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All Journal International Journal of Engineering, Science and Information Technology Journal of Data Mining and Information Systems Journal of Management and Business Analytics
Adli Abdillah Nababan
Bina Nusantara University
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Technology-Based Startup Ideas and MVP Development: A Digital Literacy Perspective among High School Students Ince Ahmad Zarqan; Dimas Yudistira Nugraha; Ganda Sitompul; Fernando Sihotang; Scherly Hansopaheluwakan; Adli Abdillah Nababan
Journal of Management and Business Analytics Vol. 2 No. 1 (2026): January
Publisher : CV. ADMITECH SOLUTIONS

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Abstract

The rapid growth of digital technologies has created new opportunities for entrepreneurship among younger generations, including high school students. This study explores the emerging trends of technology-based startup ideas and evaluates the potential for Minimum Viable Product (MVP) development from the perspective of digital literacy. Using a descriptive–qualitative approach, data were collected from student business proposals, pitch decks, and prototype outputs generated during a startup competition for high school students. The analysis categorized startup ideas into various technological domains such as fintech, edtech, agritech, and health tech, while also assessing the quality and feasibility of MVPs based on lean startup principles. Findings reveal that students demonstrate strong creativity in problem identification and solution design, yet often face challenges in translating ideas into functional MVPs due to limited technical capacity. Moreover, higher levels of digital literacy—particularly in digital creativity, technological adaptability, and collaborative skills—were positively correlated with the quality of MVP outcomes. This study highlights the importance of integrating digital literacy into early entrepreneurship education, suggesting that fostering these competencies can enhance the capacity of young innovators to develop feasible and impactful technology-driven startups.
Developing Business Intelligence Dashboard for Sales KPI Monitoring in Advertising Agency: A Human-Centered Design Approach Ince Ahmad Zarqan; Dimas Yudistira Nugraha; Ganda Tua Sitompul; Adli Abdillah Nababan
JDMIS: Journal of Data Mining and Information Systems Vol. 4 No. 1 (2026): February 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v4i1.6596

Abstract

Digital advertising agencies in South Jakarta face significant challenges in monitoring sales performance due to data fragmentation across multiple platforms such as CRM, spreadsheets, and digital advertising tools. Conventional manual reporting processes lead to data latency, high error rates, and delayed strategic decision-making. This study aims to develop a Business Intelligence (BI) dashboard to monitor Sales Key Performance Indicators (KPIs) in real-time, utilizing a Human-Centered Design (HCD) approach to ensure high usability and adoption. The research methodology follows the ISO 9241-210 standard for HCD, encompassing four iterative phases: understanding the context of use, specifying user requirements, producing design solutions, and evaluating designs. The system was developed using Google Looker Studio with a data warehouse architecture integrating Google BigQuery. Testing was conducted involving 15 internal stakeholders using the System Usability Scale (SUS) and User Experience Questionnaire (UEQ). The results demonstrated a SUS score of 82.5 (Excellent) and positive benchmarks in efficiency and perspicuity metrics. The implementation of the dashboard reduced reporting time by 60% and improved data accessibility for executive decision-making. This study contributes to the literature by demonstrating how HCD principles can bridge the gap between technical BI capabilities and end-user cognitive needs in the creative industry context.
Smart Campus Dropout Prediction: Hybrid Features and Ensemble Approach M Safii; Adli Abdillah Nababan; Husain Husain
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1183

Abstract

The issue of the high number of students dropping out of college is a major concern in higher education, especially in the smart campus ecosystem. This research aims to design a prediction system for students who are at risk of dropping out by integrating hybrid feature selection methods and ensemble learning that leverage academic data and students' digital footprints. The initial process of model development involves data cleaning and the selection of important features through a combination approach using filter-based methods (mutual information) and recursive feature elimination. A classification model is then designed using the XGBoost and Random Forest algorithms. The testing was conducted using a secondary dataset that included variables such as participation in discussions, attendance rates, interaction with learning materials, and academic achievement. The results of testing with the XGBoost model showed a satisfactory accuracy level, with an F1 score of 0.77 and a ROC AUC of 0.89. The confusion matrix recorded 67 correct predictions for students who graduated and 17 correct predictions for students who dropped out, with a total of 12 misclassifications. These findings suggest that the combination of hybrid feature selection strategies and XGBoost can produce sufficiently accurate predictions of student dropouts and has the potential to be utilized as an early warning system in the governance of a more flexible and responsive smart campus.
Co-Authors Dimas Yudistira Nugraha Fernando Sihotang Ganda Sitompul Ganda Tua Sitompul Husain Husain Ince Ahmad Zarqan Ince Ahmad Zarqan M Safii Scherly Hansopaheluwakan