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Peramalan Harga Saham ADRO dengan Geometric Brownian Motion Nafi’a, Zidni Ilma; Nuraliya, Aliffia Yasya; Mukti, Gilang Axtria; Trenggono, Iqbal Ramadhanu
PERWIRA - Jurnal Pendidikan Kewirausahaan Indonesia Vol 7 No 2 (2024): PERWIRA - Jurnal Pendidikan Kewirausahaan Indonesia
Publisher : Perkumpulan Pendidik Kewirausahaan Indonesia (Perwira Indonesia)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21632/perwira.7.2.108-116

Abstract

Stock prices often experience unpredictable changes, leading to the uncertainty of stock return values. This paper aims to find a mathematical model to predict future stock prices based on past stock price data. One of the models used to predict stock prices is the Brownian Motion Model, which is based on the concept of random movement. The research method used to solve the stock forecasting problem is by using Geometric Brownian Motion by calculating the return value of closing stock prices using Ms. Excel, testing the normality of return value data, modeling stock prices using Geometric Brownian Motion, and calculating the model’s accuracy level using MAPE. Based on the model, the accuracy level using MAPE from the calculation results is obtained with a MAPE value of less than 10%, which is 4,93%. Therefore, it can be concluded that the average error deviation generated indicates a high level of forecasting accuracy.
PENCARIAN RUTE OPTIMAL TRAVELING SALESMAN PROBLEM DENGAN ALGORITMA ANT COLONY OPTIMIZATION (ACO) Nuraliya, Aliffia Yasya; Nurshiami, Siti Rahmah; Jajang, Jajang
Jurnal Ilmiah Matematika dan Pendidikan Matematika Vol 17 No 1 (2025): Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP)
Publisher : Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jmp.2025.17.1.15866

Abstract

The implementation of product distribution requires transportation to deliver products effectively across various locations. Challenges encountered during this process include varying distribution sites, travel distances, time taken for product delivery, transportation costs, and other related factors. To address these challenges, selecting an efficient travel route is crucial. The Traveling Salesman Problem (TSP) serves as a practical application of graph theory in tackling such distribution issues. The Ant Colony Optimization (ACO) algorithm emerges as a viable solution for route optimization, particularly in addressing TSP challenges to derive optimal routes. Results derived from the TSP calculations utilizing ACO, executed through the Matlab R2018a application, employed parameters of