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All Journal Natural Science: Journal of Science and Technology Media Statistika Jurnal Gaussian JURNAL ILMIAH MATEMATIKA DAN TERAPAN BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Aplikasi Statistika & Komputasi Statistik Enthusiastic : International Journal of Applied Statistics and Data Science Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA)
Iut Tri Utami
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation Other

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Determination of the Shortest Route on the Distribution System using Ant Colony Optimization (ACO) Algorithm (Case Study: Alfamidi Palu Branch – PT. Midi Utama Indonesia) Indria, Nabila Dwi; Junaidi, Junaidi; Utami, Iut Tri
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 1 Issue 2, October 2021
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (436.645 KB) | DOI: 10.20885/enthusiastic.vol1.iss2.art5

Abstract

The distribution system of goods is one of the most important parts for every company. The company certainly has many route options to visit, and this is expected to be conducted efficiently in terms of time. In the distribution of goods by Alfamidi company in Palu City which has 51 outlets include into the category of Traveling Salesman Problem (TSP) because of many route options that can be visited. The problem can be solved by employing the Ant Colony Optimization (ACO) method which is one of the algorithms Ant Colony System (ACS). The ACS acquires principles based on the behavior of ant colonies and applies three characteristics to determine the shortest route namely status transition rules, local pheromone renewal and global pheromones. The result showed that the shortest route of the distribution of goods based on the calculation of selected iterations was ant 1 with the shortest total distance obtained 86.98 km.
A Gaussian Mixture Model Approach to Profiling Stunting Risk Across Indonesian Provinces Rochayani, Masithoh Yessi; Utami, Iut Tri
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 4 No. 2 (2025): September 2025
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v4i2.5395

Abstract

Stunting is still a major health problem in Indonesia, with notable differences between provinces. Although the national rate has decreased over time, regional gaps continue, emphasizing the role of data in helping to explain what contributes to the issue. This study aims to segment 38 provinces in Indonesia based on maternal and child health indicators associated with stunting prevalence. The variables used include the percentage of low birth weight (LBW) infants, the percentage of infants born short, the percentage of pregnant women with chronic energy deficiency (CED), exclusive breastfeeding (EBF) coverage, prevalence of diarrhea in toddlers, and prevalence of acute respiratory infections (ARI) in toddlers. The clustering analysis was performed using the Gaussian Mixture Model (GMM) with the number of clusters varied from 2 to 7. Model selection was based on the Bayesian Information Criterion (BIC), where the lowest value indicated the optimal model. The results show that the model with two clusters was selected, with a BIC value of 1358.24, which indicates the best balance between model fit and complexity. This clustering reveals that provinces are grouped based on similarities in maternal and child health profiles, not on geographic proximity, meaning that the GMM method does not rely on spatial location to form clusters.
COMPARISON OF FRUIT FLY OPTIMIZATION ALGORITHM (FOA) AND PARTICLE SWARM OPTIMIZATION (PSO) FOR SUPPORT VECTOR REGRESSION (SVR) IN UNITED TRACTORS STOCK PRICES FORECASTING Wibowo, Belva Hadaya; Utami, Iut Tri; Rochayani, Masithoh Yessi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1345-1358

Abstract

Stock price forecasting is one of the analytical approaches used by capital market participants to identify future price movement patterns. This study evaluates the performance of the Support Vector Regression (SVR) model in predicting the stock price of United Tractors (UNTR) by optimizing the model’s parameters using two metaheuristic algorithms. The selection of SVR is based on its ability to handle nonlinear regression problems through the use of the Radial Basis Function (RBF) kernel. The parameter optimization of SVR is carried out using the Fruit Fly Optimization Algorithm (FOA), an algorithm inspired by the olfactory and visual system of fruit flies in locating food sources. The advantage of FOA lies in its computational simplicity and fast convergence speed. This study also implements Particle Swarm Optimization (PSO) for comparison purposes. This algorithm adopts a collaborative mechanism among particles in the search space, inspired by the flocking behavior of birds. The stock price data used in this study, covering the period from January 2020 to December 2023, was obtained from Yahoo Finance (https://finance.yahoo.com). The results show that SVR-FOA yields a parameter combination of C = 1000, gamma = 0.9182, and epsilon = 0.9997, while SVR-PSO produces a different configuration, namely C = 1000, gamma = 0.0001, and epsilon = 1. Accuracy evaluation using Mean Absolute Percentage Error (MAPE) indicates that the SVR-PSO model achieves a MAPE of 2.3164%, suggesting a relatively low prediction error. SVR-FOA yields a MAPE of 5.8727%, which is still within the acceptable tolerance range for financial data. While this study focuses on a single stock and uses only historical closing prices, its results provide a strong baseline for applying SVR with metaheuristic optimization in financial forecasting. This research contributes by presenting a direct comparative analysis of FOA and PSO for SVR parameter tuning in an emerging market context, offering practical insights for investors and researchers seeking robust forecasting models.
Co-Authors Amalia, Nur Afnita Arief Rachman Hakim Diah Daniaty Dwi Ispriyanti Fadjryani, Fadjryani Indria, Nabila Dwi Jannah, Berliana Junaidi Junaidi Junaidi Junaidi Masithoh Yessi Rochayani Nureni, Nureni Puspita Kartikasari Rahman, Syair Dafiq Faizur Rais Rais Rancendo, H M Suparti Suparti Suryaningrum, Fahlevi Susiana, Luluk Tulak, Dewi Yuliastuti Wibowo, Belva Hadaya Yuciana Wilandari ZK Abdurahman Baizal