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All Journal International Journal of Business, Economics, and Social Development International Journal of Global Operations Research International Journal of Quantitative Research and Modeling International Journal of Ethno-Sciences and Education Research
Wahid, Alim Jaizul
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Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Transportation Other

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The Effect of Double Date Discounts on Sales Levels In E-Commerce Shopee (Case Study on Students of Padjadjaran University in Jatinangor) Wahid, Alim Jaizul; Millantika, Salwa Cendikia; Supriatna, Asep Kuswandi
International Journal of Quantitative Research and Modeling Vol 5, No 4 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i4.835

Abstract

This study aims to analyze the impact of double date sale discounts on sales levels on the Shopee e-commerce platform, focusing on students from Universitas Padjadjaran in Jatinangor, who are primarily from the millennial and Generation Z cohorts. The method used is simple linear regression, linking discount variables to sales. Additionally, the study conducts classical assumption testing to ensure the models validity and sensitivity analysis to assess the effect of parameter changes on the predicted outcomes. The results show that double date sale discounts significantly influence sales, with the double date sale coefficient (eta_1) being highly sensitive to changes. The regression model yields a low MSE, indicating good prediction accuracy. While changes in the intercept (eta_0) also affect the predictions, the impact is smaller compared to changes in the double date sale coefficient.
Game Theory as a Marketing Optimization Tool: A Case Study on Kelom Geulis Azahra, Astrid Sulistya; Saefullah, Rifki; Wahid, Alim Jaizul
International Journal of Global Operations Research Vol. 5 No. 4 (2024): International Journal of Global Operations Research (IJGOR), November 2024
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v5i4.340

Abstract

Competition in the market for traditional art products, such as Kelom Geulis, has become increasingly intense along with the growing public interest in aesthetically and culturally valuable items. This competition forces producers to develop effective marketing strategies to maintain their competitiveness. This study adopts a game theory approach to evaluate and formulate optimal marketing strategies between two major producers. The research method involves the use of questionnaires covering three main aspects: improving product quality, setting competitive prices, and enhancing customer service. Data analysis is conducted using a payoff matrix to determine the best strategies that can increase profits or reduce losses for each party.The results show that a saddle point is reached at a value of 4.57, where PT A achieves a profit increase from 4 to 4.57, while PT B reduces its loss from 6 to 4.57. This optimal strategy can be achieved if PT A prioritizes improving product quality and setting competitive prices, while PT B A prioritizes setting competitive prices and service quality enhancement. The implementation of these strategies has proven effective in strengthening the competitiveness of Kelom Geulis in the market. This study is expected to serve as a practical reference for Kelom Geulis producers to continuously adapt their marketing strategies, ensuring their relevance in the market and appealing to consumers
Portfolio Performance Analysis with Jensen's Alpha Using Single Index Model and CAPM on IDX30 Stocks Wahid, Alim Jaizul; Saputra, Jumadil
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.v6i2.1013

Abstract

This study aims to evaluate the formation of an optimal stock portfolio using the Capital Asset Pricing Model (CAPM) and Single Index Model (SIM) approaches, and to assess portfolio performance using Jensen's Alpha generated from stocks included in the IDX30 index during the period April 2024 to March 2025. This study uses a quantitative descriptive approach with a population of 30 IDX30 stocks. The methods applied include calculating stock returns and betas, as well as forming an optimal portfolio using the CAPM and SIM formulas. Portfolio performance is then measured by Jensen's Alpha. The results of the study show that based on CAPM, BRIS.JK and EMTK.JK stocks are worthy of being included in the optimal portfolio because they have a positive expected return and Jensen's Alpha that slightly outperforms the market. EMTK.JK also has a lower risk. Meanwhile, based on SIM, only BBCA.JK is included in the optimal portfolio because it meets the criteria for Excess Return to Beta (ERB) > cut-off rate (C^*), and shows a positive Jensen's Alpha. The conclusion of this study is that both models can identify superior performing stocks for the optimal portfolio in the period.
Education Revolution: Leveraging Technology to Improve Learning Quality by 2025 Saputra, Moch Panji Agung; Suhaimi, Nurnisaa binti Abdullah; Wahid, Alim Jaizul
International Journal of Ethno-Sciences and Education Research Vol 5, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijeer.v5i1.867

Abstract

The technological transformation in education in 2025 has had a significant impact on the way we teach and learn. The use of artificial intelligence (AI) and learning analytics enables a more personalized, interactive, and adaptive learning experience. AI helps provide rapid feedback and adapts learning materials to students’ needs, while learning analytics enables real-time monitoring of student progress. Despite the many benefits that can be gained, the main challenges faced are the digital divide between urban and rural areas, limited infrastructure, and issues of training for educators and protection of students’ personal data. Therefore, investment in infrastructure, training for educators, and development of data protection policies are crucial to ensure effective implementation of technology in education. Technology can play a major role in creating a more inclusive and adaptive education, provided that the existing challenges can be overcome.
Indonesian Banking Stock Portfolio Optimization Based on Ridge Regression Prediction Saputra, Moch Panji Agung; Setyawan, Deva Putra; Wahid, Alim Jaizul
International Journal of Business, Economics, and Social Development Vol. 6 No. 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v6i2.1064

Abstract

The Indonesian stock market in the banking sector is a popular investment instrument with high return potential but faces market volatility and global economic uncertainty that requires adaptive and data-driven portfolio management strategies. Traditional asset allocation strategies such as equal weighting or based on historical performance have limitations in dynamic market conditions, while the application of machine learning, especially Ridge Regression, in stock return prediction and portfolio optimization in the Indonesian market has not been widely explored. This study aims to build an integrated pipeline for portfolio prediction and optimization using Ridge Regression on Indonesian banking stocks. Methods: Daily closing price data of five major banking stocks (BBRI, BBCA, BMRI, BBNI, BBTN) for the period 2015-2025 are used with technical indicators of moving average and rolling standard deviation as input features. The Ridge Regression model is trained using TimeSeriesSplit cross-validation to predict daily returns, then the prediction results are integrated into the Mean-Variance optimization framework to maximize the Sharpe ratio. The Ridge Regression model shows excellent predictive performance with an average R² of 0.9986, MAE of 0.000466, and RMSE of 0.000720. The Ridge-based portfolio strategy achieves identical performance to the historical optimal strategy with an annualized return of 10.64% and a Sharpe ratio of 0.4705, significantly outperforming the equal-weight strategy (return of 6.63%, Sharpe ratio of 0.2562). A practical implementation simulation with IDR 100 million funds shows feasible execution with less than 1% deviation from the optimal weights. Ridge Regression is proven to be effective in capturing the return pattern of Indonesian banking stocks and enables superior portfolio performance when integrated with modern portfolio theory, providing investors with a robust and data-driven approach to portfolio optimization in emerging markets.
Co-Authors Asep Kuswandi Supriatna Azahra, Astrid Sulistya Millantika, Salwa Cendikia Saefullah, Rifki Saputra, Jumadil Setyawan, Deva Putra Suhaimi, Nurnisaa binti Abdullah