Garuda - Garba Rujukan Digital

p-Index From 2020 - 2025

5.725

P-Index

This Author published in this journals

Saputra, Jumadil

Unknown Affiliation

Author-ID : 1489171

Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

Published : 44 Documents Claim Missing Document

Claim Missing Document

Articles

Title

Prediction of Motor Vehicle Insurance Claims Using ARIMA-GARCH Models Susanti, Dwi; Maraya, Nisrina Salsabila; sukono, sukono; Saputra, Jumadil
Operations Research: International Conference Series Vol. 5 No. 3 (2024): Operations Research International Conference Series (ORICS), September 2024
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v5i3.331

Motorized vehicles are one of the means of transportation used by Indonesian people. As of 2021, the Central Statistics Agency (CSA) recorded the growth of motorized vehicles in Indonesia reaching 141,992,573 vehicles. Lack of control over the number of motorized vehicles results in losses for various parties, such as accidents, damage and other unwanted losses. The size of insurance claims has the potential to fluctuate, because it is influenced by several factors, such as policy changes, market conditions and economic conditions. This research aims to predict the size of motor vehicle insurance claims using the ARIMA-GARCH model which is used to predict the size of vehicle insurance claims by dealing with non-stationarity and heteroscedasticity in time series data. Based on research, the best model obtained is the ARIMA (2,1,3) - GARCH (1,0) model which produces seven significant parameters. Meanwhile, based on the MAPE value, it shows that the ARIMA (2,1,3)-GARCH (1,0) model is quite accurate. The results of this research can be taken into consideration in predicting the size of insurance claims in the future.

Risk of Ruin (ROR) Analysis in Casino Games Using Poisson Distribution Josua, Lancelot Julsen; Prawiro, Meivin Mulyo; Saputra, Jumadil; Yuningsih, Siti Hadiaty
Operations Research: International Conference Series Vol. 5 No. 2 (2024): Operations Research International Conference Series (ORICS), June 2024
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v5i2.312

Gambling in casino games is an uncertain business because it creates two possibilities between the hope of winning or the risk of losing. The risks faced by casinos are usually analyzed using the Risk of Ruin (ROR). The main focus of this study is to apply the mathematical model of ROR using the Poisson distribution to model random events in gambling by considering the house advantage (a) and the law of large numbers. This study discusses the relationship between variables, such as maximum bet limits and cash flows and examines how these factors affect the risk of casino bankruptcy. In its business characteristics, casinos operate as gambling business entities and utilize the house advantage to achieve their financial benefits. House advantage indicates the profitability of the casino. However, the uncertainty of this gambling can pose a risk of bankruptcy for them. In this study, the house advantage is included in our model for several popular casino games. In addition, a set of full-range scales is defined to facilitate effective assessment of the level of risk faced by the casino, considering its regulatory context. This study also uses the binomial random walk model to describe the race between the casino and the gambler, where each step has two possible outcomes, namely winning or losing. The results of this study are expected to provide insight into the risk in calculating risk in optimizing betting decisions and reducing the risk of bankruptcy.

Determination of Risk Value Using the ARMA-GJR-GARCH Model on BCA Stocks and BNI Stocks Hidayana, Rizki Apriva; Napitupulu, Herlina; Saputra, Jumadil
Operations Research: International Conference Series Vol. 2 No. 3 (2021): Operations Research International Conference Series (ORICS), September 2021
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v2i3.176

Stocks are common investments that are in great demand by investors. Stocks are also an investment instrument that provides returns but tends to be riskier. The return time series is easier to handle than the price time series. In investment activities, there are the most important components, namely volatility and risk. All financial evaluations require accurate volatility predictions. Volatility is identical to the conditional standard deviation of stock price returns. The most frequently used risk calculation is Value-at-Risk (VaR). Mathematical models can be used to predict future stock prices, the model that will be used is the Glosten Jagannathan Runkle-generalized autoregressive conditional heteroscedastic (GJR-GARCH) model. The purpose of this study was to determine the value of the risk obtained by using the time series model. GJR-GARCH is a development of GARCH by including the leverage effect. The effect of leverage is related to the concept of asymmetry. Asymmetry generally arises because of the difference between price changes and value volatility. The method used in this study is a literature and experimental study through secondary data simulations in the form of daily data from BCA shares and BNI shares. Data processing by looking at the heteroscedasticity of the data, then continued by using the GARCH model and seeing whether there is an asymmetry in the data. If there is an asymmetric effect on the processed data, then it is continued by using the GJR-GARCH model. The results obtained on the two stocks can be explained that the analyzed stock has a stock return volatility value for the leverage effect because the GJR-GARCH coefficient value is > 0. So, the risk value obtained by using VaR measurements on BCA stocks is 0.047247 and on BNI stocks. is 0.037355. Therefore, the ARMA-GJR-GARCH model is good for determining the value of stock risk using VaR.

Determination of VaR on BBRI Stocks and BMRI Stocks Using the ARIMA-GARCH Model Napitupulu, Herlina; Hidayana, Rizki Apriva; Saputra, Jumadil
Operations Research: International Conference Series Vol. 2 No. 3 (2021): Operations Research International Conference Series (ORICS), September 2021
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v2i3.178

Stocks are investment instruments that are much in demand by investors as a basis in financial storage. Return and risk are the most important things in investing. Return is a complete summary of investment and the return series is easier to handle than the price series. The movement of risk of loss is obtained from stock investments with profits. One way to calculate risk is value-at-risk. The movement of stocks is used to form a time series so that the calculation of risk can use time series. The purpose of this study was to find out the Value-at-Risk value of BBRI and BMRI stock using the ARIMA-GARCH model. The data used in this study was the daily closing price for 3 years. The time series method used is the Autoregressive Integrated Moving Average (ARIMA)-Generalized Autoregressive Conditional Heteroscedastic (GARCH) model. The stage of analysis is to determine the prediction of stock price movements using the ARIMA model used for the mean model and the GARCH model is used for volatility models. The average value and variants obtained from the model are used to calculate value-at-risk in BBRI and BMRI stock. The results obtained are the ARIMA(3,0,3)-GARCH(1,1) and ARIMA(2,0,2)-GARCH(1,1) model so with a significance level of 5% obtained Value-at-Risk of 0.04058 to BBRI stock and 0.10167 to BMRI stock.

Value-at-Risk Estimation of Indofood (ICBP) and Gas Company (PGAS) Stocks Using the ARMA-GJR-GARCH Model Napitupulu, Herlina; Hidayana, Rizki Apriva; Saputra, Jumadil
Operations Research: International Conference Series Vol. 2 No. 4 (2021): Operations Research International Conference Series (ORICS), December 2021
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v2i4.183

Stocks are one of the most widely used financial market instruments by investors in investing. The most important component of any investment is volatility. Volatility is a conditional measure of variance in stock returns and is important for risk management. In addition to volatility, the important things in investing are return and risk. Risk can be measured using Value-at-Risk (VaR) and can estimate the maximum loss that occurs. The purpose of this study is to determine VaR using the Autoregressive Moving Average-Glosten Jagannatan Runkle-Generalized Autoregressive Conditional Heteroscedasticity (ARMA-GJR-GARCH) model. The stages of data analysis used are estimating the ARMA model and the GARCH model, then estimating the GJR-GARCH model by looking at the heteroscedasticity and asymmetric effects on the GARCH model. Next, determine the VaR value from the estimated mean and variance (volatility) using the ARMA-GJR-GARCH model. The results of the model estimator obtained are based on the return data for the four stocks analyzed, namely the ARMA (5,5)-GJR-GARCH (1,1) model for ICBP stocks and ARMA (1,2)-GJR-GARCH (1,1) for PGAS shares. The Value-at-Risk values of each stock are 0.060427 and 0.024724. This research can be used by investors as a consideration in making investment decisions.

Co-Authors A Hadi Arifin Agapa, M. Fasya Febri Agung Prabowo Agus Santoso Ahmad Dailami, Ahmad Ai Nurhayati Akmal Saputra Ali Syah Putra Aliasuddin Aliasuddin Alimin, Erina Ambiya, Utari Nur Amelisqi, Lusi Bilqis Amin Hou Amiruddin Idris Amni Zarkasyi Rahman, Amni Zarkasyi Andiwi Meifilina, Andiwi Angga, Binanga Anggia Suci Pratiwi Anggita, Sahira Anggraeni, Putri Siti Annur M, Ade Isna Anugerah, Dandi Ariffin, Ku Halim Bin Ku Assidqi, Hasbi Asti Nur Aryanti Astuti, Devi Fitri Ayulita Ramadhani Azahra, Astrid Sulistya Badruzaman, Jajang Betty Subartini Brahmantia, Bayu Budi Hendrawan Chairul Furqon, Chairul Chatarina Lilis Suryani Christianingrum Cut Annisa Fitriati Dalimunthe, Doli Muhammad Jafar Dede Abdul Hasyir Detrianty, Fitria Rika Dewi Mahrani Rangkuty Dierayani, Allysa Putri DIRYO SUPARTO Dwi Susanti Dwijaningtyas, Srinata Dwiyati Pujimulyani EKO EDDYA SUPRIYANTO EKO EDDYA SUPRIYANTO Eko Wahyudi, Fendy Erlina Erlina Evi Widowati F, Ayunda Alysa Fahrul Rizal, Fahrul Fairuztama, Rafif Fauzi, Muhamad Hasman Fauziah, Irma Silvia Fauziah, Mustika Fitriyani, Seni D Geubrina, Yulia Ghazali, Puspa Liza H, Yunita Sri Hadiyani, Rahmilia Hafni Zahara Hardi, Irsan Hari Mulyadi, Hari Hendra Raza Heny Hendrayati Herlina Napitupulu Hermawan, Riki Hidayana, Rizki Apriva Idroes, Ghalieb Mutig Inriati, Dhea Intan, Dede Ira Apriyanti isfenti sadalia Iskandar Muda Islah, Tsani Jamilah Jamilah Josua, Lancelot Julsen Julieta, Salma Kardofa, Mochamad Khoeryza, Rima Laksito, Grida Saktian Lugiana, Yuda Madani, Fathi Makia Maraya, Nisrina Salsabila Maruf, Cipta Muhammad Maulana, Ar Razy Ridha Maulana, Deni Maulani, Nishfi Rahmah Meida Rachmawati Muhammad Abrar Mujiarto, Mujiarto Murni Daulay Mursyidin Mursyidin NADILA SAVIRA Nadjib, Nadjib Nasib Nazila, Siti Desi Nellis Mardhiah Neni Nuraeni, Neni Nizar P, Ajeng Noka Omalia Noor F, Deng Dewi Novitasari, Aliffiah Novitasari, Ela Nur Elfaz, Zam Zam Nurazizah, Dina Nurhayat, Firman Arif Nurjaman, Diman Nurkamilah, Milah Nurmala Nurmala Nurohmah Dona, Tiara Gita Nurul Istiqomah, Insya Siti Nuryuniarti, Rissa Palupi Permata Rahmi Permata, Shintia Pirdaus, Dede Irman Pranoto Pranoto Pratama, Muhamad Risky Pratiwi, Denisa Lestari Prawiro, Meivin Mulyo Puteh, Anwar Putra, Fadlan Gunawan Rahmad Syukur Rahman Alfarisi, Rendi Raja Masbar Rico Nur Ilham Ridho, Adil Risma, Neng Robiatul A, Wahda Saefullah, Rifki Sajidah, Febi Zulfa Salqaura, Siti Alhamra Salsabila, Nabila Saparudin, Miftahul Rizki Saputra, Nadia Sari, Risna Purnama Sariartha Sianipar, Aryanti Septiani, Lisma Septiani, Resty Shinta Dewi, Reni Shofiatun Nisa, Wafa Sidiq, Fahmi Simanjuntak, Alberto Sinurat, Mangasi Siregar, Wardah Muharriyanti Siti Maria Ulfa Sofyan Sofyan Sopia, Dea Sri Nita, Sri Suharman, Harry Sukono . Sullaida, Sullaida Suriani Suriani Suryana Suryana Syahirah, Sheila Najla Syahputra Silalahi, Amlys Syntha, Bunga T. Zulham Urrohman, Nida Wahid, Alim Jaizul Wahyuddin Wahyuni, Arni Sri Wan Ridwan Husen, Wan Ridwan Widiartanto Widiartanto Widiawaty, Shindy Cantika Windi Amelia, Windi Wintara, Jane Ayu Wiranatakusuma, Dimas Bagus Yahya, Muhammad Ilham Yunda, Lola Irma Yuningsih, Siti Hadiaty Yusril Ihza Mahendra Zikri Muhammad

Title Search

Found 5 Documents Search Journal : Operations Research: International Conference Series

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 5 Documents
Search
Journal : Operations Research: International Conference Series