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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 10 Documents
 1 
Search results for , issue "Vol 6 No 1 (2022)" : 10 Documents clear
Characteristic Estimator of Interval-Censored Binomial Data and Its Application Bernadhita Herindri Samodera Utami; Dwi Herinanto; Miswan Gumanti
Jurnal Varian Vol 6 No 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v6i1.1847

Abstract

This study aims to determine the estimation of interval-censored data with a special distribution, namely the binomial distribution. This research is using quantitative methods, the steps are estimating parameters on the interval-censored binomial distribution using the Maximum Likelihood Estimation method. The second step shows the properties of the estimator on the interval-censored binomial distribution. The last is to determine the parameter estimation of interval-censored data from the binomial distribution in survival analysis and provide an example of research containing interval-censored observations which will then be used as a case study. The results showed that the estimator is a sufficient statistic, meaning that it is unbiased. The case study was conducted using interval-censored data regarding the study of ninety-four breast cancer patients to see which group survived longer (survival value) of the two treatments, namely patients who underwent radiotherapy alone and patients who underwent radiotherapy followed by adjuvant chemotherapy.
Optimum Control of SEIR Model on COVID-19 Spread with Delay Time and Vaccination Effect in South Sulawesi Province Syafruddin Side; Irwan Irwan; Muhammad Rifandi; Muhammad Isbar Pratama; Ruliana Ruliana; Nor Zila Abdul Hamid
Jurnal Varian Vol 6 No 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v6i1.1882

Abstract

The increasing number of cases and the development of new variants of the Covid-19 virus globally including the territory of Indonesia, especially in the province of South Sulawesi are increasingly worrying and need to be prevented. Therefore, this study aims to develop a SEIR model on the spread of Covid-19 with vaccination control, optimal control analysis, stability analysis and numerical simulation of the SEIR model on the spread of Covid-19 in South Sulawesi. This study uses the SEIR epidemic model to predict the spread of Covid-19 in South Sulawesi Province with parameters such as birth rate, cure rate, mortality rate, interaction rate and vaccination. The SEIR model was chosen because it is one of the basic methods in the epidemiological model. The method used to build the model is a time delay model by considering the vaccination factor as a model parameter, model analysis using the next generation matrix method to determine the basic reproduction number and stability of the Covid-19 distribution model in South Sulawesi. Numerical model simulation using secondary data on the number of Covid-19 cases in South Sulawesi starting in 2021 which was obtained from the South Sulawesi Provincial Health Office. The results obtained are model analysis provides evidence of the existence of optimal control in the model. Based on the results obtained, it can also be seen that vaccination greatly influences the spread of Covid-19 in South Sulawesi, so that awareness is needed for the people of South Sulawesi to follow the government's recommendation to vaccinate to prevent or reduce the rate of transmission of Covid-19 in South Sulawesi.
Clustrering of BPJS National Health Insurance Participant Using DBSCAN Algorithm Wiwit Pura Nurmayanti; Dewi Juliah Ratnaningsih; Sausan Nisrina; Abdul Rahim; Muhammad Malthuf; Wirajaya Kusuma
Jurnal Varian Vol 6 No 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v6i1.1886

Abstract

In the current era of Big Data, getting data is no longer a difficult thing because they can access easily it via the internet, which is open access. A large amount of data can cause many problems in the data, such as data that deviates too far from the average (outliers). The method used to handle outlier data is DBSCAN which is density based clustering. The DBSCAN can be applied in various fields, one of which is the social sector, namely the participation of the JKN BPJS Health in West Nusa Tenggara. This study sees the distribution of BPJS Health participation groups, and to detect outliers so that objects with noise are not included in the cluster. The results of the study using the DBSCAN algorithm show that the optimal epsilon value is between 0.37 points by observing the knee of a curve. and MinPts 3, with the highest silhouette value of 0.2763. The highest JKN BPJS participants are in cluster 1 with 5 sub-districts, the second highest cluster is cluster 3 with 5 sub-districts, while the lowest cluster is cluster 2 with 93 sub-districts. The 13 sub-districts are not included in any group because they are noise data.
Determinants of Multidrug-Resistant Pulmonary Tuberculosis in Indonesia: A Spatial Analysis Perspective Ni Luh Evindia Andini; Siskarossa Ika Oktora
Jurnal Varian Vol 6 No 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v6i1.1973

Abstract

Tuberculosis is caused by Mycobacterium Tuberculosis (MT). MT usually attacks the lungs and causes pulmonary-tuberculosis. Tuberculosis cases in Indonesia keep increasing over the years. The presence of Multidrug-Resistant Tuberculosis (MDR-TB) has been one of the main obstacles in eradicating tuberculosis because it couldn’t be cured using standard drugs. In fact, the success rate of MDR-TB treatment in 2019 at the global level was only 57 percent. Research on MDR-TB can be related to the spatial aspect because this disease can be transmitted quickly. This study aims to obtain an overview and model the number of Indonesia’s pulmonary MDR-TB cases in 2019 using the Geographically Weighted Negative Binomial Regression (GWNBR) method. The independent variables used in the model are population density, percentage of poor population, health center ratio per 100 thousand population, the ratio of health workers per 10 thousand population, percentage of smokers, percentage of the region with PHBS policies, and percentage of BCG immunization coverage. The finding reveals that the model forms 12 regional groups based on significant variables where GWNBR gives better results compared to NBR. The significant spatial correlation implies that the collaboration among regional governments plays an important role in reducing the number of pulmonary MDR-TB.
Egarch Model Prediction for Sale Stock Price Ismail Husein; Machrani Adi Putri Siregar; Arya Impun Diapari Lubis; Rima Aprilia
Jurnal Varian Vol 6 No 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v6i1.1975

Abstract

Stock is an investment in the capital market that is very promising for investors. Investors can also get high returns from the shares invested. However, this stock price is not always stable, it can go up and down drastically. The purpose of this study is to predict stock prices because they often experience instability. The method used in this research is using the Exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model with the Quasi Maximum Likelihood (QML) method. The result of this research is the implementation of this model. The EGARCH model used is the stock price index model that is formed, namely the autoregressive integrated moving average (ARIMA) (0, 1, 2) EGARCH (1.4). The conclusion from the results of the research that predictions using the ARIMA model (0, 1, 2) EGARCH (1, 4) is the best model in accommodating the asymmetric nature of the volatility of the stock price index. The results of this egarch model show more optimal prediction results seen from an error of 3% compared to other modes such as the arch model and the GARCH model.
Comparison of R and GeoDa Software in Case of Stunting Using Spatial Error Model Hendra H Dukalang; Ingka Rizkyani Akolo; Muhammad Rezky Friesta Payu; Setia Ningsih
Jurnal Varian Vol 6 No 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v6i1.1993

Abstract

Gorontalo city is the capital of Gorontalo province which has a high incidence of stunting. This high incidence rate needs to get attention because stunting can further become one of the indicators of the low quality of human resources in Gorontalo. One method that can be used to analyze the factors that cause stunting is the spatial regression method, namely Spatial Error Model (SEM). SEM model can analyze used R and GeoDa software. The purpose of this study is to find out the factors that affect stunting in Gorontalo City and compare the results of the Spatial Error Model analysis based on the results of R and GeoDa software. The results showed that there are two variables that have a significant effect on stunting incidence, namely the variable number of Complete Basic Immunization (IDL) and the amount of proper sanitation. The R and GeoDa software comparison results showed there were several similar outputs i.e. LM test output, parameter estimation and R-square value, while the different outputs were Moran's I test output, Breusch-Pagan test, and AIC value. Although Moran's I test output and Breusch-Pagan’s test are different, but they produce the same conclusion. The AIC value produced by GeoDa is smaller than R software.
Application of Soft-Clustering Analysis Using Expectation Maximization Algorithms on Gaussian Mixture Model Andi Shahifah Muthahharah; Muhammad Arif Tiro; Aswi Aswi
Jurnal Varian Vol 6 No 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v6i1.2142

Abstract

Research on soft-clustering has not been explored much compared to hard-clustering. Soft-clustering algorithms are important in solving complex clustering problems. One of the soft-clustering methods is the Gaussian Mixture Model (GMM). GMM is a clustering method to classify data points into different clusters based on the Gaussian distribution. This study aims to determine the number of clusters formed by using the GMM method. The data used in this study is synthetic data on water quality indicators obtained from the Kaggle website. The stages of the GMM method are: imputing the Not Available (NA) value (if there is an NA value), checking the data distribution, conducting a normality test, and standardizing the data. The next step is to estimate the parameters with the Expectation Maximization (EM) algorithm. The best number of clusters is based on the biggest value of the Bayesian Information Creation (BIC). The results showed that the best number of clusters from synthetic data on water quality indicators was 3 clusters. Cluster 1 consisted of 1110 observations with low-quality category, cluster 2 consisted of 499 observations with medium quality category, and cluster 3 consisted of 1667 observations with high-quality category or acceptable. The results of this study recommend that the GMM method can be grouped correctly when the variables used are generally normally distributed. This method can be applied to real data, both in which the variables are normally distributed or which have a mixture of Gaussian and non-Gaussian.
Machine Learning Prediction of Anxiety Levels in the Society of Academicians During the Covid-19 Pandemic Angelina Pramana Thenata; Martinus Suryadi
Jurnal Varian Vol 6 No 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v6i1.2149

Abstract

Various sectors in Indonesia have been impacted by the COVID-19 incident, such as the trade, health, entertainment, and social sectors. Although several steps have been taken to minimize the coronavirus's impact, problems still occur, especially in the education sector, which must carry out one of the challenges faced in the learning process during the pandemic. However, the environment and learning process that turned into distance learning caused the interaction with friends to decrease, and academics could only move in a limited space, making them overwhelmed by feelings of anxiety. Anxiety must be detected early and managed properly not to cause mental deterioration. Therefore, the researcher aims to predict academic anxiety based on the self-rating anxiety scale (SAS), demography, family, lifestyle, and employment using k-means. Furthermore, tested the prediction results obtained with a confusion matrix in accuracy, precision, and recall. The test results found the accuracy rate is 99%, precision is 98% (moderate level), 100% (normal level), and recall is 97% (normal level), 100% (moderate level). These results indicate that the k-means on demographic, family, lifestyle, employment, and SAS aspects provide optimal results for predicting the anxiety level of the BM University academic community.
Measurement of DEA-Based ICT Development Efficiency Level with Modified CCR Method Defri Muhammad Chan; Herman Mawengkang; Sawaluddin Nasution
Jurnal Varian Vol 6 No 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v6i1.2197

Abstract

Data Envelopment Analysis (DEA) is the use of non-parametric mathematical programming that is useful for measuring the efficiency of the Decision Making Unit (DMU) of an organization. This study uses the Cooper and Rhodes (CCR) method known as the DEA-CCR multiplier which aims to determine the weight value of each input and output variable of the DMU being evaluated, but it is not sufficient to measure efficiency optimization. To get an efficient value of the weight value of each DMU as a reference to get updated DMU input and output values. So that the DMU efficiency value is obtained which is evaluated. The results of this study show how to modify the Multiplier Model-CCR into the Envelopment Model-CCR. Then displays the efficient level DMU which is evaluated as a result of the weight each DMU gets from the results of processing the LINDO application. Illustrations of changes in input variables and output variables are displayed in the form of tables and figures before and after the changes. The modified DEA-CCR model can also complete DMU super efficiency, effectiveness and productivity.
The Sentiment Analysis Using Naïve Bayes with Lexicon-Based Feature on TikTok Application Siswanto Siswanto; Zakiyah Mar'ah; Alfiyah Salsa Dila Sabir; Taufik Hidayat; Fadilah Amirul Adhel; Waode Sitti Amni
Jurnal Varian Vol 6 No 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v6i1.2205

Abstract

On TikTok application, there are several types of content in the form of education, cooking recipes, comedy, various tips, beauty, business, etc. However, some non-educational contents sometimes appear on TikTok homepage even though minors can access the app. As a result, TikTok application can influence the behavior of minors to be disgraceful, therefore, an assessment of the application can be one of the objects for conducting sentiment analysis. The purpose of this study is to compare the results of sentiment analysis on TikTok application using Naïve Bayes with Lexicon-Based and without Lexicon-Based features. We used the TikTok reviews on Google Play Store as our data. According to the analysis, without Lexicon-Based feature, we obtained the accuracy rate, precision rate, and recall rate of 83%, 78%, and 69%, respectively. Meanwhile, the accuracy, precision, and recall rates using the Lexicon-Based feature were 85%, 91%, and 93%, respectively. Therefore, we concluded that sentiment analysis using Naïve Bayes with Lexicon-Based feature was better than without Lexicon-Based feature on TikTok reviews.

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