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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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Klasifikasi Jenis Mangga Menggunakan Algoritma Convolutional Neural Network Risma Yati; Tatang Rohana; Adi Rizky Pratama
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6445

Abstract

The name of the mango is Mangnifera IndicaL. It originated in India and spread to Indonesia. There are various types of mango variations with different shapes and colors according to the type. To distinguish each mango is seen by its shape and color. However, if in the harvest process mango farmers have to choose manually it takes a long time and potentially mistaken in determining the type. So it needs technology that can make it easier to differentiate the type of mango based on its shape. The study aims to create models with the best accuracy on the process of classifying 5 types of mango based on its shape. The data used in the research this time there are 5 types of mango that will be classified, namely Mangga Apel, Arumanis mango, Mangga Gedong Gincu, Golek mango and Mangga Manalagi. Used 375 images of mango as data sets. The data set before entering the previous training process is undergoing a pre-processing phase that includes the augmentation and resize process. The number of images increased to 2250. The data set is divided into three parts: 70% training data, 20% validation data, and 10% test data. Next is the process of segmentation, the segmentation used in this research is otsu segmentation. The classification process uses the Convolutional Neural Network (CNN) architecture with 3 layers of convolution 16,32 and 64, also using the Adam optimizer. 4 experimental scenarios were performed to find the best accuracy value by distinguishing between learning rate and batch size. From the confusion matrix test results, the best accuracy values were obtained from the input hyperparameter size100x100, epoch 100, learning rate 0,001 and batch size 15 with accurate values of 99.56%, precision 100%, recall 100%, and f1-score 100%.
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