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Norisca Lewaherilla
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Variance : Journal of Statistics and Its Applications
Published by Universitas Pattimura
ISSN : 26858738     EISSN : 2685872X     DOI : -
Core Subject : Education,
Mathematics
Jurnal ini diterbitkan oleh Program Studi Statistik Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Pattimura, Ambon. Jurnal ini diterbitkan 2 kali pada bulan Juni dan Desember.
Arjuna Subject : -
Articles 107 Documents
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GROSS PREMIUM VALUATION METHOD IN DETERMINING PREMIUM RESERVES IN LIFE INSURANCE Rivaldo, Rendi; Perdana, Hendra; Andani, Wirda
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 2 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss2page215-222

Abstract

Abstract: Life insurance companies maintain reserve funds to pay insurance policy claims, known as premium reserves. Premium reserves are calculated using two approaches: retrospective and prospective. The prospective approach involves calculating the present value of all future expenses minus the total future income for each policyholder, using the Gross Premium Valuation (GPV) method. The GPV method takes into account initial costs, maintenance costs, and administration costs. The case study results indicate that the premium reserve using the GPV method starts at zero in the first year, increases until the last payment year, and then decreases after the payment period until the end of the coverage period. For policyholders of different genders but the same age, the premium reserve for men is greater than for women. Additionally, for male policyholders of varying ages, the premium reserves required increase with age. Furthermore, for male policyholders of the same age but with different interest rates, a higher interest rate results in a smaller premium reserve requirement.
FILLING THE PRECIPITATION GAPS: ACCURATE IMPUTATION WITH SUPPORT VECTOR REGRESSION IN NORTH SULAWESI Cahyaning, Angelin; Miftahurrohmah, Brina; Prassida, Grandys Frieska; Tikno, Tikno
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 2 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss2page183-194

Abstract

Incomplete precipitation data poses major challenges in accurate precipitation predictions, hindering the effectiveness of water resource management and disaster risk mitigation efforts in North Sulawesi, Indonesia. This research aims to develop a precipitation prediction model using Support Vector Regression (SVR) to handle missing data. The precipitation data used comes from BMKG and ERA5 stations. The results show that using the RBF kernel with parameters ∁ = 1000, ɛ = 0.1, γ = 100 produces the best predictions, except Dtatiun Meteorologi Naha with γ = 1000. The best model is shown in the model evaluation RMSE of 0.099, MAE of 0.099, and R² of 0.999. The ability of SVR to capture precipitation trends is shown in the model evaluation results. The best model obtained is used for the missing data imputation process.
SEGMENTATION OF FRESH GRADUATES' JOB INTEREST AND MOTIVATION BASED ON FINITE MIXTURE PARTIAL LEAST SQUARES (FIMIX-PLS) Hermawan, Mohamad David; Kurniawan, Ardi; Mardianto, M. Fariz Fadillah; Sediono, Sediono
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 2 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss2page229-238

Abstract

Rapid technological advancements have significantly transformed the global labor market, impacting various industries and workers in Indonesia who may need to be adequately prepared to adapt. The current landscape demands individuals who can acquire knowledge quickly and adapt to modern technologies. The unemployment rate in Indonesia, especially among fresh graduates, is still a concern. Lack of motivation and interest in finding a job and high expectations of working conditions contribute to this problem. This research aims to address the gap in research by using the Finite Mixture Partial Least Squares (FIMIX-PLS) approach to examine the segmentation of fresh graduate characteristics about their interest and motivation in finding a job. Segmentation based on latent variable relationships in the structural model can be overcome with Finite Mixture Partial Least Square (FIMIX-PLS) to identify more homogeneous characteristics. This research analyzes explicitly the impact of compensation, work environment, and company reputation on the interest and motivation of fresh graduates in finding a job. This research resulted in the best segmentation of two segments: the 1st segment at 77.8% (265 samples) and the 2nd segment at 22.2% (75 samples).
CLASSIFICATION OF MYPERTAMINA APP REVIEWS USING SUPPORT VECTOR MACHINE Fadlurohman, Alwan; Yunanita, Novia; Rohim, Febrian Hikmah Nur; Wardani, Amelia Kusuma; Ningrum, Ariska Fitriyana
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 2 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss2page223-228

Abstract

Indonesia is rich in natural resources, including oil and gas, and it manages these strategic assets through state-owned enterprises, one of which is PT Pertamina. Pertamina is responsible for domestic fuel production, distribution, and price stabilization. To improve efficiency and transparency, Pertamina developed the MyPertamina application that enables cashless fuel purchases, stock monitoring, and up-to-date price information. The application aims to streamline distribution and control fuel prices, thus helping to stabilize the cost of goods and services. MyPertamina also ensures subsidized fuel distribution is more effective and targeted by identifying and verifying subsidy recipients, reducing the potential for abuse. A sentimental analysis of subsidized fuel user reviews using this application is needed to understand the public's views. This research uses the Support Vector Machine (SVM) method to analyze the sentiment of MyPertamina app reviews. This research produced a stable model. Out of 200 reviews, 190 were negative, and nine were positive, with an SVM model accuracy of 97%. Wordcloud visualization shows the words that appear frequently in each sentiment. Positive reviews appreciated the photo verification feature, easy payment, and good service. Negative reviews included verification difficulty, app error, and feature failure.
THE EFFECT OF ECONOMIC GROWTH, LABOR FORCE PARTICIPATION RATE, AND HUMAN DEVELOPMENT INDEX ON POVERTY IN MALUKU (2010-2022): MULTIPLE LINEAR REGRESSION APPROACH Tipka, Jefri; Ramly, Fahrudin
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 1 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss1page61-72

Abstract

This study examines the impact of economic growth, labor force participation rate (LFPR), and the human development index (HDI) on poverty in Maluku Province using secondary data from 2010–2022. Employing multiple linear regression via SPSS, the findings indicate that all three variables significantly affect poverty levels. Inclusive economic growth and higher LFPR tend to reduce poverty when aligned with quality job creation. Improvements in HDI reflecting better health, education, and living standards also contribute to poverty reduction. These results underscore the need for targeted and effective poverty alleviation strategies by both regional and national governments.
FORECASTING THE COMBINED STOCK PRICE INDEX (IHSG) USING THE RADIAL BASIS FUNCTION NEURAL NETWORK METHOD Fitriawan, Della; Satyahadewi, Neva; Andani, Wirda
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 1 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss1page83-92

Abstract

The capital market is one of the most critical factors in national economic development in Indonesia, as many industries and companies have previously used the capital market as a medium to absorb investment so that their financial position can be strengthened. The main indicator that can reflect the performance of the capital market is the Composite Stock Price Index (IHSG). The IHSG can be used to assess the general situation occurring in the market. Data IHSG is data obtained from the past and used to predict the future, also called time series data. Predictions on IHSG data need to be made so that investors can easily see capital market movements and know the policies that will be taken in the future. The Radial Basis Function Neural Network (RBFNN) method is used. RBFNN aims to get more efficient results because this method does not need to make the data stationary. The analysis results were carried out on a secondary data sample size of 1114 data, which obtained the highest forecasting price of Rp6157,619 on August 2, 2023. Meanwhile, the lowest forecast price on August 5, 2023, is IDR 5564,828 from August 1, 2023, to August 5, 2023.
CLUSTERING OF DISTRICTS IN CENTRAL JAVA ACCORDING TO PEOPLE'S WELFARE INDICATORS USING WARD'S METHOD Purwanto, Dannu; Pratama, Rizky Adi; Lein, Raymond Bolly; Prastyo, Ikwan; Haris, M. Al
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 1 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss1page73-82

Abstract

One of the main goals of development activities carried out by every country was to improve people's welfare. Community welfare was a situation where citizens could fulfill and adequately fulfill their material and spiritual needs. The poverty rate of Central Java Province was recorded: out of a total population of 37.03 million people, around 3,831.44 thousand people were poor. The population density of Central Java Province reaches 1,120 people per km2, the third largest number of poor people in Indonesia. This study aimed to group regencies/cities in Central Java based on the characteristics of the community welfare indicators. The indicators used in this study were the Open Unemployment Rate (UR), Labor Force Participation Rate (LFPR), Poverty, Human Development Index (HDI), and District Minimum Wage (DMW). The method used in this research was Ward's Agglomerative Hierarchical Clustering. The final results concluded that the best number of clusters formed was 6 clusters. The first cluster consists of 13 Regencies/Cities, the second cluster consists of 8 Regencies/Cities, the third cluster consists of 3 Regencies/Cities, the fourth cluster consists of 1 Regency/City, the fifth cluster consists of 5 Regencies/Cities, the sixth cluster consisting of 5 Regencies/Cities.
PERFORMANCE ANALYSIS OF RANDOM FOREST CLASSIFICATION ON UNEMPLOYMENT RATE IN MALUKU PROVINCE BASED ON DATA BALANCING METHOD Yunizar, Mahdayani Putri; Sinay, Lexy Janzen; Yudistira, Yudistira
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 1 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss1page31-38

Abstract

In 2023, the number of unemployed people in Maluku will reach 59,800 or 6.08% of the total population. To reduce unemployment in Maluku, it is essential to understand the unemployment situation of the Moluccan population based on socioeconomic factors immediately. Therefore, applying classification methods such as random forests is the right step, but it is recommended that the data be balanced to get accurate results. However, the unemployment rate in Maluku is much lower than that of the unemployed, so data imbalance affects the accuracy of the classification results. Therefore, a data balancing process is needed, among others, using the Random Oversampling of Sample (ROSE), Synthetic Minority Oversampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN) methods. This study uses data from the 2023 National Labor Force Survey (SAKERNAS) conducted in February by the Central Statistics Agency (BPS) of Maluku. The number of unemployed people is smaller than the number of unemployed residents. Therefore, action needs to be taken to address data inequality. The results of this study show that the random forest classification model with SMOTE has the best performance with a combination of 90% training data and 10% testing data, with a higher AUC value than other methods, and age variables are the most essential variables built into the model.
ANALYSIS OF ECONOMIC GROWTH AND DEVELOPMENT INEQUALITY AMONG DISTRICTS/CITIES IN WEST NUSA TENGGARA PROVINCE USING THE WILLIAMSON INDEX, KLASSEN TYPOLOGY, AND LOCATION QUOTIENT METHODS Purnamasari, Nur Asmita; Hadijati, Mustika; Hidayati, Lilik; Saputra, Dede; Graha, Syifa Salsabila Satya
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 1 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss1page93-104

Abstract

West Nusa Tenggara Province faces low GRDP growth and significant economic disparities among its districts and cities. This study aims to analyze economic growth and development inequality using the Williamson Index, Klassen Typology, and Location Quotient methods. The Williamson Index shows high inequality, with values approaching 1 from 2018 to 2022. Klassen Typology categorizes districts into four quadrants: fast-growing (West Sumbawa, Mataram City), developing (Dompu, Bima City), developed but under pressure (West Lombok, Central Lombok, East Lombok, Sumbawa, Bima, North Lombok), and none in the underdeveloped category. The Location Quotient analysis highlights sectors with growth potential; however, some regions still rely on imports to meet local demand. The findings suggest targeted policies to enhance sector development and reduce economic disparities, fostering sustainable growth and improving welfare in West Nusa Tenggara Province.
FORECASTING STOCK PRICES OF PT. BANK RAKYAT INDONESIA USING THE HYBRID ARIMA-BACKPROPAGATION NEURAL NETWORK METHOD Alaina, Silvana Rahmayanti; Hasan, Isran K.; Abdussamad, Siti Nurmardia
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 1 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss1page39-48

Abstract

PT. Bank Rakyat Indonesia (Persero) Tbk is classified as a blue-chip stock. Although investing in BRI shares has the potential to generate profits, stock price fluctuations can pose risks, making forecasting necessary. The ARIMA model is frequently used to predict such fluctuations, but struggles to capture non-linear patterns. ARIMA is combined with an Artificial Neural Network (ANN), specifically the Backpropagation Neural Network, to address this issue and improve forecasting accuracy. Although Backpropagation is weak in slow convergence, this can be overcome using the Conjugate Gradient Powell Beale (CGB) algorithm. The research results show that the closing stock price data of BRI from January 2023 to February 2024 produced an ARIMA (1,1,1)-Backpropagation [4-4-1] model with higher accuracy, achieving a MAPE of 2.516% and RMSE of 200.1592, Relative to the standalone ARIMA (1,1,1) model, which had a MAPE of 6.203% and RMSE of 421.5896.

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