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All Journal Jurnal Studi Manajemen Organisasi Journal of Advanced in Information and Industrial Technology (JAIIT)
Ardiani, Ardia Eva
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Prediction Of Loss Risk Investment On The Idx Indonesia: Quantitative Approach With Var And Adj-Es Trimono, Trimono; Fahrudin, Tresna Maulana; Ardiani, Ardia Eva
JURNAL STUDI MANAJEMEN ORGANISASI Vol 22, No 1 (2025)
Publisher : Faculty of Economics and Business | Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jsmo.v22i1.73062

Abstract

Loss is the primary risk associated with any investment. In stock investments, the risk of loss can occur at any time and its magnitude cannot be precisely determined. Improper risk management can negatively impact the investment activities carried out by investors. One way to manage risk effectively and prevent bankruptcy is by estimating the potential future risk. This study aims to predict the risk of loss using the quantitative Value-at-Risk (VaR) model, particularly for stocks listed on IDX Indonesia. VaR has the main advantage of being a simple model that can be applied to various types of financial assets. However, VaR also has a drawback it does not satisfy the subadditivity principle. Therefore, this study also employs the Adjusted-Expected-Shortfall (Adj-ES) model as an improvement to VaR. The VaR and Adj-ES models will be implemented on the stocks AMRT.JK and BBCA.JK. These two stocks are part of the IDX Indonesia 2024 blue chip stocks, with a significant increase in market capitalization. The results show that VaR provides prediction results for the risk of loss in the range of 1.2% - 3.4 for AMRT.JK data, and 1.1 - 3.2% for BBCA.JK data. Referring to the Violation Ratio value, it is known that both VaR and Adj-ES have VR values <1 so it is concluded that the prediction accuracy is very good
Spatial Autocorrelation Analysis of East Java Stunting Prevalence Cases in 2023 Trimono; Amri Muhaimin; Ekacitta, Puti Cresti; Ardiani, Ardia Eva
Journal of Advances in Information and Industrial Technology Vol. 7 No. 1 (2025): May
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v7i1.689

Abstract

Stunting is one of the chronic nutritional problems occur in East Java. In 2022, the percentage of stunting in East Java reached 19.2% and decreased to 17.7% in 2023. The less significant decrease occurred due to various factors, including malnutrition, poor sanitation, and environmental influences. This study will analyze the spatial influence on the prevalence of stunting in East Java, especially in 2023. The methods used include the Morans Index and the Local Indicator of Spatial Association (LISA). Spatial correlation analysis will help in determining the pattern of regional grouping based on stunting cases. This model works by testing whether the values of a variable at a location are related to the values of the same variable at neighboring locations, with the nature of the relationship being positive (clustering) or negative (dispersion). Using stunting prevalence data in 2023, the Moran Index = 0.3233 was obtained with a Zvalue = -1.0776. This value indicates that there is positive spatial autocorrelation, but is not significant enough. Then, through the Moran Scatterplot analysis, there are indications of regional grouping in four spatial quadrants. The results of the LISA analysis show that there are five cities/regencies included in the High-High cluster (Jember, Probolinggo City, Lumajang, Malang, and Probolinggo), one area in the Low-High cluster (Situbondo), and one area in the Low-Low cluster (Gresik). These findings indicate the existence of a spatial concentration of stunting problems that can be used as a basis for developing appropriate handling strategies by the provincial government based on regions.
Co-Authors Amri Muhaimin Fahrudin, Tresna Maulana Puti Cresti Ekacitta Trimono Trimono, Trimono