Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal International Journal of Research in Community Services International Journal of Quantitative Research and Modeling
Renda Sandi Saputra
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Environmental Science Physics Social Sciences Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Education on the use of Social Media as a Marketing Medium for Small Micro and Medium Enterprises (SME) in Cipamokolan Village during the COVID-19 Pandemic Renda Sandi Saputra
International Journal of Research in Community Services Vol 2, No 4 (2021)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijrcs.v2i4.225

Abstract

The economy in Indonesia is now growing and developing, especially for SME (small micro and medium enterprises) businesses, in the micro, small and medium movement, many Indonesian people are opening small businesses, by marketing products that are still sought after by buyers, Many Indonesian traders have not used the internet or social media to market their products, even though there are many social media that can be used to market their products, such as Facebook, Instagram, Tiktiok and many more. Currently, social media or the internet can be accessed by anyone from all walks of life. including SME businessmen who use the internet as a means of providing and sharing information about products offered to consumers online. This study discusses how the use of social media by SME in marketing products during the COVID19 pandemic. The purpose of the research is to introduce traders in Cipamokolan village to be able to better utilize social media as a marketing tool in order to have a bigger market potential. This research method is a qualitative method using a descriptive approach by utilizing secondary data from various literatures such as books, journals/articles and homepages to access the latest data and information related to the impact of COVID-19 and the use of social media for the recovery of SME. The result of the research is that social media is an effective means of communication, can increase market share and help business decisions. The community in Cipamokolan village can increase sales volume by more than 100% if the information is updated every day and consistently.
The Role of Industrial Operators and IIoT in AI/ML-Based Process Optimization: A Bibliometric Analysis and Research Gap Identification in the Industry 4.0 Era Renda Sandi Saputra; Rifki Saefullah
International Journal of Quantitative Research and Modeling Vol. 7 No. 2 (2026): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v7i2.1345

Abstract

The rapid adoption of Artificial Intelligence (AI) and Machine Learning (ML) technologies has transformed manufacturing systems under the Industry 4.0 paradigm, enabling data-driven process optimization, predictive decision-making, and intelligent production management. Despite substantial growth in this research domain, previous bibliometric studies reported limited visibility of the Industrial Internet of Things (IIoT) and industrial operators within the AI/ML-based process optimization literature. This study aims to examine the evolution of these research themes and assess how the knowledge structure of the field has developed during the transition from Industry 4.0 to Industry 5.0. A bibliometric analysis was conducted using 362 publications retrieved from Dimensions.ai covering the period 2020–2026. Bibliometric performance indicators were analyzed using Bibliometrix (R), while science mapping and keyword co-occurrence analyses were performed using VOSviewer 1.6.20. The results reveal a continuous increase in publication output and the emergence of six major thematic clusters. AI and Smart Factory technologies remain the dominant research themes, followed by Smart Manufacturing and Cyber-Physical Systems. The analysis further shows that IIoT has evolved into a distinguishable thematic component connected to industrial connectivity, edge computing, and sensor infrastructures. In addition, a new human-centered cluster has emerged, characterized by concepts such as Operator 4.0, human-in-the-loop systems, collaborative robotics, and human-centered AI. Although both IIoT and operator-related themes have gained visibility, their thematic prominence remains lower than that of the dominant AI and smart manufacturing clusters. The findings indicate a gradual shift toward a more integrated manufacturing paradigm that combines intelligent algorithms, industrial connectivity, and human expertise, reflecting the broader transition from Industry 4.0 to Industry 5.0.
Extreme Value Theory-Based Tail Risk Estimation for Indonesian Cocoa Commodity Prices Using the Generalized Pareto Distribution and Peaks-Over-Threshold Method Renda Sandi Saputra; Setyo Luthfi Okta Yohandoko
International Journal of Quantitative Research and Modeling Vol. 7 No. 3 (2026): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v7i3.1436

Abstract

Cocoa (Theobroma cacao) is one of Indonesia's most strategically significant agricultural export commodities, yet its price dynamics are characterized by extreme volatility that imposes substantial financial risk on stakeholders across the supply chain. Standard statistical models that assume Gaussian or light-tailed distributions systematically underestimate the probability and severity of extreme price movements, leading to inadequate risk management practices. This paper proposes a rigorous tail risk estimation framework for Indonesian cocoa commodity prices using Extreme Value Theory (EVT), specifically the Generalized Pareto Distribution (GPD) fitted via the Peaks-Over-Threshold (POT) method. The optimal threshold is selected using the Mean Excess Plot (MEP) and the Hill estimator, while GPD parameters are estimated through Maximum Likelihood Estimation (MLE). The framework quantifies tail risk through Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) at multiple confidence levels (90%, 95%, 99%, 99.5%, and 99.9%) and constructs return-level plots for risk horizon analysis. Comparative analysis against the Normal distribution and Student-t distribution confirms that the GPD-POT approach provides a significantly superior fit to the empirical tail of cocoa log-returns, as evidenced by the Kolmogorov-Smirnov test, Anderson-Darling test, and Q-Q diagnostic plots. The shape parameter estimate ξ = 0.2758 confirms the presence of heavy tails (Fréchet domain), indicating a higher frequency of extreme price events than predicted by thin-tailed models. The estimated 99% VaR of 6.301% and CVaR of 9.142% provide actionable benchmarks for hedging strategies, insurance premium calculations, and commodity trading risk limits. This study contributes a statistically sound, threshold-based EVT methodology applicable to agricultural commodity risk management in emerging markets.
Co-Authors Rifki Saefullah Setyo Luthfi Okta Yohandoko