cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Soraya
Contact Email
jurnal.varian@stmikbumigora.ac.id
Phone
+6282339979545
Journal Mail Official
jurnal.varian@stmikbumigora.ac.id
Editorial Address
Jln. Ismail Marzuki - Cilinaya - Cakranegara - Mataram 83127
Location
Kota mataram,
Nusa tenggara barat
INDONESIA
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 11 Documents
 1   2 
Search results for , issue "Vol 5 No 2 (2022)" : 11 Documents clear
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.
Expansion of Stock Portfolio Risk Analysis Using Hybrid Monte Carlo-Expected Tail Loss Wisnowan Hendy Saputra; Ika Safitri
Jurnal Varian Vol 5 No 2 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v5i2.1813

Abstract

Monte Carlo-Expected Tail Loss (MC-ETL) is the new expansion method that combines simulation and calculation to measure investment risk. This study models US stock prices using ARIMA-GARCH and forms an optimized portfolio based on Multi-Objective that aims to analyze the portfolio investment return. The next portfolio return will be simulated using the Monte Carlo (MC) method, measured based on the Expected Tail Loss (ETL) calculation. The optimized portfolio comprises 5 US stocks from 10 years of data, with the biggest capitalization market on February 25, 2021. MSFT has the most considerable weight in the optimized portfolio, followed by GOOG, AAPL, and AMZN, whereas TSLA shares have negligible weight. Based on the simulation result, the optimized portfolio has the smallest ETL value compared to its constituent stocks, which is ±0.029 or about 2.9%. This value means that the optimized portfolio is concluded as an investment choice for investors with a low level of risk.
The Defuzzification Methods Comparison of Mamdani Fuzzy Inference System in Predicting Tofu Production Grandianus Seda Mada; Nugraha Kristiano Floresda Dethan; Andika Ellena Saufika Hakim Maharani
Jurnal Varian Vol 5 No 2 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v5i2.1816

Abstract

One of the tofu-producing companies in Kupang City is Bintang Oesapa. With the Covid-19 pandemic,the factory needs to reconsider the amount of production by taking into account the unpredictability ofdemand and resources to minimize losses due to excessive accumulation or shortages of supplies. Indetermining the amount of production, Mamdani’s Fuzzy Inference System (FIS) can be used, whichis a method for the analysis of an uncertain system. This method has three stages in the process ofdecision making, namely fuzzification, inferencing and defuzzification. In the defuzzification stage,the FIS Mamdani has five methods, namely Centroid, Bisector, Mean of Maximum (MOM), Smallestof Maximum (SOM), and Largest of Maximum (LOM). This study discusses an application of FISMamdani with five defuzzification methods for determining daily tofu production. The purpose of thisstudy is to offer a solution by first comparing the five defuzzification methods in assessing the amount oftofu production at the Bintang Oesapa factory and then determining that which is most appropriate. Theinput variables used in this research are the amount of demand and the amount of available stock, whilethe amount of production is our variable of interest. The results showed that the best defuzzificationmethod was the MOM method with an accuracy level of 94.73% and a small error value, 5.27%. TheMOM defuzzification is expected to aid decision makers in determining the best amount of productionduring the pandemic.
Modified Hungarian Method for Solving Balanced Fuzzy Transportation Problems Fried Markus Allung Blegur; Nugraha K. F. Dethan
Jurnal Varian Vol 5 No 2 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v5i2.1865

Abstract

This paper discusses how to solve balanced transportation problems, with transportation costs in the form of trapezoidal fuzzy numbers. Fuzzy costs are transformed into crisp costs using the Robust’s method as a ranking function. A new approach of modified Hungarian method has been applied to solve the problem of fuzzy transportation. This approach solves the fuzzy transportation problem in one stage of optimization and yields the same results as other methods that solve the problem in two stages.
Cluster Analysis of Inclusive Economic Development Using K-Means Algorithm Riska Yanu Fa'rifah; Dita Pramesti
Jurnal Varian Vol 5 No 2 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v5i2.1894

Abstract

This study aims to cluster 38 Districts/Cities in East Java based on the 10 forming indicators of inclusive economic development and to determine the inclusive economic growth of Districts/Cities above or below the total average. 10 indicators used in this study are GRDP per capita, GRDP by business field, Labor force participation rate, Unemployment rate, Gini ratio, Expenditure per capita, the number of poverty, Life expectancy, expectation years of schooling, and mean years of schooling. There are 3 scenarios in this study, namely 2 clusters, 3 clusters, and 4 clusters. The method of clustering in this study is using the K-means algorithm. This study uses the silhouette coefficient to evaluate the best cluster of 3 scenarios. The best k-means algorithm in this study is using 2 clusters with a silhouette coefficient of 0.87. There are 29 Districts/Cities included in cluster 1 with inclusive economic development below the total average and 9 Districts/Cities included in cluster 2 with inclusive economic development above the total average. The members of cluster 1 are mostly district areas and located in coastal or border areas and the members of cluster 2 are mostly urban or industrial areas.
Mask Compliance Modeling Related COVID-19 in Indonesia Using Spline Nonparametric Regression Citra Imama; M. Haykal Adriansyah; Hadi Prayogi; Ferdiana Friska Rahmana Putri; Naufal Ramadhan Al Akhwal Siregar; Alfredi Yoani; Fariz Mardianto
Jurnal Varian Vol 5 No 2 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v5i2.1895

Abstract

Until now, Coronavirus disease (COVID-19) has become a concern for Indonesia because of its significant development and impact on various sectors of life and hampering the target of achieving Sustainable Development Goals (SDGs). The achievements targeted in the SDGs, such as reducing poverty, hunger, and many more are very difficult to realize in the current pandemic conditions. The uncertain conditions of the pandemic made the government need some new ideas for consideration in creating policies to encourage sustainable development in this situation. This article covers modeling the effect of achieving the second dose of vaccination and the total cases of COVID-19 cases, which are often considered the reason for general negligence in complying with health protocols, especially wearing masks. This research was conducted using spline nonparametric regression because of its flexibility to handle uncertain data patterns. The results of this study are truncated spline nonparametric regression with 3 knots that produce a R-sq equal to 69.952%. Based on the results, the second dose vaccination coverage variables and the total COVID-19 cases together affect mask compliance. This result is expected to be a benchmark for the government to handle COVID-19 and efforts to achieve the SDGs.
K-Prototypes Algorithm for Clustering The Tectonic Earthquake in Sulawesi Island Suwardi Annas; Irwan Irwan; Rahmat H Safei; Zulkifli Rais
Jurnal Varian Vol 5 No 2 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v5i2.1908

Abstract

Natural disasters that had occurred in Indonesia consist of hydro-meteorology: floods, droughts, and landslides, geophysical: volcanic earthquakes and volcanic eruptions, and biological: epidemics. Regarding the tectonic earthquake on Sulawesi Island, there are at least 2 earthquake disasters that became national disasters, namely in Central Sulawesi and West Sulawesi in the range of 2017 to 2021. This study aims to cluster tectonic earthquakes on Sulawesi Island, from 2017 to 2020, as the basis for formulating disaster mitigation plans. This study used tectonic earthquake data from 2017 to 2020 obtained from BMKG Gowa, Indonesia. The variables used are magnitude, depth, and distance category. Because they are mixed variables, this study used a k-prototype algorithm. There are four clusters in 2017, six clusters in 2018, five clusters in 2019, and six clusters in 2020 based on the ratio of within-cluster distance against between-cluster distance. It can be related to the active fault on Sulawesi Island. The characteristics of clusters form each year are the greater magnitude of the earthquake, the deeper of deep and the category distance is dominated by the regional level.

Page 1 of 2 | Total Record : 11

 1   2 

Filter by Year

2022 2022


Reset
Filter By Issues
All Issue Vol. 8 No. 2 (2025) Vol 8 No 1 (2024) Vol. 8 No. 1 (2024) Vol. 7 No. 2 (2024) Vol 7 No 2 (2024) Vol 7 No 1 (2023) Vol. 7 No. 1 (2023) Vol 6 No 2 (2023) Vol 6 No 1 (2022) Vol 5 No 2 (2022) Vol 5 No 1 (2021) Vol 4 No 2 (2021) Vol 4 No 1 (2020) Vol 3 No 2 (2020) Vol 3 No 1 (2019) Vol 2 No 2 (2019) Vol 2 No 1 (2018) Vol 1 No 2 (2018) Vol 1 No 1 (2017) More Issue