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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Algoritma
Luhur Bayuaji
Universitas Budi Luhur
Computer Science & IT Electrical & Electronics Engineering

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Perbandingan Genetic Algorithm, Nearest Neighbour, dan Particle Swarm Optimization untuk Penentuan Rute Pengiriman Barang Sandy Achmadi; Prabowo Murti Saputro; Luhur Bayuaji
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3201

Abstract

The goods distribution process carried out by PT Saqo Putra Utama, a logistics transportation service company that delivers goods from warehouses to customers in the Jabodetabek area, is still performed manually. As a result, its effectiveness cannot be measured based on the travel distance from one location to another, leading to high operational costs for the company. This study aims to determine the shortest route for goods delivery by minimizing travel distance. The study compares and analyzes route determination results using three methods: Genetic Algorithm, Nearest Neighbour, and Particle Swarm Optimization. The comparison of these three algorithms in goods distribution routing aims to find a balance between processing speed and solution quality, namely the shortest distance or lowest cost. This research was conducted in the Jabodetabek area at PT Saqo Putra Utama, a logistics transportation service company that distributes goods from warehouses to customers. Based on the average calculation results of the three compared methods, it can be concluded that the best method for determining goods delivery routes at PT Saqo Putra Utama is the Genetic Algorithm method, with an average total distance of 222.57 km and an average total cost of IDR 355,809.66.
XgBoost Hyper-Parameter Tuning Using Particle Swarm Optimization for Stock Price Forecasting Dwi Pebrianti; Haris Kurniawan; Luhur Bayuaji; Rusdah Rusdah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27712

Abstract

Investment in the capital market has become a lifestyle for millennials in Indonesia as seen from the increasing number of SID (Single Investor Identification) from 2.4 million in 2019 to 10.3 million in December 2022. The increase is due to various reasons, starting from the Covid-19 pandemic, which limited the space for social interaction and the easy way to invest in the capital market through various e-commerce platforms. These investors generally use fundamental and technical analysis to maximize profits and minimize the risk of loss in stock investment. These methods may lead to problem where subjectivity and different interpretation may appear in the process. Additionally, these methods are time consuming due to the need in the deep research on the financial statements, economic conditions and company reports. Machine learning by utilizing historical stock price data which is time-series data is one of the methods that can be used for the stock price forecasting. This paper proposed XGBoost optimized by Particle Swarm Optimization (PSO) for stock price forecasting. XGBoost is known for its ability to make predictions accurately and efficiently. PSO is used to optimize the hyper-parameter values of XGBoost. The results of optimizing the hyper-parameter of the XGBoost algorithm using the Particle Swarm Optimization (PSO) method achieved the best performance when compared with standard XGBoost, Long Short-Term Memory (LSTM), Support Vector Regression (SVR) and Random Forest. The results in RSME, MAE and MAPE shows the lowest values in the proposed method, which are, 0.0011, 0.0008, and 0.0772%, respectively. Meanwhile, the  reaches the highest value. It is seen that the PSO-optimized XGBoost is able to predict the stock price with a low error rate, and can be a promising model to be implemented for the stock price forecasting. This result shows the contribution of the proposed method.
Co-Authors Dwi Pebrianti Haris Kurniawan Prabowo Murti Saputro Rusdah Rusdah Sandy Achmadi