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All Journal VISI SAKTI: Jurnal Industri Kreatif dan Inovatif
Rudi Lingga
Universitas Efarina
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PEMBELAJARAN MESIN UNTUK OPTIMASI ALOKASI MODAL DAN PROYEK DI INDUSTRI Riduan Siagian; Adrian K. Tarigan; Rudi Lingga
Jurnal Industri Kreatif dan Inovatif Vol. 3 No. 1 (2025): Desain Komunikasi Visual
Publisher : Institut Teknologi dan Bisnis Kristen Bukit Pengharapan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61696/visisakti.v3i1.1043

Abstract

This study investigates the application of machine learning for optimizing capital allocation and project management in the industrial sector under financial market complexity and macroeconomic uncertainty. The research aims to explore machine learning algorithms, analyze their effectiveness in improving investment decision efficiency and risk mitigation, integrate real-time risk-based allocation approaches, and compare the proposed methods with traditional frameworks such as the mean-variance model (MVO). The methodology combines literature review, case studies, and numerical simulations using historical data from 2017 to 2022. The framework consists of volatility forecasting using LSTM, differentiable risk budgeting for adaptive target-risk adjustment, and deep reinforcement learning (DDPG-TiDE) to optimize asset allocation policies within a Markov Decision Process (MDP). Model performance is evaluated using Sharpe ratio, maximum drawdown, and portfolio turnover efficiency, while interpretability is validated using SHAP. Simulation results show a 23–55% improvement in Sharpe ratio compared to traditional risk parity strategies and a 41% reduction in maximum drawdown during volatile market periods. The study also demonstrates that SHAP enhances transparency by identifying key drivers such as market volatility, credit spread, and the yield curve. The findings conclude that machine learning can be a game changer for improving efficiency, real-time risk mitigation, and adaptive decision-making, while highlighting challenges related to data quality, model complexity, AI governance, and integration with legacy systems.
PENERAPAN SISTEM PENILAIAN KINERJA BERBASIS KOMPETENSI UNTUK PELAKU USAHA MIKRO, KECIL, DAN MENENGAH (UMKM) Riduan Siagian; Adrian K. Tarigan; Rudi Lingga
Jurnal Industri Kreatif dan Inovatif Vol. 3 No. 1 (2025): Desain Komunikasi Visual
Publisher : Institut Teknologi dan Bisnis Kristen Bukit Pengharapan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61696/visisakti.v3i1.1044

Abstract

This Community Service Program (PKM) activity aims to help MSMEs implement a competency-based performance assessment system to ensure clearer division of labor, increased productivity, and more consistent service/product quality. Partners' issues include the lack of written work standards, subjective employee assessments, unclear targets, and difficulty in determining training needs. The program is implemented through needs assessments, the development of a simple MSME competency dictionary, the creation of assessment tools (competency forms and operational KPIs), assessment implementation training, and trial assistance for a specific period. The output is a performance assessment system package (competency + KPI) ready for use by MSMEs, complete with assessment SOPs, sample forms, and competency development plans.
Co-Authors Adrian K. Tarigan Riduan Siagian