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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Bulletin of Computer Science Research
Adi Rizky Pratama
Universitas Buana Perjuangan Karawang, Karawang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Journal : bulletin of computer science research

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Prediksi Pola Pergerakan Saham Adro.Jk Melalui Model LSTM Berbasis Data Historis Muhammad Irsyad Iskandar; Tohirin Al Mudzakir; Yana Cahyana; Adi Rizky Pratama
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.554

Abstract

The fluctuating nature of stock price movements presents a significant challenge in investment decision-making. To address this issue, a predictive model capable of capturing historical patterns and accurately forecasting stock prices is required. This study aims to develop a stock price prediction model for PT Alamtri Resources Indonesia Tbk (ADRO.JK) using the Long Short-Term Memory (LSTM) algorithm. The dataset comprises daily closing prices from January 1, 2020, to December 30, 2024, obtained from Yahoo Finance. The data was processed in a time series format using a sliding window approach, employing 30 historical data points to predict the next price point. The model was constructed using two LSTM layers, one Dense layer, and techniques such as Dropout and EarlyStopping to prevent overfitting.The training and testing results indicate that the model performs exceptionally well, achieving a Mean Absolute Percentage Error (MAPE) of 0.0341 or 3.41%, corresponding to a prediction accuracy of 96.59%. In a short-term prediction scenario over seven days, the model achieved an accuracy of 99.07% (MAPE = 0.0093), while in a medium-term scenario up to May 19, 2025, it achieved an accuracy of 98.76% (MAPE = 0.0124). The predicted stock price on May 19, 2025, is estimated at IDR 1,913.76. With its high accuracy and low error rate, the LSTM model has proven to be a reliable tool for forecasting stock prices based on historical data.
Identifikasi Jenis Buah Apel berdasarkan Ektraksi Ciri Warna Fitur HSV dengan Model Jaringan Syaraf Tiruan Backpropagation Ayu Ratna Juwita; Cici Emilia Sukmawati; Adi Rizky Pratama; Resi Sujiwo Bijokangko; Agung Susilo Yudha Irawan
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.1010

Abstract

Automatic identification of apple varieties is one of the challenges in the field of digital image processing, especially due to the similarity of visual characteristics between varieties and the influence of lighting conditions. This study aims to develop an apple variety classification system based on color feature extraction in the HSV (Hue, Saturation, Value) color space combined with Gray Level Co-occurrence Matrix (GLCM) texture features and classified using a Multilayer Perceptron (MLP) Artificial Neural Network. The research process begins with apple image segmentation using the Otsu thresholding method to separate objects from the background, followed by extraction of HSV color features and texture features in the form of contrast and energy. The obtained feature data is then normalized using StandardScaler and divided into training data of 80% and test data of 20%. The MLP model is trained with two hidden layers of 64 and 32 neurons, using the ReLU activation function and the Adam optimization algorithm with a maximum of 500 epochs. The test results show that the developed system is able to achieve a classification accuracy of 87.5% on the test data. These results indicate that the combination of HSV color features and GLCM texture classified using Backpropagation Neural Network is quite effective in identifying apple types, although there are still challenges in classes that have similar color characteristics.
Co-Authors Agung Susilo Yudha Irawan Ayu Ratna Juwita Cici Emilia Sukmawati Muhammad Irsyad Iskandar Resi Sujiwo Bijokangko Risma Yati Tatang Rohana Tohirin Al Mudzakir Yana Cahyana