Model Optimasi Alokasi Dana Investasi Terbatas Menggunakan Pendekatan Riset Operasi dan Orange Data Mining
DOI:
https://doi.org/10.70340/jirsi.v5i2.350Keywords:
Investment Optimization, Linear Programming, Limited Capital, Orange Data Mining, Operations Research, Information SystemsAbstract
Limited capital is a major obstacle in investment decision-making, especially for novice investors. This study aims to develop an optimization model for investment fund allocation with limited capital using an operations research approach (linear programming) and its implementation through Orange Data Mining. A case study was conducted on three digital investment instruments (stocks, mutual funds, gold) with a total capital of IDR 100 million and a maximum risk limit of 10%. The method used is linear programming solved using the Simplex method and Orange Data Mining. The results showed an optimal solution of 25% stocks, 50% mutual funds, 25% gold with an expected return of IDR 10.75 million/year. Method comparison showed identical accuracy (100%) with Orange computation time of 0.3 seconds vs 15 minutes manually. Sensitivity analysis revealed that increasing risk tolerance from 10% to 15% increases returns to IDR 12.5 million. This research contributes to the development of investment decision support tools that are easy to use by novice investors as well as teaching materials for Operations Research.
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References
Otoritas Jasa Keuangan, "Statistik Pasar Modal Indonesia 2025," OJK Publishing, Jakarta, 2026.
Kustodian Sentral Efek Indonesia, "Laporan Tahunan KSEI 2025," KSEI, Jakarta, 2026.
R. J. Shiller, Narrative Economics: How Stories Go Viral and Drive Major Economic Events. Princeton University Press, 2020.
F. S. Hillier and G. J. Lieberman, Introduction to Operations Research, 11th ed. McGraw-Hill Education, 2021.
H. A. Taha, Operations Research: An Introduction, 10th ed. Pearson Education, 2020.
F. S. Hillier and G. J. Lieberman, "The History and Future of Operations Research," INFORMS Journal on Applied Analytics, vol. 52, no. 3, pp. 214-228, 2022.
W. L. Winston, Operations Research: Applications and Algorithms, 4th ed. Cengage Learning, 2020.
B. Render, R. M. Stair, and M. E. Hanna, Quantitative Analysis for Management, 14th ed. Pearson Education, 2023.
Orange Data Mining Team, "Orange: Data Mining Toolbox in Python," Journal of Machine Learning Research, vol. 20, no. 1, pp. 1-5, 2019.
J. Demšar et al., "Orange: Data Mining Fruitful and Fun – A Historical Perspective," Informatica, vol. 41, no. 1, pp. 7-14, 2017.
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Copyright (c) 2026 Zuhri Zuhri, Fajrillah Fajrillah, Almastoni Almastoni
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
