Article Per Year (5 Year)
p-Index From 2021 - 2026
1.829
P-Index
This Author published in this journals
All Journal JDM (Jurnal Dinamika Manajemen) Jurnal Ad'ministrare: Jurnal Pemikiran Ilmiah dan Pendidikan Administrasi Perkantoran JAF- Journal of Accounting and Finance Jurnal Syntax Transformation Journal of Comprehensive Science Asean International Journal of Business West Science Business and Management Blantika : Multidisciplinary Journal
Edi Witjara
Universitas Telkom
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : journal of comprehensive science

 1 
Business Model Development with Business Model Canvas Approach and SWOT Analysis at PT. Kita Muda Bisa Prabowo Leksono, Teguh; Riza Sutjipto, Mohammad; Witjara, Edi
Journal of Comprehensive Science Vol. 4 No. 1 (2025): Journal of Comprehensive Science (JCS)
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/jcs.v4i1.3003

Abstract

In an era characterized by rapid technological advances and continuously evolving consumer preferences, companies are required to continue to innovate and optimize their strategies to maintain relevance and competitiveness in the market. With this background, this research focuses on PT Kita Muda Bisa, a company that has an online learning platform in Indonesia, which is specifically involved in the world of pre-employment which facilitates online learning services to the community. By using a qualitative research approach, this study aims to gain in-depth insight into the current condition of the business model and strategic landscape of pre-employment training institutions in Indonesia. Through a comprehensive analysis using the BMC and SWOT frameworks, this research seeks to identify existing strengths and weaknesses, as well as external opportunities and threats related to training service products. By utilizing this analysis, this research seeks to develop an understanding of the complex interactions between internal capabilities and external market dynamics that shape the competitiveness of learning service products in Indonesia. The proposed strategy is designed to exploit strengths, mitigate weaknesses, exploit opportunities, and counter threats identified through the research process. Additionally, this research highlights the importance of strategic agility and adaptability in navigating the dynamic training industry landscape, emphasizing the need for continuous strategy reassessment and refinement. In Conclusion, this research contributes to existing knowledge by offering practical insights and recommendations to increase the competitiveness of online learning service products. By integrating the BMC and SWOT frameworks, this research provides a qualitative approach to strategic analysis and formulation, equipping PT Kita Muda Bisa's work plan with the tools and insights needed to thrive in an increasingly competitive market. Ultimately, the findings of this research aim to inform the strategic decision-making process in online learning services and inspire similar efforts in a broader business and innovation strategic context.
Analysis of Random Forest Machine Learning Integration in Enterprise Resource Planning (ERP) Systems to Improve Operational Efficiency in Supply Chain Management: A Case Study at PT Sentosa Laju Sejahtera Putri Hardiyanti; Mohammad Riza Sutjipto; Edi Witjara
Journal of Comprehensive Science Vol. 5 No. 6 (2026): Journal of Comprehensive Science
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/jcs.v5i6.4189

Abstract

The rapid advancement of digital technologies and volatile market dynamics have challenged mining companies to optimize supply chain management (SCM) processes. PT Sentosa Laju Sejahtera (SLS) experiences inefficiencies in procurement and inventory, causing decision latency and increased operational costs. This study aims to explore how integrating Machine Learning, specifically the Random Forest algorithm, into the company’s Enterprise Resource Planning (ERP) system can enhance predictive capabilities, reduce costs, and strengthen operational resilience. A qualitative case study approach was employed, combining in-depth interviews with key managerial stakeholders, participatory observations of operational workflows, and analysis of 63,818 historical Purchase Order documents. Triangulation techniques ensured data credibility and reliability. Findings reveal that Random Forest integration transforms decision-making from reactive to data-driven, reducing procurement cycle times from seven days to 2–3 days and lowering Total Cost of Ownership by 15% through fewer emergency orders. Feature Importance metrics enabled management to identify key inefficiency drivers, improving strategic interventions. Organizational readiness and data governance were identified as critical factors for successful implementation. In conclusion, the integration of predictive analytics into ERP systems provides significant operational and strategic benefits, enhancing efficiency, agility, and competitiveness. The study contributes both empirically to the literature on digital business transformation and practically as a model for multi-site mining enterprises seeking data-driven SCM optimization.
Co-Authors Abbadi Said Thalib Adhanissa, Salsa Arif Partono Prasetio, Arif Partono Bachruddin Saleh Luturlean Dodie Tricahyono Eri Hermawan Ferdio Ghifary Fidhien Fetty Poerwita Sary Fidhien, Ferdio Ghifary Hutapea, Vico Metriwan Perluozon Ismu Widodo Mohammad Riza Sutjipto Mohammad Riza Sutjipto Muhammad Luthfi Siraj Prabowo Leksono, Teguh Putri Hardiyanti Rina Djunita Pasaribu Siska Noviarisanti Vico Metriwan Perluozon Hutapea