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 168 Documents
 8   9   10   11   12 
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.
Jurnal Varian Full Text Siti Soraya
Jurnal Varian Vol 5 No 2 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v5i2.2250

Abstract

The NADI Mathematical Model on the Danger Level of the Bili-Bili Dam Sukarna Sukarna; Andi Muhammad Ridho Yusuf Sainon Andin P; Syafruddin Side; Aswi Aswi; Supriadi Yusuf
Jurnal Varian Vol 6 No 2 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v6i2.2237

Abstract

The research discusses the NADI mathematical model due to the overflow of the Bili-Bili dam, using secondary data obtained through online literature review by collecting various information related to the Bili-Bili Dam, starting from the Jeberang River Scheme, the chronology of floods, normal or dry conditions, and dam operation patterns. The aim of this study is to predict the level of danger of Bili-bili dam overflow over time, considering extreme weather factors and standard operating procedures performed by humans. The research uses analytical and computational methods. The study obtained the NADI mathematical model due to the overflow of the Bili-Bili dam, with two equilibrium points: (1) the equilibrium point free of disaster, (2) the disaster equilibrium point, and a basic disaster reproduction number of R0 = 1.219. This indicates that the water discharge from the dam is high and has an impact on the overflowing water for communities around the Jeneberang river. Therefore, it can be concluded that the NADI model can be used to simulate the Bili-bili dam process based on extreme weather and dam SOP, and predict the level of danger of Bili-bili dam overflow, which is also a novelty that has not been done in previous studies.
Impact of SST Anomalies on Coral Reefs Damage Based on Copula Analysis Pratnya Paramitha Oktaviana; Kartika Fithriasari
Jurnal Varian Vol 6 No 2 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v6i2.2324

Abstract

The condition of coral reefs in Indonesia is alarming. One of the influenting factors of coral reefs damage is extreme climate change. The aim of this study is to determine the relationship of climate change, that is Sea Surface Temperature (SST) anomaly index, and coral reefs damage in West, Central and East Region of Indonesia. The method used in this study is Copula analysis. Copula is one of the statistical methods used to determine the relationship of two or more variables, in which case the distribution can be normal or not. First, data is transformed into Uniform [0,1] domain. Then, Copula parameter is estimated to get significance parameter. Lastly, the best Copula that has the highest log likelihood value is selected to represent the relationship of data. The result indicates that percentage of coral reefs damage in West and Central Region has relationship with SST Nino 4, while coral reefs damage in East Region does not have relationship with any of SST Nino anomalies. In West Region, the best Copula represents the relationship is Gaussian Copula (parameter = -0.32); it concludes that the higher the value of SST Nino 4, the lower the percentage of coral reefs damage and otherwise. While in Central Indonesia, Frank Copula (parameter = -4.89) is selected; it does not have tail dependency so that the SST Nino 4 and the percentage of coral reefs in damage condition in Central Region has low correlation.
Application of Principal Component Regression in Analyzing Factors Affecting Human Development Index Sumarni Susilawati; Didiharyono Didiharyono
Jurnal Varian Vol 6 No 2 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v6i2.2366

Abstract

The human development index is an indicator to measure the quality of people's lives. If the human development index number increases, the better the quality of people's lives. There are many factors or variables that affect the level of the human development index, ranging from economic issues, education, health and other factors. However, not all factors have a positive and significant effect. Thus, this study aims to determine the factors that significantly affect the human development index in South Sulawesi. The method used in this study is principal component regression which involves many variables. The variables involved are expected length of schooling, average length of schooling, percentage of population with the highest Diploma, Bachelor and Masters education, school enrollment rate for people aged 7-24 years, percentage of poor people, spending per capita, and life expectancy. From the results of data processing using principal component analysis, 4 main components are obtained which represent the other components, for principal component regression, taking into account the cumulative proportion of > 80%. The results of this study indicate that the human development index in South Sulawesi is influenced by all the variables involved, which is equal to 95.7%. With the variable percentage of poverty being one of the variables that has a negative effect on HDI in South Sulawesi which shows that the higher the percentage of poverty, the lower the human development index. Thus, in order to increase the human development index in Indonesia, it is necessary to take strategic steps to improve people's welfare.

Page 10 of 17 | Total Record : 168

 8   9   10   11   12 

Filter by Year

2017 2025


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