Article Per Year (5 Year)
p-Index From 2021 - 2026
2.686
P-Index
This Author published in this journals
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
Unknown Affiliation
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

Published : 19 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : International Journal of Mathematics, Statistics, and Computing

 1 
Community Readiness for the Use of the Cash on Delivery (COD) Application Without a Marketplace Using an SPSS-based Likert Scale: Community Readiness for the Use of the Cash on Delivery (COD) Application Without a Marketplace Using an SPSS-based Likert Scale Saputra, Renda Sandi; Salih, Yasir
International Journal of Mathematics, Statistics, and Computing Vol. 1 No. 3 (2023): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijmsc.v1i3.5

Abstract

This study aims to analyze the community's readiness for the Cash on Delivery (COD) without marketplace application as a payment method in online shopping transactions. Questionnaires were distributed to 30 respondents aged 18 to 25 years with various levels of education. The results of the questionnaire were analyzed using the SPSS statistical software, and the data obtained included age, gender, education level, and responses to several questions that were relevant to community readiness for COD services. The results of the analysis show that the majority of the community shows a high level of readiness for the COD application, with around 50.0% to 60.0% of respondents belonging to the ready and very ready categories. The diverse age range of respondents indicates that the COD application can reach young age groups, and the varying levels of education indicate that this application attracts interest from various walks of life. The gender of the respondents did not show a significant difference in the level of interest in COD services. Even though the majority showed high readiness, there were some respondents who showed a lower level of readiness. Therefore, a special approach is needed to provide further information and education to this group to increase their readiness to use COD services. In conclusion, the COD application has great potential to become a popular and reliable choice for users to make purchases online. However, effective marketing, education and product development efforts need to be continued to reach the full potential of COD services and provide better benefits to potential users. By focusing on good user experience, data security, and the right marketing strategy, COD applications have a chance of success in a competitive market.
Optimization of Renewable Energy Company Stock Portfolio for Investment Decision Making using the Markowitz Model Saputra, Renda Sandi; Rahayu, Alpi fauziah; Suhaimi, Nurnisaa Binti Abdullah
International Journal of Mathematics, Statistics, and Computing Vol. 2 No. 4 (2024): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijmsc.v2i4.142

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
Stock Price Prediction of PT. Pertamina Geothermal Energy Tbk Using Gated Recurrent Unit (GRU) Model Saputra, Renda Sandi; Hasan, Mohammad Tanzil; Azahra, Astrid Sulistya
International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 2 (2025): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijmsc.v3i2.203

Abstract

This study aims to predict the stock price of PT. Pertamina Geothermal Energy Tbk (PGEO.JK) using the Gated Recurrent Unit (GRU) model, a neural network architecture in the Recurrent Neural Network (RNN) category that is known to be effective in handling time series data. The data used is historical stock price data from 2022 to 2024 taken from Yahoo Finance. The GRU method was chosen because of its ability to remember long-term information and overcome the vanishing gradient problem. In the research process, the data was divided into two parts, namely training data and testing data. The GRU model was trained without adjusting hyperparameters to measure its performance by default. Model evaluation was carried out using the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R²) metrics. The results of the study indicate that the GRU model is able to provide good prediction results with an RMSE value of 0.0271, MAE of 0.0180, MAPE of 22.25%, and an R² value of 0.9112. These values ​​indicate that the GRU model is quite accurate in predicting the price of PGEO.JK shares. These findings indicate that GRU is a potential method in stock prediction analysis, especially in the renewable energy sector.
Optimization of Machine Learning Models for Sentiment Analysis of TikTok Comment Data on the Progress of the Ibu Kota Nusantara as New Capital City of Indonesia Saputra, Renda Sandi; Dwiputra, Muhammad Bintang Eighista; Saputra, Moch Panji Agung; Ismail, Muhammad Iqbal Al-Banna
International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 3 (2025): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijmsc.v3i3.232

Abstract

Sentiment analysis plays a crucial role in understanding public opinion on social media platforms, especially in discussions related to government policies such as the relocation of Indonesia’s new capital city, known as Ibu Kota Nusantara (IKN). While machine learning algorithms like Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression (LR) are widely used for sentiment classification tasks, previous studies often focus on performance comparisons without addressing the impact of data imbalance or regularly optimizing model parameters. These issues can lead to suboptimal classification performance, especially in real-world social media data. This study aims to improve the accuracy and robustness of sentiment classification by applying two enhancement strategies: text data augmentation and hyperparameter tuning. Three models Naïve Bayes, SVM, and Logistic Regression were trained and evaluated in three experimental stages: (1) using original data, (2) after applying augmentation, and (3) after augmentation combined with hyperparameter tuning via GridSearchCV. The evaluation results show progressive improvements across the three stages. In the first stage (original data), Logistic Regression achieved the highest accuracy of 80.41%, while Naïve Bayes and SVM reached 79.73% and 76.98%, respectively. However, all models struggled to classify the minority class (positive sentiment), as reflected in their lower recall and F1-scores. After applying augmentation, performance improved significantly across all models. SVM, in particular, reached an accuracy of 92.77%, followed by Logistic Regression (86.57%) and Naïve Bayes (86.22%), with better balance between precision and recall for both sentiment classes. hyperparameter tuning further optimized model performance. Logistic Regression became the best-performing model, achieving an accuracy of 93.80%, along with high precision, recall, and F1-scores for both classes. SVM and Naïve Bayes also showed stable improvements, with accuracies of 92.88% and 87.72%, respectively.
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