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The Study of Value-At-Risk Calculation and Back-testing Using the ARMA-GARCH Model Based on Stock Returns: An Overview Rizki Apriva Hidayana; Subiyanto Subiyanto; Sudradjat Supian
International Journal of Research in Community Services Vol 3, No 4 (2022)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijrcs.v3i4.368

Stocks are investment instruments that provide returns but tend to be risky. The most important component of investing is volatility, where volatility is identical to the standard conditional deviation of stock price return. The important thing in investing in addition to return is a risk. Value-at-Risk (VaR) is a statistical method of estimating maximum losses. To evaluate the quality of VaR estimates, models should always be back-tested with appropriate methods. Back-testing is a statistical procedure in which actual gains and losses are systematically compared to appropriate VaR estimates. To evaluate the quality of VaR estimates, models should always be back-tested with appropriate methods. Back-testing is a statistical procedure in which actual gains and losses are systematically compared to appropriate VaR estimates. The goal of the study was to estimate the Autoregressive Moving Average-Generalized Conditional Heteroscedastic (ARMA-GARCH) model to determine Value-at-Risk and back-testing. ARMA is a combination of AR and MA models, while GARCH is a time series model with symmetrical properties. The method in this study is systematic browsing of libraries. Systematic library tracing is an attempt to identify, evaluate, and interpret all research relevant to a particular phenomenon.

Inventory Control for MSME Products Using the Q Model with Lost Sales Condition Based on Products Sales Forecasting Dita Aulia Nissa; Sudradjat Supian; Julita Nahar
International Journal of Quantitative Research and Modeling Vol 4, No 1 (2023)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v4i1.417

Micro, Small and Medium Enterprises (MSMEs) have an important role in economic development in order to achieve thq quality of economic growth. Intense competition among MSMEs requires MSMEs to have a good inventory control that can help them minimize costs and maximize profits. One of the MSMEs that often experiences problems in inventory control is Sabun Bening Official. To solve the inventory problems in Sabun Bening Official, Holt-Winter Exponential Additive forecasting method is used as a guide to predict future product demand because product demand graph is seasonal and has trend pattern. After getting the value of product demand forecast, inventory control calucaltions are carried out using the Q Model probabilistic inventory method with lost sales condition. The uncertain and fluctuating demand causing the inventory system in Sabun Bening Official is probabilistic and the company will lose sales if it does not able to fulfill customer demands. Based on the research results, product forecasting for the coming period and inventory control policies which include the optimal number of product order, safety stock, reorder point, and product inventory costs can be obtained.

Basic Programming Training in Python for Junior High School Students at Al Fitrah Islamic Boarding School Sudradjat Supian; Mohammad Fadhli Ahmad; subiyanto subiyanto
International Journal of Research in Community Services Vol 4, No 4 (2023)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijrcs.v4i4.489

This research discusses the implementation of basic programming training using the Python programming language for junior high school (SMP) students at Al Fitrah Islamic Boarding School. The aim of this study is to provide an overview of the training experience and its impact on students' understanding of programming and their proficiency in using Python. The research method used is qualitative with a case study approach. The participants of the study consist of students from the seventh and eighth grades at Al Fitrah Islamic Boarding School. Data was collected through observations, interviews, and tests of students' learning outcomes. The results of the research show that the basic programming training with Python effectively improves students' understanding of programming concepts and develops their computational skills. Additionally, the students demonstrate high interest and enthusiasm in programming activities, indicating strong potential for developing technological skills in the future. This research concludes that basic programming training with Python can be well integrated into the junior high school curriculum to enhance digital literacy and prepare students to face the challenges of an increasingly advanced technological world.

Probability distributions of COVID-19 tweet posted trends uses a nonhomogeneous Poisson process Devi Munandar; Sudradjat Supian; Subiyanto Subiyanto
International Journal of Quantitative Research and Modeling Vol. 1 No. 4 (2020): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v1i4.74

The influence of social media in disseminating information, especially during the COVID-19 pandemic, can be observed with time interval, so that the probability of number of tweets discussed by netizens on social media can be observed. The nonhomogeneous Poisson process (NHPP) is a Poisson process with dependent on time parameters and the exponential distribution having unequal parameter values and, independently of each other. The probability of no accurence an event in the initial state is one and the probability of an event in initial state is zero. Using of non-homogeneous Poisson in this paper aims to predict and count the number of tweet posts with the keyword coronavirus, COVID-19 with set time intervals every day. Posting of tweets from one time each day to the next do not affect each other and the number of tweets is not the same. The dataset used in this study is crawling of COVID-19 tweets three times a day with duration of 20 minutes each crawled for 13 days or 39 time intervals. Result of this study obtained predictions and calculated for the probability of the number of tweets for the tendency of netizens to post on the situation of the COVID-19 pandemic.

Wireless Chaos-Based Communication System: Literature Review Siti Hadiaty Yuningsih; Sudradjat Supian; Sukono Sukono; Subiyanto Subiyanto
International Journal of Quantitative Research and Modeling Vol. 2 No. 1 (2021): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v2i1.128

Since the early 1990s, a slew of chaotic-based communication systems have been proposed, all of which take advantage of chaotic waveform properties. The inspiration stems from the substantial benefits that this form of nonlinear signal offers. Many communication schemes and applications have been specifically designed for chaos-based communication systems to achieve this goal, with energy, data rate, and synchronization awareness being taken into account in most designs. However, non-coherent chaos-based systems have recently received a lot of attention in order to take advantage of the benefits of chaotic signals and non-coherent detection while avoiding the use of chaotic synchronization, which has poor performance in the presence of additive noise. This paper provides a thorough examination of all wireless radio frequency chaos-based communication systems. It begins by describing the difficulties of chaos implementations and synchronization processes, then moves on to a thorough literature review and study of chaos-based coherent techniques and their applications.

IDX30 Stocks Clustering with K-Means Algorithm based on Expected Return and Value at Risk Ahmad Fawaid Ridwan; Subiyanto Subiyanto; Sudradjat Supian
International Journal of Quantitative Research and Modeling Vol. 2 No. 4 (2021): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v2i4.157

Stocks are one of the investment instruments available in the capital market. Several indices show the characteristics of stocks listed on the Indonesia Stock Exchange. IDX30 is one of several indications that show the combined stocks are stocks with large market capitalization, high liquidity, and good fundamentals. The selection of assets to be allocated in the portfolio is an important factor in investing where the purpose of investing is to maximize returns and minimize risk. This study aims to classify stocks that have certain characteristics based on the expected return and value at risk of the stocks incorporated in IDX30 with a clustering algorithm. The clustering algorithm used is the K-Means algorithm. K-Means is a non-hierarchical clustering algorithm by groups each object based on its proximity to the cluster center. The method used in this research is a clustering simulation study using the K-Means algorithm on IDX30 stock data. By identifying the characteristics of the stock based on the characteristics of the cluster formed, it is hoped that it can be considered in choosing the assets to be used in the formation of an optimal portfolio.

Inventory Control for MSME Products Using the Q Model with Lost Sales Condition Based on Products Sales Forecasting Dita Aulia Nissa; Sudradjat Supian; Julita Nahar
International Journal of Quantitative Research and Modeling Vol. 4 No. 1 (2023): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v4i1.417

Micro, Small and Medium Enterprises (MSMEs) have an important role in economic development in order to achieve thq quality of economic growth. Intense competition among MSMEs requires MSMEs to have a good inventory control that can help them minimize costs and maximize profits. One of the MSMEs that often experiences problems in inventory control is Sabun Bening Official. To solve the inventory problems in Sabun Bening Official, Holt-Winter Exponential Additive forecasting method is used as a guide to predict future product demand because product demand graph is seasonal and has trend pattern. After getting the value of product demand forecast, inventory control calucaltions are carried out using the Q Model probabilistic inventory method with lost sales condition. The uncertain and fluctuating demand causing the inventory system in Sabun Bening Official is probabilistic and the company will lose sales if it does not able to fulfill customer demands. Based on the research results, product forecasting for the coming period and inventory control policies which include the optimal number of product order, safety stock, reorder point, and product inventory costs can be obtained.

Mathematical Model Analysis of Mosaik Disease Spread on Jatropha Plants: Article Review Ayun Sri Rahmani; Subiyanto Subiyanto; Sudradjat Supian
International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i2.669

Mosaic disease is one of the plant diseases that can be detrimental and cause crop failure, the disease is caused by Begomovirus. Begomovirus is spread by whitefly vectors. The whitefly as a vector can infect healthy plants because once the whitefly is infected, the whitefly body will forever contain the disease. Therefore, we need a mathematical model to prevent the spread of mosaic disease on Jatropha plants and make a strategy to prevent mosaic disease with optimal control and other factors. In this study, mathematical modeling of the spread of jatropha mosaic disease will be discussed, with the addition of various compartments, parameters, and optimal control. Several strategies that can be used to prevent mosaic disease in Jatropha are adding effect awareness, delay, insecticides, interventions, natural predators, yellow stick, rouging, and a combination of all strategies.

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