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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 168 Documents
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Analysis of Underdeveloped Regency Using Logistic Threshold Regression Model Salsabila, Annisa Nur; Oktora, Siskarossa Ika
Jurnal Varian Vol. 8 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i1.3570

Abstract

Regional development inequality causes some regions to lag behind other regions. An underdevelopedregency is a regency where territories and people are less developed than other regions nationally. Thegovernment has set a Human Development Index (HDI) target of 62.2 to 62.7 to accelerate the development of underdeveloped regency and prevent the regions from lagging. This study aims to evaluatethe HDI target and obtain the HDI value that reduces the risk of underdeveloped regency and acquiresvariables that affect underdeveloped regency’s status. The logistic threshold regression model is usedin this study with HDI as the threshold variable, 22 indicators for determining underdeveloped regencyas explanatory variables, and the underdeveloped regency’s status as the response variable. Thresholdregression can handle non-linear relationships between response and explanatory variables, includingvarious types of threshold models such as step, segmented, hinge, stegmented, and upper hinge. By applying a hinge threshold regression model using the R package ’chngpt,’ this study addresses non-linearrelationships and categorical responses. The results showed a threshold effect with a threshold value of62.9, indicating that the HDI target can reduce the region’s risk of being left behind.
The Weibull Regression Model Analysis of Mahakam River Water Pollution Potential Pradipa, Zalva; Suyitno, Suyitno; Siringoringo, Meiliyani
Jurnal Varian Vol. 8 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i1.3699

Abstract

Mahakam River has a vital role in the lives of the people of the East Kalimantan province, includingproviding a raw source of clean water. The multi-activity of the Mahakam River watershed, as a watertraffic lane, mining, fisheries, hotels, restaurants, and resident houses, has the potential to produce wasteinto the water. Increasing waste in the water flow can increase the pollution potential of river water,threatening people’s health. Therefore, precaution is necessary. In this research, statistical preventionwas proposed, providing information to the East Kalimantan people regarding the factors affecting thepollution potential of the Mahakam River through Weibull regression (WR) modeling on dissolved oxygen (DO) data 2022. Research data was secondary data provided by the Life Environmental Departmentof East Kalimantan province. The WR model is a Weibull distribution that is directly influenced by covariates. WR model consists of Weibull survival regression, cumulative distribution regression, hazardregression, and Weibull mean regression. This research aims to obtain the factors affecting the pollution potential and to provide the pollution potential information of Mahakam River 2022. The researchconcluded that factors influencing the pollution potential of the Mahakam River were watercolor degreeand nitrate concentration. Applying the WR model to DO data 2022 was able to provide the pollutionpotential information of Mahakam River, namely the probability of river water isn’t polluted is 0.6555,or the probability of the polluted river water is 0.3445, the pollution rate is 6 locations are polluted forevery 10 mg/L DO, and the DO average of river water is 5.7450 mg/L. Increasing water color degreeand nitrate concentration will decrease the probability of the Mahakam River being polluted, increasethe probability of the Mahakam River being polluted, increase the pollution rate, and reduce the DO ofMahakam River water.
Finding the Factors Influencing the Severity of Traffic Accident Victims in Sleman Regency Using Ordinal Logistic Regression Analysis Cahyani, Amalia Rizqi; Kartikasari, Mujiati Dwi
Jurnal Varian Vol. 8 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i1.3769

Abstract

Special Region of Yogyakarta (Daerah Istimewa Yogyakarta, DIY) is well-known for its tourist, cultural, and educational attractions, but it also has a high accident rate. Sleman Regency is among the DIY regions with the greatest number of traffic accidents. According to Yogyakarta Police records, Sleman Regency had 1,825 traffic incidents in 2022, while 637 accidents occurred there in a short period of time in 2023, specifically from January to April. To stop the rising number of people injured in road accidents, this issue needs to be taken into account. The objective of this study was to examine the profile of traffic accidents that happened in Sleman Regency between January and April of 2023 and use the ordinal logistic regression method to find characteristics that influence the severity of traffic accidents. Sleman Regency traffic accident data was used in this study. The opponent's vehicle factor, with the category of four or more wheeled vehicles and non-motorized vehicles, is one of the elements that influences the severity of traffic accident victims in Sleman Regency, according to the study's findings.
Classification Classification of Criminal Events Based on Biplot Analysis Fauzi, Doni Muhammad; Dewanty, Sanda Insania; Putri, Farah Fauziah; Inneztiana, Alya Rahma; Mardianto, M. Fariz Fadillah; Amelia, Dita; Ana, Elly
Jurnal Varian Vol. 7 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.3795

Abstract

Kriminalitas merupakan suatu perilaku yang melanggar hukum dan aturan dalam masyarakat. Penelitian ini dilakukan untuk menganalisis data biplot jumlah kejahatan di berbagai provinsi di Indonesia. Biplot merupakan analisis yang berguna untuk menafsirkan hubungan antara variabel dan objek dalam bentuk grafik tunggal. Sumber data dalam penelitian ini adalah data sekunder yang berasal dari website Badan Pusat Statistik yang berjudul “Statistik Kriminal 2022”. 34 kepolisian daerah yang mewakili setiap provinsi di Indonesia menjadi objek pengamatan dan 9 klasifikasi kejahatan menjadi variabel. Metode penelitian ini menggunakan analisis biplot dengan bantuan fiton. Dari nilai Dekomposisi Nilai Singular, keragaman data yang dapat dijelaskan sebesar 73,714%. Pada grafik analisis biplot hubungan antar observasi diperoleh bahwa observasi atau objek polda dari setiap provinsi tersebar terpusat pada satu kuadran. Hubungan antar variabel yang paling tinggi adalah korelasi antara variabel kejahatan narkotika dengan kejahatan yang berkaitan dengan penggelapan, penipuan, dan korupsi, sedangkan hubungan yang paling rendah adalah korelasi antara kejahatan narkotika dengan kejahatan terhadap ketertiban umum. Dalam hubungan observasi dengan variabel diperoleh 4 kelompok. Keberagaman variabel yang paling tinggi terletak pada kejahatan terhadap kebebasan masyarakat, sedangkan keberagaman variabel yang paling rendah terletak pada kejahatan terhadap kesusilaan.
Application of VAR-GARCH for Modeling the Causal Relationship of Stock Prices in the Mining Sub-sector Nasrudin, Muhammad; Setyowati, Endah; May Wara, Shindi Shella
Jurnal Varian Vol. 8 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i1.4239

Abstract

Accurate modeling is expected to minimize risk and maximize profit in investment portfolios, one ofwhich is in stock price modeling. This research aims to model the causal relationship between stockprices using the Vector Autoregressive - Generalized Autoregressive Conditional Heteroskedasticity(VAR-GARCH) model. The VAR-GARCH model is used to overcome heteroscedasticity and modeldynamic volatility. The data used for the modeling consists of daily stock prices from July 2023 toMay 2024 for mining sub-sector companies listed on the Jakarta Islamic Index (JII), including ADMR,ADRO, and ANTM. The results showed that the VAR(1) model is stable, but this model indicates thepresence of heteroskedasticity or ARCH effects. Therefore, the VAR(1) model was combined with theGARCH model, and the results showed that the best model is VAR(1)-GARCH(1,1). The VAR(1)-GARCH(1,1) model is appropriate and meets the homoskedasticity assumptions for modeling the stockprices of the mining sub-sector in the Jakarta Islamic Index (JII). This indicates that the VAR-GARCHmodel could successfully handle the volatility of stock price data. In general, this research is in linewith previous research, i.e., the VAR-GARCH model showed a better model for capturing the volatilitypatterns in the data.
Pemodelan Jumlah Siswa Putus Sekolah Tingkat SMA di Indonesia Menggunakan Geographically Weighted Generalized Poisson Regression Azizah, Nur; Gamayanti, Nurul Fiskia; Junaidi, Junaidi; Sain, Hartayuni; Fadjriyani, Fadjryani
Jurnal Varian Vol. 8 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i1.4248

Abstract

In 2022, the high school dropout rate is the highest compared to other levels of education in Indonesia.Seeing the urgency of the 12-year Compulsory Education program, completing education up to the highschool level is an important thing that needs to be considered. Thus, it is necessary to know the factorsthat influence the dropout rate in the hope that this problem can be reduced. This study aims to modelthe high school dropout rate using geographically weighted generalized poisson regression (GWGPR)based on the factors that influence it. GWGPR is used if the response variable is overdispersed anddepends on the location observed. The results of this study indicate that each province has a different regression model. The GWGPR model with the adaptive tricube kernel weighting function is thebest model because it has the smallest AIC value compared to other weighting functions. In CentralSulawesi Province, the GWGPR model with the adaptive tricube kernel weighting function formed isµˆ26 = exp (8, 1267 − 0, 1267X4 + 0, 0344X5 + 0, 0957X6 + 0, 1173X7). With the significant variables are the average length of schooling, the percentage of the population aged 7-17 years who receivePIP, the open unemployment rate, and the percentage of children who do not live with parents.
Stock Price Index Prediction Using Random Forest Algorithm for Optimal Portfolio Humairah, Putri; Agustina, Dina
Jurnal Varian Vol. 8 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i1.4276

Abstract

With a majority Muslim population in Indonesia, Islamic capital markets such as the Jakarta IslamicIndex (JII) are a relevant choice because the JII is an investment index that complies with Sharia principles. This research aims to predict stock prices in the JII using the Random Forest (RF) algorithm andform an optimal portfolio with the Mean-Variance Efficient Portfolio (MVEP) model. The data used isthe daily closing price of JII stocks from April 2023 to March 2024, obtained from the Indonesia StockExchange and Yahoo Finance. The RF method is used to predict stock prices, with model performanceevaluation using Mean Absolute Percentage Error (MAPE). The results showed that the application ofML with the RF algorithm in predicting stock prices produced very good predictions because the evaluation results using MAPE were in the 0%-10% range, namely a value of 2.522% for ACES shares;1.222% for ICBP shares, and 0.760% for INDF shares. The optimal portfolio formed using MVEPproduces a stock composition with a weight of 7.64% for ACES, 22.46% for ICBP, and 69.90% forINDF. The optimal portfolio’s estimated expected return and risk are 0.0546% and 0.0103%.
A Kernel Logistic Regression Approach to Understanding the “Banyak Anak Banyak Rezeki” Stigma Tjabbe. Suwardi, Assyifa’ Nur Qalby. A.; Sanur, Muh. Alwi; Chaerunnisa, Nurul Mutmainnah; Maulina, Azzahra Dwi Nur; Poerwanto, Bobby
Jurnal Varian Vol. 8 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i1.4281

Abstract

Indonesia, the world’s 4th largest country with a population of 270 million in 2020, faces many challenges due to rapid population growth, including biodiversity loss and increased consumption of naturalresources. One of the cultural factors underlying the high rate of population growth is the perception of“Banyak Anak Banyak Rezeki“ that develops in the community. This study aims to identify and modelthe factors that influence the “Banyak Anak Banyak Rezeki” stigma and find solutions to overcome thisproblem. The research method used was quantitative, with a sample of 384 people in South Sulawesi,consisting of Bugis, Makassar, Toraja, and Mandar tribes. The variables studied include religiosity,tradition, number of children, and cognitive dissonance. The analysis techniques used were logisticregression (LR) and kernel logistic regression (KLR). The results showed that religiosity, number ofchildren, and cognitive dissonance had a significant effect on the “Banyak Anak Banyak Rezeki” stigma.The accuracy of the LR model reached 87.01% and increased to 93.51% after using KLR.

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