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Nurul Gusriani, Nurul
Departemen Matematika Fakultas MIPA, Universitas Padjadjaran
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Analysis of Health Insurance Claims Factors using The Stochastic Restricted Maximum Likelihood Estimation (SRMLE) Binary Logistic Regression Model: (Case Study: Health Insurance Claims at XYZ Company in 2023) Bagariang, Elizabeth Irene; Riaman; Gusriani, Nurul
International Journal of Global Operations Research Vol. 6 No. 3 (2025): International Journal of Global Operations Research (IJGOR), August 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i3.389

Abstract

The health insurance claim approval process is a crucial aspect for insurance companies. Inaccuracy in predicting claim status can pose financial risks to the company and reduce policyholder trust. This study aims to identify the factors that influence the approval or rejection of health insurance claims. In this type of data analysis, the problem of multicollinearity among predictor variables is often encountered, which can lead to unstable parameter estimates. To address this issue, this study utilizes a binary logistic regression model with the Stochastic Restricted Maximum Likelihood Estimation (SRMLE) method, which is better suited to handle such conditions. The data used in this research includes the variables of total claim amount, premium price, number of insured individuals, employee age, and the number of previous claims recorded at XYZ Company. The results of the factor analysis, through the developed logistic regression model, show that the variables of total claim amount, premium price, and the number of insured individuals are significant factors influencing the probability of claim approval.
REGRESI LOGISTIK MULTINOMIAL BAYESIAN DENGAN ALGORITMA GIBBS SAMPLING UNTUK MENENTUKAN FAKTOR-FAKTOR TINGKAT KEMISKINAN DI INDONESIA Syifana, Hani; Gusriani, Nurul; Parmikanti, Kankan
Jurnal Matematika Integratif Vol 21, No 1: April 2025
Publisher : Department of Matematics, Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jmi.v21.n1.62937.89-102

Abstract

Poverty is a state of deprivation experienced by individuals or groups with monthly per capita expenditure that is insufficient to meet basic needs. Based on Indonesia's poverty profile released by the Statistics Indonesia (BPS) in March 2024, it was recorded that 9.03% of Indonesia's population was declared poor, which is still far from the poverty reduction target of 6.5% to 7.5% targeted in the National Medium-Term Development Plan 2020-2024. One of the efforts that can be made to end poverty in Indonesia is to analyze what factors affect the poverty rate. The method used in this study is Bayesian multinomial logistic regression using the Markov Chain Monte Carlo (MCMC) Gibbs Sampling algorithm and the response variable used as a measure of poverty level is the poverty line which is an official indicator sourced from BPS. The results show that after 20,000 iterations, the Markov chain reaches a stationary state with the results of the credible interval test supported by the deviance test results stating that the factors that have a significant effect on the poverty rate in Indonesia in 2024 are GRDP at constant prices and average years of schooling.
GEOGRAPHICALLY WEIGHTED PANEL REGRESSION (GWPR) MODEL FOR POVERTY DATA IN WEST JAVA PROVINCE 2019-2021 Nasri, Ramadhoni; Gusriani, Nurul; Anggriani, Nursanti
JURNAL DIFERENSIAL Vol 5 No 2 (2023): November 2023
Publisher : Program Studi Matematika, Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jd.v5i2.12213

Abstract

The problem of poverty in West Java shows a pattern that tends to be concentrated in adjacent areas, indicating spatial heterogeneity in the problem. On the other hand, poverty in West Java also shows an increasing trend from year to year so that dynamic changes occur in various regions. From this situation, it is necessary to know the factors that affect poverty spatially using panel data. One way is to model the poverty problem with the Geographically Weighted Panel Regression (GWPR) model. The GWPR model is the development of a regression model that combines Geographically Weighted Regression (GWR) with panel data regression assuming a Fixed Effect Model (FEM). The data used in this study are secondary data in the 2019-2021 range from the Central Bureau of Statistics and Open Data Jabar which consists of the dependent variable (Y), namely the percentage of poor people and the independent variable (X), namely the factors that influence the percentage of poverty. The purpose of this study is to produce a GWPR model using the Weighted Least Square (WLS) method with the Tricube adaptive kernel weighting function. By conducting overall and partial testing through the F test and t test, the results show that the model for each location and the factors that influence the percentage of poor people in West Java are different for each location due to spatial variations in the relationship between the independent variable and the dependent variable.
REPRESENTASI su(2) DAN KOMPLEKSIFIKASI su(2)_C=sl(2,C) PADA RUANG VEKTOR POLINOM HOMOGEN Kurniadi, Edi; Badrulfalah, Badrulfalah; Gusriani, Nurul
Jurnal Silogisme : Kajian Ilmu Matematika dan Pembelajarannya Vol 9 No 2 (2024): Desember
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/silogisme.v9i2.9259

Abstract

Aljabar Lie su(N) mempunyai kompleksifikasi sl(N,C). Dengan kata lain, suNC≅sl(N,C). Dalam artikel ini, dipelajari representasi aljabar Lie su(N) dan sl(N,C) khususnya untuk N=2 yang direalisasikan pada ruang vektor polinom homogen kompleks dua variabel berderajat dua. Tujuannya adalah untuk mengkonstruksi representasi sl(2,C) dari representasi su(2) dan  membuktikan bahwa representasi yang diperoleh bersifat unitar dan tak tereduksi. Selanjutnya, karena grup Lie dari  su(2) bersifat simply connected maka representasi su(2) dapat dikonstruksi dari grup Lie-nya. Di sisi lain, karena su2C≅sl(2,C) maka representasi dari sl(2,C) dapat dikonstruksi melalui perluasan linear-kompleks dari representasi su(2) dan hasilnya dapat dinyatakan dalam bentuk operator linear.
Pemodelan Indeks Pembangunan Manusia di Kalimantan Timur Menggunakan Spasial Durbin Data Panel Kaerudin, Nandira Putri; Gusriani, Nurul; Ruchjana, Budi Nurani
Jurnal Matematika Integratif Vol 20, No 1: April 2024
Publisher : Department of Matematics, Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jmi.v20.n1.55158.101-115

Abstract

Indeks Pembangunan Manusia (IPM) merupakan salah satu indikator yang dapat digunakan untuk mengukur kemajuan suatu negara. Di Indonesia sendiri masih terdapat ketimpangan IPM antar provinsinya. Provinsi Kalimantan Timur merupakan salah satu provinsi yang memiliki rata-rata IPM tinggi di Indonesia, sehingga perlu dilakukan studi mengenai IPM untuk memberikan gambaran bagi provinsi dengan IPM rendah. IPM di suatu wilayah dipengaruhi oleh wilayah sekitarnya, hal ini disebabkan oleh efek spasial. Analisis regresi spasial merupakan metode yang mampu mengakomodasi efek spasial. Spatial Durbin Model (SDM) adalah salah satu pengembangannya. Selain itu, penggunaan data panel pada model menyebabkan variabilitas pada data. Penelitian ini bertujuan untuk memodelkan IPM di Kalimantan Timur menggunakan spasial durbin data panel meliputi lima kategori: Persentase penduduk miskin; Tingkat Partisipasi Angkatan Kerja (TPAK); Persentase penduduk; Angka Partisipasi Murni (APM); Persentase rumah tangga menurut fasilitas toilet sendiri. Berdasarkan hasil uji Hausman dan Chow, terdapat efek tetap pada setiap kabupaten/kota sehingga FEM merupakan jenis data panel yang digunakan. Selain itu, Hasil uji Moran’s I mengindikasikan adanya dependensi spasial positif dalam data IPM. Koefisien determinasi pada model spasial Durbin data panel menunjukkan nilai 99,92417% yang berarti model ini baik digunakan untuk memodelkan IPM di Kalimantan Timur.
Estimation of Labor Force Participation Rate (TPAK) in Java's Data-Scarce Areas Using Ordinary Cokriging Angelina, Sofia; Gusriani, Nurul; Firdaniza, Firdaniza
Jurnal Matematika Integratif Vol 21, No 2: Oktober 2025
Publisher : Department of Matematics, Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jmi.v21.n2.65239.229-244

Abstract

The quality of the labor force is crucial for economic development, and the Labor Force Participation Rate (TPAK) is a key employment indicator. In 2024, TPAK data collection in Java Island faced gaps in DKI Jakarta and Banten Provinces, limiting comprehensive labor mapping. To overcome this, spatial estimation methods are needed using data from surrounding areas and auxiliary variables. The Open Unemployment Rate (TPT) has a strong inverse relationship with TPAK, each 1\% TPAK increase lowers TPT by 14,82\%, making it a suitable auxiliary variable. This study estimates the 2024 TPAK for DKI Jakarta and Banten using the ordinary cokriging method, with TPT as the secondary variable. Spatial autocorrelation analysis confirmed that TPAK and TPT exhibit spatial patterns, are normally distributed, and meet stationarity assumptions. The best cross semivariogram model was identified using k-fold cross validation, which selected the spherical model with the lowest average RMSE of 4,24. The resulting ordinary cokriging model accurately predicted TPAK values, achieving a MAPE of 3,25\%. These estimates enable spatial visualization of TPAK in previously unobserved areas, contributing to a more complete understanding of labor participation across Java Island.
Struktur Aljabar untuk Barisan Kodon dari Asam Deoksiribonukleat (DNA) Hasan, Nabila Nurmala; Kurniadi, Edi; Gusriani, Nurul
Jurnal Matematika Integratif Vol 21, No 2: Oktober 2025
Publisher : Department of Matematics, Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jmi.v21.n2.67778.213-228

Abstract

This article discusses the algebraic structure of codon sequences as a representation of DNA nitrogen base sets in mathematical terms. The study aims to prove what algebraic structures are obtained for codon sequences from DNA bases. The methods used include qualitative research methods in the form of literature studies and quantitative research methods in the form of experiments on DNA base sets. In mathematical notation, the nitrogen bases of DNA can be collected in a set and connected into algebraic structures through a bijective mapping on the Galois field of order 4. This results in the set B being viewed as a Galois field of order 4. Additionally, DNA base triplets or codons can be represented in mathematical form. Furthermore, these codons are bijectively mapped onto the Galois field of order 64, so that the resulting algebraic structure is a field. The result of this study show that the codon sequences have an algebraic structure in the form of a one-dimensional vector space over the Galois field on the codon. For further research, the Lie structure in codon can be investigated through the construction of its Lie brackets, where this vector space is a necessary condition for Lie algebras.
Co-Authors Angelina, Sofia Anita Triska Arif Rahman Aulia Wanda Puspitasari Azzahra, Rediva Badrulfalah Badrulfalah Bagariang, Elizabeth Irene Balqis, Viona P Betty Subartini Budi Nurani Ruchjana Clarita Simar DHEA ARIESTA Diah Chaerani Edi Kurniadi Edi Kurniadi Edi Kurniadi Edi Kurniadi Edi Kurniadi Endang Rusyaman Firdaniza Firdaniza Ghiffari, Alif Muhamad Hasan, Nabila Nurmala Herlina Napitupulu Herlina Napitupulu Iin Irianingsih Irmansyah, Athaya Zahrani Kaerudin, Nandira Putri Kankan Parmikanti Kusuma, Dianne Amor Luthfi Ghiffari Melda Noereast Talytha Michelle Selina Buntara Milani Destriana Mohamad Nurzaman Muslihin, Khoirunnisa R A Nabila Putri Nasri, Ramadhoni Nursanti Anggriani Ramdhani, Muhammad Dhafin Qinthar Rediva Azzahra Reffa Ayu Anggraeni Reffa Ayu Anggraeni Riaman Samantha Surya Satyaputra, Ida Bagus Wira Krishna Sri Rahayu Sunarta Susanto Syifana, Hani Tiara Aprilia K Tiara Aprilia K, Tiara Tomy Perdana Wahyu, Azkanul