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All Journal Jurnal Sistem Informasi dan Informatika (SIMIKA)
Kartika Maulida Hindrayani
University of Pembangunan Nasional Veteran Jawa Timur
Computer Science & IT Control & Systems Engineering

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PREDIKSI HARGA SAHAM DI INDONESIA DENGAN EXTREME GRADIENT BOOSTING YANG DIOPTIMALKAN OLEH ADAPTIVE PARTICLE SWARM OPTIMIZATION Alya Mirza Safira; Trimono Trimono; Kartika Maulida Hindrayani
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/5r67ag12

Abstract

Volatile stock price movements are a big problem in making investment decisions, especially in stocks with high volatility. This research aims to build a stock price prediction model by utilising the Extreme Gradient Boosting (XGBoost) algorithm optimised using the Adaptive Particle Swarm Optimization (APSO) method. The research focuses on two stocks with high volatility levels, namely PT Jaya Agra Wattie Tbk (JAWA) and PT Charnic Capital Tbk (NICK) with historical closing price data from August 2019 to July 2024. The research process includes data collection, preprocessing, modelling, optimazing and model performance evaluation using the Mean Absolute Percentage Error (MAPE) metric. The results showed that the XGBoost-APSO combination proved superior to the standard XGBoost-PSO and XGBoost methods in predicting stock prices without overfitting. The MAPE value on JAWA stock is 5.20 (training) and 5.95 (testing) with a difference of 0.75. As for NICK stock, the MAPE on training data is 4.50 and testing is 5.40, with a difference of 0.90. The model also successfully predicts the closing price movement in the next five days realistically according to its historical volatility characteristics. This research proves that the combination of XGBoost with APSO optimisation is effective in handling stock data with high volatility and can be used as a predictive tool in investment decision making. Keyword: prediction, stock price, volatile, XGBoost, APSO Data, Source Code, dan Plagiarisme: https://drive.google.com/file/d/1GRhEguXHYj-mzbE2fW7MsnpjUuaJnyxl/view?usp=sharing
ANALISIS PERBANDINGAN ALGORITMA APRIORI DAN FP-GROWTH DALAM MENENTUKAN POLA PEMBELIAN KONSUMEN TOKO BANGUNAN Muhimmatul Arofah; Mohammad Idhom; Kartika Maulida Hindrayani
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/v9bepx08

Abstract

Micro, Small, and Medium Enterprises (MSMEs) are vital to Indonesia's economy but often face challenges in inventory control and understanding consumer behavior. This study aims to compare the performance of the Apriori and FP-Growth algorithms in identifying consumer purchasing patterns from 7,778 transaction records at UD. Kurnia, a building material store, between August 2023 and July 2024. Unlike previous research that relied only on support and confidence metrics, this study applies the lift metric, which measures the strength of item associations, to minimize misleading rules. The algorithms were tested under 15 combinations of minimum support and lift threshold values. Results show that both algorithms generate the same association rules, but Apriori is significantly faster. At a minimum support of 0.0005 and a lift threshold of 1.5, Apriori completes processing in 3.23 seconds, while FP-Growth takes 21.81 seconds. With these findings, store owners can make more precise inventory decisions and implement data-driven cross-selling strategies, such as offering semen gresik when colt pasir is purchased.
Co-Authors Alya Mirza Safira Mohammad Idhom Muhimmatul Arofah Trimono Trimono