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All Journal Scientific Journal of Informatics Jurnal Pengabdian IPTEK
Dwi Utari Iswavigra
Department of Computer Science, Universitas Sugeng Hartono, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Practice-Based Entrepreneurship Training Through Market Validation and Mentoring: A Community Engagement Program at Universitas Sugeng Hartono Suyahman Suyahman; Deny Prasetyo; Ardy Wicaksono; Dwi Utari Iswavigra; Yulaikha Mar'atullatifah; Ahmad Aufar Ribhi; Muhamad Wisnu Pangestu
Jurnal Pengabdian IPTEK Vol. 1 No. 2 (2026): May 2026
Publisher : Sah Publisher

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Abstract

Entrepreneurship courses can become overly centered on concepts and business plans when students have limited contact with customers and market uncertainty. This article describes the implementation of a practice-based entrepreneurship training program for 24 students at Universitas Sugeng Hartono from May 14 to June 25, 2026. The program combined entrepreneurial-mindset orientation, business proposal development, preparation of marketing materials, direct market practice, weekly progress reporting, mentoring, and final reflection. A descriptive qualitative approach was used to organize evidence from proposals, progress reports, classroom observations, supporting documentation, and student reflections. The implementation produced a structured learning sequence that moved from opportunity identification to customer contact, feedback, and strategy revision. Market validation and minimum viable product concepts gave students a practical basis for testing assumptions before expanding a business idea. Weekly reports made activity, obstacles, and follow-up decisions visible, while mentoring sessions converted individual difficulties into shared problem-solving material. Because participant records were heterogeneous and did not provide a complete standardized outcome dataset, the program cannot support claims about causal effects, average revenue gains, or changes in entrepreneurial intention. Its contribution is an implementable model for connecting entrepreneurship concepts with customer-facing action and reflective mentoring. Future delivery should retain the practice cycle while adding common indicators, baseline and endline measures, and systematic reflection records
Implementation of IndoBERT for Sentiment Analysis of the Constitutional Court's Decision Regarding the Minimum Age of Vice Presidential Candidates Very Dwi Setiawan; Dwi Utari Iswavigra; Endang Anggiratih
Scientific Journal of Informatics Vol. 12 No. 3: Special Issue
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.26320

Abstract

Purpose: This study aims to analyze the effectiveness of the IndoBERT model for sentiment analysis of Indonesian anguage YouTube comments related to the legal Court’s ruling on the minimum age of vice presidential candidates for 2024. While previous research applied conventional machine learning methods, this study addresses the challenge of understanding nuanced public opinion using a language-specific transformer model. Methods: A dataset of 23,796 YouTube comments was collected using the YouTube Data API in January 2025. The comments underwent extensive preprocessing including normalization, case folding, text cleansing, symbol removal, stopword elimination, and stemming. Sentiment labels (positive, negative, neutral) were assigned through a lexicon based approach. Three models IndoBERT, BERT, Support Vector Machine (SVM), and Random Forest were trained and tested using an 80% and 20% split. Model result was evaluated with accuracy, precision, recall, and F1-score metrics. Result: IndoBERT achieved the maximum result with 95% accuracy, outperforming BERT 92%, SVM 88%, and Random Forest 85%. This confirms IndoBERT’s superior ability to capture contextual nuances in Indonesian sentiment analysis compared to other models. Novelty: This research demonstrates the advantage of transformer based models, particularly IndoBERT, in analyzing complex Indonesian social media texts. The findings support the use of IndoBERT for automated sentiment monitoring to inform government and media responses. Future work could extend to broader discourse analysis across diverse public sectors.
Co-Authors Ahmad Aufar Ribhi Ardy Wicaksono Deny Prasetyo Endang Anggiratih Muhamad Wisnu Pangestu Suyahman Suyahman Very Dwi Setiawan Yulaikha Mar'atullatifah