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Jurnal Varian
Published by Universitas Bumigora
ISSN : -     EISSN : 25812017     DOI : https://doi.org/10.30812/varian
Core Subject : Economy, Science, Education, Social,
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Social Sciences Other
Jurnal Varian adalah salah satu Jurnal Ilmiah yang terdapat di Universitas Bumigora. Jurnal ini bertujuan untuk memberikan wadah atau sarana publikasi bagi para dosen, peneliti dan praktisi baik di lingkungan internal maupun eksternal Universitas Bumigora Mataram. Jurnal ini terbit 2 (dua) kali dalam 1 tahun pada periode Genap (April) dan Ganjil (Oktober). Jurnal Varian fokus memuat publikasi pada Bidang Matematika dan Statistika.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 168 Documents
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Mathematical Modelling of Deforestation Due to Population Density and Industrialization Didiharyono Didiharyono; Irwan Kasse
Jurnal Varian Vol 5 No 1 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v5i1.1412

Abstract

The focus of the study in this paper is to model deforestation due to population density and industrialization. To begin with, it is formulated into a mathematical modelling which is a system of non-linear differential equations. Then, analyze the stability of the system based on the Routh-Hurwitz stability criteria. Furthermore, a numerical simulation is performed to determine the shift of a system. The results of the analysis to shown that there are seven non-negative equilibrium points, which in general consist equilibrium point of disturbance-free and equilibrium points of disturbances. Equilibrium point TE7(x, y, z) analyzed to shown asymptotically stable conditions based on the Routh-Hurwitz stability criteria. The numerical simulation results show that if the stability conditions of a system have been met, the system movement always occurs around the equilibrium point.
Comparative Analysis of The Growth of School Students Using Autoregressive Integrated Moving Average Methods Analisis Kiki Riska Ayu Kurniawati; Sumeet Goyal; Biswadip Basu Mallik; Habib Ratu Perwira Negara; Syaharuddin Syaharuddin
Jurnal Varian Vol 5 No 1 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v5i1.1437

Abstract

This study aims to analyze and predict the number of Elementary School Students using Autoregressive Integrated Moving Average (ARIMA) method using data from the last 17 years, case studies in three provinces namely Bali, West Nusa Tenggara (NTB), and East Nusa Tenggara (NTT). This type of research is quantitative by comparing the final value on the first graph to the fourth graph to analyze on the graph what the predictive value is most accurate. Based on the results of the simulation of the number of elementary school students in Bali, NTB, and NTT provinces using the G-MFS application program and mathematical model calculations that the predicted results in 2021 on the data of the number of elementary school students in Bali province amounted to 417,805.40 with a percentage decrease of 0.1%, then the predicted result in the data of the number of elementary school students in NTB province of 512,381.76 with a percentage increase of 1.0%. The predicted result on the data of the number of elementary school students in NTT province amounted to 705,335.11 with an increase of 1.0%. The results of the forecasting of the number of elementary school students are expected to provide important information for the government to improve development in the education sector, especially at the elementary school education level in one way that is to improve the quality of educational infrastructure and many more developments that need to be done by the number of students in the future.
Forecasting Foreign Tourist Visits to West Nusa Tenggara Using ARIMA Method Siti Soraya; Maulida Nurhidayati; Baiq Candra Herawati; Anthony Anggrawan; Lalu Ganda Rady Putra; Didiharyono D
Jurnal Varian Vol 5 No 1 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v5i1.1441

Abstract

West Nusa Tenggara (NTB) is one of the provinces in Indonesia that has its own charm in the world of tourism and is known as a pioneer of halal tourism. In addition to domestic tourists, NTB tourism always has an attraction for foreign tourists. This is evidenced by the increasing number of foreign tourists visiting NTB from year to year before the Covid-19 pandemic. This condition certainly has a positive impact on increasing NTB’s economic growth in the tourism sector and indirectly on the optimization of existing infrastructure. The purpose of the study is to predict the number of foreign tourist visits to NTB so that it can assist the government in making decisions in preparing adequate facilities and infrastructure in the event of a surge in tourist visits. The method used in this study is the Box-Jenkins-ARIMA model. The ARIMA method is based on 3 models that are formed from the results of plot data. The data used in this study is secondary data sourced from the Central Statistics Agency (BPS) of West Nusa Tenggara (NTB), from January 2010 to June 2019. The results show that the ARIMA (4,1,1) model is the most widely used model. This model is suitable for predicting the number of foreign tourists visiting NTB because this model produces the lowest SSE and MSE values compared to other models.
Modeling The Types of Online Learning Media Using Multiple Linear Regression Analysis Isma Muthahharah; Inayanti Fatwa
Jurnal Varian Vol 5 No 1 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v5i1.1459

Abstract

The research objective is to model the types of learning media Whats App Group, Google Classroom, Zoom and YouTube with multiple linear regression analysis. Multiple linear regression analysis is a linear regression model with one continuous variable and k (two or more) independent variables. This type of research is quantitative research that can model several types of online learning. The object of study in this research is STKIP Pembangunan Indonesia students with a sample of 25 people. The source of data comes from primary data by giving questionnaires to students. Based on the results of the analysis, the type of online learning model obtained is = 70, 376 + 0, 357x1 + 0, 322x2 − 0, 279x1 − 0, 321x2 + ε with a contribution of 21.2%. From the resulting regression model, the best learning models or those often used by Lecturers at STKIP development are WhatsApp Group and Google Classroom. In addition to multiple linear regression analysis, other methods can also be used to model types of online learning media with the addition of media such as LMS Moodle, Edmodo and others.
Value-At-Risk Analysis Using ARIMAX-GARCHX Approach For Estimating Risk Of Bank Central Asia Stock Returns Felinda Arumningtyas; Alan Prahutama; Puspita Kartikasari
Jurnal Varian Vol 5 No 1 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v5i1.1474

Abstract

Before buying a stock, an investor must estimate the risk which will be received. VaR is one of the methods that can be used to measure the level of risk. Most stock returns have a high fluctuation, so the variant is heteroscedastic, which is thought to be caused by exogenous variables. The time series model used to model data that is not only influenced by the previous period but is also influenced by exogenous variables is ARIMAX. In contrast, the GARCHX model is used to obtain a more optimal stock return data model with heteroscedasticity cases and is influenced by exogenous variables. This study uses the ARIMAX-GARCHX model to calculate the VaR of the stock returns of PT Bank Central Asia Tbk. The exogenous variables used are the exchange rate return of IDR/USD and the return of the JCI in the period January 3, 2017, to March 31, 2021. The best model chosen is the ARIMAX(2,0,1,1)-GARCHX(1,1,1). VaR calculation is carried out with the concept of moving windows with time intervals of 250, 375, and 500 transaction days. The results obtained at the 95% confidence level, the maximum loss obtained by an investor is 1,4%.
Hausman and Taylor Estimator Analysis on The Linear Data Panel Model Bernadhita Herindri Samodera Utami; Agus Irawan; Miswan Gumanti; Gilang Primajati
Jurnal Varian Vol 5 No 1 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v5i1.1481

Abstract

Panel data modelling in the field of econometrics applies two main approaches, namely fixed effect estimators and random effects. The application of the Hausman and Taylor estimator to real data is used to test for fixed effects or random effects based on the idea that the set of estimated coefficients obtained from the fixed effect estimates is taken as a group. A good estimator is an estimator that is as close as possible to represent the characteristics of the population. The characteristics of a good estimator include unbiasedness, efficiency, and consistency. The purpose of this study is to identify the properties of the Hausman and Taylor estimator in the linear model of panel data. Based on the analysis using panel data, it is found that the Hausman and Taylor estimator on the random effects panel data is an estimator that is consistent and efficient even though it is not unbiased.
Forcasting Stock Price PT. Indonesian Telecomunication with ARCH-GARCH Model Wahidah Alwi; Aprilia Pratiwi S; Ilham Syata
Jurnal Varian Vol 5 No 2 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v5i2.1543

Abstract

This research discusses the modeling of time series using R software, focusing on forecasting the stock price of PT. Indonesian telecommunications with ARCH-GARCH model. The data used daily closing data on stock prices from January 6, 2020, to January 6, 2021 was obtained from the website www.finance.yahoo.com. The goal is to find out the best model arch-garch on PT. Indonesian telecommunications to find out the results of stock price forecasting the next day using the ARCH-GARCH model. The best model was ARIMA (2,1,3). The results of the ARCH-LM test showed the data contained heteroskedasticity effects or ARCH elements. The research models proposed in this study are ARCH (1) and ARCH-GARCH (1,1). The smallest AIC and BIC values of these two models are ARCH-GARCH (1,1) which is the best model for forecasting the stock price of PT. Indonesian telecommunications for the next 10 days. The study attempts to conduct stock price forecasting with the ARCH-GARCH model. The result of the forecasting of the share price of PT. Indonesian telecommunications from January 07, 2021 to January 20, 2021 respectively except for holidays is IDR 3374.884, IDR 3379.617,IDR 3378.305, IDR 3376.610, IDR 3380.050, IDR 3376.372, IDR 3379.071, IDR 3377.964, IDR 3377.515, IDR 3379.002. Forecasting results are close to factual data for forecasting the next 10 days so that they can be taken into consideration in investing by investors.
Convolutional Neural Network for Cataract Maturity Classification Based LeNet Radimas Putra Muhammad Davi Labib; Sirojul Hadi; Parama Diptya Widayaka; Irmalia Suryani Faradisa
Jurnal Varian Vol 5 No 2 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v5i2.1629

Abstract

The eyes are one of the vital organs owned by humans. One of the common eye diseases is cataracts. This disease is characterized by clouding of the lens of the eye and can interfere with vision. Worst case, sufferers can experience blindness. Cataract maturity can be divided into four categories, namely incipient, immature, mature, and hypermature. Cataracts can be removed through surgery when the cataract is in the mature or hypermature phase. Cataract examination is usually done using a slit lamp. The lack of hospitals that have this equipment can cause delays in the healing process for cataract sufferers. This study created an image processing algorithm for the maturity classification process of cataracts using the Convolutional Neural Network method with LeNet network architecture. The algorithm that has been built is capable of classifying the maturity of cataracts with an accuracy rate of 93.33%
Determinants of Leprosy Prevalence in Sulawesi Island Using Spatial Error Model Geraldi Putra P Balebu; Siskarossa Ika Oktora
Jurnal Varian Vol 5 No 2 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v5i2.1632

Abstract

Leprosy is one of the infectious diseases and has become a serious health problem in Indonesia. Based on the publication of the Health Ministry of Republik Indonesia, there are still many areas in Indonesia that have not reached the leprosy elimination status, one of which is Sulawesi Island. The condition of leprosy prevalence in Sulawesi Island is still fluctuating and tends to be high. In addition, leprosy can also be spread across regions. This study aims to analyze whether a spatial effect is present on leprosy prevalence and determine the variables that possibly affect leprosy prevalence. Data used are from Health Profile and Province in Figure publications with an analysis unit consisting of 81 districts/cities. The results show that there is a spatial effect on leprosy prevalence in Sulawesi Island. Queen contiguity-based spatial weights are also considered while performing the spatial analysis. Based on the results of Spatial Error Models can be concluded that population density, the number of multibacillary (MB) leprosy cases, and spatial effect significantly affect the leprosy prevalence. In contrast, a clean and healthy lifestyle, proper water access, and proper sanitation access do not significantly affect the leprosy prevalence.
Workload and Performance of Nurses During The Covid-19 Pandemic: A Meta Analysis Study Gde Palguna Reganata; I Gusti Ngurah Made Yudhi Saputra
Jurnal Varian Vol 5 No 2 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v5i2.1657

Abstract

The surge in Covid-19 cases has caused hospitals and health workers to experience functional collapse. The high workload in handling Covid-19 cases by nurses is happening everywhere. Many studies have been conducted to look at the effect of workload on nurse performance during a pandemic. This research was conducted to determine the effect of workload on the performance of nurses with a meta- analysis approach. This type of research is observational with a retrospective approach. This research conducted through secondary data obtained from relevant sources related to the workload of nurses and nurse performance in various journals. The population and samples were taken from studies that met the criteria. Data analysis using meta-analysis. The result showed that there is a negative correlation between workload and performance of nurses, with ρ= 0 .334 are in the reception area of the 95% (0.334±0.219) confidence interval with p-value < 0.0001. Workload has a contradictory effect on performance, where when the workload of nurses is high, nurses tend to experience a decrease in performance. This needs to be a serious concern, because nurses are at the forefront of health services. If the nurse’s performance has started to decline, then the patient’s handling becomes not optimal and can increase the risk of death for the patient.

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