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All Journal International Journal of Business, Economics, and Social Development International Journal of Research in Community Services International Journal of Global Operations Research International Journal of Quantitative Research and Modeling International Journal of Ethno-Sciences and Education Research International Journal of Mathematics, Statistics, and Computing International Journal of Linguistics, Communication, and Broadcasting
Saputra, Renda Sandi
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Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Transportation Other

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Indofood CBP Sukses Makmur Tbk Stock Price Prediction Using Long Short-Term Memory (LSTM) Saputra, Moch Panji Agung; Saputra, Renda Sandi; Dwiputra, Muhammad Bintang Eighista
International Journal of Global Operations Research Vol. 6 No. 1 (2025): International Journal of Global Operations Research (IJGOR)
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i1.363

Abstract

Fluctuating stock price movements are a challenge in the investment world, so an accurate prediction model is needed to assist decision making. This study aims to evaluate the ability of the LSTM model to predict ICBP stock prices based on historical data and will compare the results of the LSTM model predictions with actual stock price movements to determine the extent to which this model is able to capture trends and patterns of ICBP stock prices. The results show a comparison of the original price and the predicted price indicating that the model can follow market trends, although there are still deviations at some points, especially when volatility is high. Residual analysis shows a distribution of prediction errors that is close to normal, indicating that the model does not experience significant bias. In addition, evaluation of the loss function on the training and validation data confirms that the model has converged well. In the performance evaluation, the model is able to capture stock movement patterns quite well, indicated by the Mean Absolute Error (MAE) value of 0.0231, Root Mean Squared Error (RMSE) of 0.0305, and Mean Absolute Percentage Error (MAPE) of 19.21%.
Implementation of the Gated Recurrent Unit (GRU) Model for Bank Mandiri Stock Price Prediction Saputra, Moch Panji Agung; Saputra, Renda Sandi; Pirdaus, Dede Irman
International Journal of Quantitative Research and Modeling Vol. 6 No. 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i1.894

Abstract

Stock price prediction is a crucial aspect in the financial world, especially in making investment decisions. This study aims to analyze the performance of the Gated Recurrent Unit (GRU) model in predicting Bank Mandiri (BMRI.JK) stock prices using historical data for five years. Stock data was collected from Yahoo Finance and normalized using Min-Max Scaling to improve model stability. Furthermore, the windowing technique was applied to form a dataset that fits the architecture of the time series forecasting-based model. The developed GRU model consists of two GRU layers with 128 neuron units, two dropout layers to prevent overfitting, and one output layer with one neuron to predict stock prices. Model evaluation was carried out using the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared (R² Score) metrics. The experimental results show that the GRU model is able to produce predictions with a high level of accuracy, indicated by the R² Score value of 0.9636, which indicates that the model can explain 96.36% of stock price variability based on historical data.
Comparison of Random Forest and SVM Algorithms in Classification of Diabetic Retinopathy Based on Fundus Image Texture Features Saputra, Moch Panji Agung; Saputra, Renda Sandi
International Journal of Quantitative Research and Modeling Vol. 6 No. 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i2.1011

Abstract

Diabetic Retinopathy (DR) is a microangiopathic complication of diabetes mellitus that can cause visual impairment to permanent blindness. Early detection of DR is essential to prevent disease progression, but conventional methods require time, cost, and expertise that are not always available. This study aims to compare the performance of the Random Forest (RF) and Support Vector Machine (SVM) algorithms in DR classification based on texture features extracted from retinal fundus images. The dataset used consists of 3,000 retinal fundus images obtained from the Kaggle platform, divided into 2,400 training data and 600 test data. Image preprocessing includes conversion to grayscale, resizing to a resolution of 128×128 pixels, and normalization. Feature extraction is performed using a combination of Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) to produce a 14-dimensional feature vector. Performance evaluation uses accuracy, precision, recall, F1-score, ROC curve, and 5-fold cross-validation metrics. The results showed that Random Forest significantly outperformed SVM with an accuracy of 96% compared to 64%, an AUC value of 0.99 compared to 0.72, and an average cross-validation accuracy of 94.5% compared to 63.42%. Random Forest also showed balanced performance in both classes with precision, recall, and F1-score of 0.96, while SVM experienced classification imbalance especially in the disease class. This study proves that Random Forest is a more optimal algorithm for an automatic DR detection system based on fundus image texture features and can support increasing the accessibility of DR screening in areas with limited specialist medical personnel.
Information Quality and Compatibility as Determinants of M-Wallet Usage in Indonesia. Prakarsa, Graha; Nursyanti, Reni; Putra, Prayuda Mulyadi; Saputra, Renda Sandi
International Journal of Global Operations Research Vol. 6 No. 3 (2025): International Journal of Global Operations Research (IJGOR), August 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i3.393

Abstract

This study aims to assess the acceptance of mobile wallet applications in Indonesia by incorporating Information Quality and Compatibility as external factors within the framework of the Technology Acceptance Model (TAM). A quantitative approach was employed, and data from 208 respondents were analyzed using Partial Least Squares - Structural Equation Modeling (PLS-SEM). The findings indicate that both Information Quality and Compatibility have a positive and significant influence on Perceived Usefulness and Perceived Ease of Use. Furthermore, these two variables also significantly affect Continuance Intention to Use, which subsequently impacts the Actual Use of mobile wallets. Overall, Information Quality and Compatibility contribute 56% to Perceived Usefulness, 52.4% to Perceived Ease of Use, and 43.8% to Continuance Intention to Use. These findings offer valuable insights for application developers seeking to enhance mobile wallet adoption in Indonesia.
Stock Portfolio Optimization of Several Companies Engaged in Renewable Energy for Investment Decision-Making Using the Markowitz Model Saputra, Moch Panji Agung; Saputra, Renda Sandi
International Journal of Business, Economics, and Social Development Vol. 5 No. 4 (2024)
Publisher : Rescollacom (Research Collaborations Community)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v5i4.805

Abstract

This study focuses on optimizing the renewable energy company's stock portfolio using the Markowitz model, which aims to balance risk and return for proper investment decision making. With the increasing demand for clean energy, portfolio optimization in the renewable energy sector is important for investors. This research takes into account historical stock performance and applies the Mean-Variance Optimization framework to minimize risk while maximizing return. This portfolio consists of selected renewable energy companies, and the analysis runs from September 2021 to August 2024. This study aims to analyze the allocation of investment portfolios in renewable energy company stocks in Indonesia. Based on the analysis results, the investment portfolio is allocated to five main stocks, namely BUMI.JK with an investment value of IDR 17,075,844 (17.08%), INDY.JK of IDR 5,825,852 (5.83%), KEEN.JK of IDR 33,766,798 (33.77%), RAJA.JK of IDR 43,084,876 (43.08%), and WIKA.JK of IDR 246,630 (0.25%). These results indicate that most of the funds are invested in RAJA.JK and KEEN.JK stocks, which contribute more than 75% of the total investment portfolio.
Comparative Analysis of LSTM and GRU Models for Ethereum (ETH) Price Prediction Saputra, Moch Panji Agung; Ibrahim, Riza Andrian; Saputra, Renda Sandi
International Journal of Business, Economics, and Social Development Vol. 6 No. 1 (2025)
Publisher : Rescollacom (Research Collaborations Community)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v6i1.887

Abstract

The increasing use of cryptocurrencies has changed the dynamics of investment, presenting both opportunities and challenges for investors. Although various studies have compared the performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) in predicting financial asset prices, there are still differences in results regarding which model is superior. Therefore, this study aims to compare the performance of LSTM and GRU in predicting Ethereum prices using a hyperparameter tuning approach. The data used is historical data of Ethereum (ETH) shares from 2020 to 2025. The research methodology includes data preprocessing using Min-Max scaling, model development with various layer configurations, and comprehensive evaluation using several performance metrics. The results show that the GRU Model provides superior performance with a lower Root Mean Squared Error (RMSE) of 0.0234 and Mean Absolute Error (MAE) of 0.0168, compared to LSTM's RMSE of 0.0265 and MAE of 0.0193. While LSTM exhibits a slightly better Mean Absolute Percentage Error (MAPE) of 18.08% compared to GRU at 18.17%, the GRU model achieves a higher R² Score of 0.9442 compared to LSTM at 0.9282. Visual analysis of the prediction patterns and residual distributions further demonstrates GRU’s more consistent and accurate performance in capturing Ethereum price movements. These findings suggest that while both models are effective for cryptocurrency price prediction, GRU offers slightly better overall performance and stability, especially in maintaining consistent prediction accuracy across different market conditions.
Markowitz Portfolio Learning Design in Financial Mathematics with Technology-Based Stock Investment Simulation Practice Saputra, Moch Panji Agung; Saputra, Renda Sandi; Laksito, Grida Saktian
International Journal of Research in Community Services Vol. 6 No. 1 (2025)
Publisher : Research Collaboration Community (Rescollacom)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijrcs.v6i1.846

Abstract

In an era of global economic uncertainty, mastering the concept of portfolio management is important for students in preparing themselves to face the challenges of the financial world. This study aims to design financial mathematics learning by integrating Markowitz's portfolio theory and technology-based stock investment simulation practices. The approach used includes the use of digital platforms to obtain historical stock data and stock market simulations for virtual investment practices. The results of the study indicate that the use of this method can improve students' understanding of the concepts of diversification, expected returns, and risk management in investment portfolios. With an interactive and practical approach, students can gain direct experience in building an optimal portfolio based on the Markowitz mean-variance model and implementing it in stock investment simulations. This study makes a significant contribution to the development of students' financial literacy by utilizing digital technology effectively.
Seven Segment Display Circuit Simulation using Electronics Workbench Praja, Muhamad Januar Indra; Saputra, Renda Sandi
International Journal of Ethno-Sciences and Education Research Vol. 2 No. 2 (2022): International Journal of Ethno-Sciences and Education Research (IJEER)
Publisher : Research Collaboration Community (Rescollacom)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijeer.v2i2.286

Abstract

A seven segment display, or in Indonesian it is called a seven segment display, is an electronic device consisting of segments that are used to display decimal numbers or numbers. This journal will explain how to simulate a seven segment display circuit using computer software called Electronics Workbench. This simulation is done to help understand how a seven segment display circuit can work, and because this is a simulation, there is no need to be afraid of failure, or making mistakes. This simulation is done by connecting a seven segment display with a 74 series Digital IC, you can use a 7447 or 7448 IC, because the function is still the same, and the IC formation is still the same, and is suitable for connecting seven segment displays. Then the IC is controlled via a switch / button that is connected directly to the IC, and the switch and IC are also connected to a power source, so that the circuit simulation can run. The results of this simulation show that the simulation can be done well in the Electronics Workbench software, can be a medium of learning about how seven segment displays work, and so that we can more easily understand how electronic devices work.
Parents' Positive Behavior in Children in Online Learning at Sds Rancakasumba Wangi, Risfa Mustifa; Saputra, Renda Sandi
International Journal of Ethno-Sciences and Education Research Vol. 2 No. 2 (2022): International Journal of Ethno-Sciences and Education Research (IJEER)
Publisher : Research Collaboration Community (Rescollacom)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijeer.v2i2.287

Abstract

The existence of COVID-19 pandemic struck across the country in the world, including Indonesia has disrupted human activities in various sectors of life. Online learning can not be separated from internet networks. The internet network connection is one of the obstacles that the students live in the suburbs. The problem with online learning during the COVID-19 pandemic is environmental incompatibility by changing old habits so that parents do various ways so that parents can provide positive behavior toward children. This paper focuses on a positive behavior model in parents as an essential part of online learning by children amid the uncertainty of the end of the COVID-19 pandemic. The method in this paper is a literature study or literature review on positive behavior with logical model analysis. The results show that the factors that influence parents' positive behavior towards children in online learning amid the COVID-19 pandemic must fulfill the elements of confidence, hope, optimism, resilience, and intelligence of parents in responding to online learning carried out by their parent’s children. In addition, social capital as an alternative strengthens parents' positive behavior towards children so that they feel comfortable with the presence of parents to accompany online learning.
Co-Authors Azahra, Astrid Sulistya Dwiputra, Muhammad Bintang Eighista Graha Prakarsa Hasan, Mohammad Tanzil Ibrahim, Riza Andrian Ismail, Muhammad Iqbal Al-Banna Laksito, Grida Saktian Pirdaus, Dede Irman Praja, Muhamad Januar Indra Putra, Prayuda Mulyadi Rahayu, Alpi fauziah Reni Nursyanti Salih, Yasir Suhaimi, Nurnisaa binti Abdullah Wangi, Risfa Mustifa