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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 10 Documents
 1 
Search results for , issue "Vol 7 No 1 (2023)" : 10 Documents clear
Finding the Best Model in Nonlinear Regression: Using the Coefficient of Determination Vitri Aprilla Handayani; Widya Reza; Saba Mehmood
Jurnal Varian Vol 7 No 1 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i1.2322

Abstract

In Indonesia, inflation plays a significant role in shaping economic growth. Therefore, it is essential to examine the impact of inflation on economic growth through a comprehensive analysis. This analysis aims to identify the factors influencing economic growth in Indonesia by utilizing nonlinear regression analysis. The study focuses specifically on modeling economic growth in Batam City and its correlation with inflation. The primary goal is to identify the most effective nonlinear regression model that accurately represents the relationship between economic growth and inflation, as determined by the coefficient of determination. The method used in this research is nonlinear regression methods provide a more accurate and comprehensive analysis when dealing with complex relationships and can help uncover valuable insights that may be missed by simpler linear models. The results of the analysis finding the model that is suitable for modeling inflation on economic growth is a quadratic model with a coefficient of determination of 73.4%. The research has found that the best model for explaining the impact of inflation on economic growth is the Quadratic model with an R-value of 0.734 or 75%. These results indicate that the Quadratic model can account for 75% of the influence of inflation on economic growth.
Application of Artificial Neural Network in Predicting Direct Economic Losses Due to Earthquake Ulil Azmi; Soehardjoepri Soehardjoepri; Rudi Prihandoko; Iqra Asif
Jurnal Varian Vol 7 No 1 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i1.2326

Abstract

Accurately predicting the direct economic losses caused by earthquakes is important for policy makers for disaster budgets. Before a disaster strikes, it is important to consider the public policy costs associated with disaster relief and recovery. The aim of this study is to provide a risk assessment approach, which can benefit all parties involved. Artificial neural networks are widely used for time series forecasting, especially financial forecasting. Therefore, this study proposes a cutting-edge forecasting method such as backpropagation neural network (BPNN) and other prediction methods: neural network autoregressive (NNAR) and ARIMA-GARCH to obtain the best prediction results. This paper applies interpolation data to increase the amount of data used. Two interpolations were applied to amplify the original small sample with virtual points, namely cubic splines and further piecewise interpolation using. The results of this study are the cubic spline interpolation is the most effective way to solve the small sampling problem to predict direct economic losses due to the Indonesian earthquake and the BPNN method outperforms other traditional methods with an RMSE of 0.024 in the training period and 0.174 in the testing period, significantly lower than other methods. The results of this research can be used as reference material for the government in estimating the level of earthquake losses and can be used to develop risk reduction strategies.
Improved Chi Square Automatic Interaction Detection on Students Discontinuation to Secondary School Fadhil Al Anshory; Siswanto Siswanto; Sri Astuti Thamrin; Ika Inayah
Jurnal Varian Vol 7 No 1 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i1.2627

Abstract

Improved Chi Square Automatic Interaction Detection (CHAID) with bias correction is the development of the CHAID method by relying on Tschuprow's T test calculations with bias correction in the process of forming a classification tree. This study aims to obtain a classification of factors which influence students for not continuing their education from junior high school or equivalent to high school or equivalent. The results obtained in the classification tree produce nine classifications. Based on the results of the classification tree, the classification of students who do not continue their education to high school or equivalent is: students with disabilities who do not have access to Information and Communication Technology (ICTs) (0.89); students who work without disability but do not have access to ICTs (0.73); and students who do not work without disability but do not have access to in ICTs (0.60). Based on the classification obtained the factors which influence students for not continuing their education to high school or equivalent are access to ICTs, employment status, and persons with disabilities. The classification accuracy of the results uses the Improved-CHAID method with bias correction with a proportion of 80% training data and 20% testing data, namely 72.3033% on training data and an increase of 73.3300% on testing data.
Modeling The Influenced Factors of Remaining Operation Results Using Multiple Linear Regression Widiya Astuti Alam Sur; Ines Saraswati Machfiroh; Ricky Iskandar
Jurnal Varian Vol 7 No 1 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i1.2635

Abstract

The amount of Remaining Operation Results (ROR) obtained by Koperasi can reflect the health of its financial management. Many economic factors can affect the ROR of Koperasi itself. This study aims to determine the economic factors that significantly influence ROR based on a statistical model of multiple linear regression. The novelty of this research provides new analytical results that compare the most three influential factors based on previous research to show the factors significantly influencing the ROR. The quantitative data used the five years financial statements of Koperasi Sawit Makmur in 2017-2021 consists of 20 data on liabilities, net worth, assets, and ROR of Koperasi Sawit Makmur. The results show that asset variable as an excluded variable that does not affect the ROR of Koperasi with R2 = 0.961. Liabilities and net worth variables can explain 96.1% of ROR, with 3.9% can be explained by assets and other variables not included in the study. Liabilities have a negative and tangible effect, and net worth has a positive and partially real influence on ROR. The factor with the most dominant influence on ROR is net worth, with an influence percentage of 63.80%. The future research is expected to provide an overview of net worth and asset determinants using related statistical methods.
Application of Mamdani’s Fuzzy Inference System in the Diagnosis of Pre-eclampsia Grandianus Seda Mada; Maria Julieta Esperanca Naibili; Siprianus Septian Manek; Estevania Daonce Mau; Wasim Raza
Jurnal Varian Vol 7 No 1 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i1.2764

Abstract

Pre-eclampsia is the second of the top three causes of death in pregnant women after bleeding and followed by infection. By knowing the risk factors, early detection of pre-eclampsia in pregnant women needs to be done so that later it can be treated more quickly to prevent further complications. This study aims to design a practical application of a decision-making system for the diagnosis of pre-eclampsia in pregnant women using the Fuzzy Inference System (FIS) method so it can be used efficiently and effectively for the early diagnosis of pre-eclampsia. The method used in data analysis is the FIS Mamdani method with defuzzification using the centroid method. The designed system considers blood pressure and proteinuria as input variables and pre-eclampsia status as output variables. The research results show that the system has 7.27% of Mean Absolute Percentage Error (MAPE) value and when comparing the final diagnosis of the system and expert diagnoses (doctors) from 20 patients at two hospitals, it was found that the system diagnosis was 100% in accordance with the expert diagnoses.
Determinants of Under-five Mortality Due to Pneumonia: A Negative Binomial Regression Analysis Dhea Prawidia; Lidya Zhafirah; Nurzikri Saputra; Fitri Kartiasih; Uma Sahu
Jurnal Varian Vol 7 No 1 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i1.2768

Abstract

Pneumonia is one of the main causes of under-five mortality in Indonesia. In under-fives, pneumonia is the number one killer in the world. Meanwhile, in Indonesia, it ranks second after diarrhea. On average, the disease affects half a million children a year. This study aims to identify and analyze the risk of variables that affect the number of under-five mortality due to pneumonia in Indonesia in 2021. The novelty of this research focuses on the macro variables used, making it easier for policy makers to make decisions. The research method used is negative binomial regression. The results showed that the highest number of under-five mortality due to pneumonia was in Central Java Province. Meanwhile, the lowest was in Jambi Province, South Sumatra, Riau Islands, DKI Jakarta, North Kalimantan, Southeast Sulawesi, and Papua. The per capita income significantly reduces the number of under-five mortality due to pneumonia, while the number of under-fives with severe pneumonia significantly reduces the number of under-five mortality due to pneumonia in Indonesia. The government needs attention to reduce the death rate of children under five due to pneumonia by providing social protection in the fields of health and education for underprivileged communities.
The Role of Time Management on Behavior and Perception with Qualitative Descriptive Suci Pertiwi; Sophya Hadini Marpaung; Joosten Joosten
Jurnal Varian Vol 7 No 1 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i1.2795

Abstract

Problems that occur at Mikroskil University student organizations include obstacles from the administrative side, such as delays in submitting proposals and reporting activities which result in delays/especially delays in the funding process, to issues of morality and management of each student organization as well as time management itself. This research aims to determine and analyze the condition of Mikroskil University student organizations in running student organizations so that student perceptions and behavior can be known, and this is the novelty of this research. This research used a qualitative descriptive survey method with a purposive sampling technique involving core administrators and active members of divisions within Mikroskil University as samples. The results of the partial test (t-test) show that the time management variable has a positive effect on student behavior and perceptions at the Mikroskil University Student Organization. The Adjusted R2 value of the time management variable on student behavior was obtained at 44.8%, while the time management variable on behavior towards student perceptions was 32.8% and the remaining 55.2% and 67.2% were influenced by other variables not examined in this research. The research results show that time management partially has a positive and significant effect on behavior and perceptions
Comparison K-Means and Fuzzy C-Means In Regencies/Cities Grouping Based on Educational Indicators Gerald Claudio Messakh; Memi Nor Hayati; Sifriyani Sifriyani
Jurnal Varian Vol 7 No 1 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i1.2879

Abstract

Cluster analysis is an analysis that aims to classify data based on the similarity of specific characteristics. The methods used in this research are K-Means and Fuzzy C-Means (FCM). K-Means is a partition-based non-hierarchical data grouping method. FCM is a clustering technique in which the existence of each data is determined by the degree of membership. The purpose of this study is to classify regencies/cities in Kalimantan based on education indicators in 2021 using K-Means and FCM and find out which method is better to use between K-Means and FCM based on the standard deviation ratio so it can be used efficiently and effectively for decision making by the government to advance the level of education on the island of Kalimantan. Based on the results of the analysis, it's concluded that K-Means is the better method with the ratio of the standard deviation within a cluster against the standard deviation between clusters of 0.6052 which produces optimal clusters of 2 clusters, namely the first cluster consisting of 14 Regencies/Cities, while the second cluster consists of 42 Regencies/Cities in Kalimantan.
Survival Analysis with Cox Proportional Hazard Model for Tuberculosis (TBC) Patients Zahratun Nisa; Bobby Poerwanto; Muhammad Fahmuddin Sudding
Jurnal Varian Vol 7 No 1 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i1.2994

Abstract

Survival analysis is a method in statistics which aims to analyze the relationship between time from the beginning of observation until the occurrence of an event (response variable) with factors that have an influence on the event (predictor variables). To determine the relationship between the response variable and the predictor variable, where the response variable is the time until the event occurs, one method that can be used is the cox proportional hazard regression method. The data used in this research is data on hospitalizations of tuberculosis sufferers at Haji Makassar Hospital in 2022 because it has characteristics that are in accordance with the aim of survival analysis, namely to determine the relationship between the life span of TBC patients and the factors that influence TBC disease. The results of the analysis obtained factors that significantly influence the recovery rate of patients with TBC are shortness of breath and smoking habits. The shortness of breath variable has an influence on the recovery rate of TBC patients, namely 0.3506, which means that TBC patients who do not experiencing shortness of breath has a recovery rate of 0.3506 times the likelihood of recovery compared to patients who experience shortness of breath. Variable smoking habit was 0.7367, which means that patients with TBC did not smoking habit has a recovery rate of 0.7367 times recovered compared to patients who had a smoking habit.
Gold Price Fluctuation Forecasting Based on Newton and Lagrange Polynomial Interpolation Andika Ellena Saufika Hakim Maharani; Dea Alvionita Azka; Darlena Darlena
Jurnal Varian Vol 7 No 1 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i1.3230

Abstract

Gold is a highly valuable commodity and an investment opportunity for many people. However, there are often significant fluctuations in gold prices that affect investment decisions. Various mathematical forecasting methods have been developed to anticipate gold price fluctuations. This study uses historical daily data of gold prices during January-May 2023. The method used in this study is the Newton and Lagrange polynomial interpolation method with several orders to analyze data and forecast gold price fluctuations. The purpose of this study is to compare the performance and accuracy of the order levels of the Newton and Lagrange polynomial interpolation forecasting models. In this study, the test data points and orders are selected so that a range is formed that matches the amount of data available. The test orders used in this study include orders 2, 3, 5, 6, and 10. This study found that the 2nd order polynomial interpolation method is more effective and accurate in forecasting gold price fluctuations compared to the higher orders tested. This is indicated by the results of the calculation of MAE, RMSE, and MAPE values in 2nd order polynomial interpolation which are smaller than in 3rd, 5th, 6th, and 10th order polynomial interpolation. This suggests that a polynomial of 2nd order has been able to model and forecast gold price fluctuations well. However, it is important to remember that these conclusions are based on the data and methods used in this study. Variability in forecasting results can occur depending on the quality of the data, the time period used, and the interpolation method applied, among others. Therefore, further research and wider testing needs to be conducted to validate these conclusions.

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