed using NVIVO. Digital literacy emerges as the dominant barrier, followed by technical, financial, operational, and external barriers. The study reveals three new theoretical findings: digital financial literacy as an overlooked dimension, time constraints creating repetitive cycles, and optimization paradox showing the phenomenon of "digital presence without digital performance". Barrier distribution varies based on MSME characteristics. Education-digital capability paradox shows bachelor's degree owners face the highest barriers. Middle-income digital trap appears in MSMEs with monthly revenue of Rp 5-8 million experiencing maximum technical complexity. Early-stage digital struggle dominates new MSMEs, while maturity advantage provides adaptation benefits for established MSMEs. The findings validate the Technology-Organization-Environment framework and generate a layered barrier system theory requiring comprehensive intervention approaches to break the mutually reinforcing barrier cycle.
Adoption Without Optimization: Text Mining and Business Intelligence Analysis on Culinary SME Digital Literacy Dafitri, Rahma; Daryani, Dian; Sutisna, Entis
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2622

Abstract

Digital transformation has become critical for culinary Micro, Small, and Medium Enterprises (MSME), yet most remain digitally underperforming despite platform adoption, a gap this study terms "adoption without optimization." This study employs a sequential exploratory mixed-methods design to examine underlying barriers. Phase 1 applied NVivo-assisted thematic coding and word frequency analysis to transcripts from 10 informants. Phase 2 administered a 78-item structured questionnaire measuring five TOE-derived barrier categories (financial, technical, human resource, regulatory, and market barriers) and digital platform usage frequency across six platform types to 39 respondents, analyzed using descriptive statistics, cross-tabulation, and Pearson correlation in SPSS. Phase 3 conducted VOSviewer keyword co-occurrence analysis of 87 Scopus documents. Results reveal financial barriers rank highest (M=3.46), followed by human resource (M=3.31) and market competition barriers (M=3.25). Digital literacy is predominantly low, with 48.7% scoring in the low category (mean=14.87/30). Thematic analysis identified three novel phenomena: digital financial literacy as a distinct barrier dimension, a recursive time-constraint cycle, and the adoption-without-optimization gap, confirmed by a 79.4-percentage-point difference between platform adoption (100%) and meaningful digital performance (20.6%). Digital literacy significantly correlates with sales improvement (r=0.432, p=0.006). A five-pillar digital adaptation framework is proposed for SME digitalization policy.
The Lexicon-Based Sentiment Analysis for Stock Price Prediction in Islamic Fashion Industry Entis Sutisna; Rahma Dafitri; Dian Daryani
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2586

Abstract

This study develops a lexicon-based sentiment analysis method to examine the relationship between social media sentiment and stock price movements in Indonesia's Islamic fashion industry. We collected 6,087 social media comments from TikTok, Instagram, and Twitter over 396 days, alongside daily stock data from Elzatta (ZATA). Using an Indonesian fashion-specific lexicon with 42 positive and 38 negative indicators validated through inter-annotator agreement (Cohen's Kappa = 0.82), we employed ordinary least squares regression with heteroskedasticity-robust standard errors to test sentiment-return relationships. Results show no economically significant relationship between social media sentiment and stock returns (correlation = -0.06, p = 0.240), with models explaining only 2.09% of return variance. We identify three boundary conditions limiting sentiment-based prediction: (1) feature mismatch 82% of comments discuss product attributes rather than business fundamentals, (2) demographic disconnect social media users (78% female, aged 18-29) differ markedly from investors (68% male, aged 35-55), and (3) market microstructure constraints 43% zero-volume days and 2.3% bid-ask spreads impede price discovery. This study provides the first Indonesian fashion-specific sentiment lexicon and establishes actionable validation guidelines for practitioners: sentiment features must be verified for demographic alignment and market liquidity before deployment. For machine learning applications in emerging markets
Co-Authors Entis Sutisna Entis Sutisna Rahma Dafitri