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Journal : Jurnal Varian

Factor Extraction and Bicluster Analysis on Halal Destinations in Lombok Island Desy Komalasari; Mustika Hadijati; Nurul Fitriyani; Agus Kurnia
Jurnal Varian Vol 4 No 1 (2020)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v4i1.743

Abstract

Indonesia is one of the countries currently developing the concept of halal tourism. Halal tourism includes many variables that are related to each other, which need to be grouped into several main factors that affect tourist visits. This study was conducted to group the variables associated with halal tourism visits to Lombok Island using factor analysis and to classify sub-districts and halal tourism destinations on Lombok Island using the Plaid Bicluster algorithm. Based on the analysis using the main component extraction technique in factor analysis with varimax rotation, it can be concluded that the 9 halal tourism characteristic variables can be grouped into 2 main factors. Furthermore, by using the Plaid Bicluster algorithm, 2 Bicluster were produced. There were 7 sub-districts and 9 destinations formed in Bicluster I, and 8 sub-districts and 3 destinations formed in Bicluster II.
Spline and Kernel Mixed Nonparametric Regression for Malnourished Children Model in West Nusa Tenggara Muhammad Sopian Sauri; Mustika Hadijati; Nurul Fitriyani
Jurnal Varian Vol 4 No 2 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v4i2.1003

Abstract

Health sector development is essential to improve human life quality, especially in West Nusa Tenggara (NTB) Province. Based on data from the NTB Provincial Health Office from 2011 to 2016, children under five suffering from malnutrition continued to increase, caused by several factors that affected the incident. Therefore, appropriate analysis is needed to model children who suffer from malnutrition in NTB Province in 2016, consisting of 10 districts based on the variables that influence it. The analysis in this study was carried out using a nonparametric regression mixed-model spline truncated and kernel. The estimation of the nonparametric regression curve depends on the optimal knot points and bandwidths parameter. Therefore, in determining the optimal knot points and bandwidths obtained from Generalized Cross-Validation (GCV). Based on the analysis that has been done, we obtained a nonparametric regression mixed-model spline truncated and kernel optimal knot points, such as for each variable and optimum bandwidths, such as and , with the value of GCV. The mixed model acquired has a good model by considering the values of and MSE. Besides, the MAPE value indicated a high degree of accuracy, so that the model obtained has an excellent forecast.
Forecasting the Amount of Water Discharge Based on the VARIMA Model Meliyana, Hesti; Hadijati, Mustika; Harsyiah, Lisa
Jurnal Varian Vol. 8 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i2.3278

Abstract

Water is an absolutely necessary substance for every living thing. Clean water is the main requirement for ensuring human health and the environment PT. Air Minum Giri Menang (Perseroda). The purpose of this study is to determine the model and then predict the water discharge of PT. Air Minum Giri Menang using the obtained model which will be useful for the community and agencies so that the management, distribution, and use of clean water are more optimal. The method used in this study is VARIMA (Vector Autoregressive Integrate Moving Average) which can process data for more than one variable. The data used in this study is water discharge data produced and distributed in the period January 2018 to December 2021. The results show that the best model obtained is VARIMA(0,1,1) with model accuracy for water discharge data that produced and distributed based on the MAPE value of 4% and 5% which states that the forecasting results can be categorized as very good. This means that the VARIMA (0,1,1) model has provided very accurate results in predicting water discharge with very small forecasting errors, thus indicating that the model is very effective. Suggestions for further research are look for the alternative forecasting method that are overcome non-stationarity data other than data transformation.