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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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ANALISIS FAKTOR KEMISKINAN DI PROVINSI SUMATERA UTARA BERDASARKAN REGRESI KOMPONEN UTAMA Aldawiyah, Najwa Khoir; Astuti, Aprillia; Kurnia, Rizky Dwi; Amalia, Nadinta Kasih; Amelia, Dita; Mardianto, M. Fariz Fadillah; Ana, Elly
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 1 (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/variancevol6iss1page63-74

Abstract

Kemiskinan merupakan permasalahan terkait kesejahteraan masyarakat yang serius dan menjadi indikator keberhasilan ekonomi dari suatu negara. Provinsi Sumatera Utara merupakan salah satu provinsi dengan jumlah penduduk miskin terbanyak dengan total 1,2 juta jiwa di pulau Sumatera pada tahun 2022. Tujuan penelitian ini yaitu menangani masalah multikolinearitas pada faktor-faktor yang mempengaruhi kemiskinan di Provinsi Sumatera Utara dengan menggunakan analisis komponen utama. Data yang diperoleh merupakan data yang didapatkan dari Badan Pusat Statistik Provinsi Sumatera Utara. Terdapat 8 variabel prediktor yang digunakan dan terbentuk 3 komponen utama dengan keragaman total sebesar 84,5%. Komponen utama yang terbentuk kemudian diregresikan dan diperoleh persamaan . Model regresi tersebut terbebas dari masalah multikolinearitas dan ketiga komponen secara signifikan berpengaruh terhadap jumlah penduduk miskin di Provinsi Sumatera Utara.
PENENTUAN PREMI MURNI DARI DATA KLAIM ASURANSI KENDARAAN RODA EMPAT DENGAN JENIS PERLINDUNGAN COMPREHENSIVE Yulita, Tiara; Patricia, Mitha; Hidayat, Agus Sofian Eka
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 1 (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/variancevol6iss1page75-86

Abstract

Produk asuransi kendaraan bermotor sudah banyak digunakan oleh banyak orang karena semakin banyak yang membutuhkan untuk meminimalkan risiko yang didapat oleh pihak tertanggung. Sehingga pihak tertanggung perlu membayarkan kewajiban berupa premi serta mengikuti syarat dan ketentuan yang telah disepakati bersama sebelumnya, untuk mendapatkan haknya berupa pembayaran klaim ketika terjadi suatu kejadian yang merugikan tertanggung. Besarnya premi dapat dilihat dari berbagai faktor seperti jenis perlindungannya yaitu Total Loss Only dan Comprehensive, usia kendaraan, riwayat klaim sebelumnya, dan faktor-faktor lainnya. Pada penelitian ini perhitungan yang dilakukan adalah perhitungan premi yang menggunakan data riwayat klaim dari asuransi kendaraan roda empat periode 2020-2022 dengan jenis perlindungan comprehensive dengan menggunakan metode compound model. Dimana data banyak klaim mengikuti model distribusi Negative Binomial, dan data besar klaim mengikuti model distribusi Lognormal. Selanjutnya nilai ekspektasi dari kedua distribusi ini akan dikalikan untuk menentukan premi murni dari asuransi kendaraan roda empat.
IMPACT OF INVESTMENT, REGIONAL AND NATIONAL BUDGET CAPITAL EXPENDITURES ON MALUKU PROVINCE'S ECONOMIC GROWTH (2010-2022) USING MULTIPLE LINEAR REGRESSION Tipka, Jefri; Matitaputty, Izaac Tonny
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/variancevol6iss2page123-132

Abstract

Economic growth in Maluku Province is lower compared to several provinces in the Sulawesi Maluku Papua region. This research aims to analyze the influence of investment (foreign and domestic investment), regional and national budget capital expenditures (APBD capital expenditure and APBN capital expenditure) on economic growth in Maluku Province. This research uses secondary data from Maluku Province sourced from BPS for 2010-2022. The research method uses multiple linear regression with the SPSS application. The research results show that investment negatively and significantly affects economic growth. APBD capital expenditure and APBN capital expenditure have a positive and insignificant impact on economic growth in Maluku Province. This research implies that local governments must increase foreign and domestic investment and use capital expenditure effectively and efficiently to encourage economic development in Maluku.
A ORDINAL LOGISTIC REGRESSION BAGGING FOR MODELING AND CLASSIFICATION OF THE NUTRITIONAL STATUS OF TODDLERS IN SOUTHEAST PONTIANAK SUB-DISTRICT Sista, Sekar Aulia; Kusnandar, Dadan Tonny; Satyahadewi, Neva
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/variancevol6iss2page195-204

Abstract

Although Pontianak's 2022 stunting rate of 19.7% is higher than the RPJMN's 2020–2024 target of 14%, this is still significant. The categories of stunts are very short (severely stunted), short (stunted), normal, and high, based on a high index of the body by age (TB/U). Ordinal Logistic Regression is one classification that can be used to group stunts based on the TB/U index. This approach makes the unstable parameter. Use the bagging to get stable parameters. The study aims to model and classify toddlers' nutritional status using the TB/U index. Utilizing secondary data for 150 toddlers from Pontianak Tenggara's UPT Puskesmas Parit Haji Husin II. This will monitor kids' growth from 24 to 59 months in 2022. Response factors include short, very short, normal, and high. The mother's job position, birth weight, length, and gender are the predictive variables. Due to imbalanced data utilized in the first analysis using Ordinal Logistics Regression, a decent model, and the final classification result, they used the Bagging OLR ensemble method. The study's findings are a very effective model using OLR Bagging, with an accuracy rate of 99.33%, a sensitivity value of 98.91%, and a specificity value of 98.52%. The results also revealed significant variables that influence the mother's employment status and the birth length variable.
POVERTY MODELING IN WEST KALIMANTAN USING STRUCTURAL EQUATION MODELING - PARTIAL LEAST SQUARE Perdana, Hendra; Novita, Irene; Fauzan, Muhammad; Nurhanifa, Nurhanifa
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/variancevol6iss2page205-2014

Abstract

Poverty remains a pervasive issue across various regions in Indonesia, often leading to significant repercussions such as hindering economic growth and exacerbating inflationary pressures within localities. Poverty has emerged as a formidable challenge for the global community, particularly for developing nations, including Indonesia. West Kalimantan is a province in Kalimantan Island with high poverty rates. This research examined the factors influencing poverty within West Kalimantan Province in 2022 using the Structural Equation Modeling-Partial Least Square (SEM-PLS) method. This research analyzed the interrelations among poverty, education, economy, and health dimensions. Findings indicated a significant relationship between health and education dimensions. In contrast, the relationships between economic dimensions and poverty, health and the economy, and education and the economy were insignificant.
IMPLEMENTATION GRID SEARCH OF RBF AND POLYNOMIAL ON SUPPORT VECTOR REGRESSON FOR CLOSING STOCK PRICES PREDICTION ON PT INDOFARMA (INAF) Salsabilla, Arla; Satyahadewi, Neva; 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/variancevol6iss2page133-142

Abstract

Stocks represent evidence of ownership of an asset. The highly volatile nature of stock prices makes it difficult for investors to predict stock prices, necessitating the analysis of stock investments. This research aims to forecast for the next 30 days the closing price of PT Indofarma (INAF) stocks using the best model, and the accuracy level of the employed model was analyzed based on the data from the last seven years. The research used the Support Vector Regression (SVR) method, which is known for its capability to handle nonlinear data through kernel functions. The Radial Basis Function (RBF) and polynomial kernels are used in this case. The challenge with SVR lies in determining the optimal hyperparameter, which can be addressed through hyperparameter tuning using grid search. The research results show that the best model is the SVR kernel RBF model with optimal hyperparameter C=1,γ=0.01, and ε=0.01. Based on the performance evaluation results of the best model, the MAPE, MSE, and MAE values are equal to 1.537%,1483.936, and 23.409.
SPATIAL AUTOREGRESSIVE (SAR) POISSON MODELING IN DENGUE FEVER CASES ON LOMBOK ISLAND IN 2021 Husnaeni, Ririn Robiatul; Hauliati, Siti; Sholihah, Imroatun; Lasmiani, Bq Tia Ayu; Hastuti, Siti Hariati; Gazali, Muhammad
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/variancevol6iss2page143-154

Abstract

Indonesia, the fourth most populous country in the world with 275.5 million people, faces increasing human activity that can lead to negative impacts such as the spread of infectious diseases. One of these diseases is Dengue Hemorrhagic Fever (DHF), which is particularly susceptible in residential areas with poor environmental hygiene. The rising number of DHF cases on Lombok Island is a significant concern. This study employs a spatial analysis modeling approach, specifically the Spatial Autoregressive Poisson (SAR Poisson) model, which considers the spatial dependence of dengue cases assumed to follow a Poisson distribution. The objective is to model and map the potential distribution of DHF cases on Lombok Island in 2021. The analysis reveals spatial autocorrelation in the data based on Moran's I. Significant variables affecting DHF cases include the number of permanent sanitation facilities (X2) and the number of drinking water facilities (X3). Mapping results based on the SAR Poisson model indicate that the distribution of DHF cases is relatively uniform across most sub-districts, with the highest incidence suspected in Tanjung Sub-district
APPLICATION OF THE QUEST AND CHAID METHODS IN CLASSIFYING STUDENT GRADUATION Banu, Syarifah Syahr; Sulistianingsih, Evy; Debataraja, Naomi Nessyana; Satyahadewi, Neva
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/variancevol6iss2page155-164

Abstract

Graduation is the final result of the learning process during the course. Student graduation time is affected by many factors. Whether or not the time of student graduation is appropriate is an important thing that must be considered. Graduating well and on time is one measure of success in the learning process. This research aims to build a student graduation classification model by applying the QUEST (Quick, Unbiased, and Efficient, Statistical Tree) and CHAID (Chi-squared Automatic Interaction Detection) methods, examining the factors that affect student graduation, and comparing the classification results of the two methods. Both methods produce output in the form of tree diagrams, making it easier to interpret. Based on the classification tree formed from the two methods, four final nodes of the classification tree were generated, and three categories were grouped. Factors that affect student graduation include age and IPK. The classification results show that the percentage of classification accuracy for student graduation with QUEST and CHAID methods is 76.1%.
APPLICATION OF C4.5 ALGORITHM WITH FEATURE SELECTION IN CLASSIFICATION OF DISCHARGE STATUS OF HEAD INJURY PATIENTS ., Putri; Sulistianingsih, Evy; Imro'ah, Nurfitri; Debataraja, Naomi Nessyana
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/variancevol6iss2page165-174

Abstract

Head trauma is a medical emergency that can cause brain damage and disability, leading to death. The discharge status of injured patients is classified into two: alive and dead. The purpose of this study is to apply the C4.5 algorithm without feature selection and by using Chi-Square and Mutual Information feature selection to show independent variables that significantly influence the discharge status of head injury patients. This research data is secondary data of patients who suffered head injuries at Dr. Abdul Aziz Hospital, Singkawang City, in 2019-2021. The independent variables used were age, gender, length of hospitalization, etiology of head injury, Suprasellar Cistern, and Glasscow Coma Scale, with the dependent variable being discharge status. Based on the study results, the Chi-Square feature selection results identified two variables that had a significant effect. In contrast, for the Mutual Information feature selection results, five variables had a significant impact on the dependent variable. The C4.5 Algorithm classification model without feature selection produces an accuracy of 88.57%, the C4.5 Algorithm classification model with Chi-Square feature selection produces an accuracy of 88.57%, and the C4.5 Algorithm classification model with Mutual Information feature selection produces an accuracy value of 91.42% with the highest accuracy obtained from the results of the C4.5 Algorithm model formation with Mutual Information feature selection.
LSTM MODELING WITH AN AUTOREGRESSIVE APPROACH FOR DAILY TEMPERATURE PREDICTION IN GRESIK REGENCY Ifadah, Azlia Septy; Miftahurrohmah, Brina; Amelia, Putri; Firmansyah, Ardhi Dwi
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/variancevol6iss2page175-182

Abstract

The zero-hunger program is one of the primary goals of the SDGs, especially in large countries like Indonesia, where hunger remains a serious issue. The agricultural sector plays a crucial role in addressing this problem. However, the effectiveness of this sector is highly dependent on climate changes, such as temperature. Therefore, this research aims to develop a daily temperature prediction model in Gresik Regency using the LSTM method with an autoregressive approach. This model is expected to assist farmers in optimizing planting and harvesting times. The autoregressive approach is applied by analyzing the ACF and PACF plots to determine the lags used as lookback parameters. The research results show that the LSTM model with five lookbacks and 150 epoch parameters provides the best outcomes, with an RMSE value of 0.50, MAE of 0.39, R2 of 0.69, and MAPE of 0.01.

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