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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Dharmakarya : Jurnal Aplikasi Ipteks Untuk Masyarakat Kubik Jurnal Matematika: MANTIK Desimal: Jurnal Matematika Teorema: Teori dan Riset Matematika Jurnal Cendekia : Jurnal Pendidikan Matematika JURNAL SILOGISME : Kajian Ilmu Matematika dan Pembelajarannya Eksakta : Berkala Ilmiah Bidang MIPA M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jurnal Matematika UNAND Kumawula: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Statistika dan Aplikasinya KUBIK: Jurnal Publikasi Ilmiah Matematika Jurnal Diferensial International Journal of Business, Economics, and Social Development International Journal of Global Operations Research Leibniz: Jurnal Matematika SisInfo : Jurnal Sistem Informasi dan Informatika Innovative: Journal Of Social Science Research Jurnal Matematika Integratif In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism)
Nurul Gusriani
Departemen Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Padjadjaran
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Analisis Regresi TELBS Untuk Menentukan Pengaruh Lahan Kopi Terhadap Produksi Kopi di Indonesia Tahun 2023 Menggunakan Bahasa Pemrograman Python Ramdhani, Muhammad Dhafin Qinthar; Gusriani, Nurul; Firdaniza, Firdaniza
In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism) Vol 23 No 2 (2024): In Search
Publisher : LPPM UNIBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37278/insearch.v23i2.889

Abstract

Indonesia, as one of the world's largest coffee producers, is renowned for its diverse range of high-quality coffees such as Arabica, Robusta, and Liberica. Coffee production is influenced by various factors, including the extent of plantation land. Coffee production data may contain outliers due to factors like weather changes, pest attacks, inconsistent farming practices, or recording errors. These challenges can be addressed using robust regression methods, with one such estimation being Tabatabai Eby Li Bae Singh (TELBS) estimation. TELBS estimates model parameters by minimizing an objective function. In this study, a TELBS estimation model was applied to Indonesian coffee production data in 2023, with the dependent variable being coffee production quantity and the independent variable being plantation land area. Parameter testing using t-tests indicated that plantation land area significantly influences coffee production in that year at a significance level of 0.05. The TELBS estimation model yielded a coefficient of determination of 96.51%, demonstrating its capability to explain a substantial portion of the data's variance.
Generalized Additive Models for Modeling Pneumonia Cases in Toddlers in West Java based on the Penalized Spline Estimator Wahyu, Azkanul; Nurul Gusriani; Kankan Parmikanti
EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 25 No. 02 (2024): Eksakta : Berkala Ilmiah Bidang MIPA (E-ISSN : 2549-7464)
Publisher : Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Negeri Padang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/eksakta/vol25-iss02/491

Abstract

Acute Respiratory Infections (ARI) are one of the causes of high mortality in the world, such as pneumonia in toddlers. Pneumonia cases in West Java are high compared to other provinces. In this study, pneumonia cases will be modeled with Generalized Additive Models (GAM) based on penalized spline estimators. The optimal number of knots is determined using the full search algorithm and the optimal smoothing parameter is obtained based on the minimum Generalized Cross Validation (GCV) value of order one or two. Then, GAM parameter estimation is performed using the local scoring algorithm. Formed model based on the order, number of knots, and smoothing parameters of each predictor variable with order one, number of knots two, and optimal smoothing parameter one for , order two, number of knots three, and optimal smoothing parameter one for , and order one, number of knots two, and optimal smoothing parameter for  whose parameters were estimated by local scoring resulted in a coefficient of determination of 0.679. This indicates that 67.9% of the factors from the predictor variables affect the percentage of pneumonia cases among under-fives while the remaining 32.1% is influenced by other factors outside the model.
Robust Linear Discriminant Analysis with Modified One-Step M-Estimator Qn Scale for Classifying Financial Distress in Banks: Case Study Nabila Putri; Parmikanti, Kankan; Gusriani, Nurul
EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 25 No. 02 (2024): Eksakta : Berkala Ilmiah Bidang MIPA (E-ISSN : 2549-7464)
Publisher : Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Negeri Padang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/eksakta/vol25-iss02/515

Abstract

The COVID-19 pandemic has significantly disrupted the banking sector, leading to a decline in profit growth as an indicator of financial distress. Bank financial health can be evaluated using the RGEC (Risk Profile, Good Corporate Governance, Earnings, Capital) analysis. While Linear Discriminant Analysis (LDA) ideally requires normality and homogeneity of covariance matrices, financial data often fail to meet these assumptions. Therefore, this study employs robust linear discriminant analysis using the Modified One-Step M-Estimator with Qn scale estimator (MOM-Qn) to classify ‘distress’ and ‘non-distress’ bank conditions. Given these challenges, this study acts as a preventive measure for banks to evaluate financial health simultaneously. The objective is to provide a robust discriminant function for more accurate and stable classification, particularly in the presence of outliers. It focuses on conventional private banks listed on the Indonesia Stock Exchange (IDX) during December 2021-2022. The results show a classification accuracy of 69.23% and a Press’s Q value of 11.53846, indicating the method’s effectiveness in classifying real financial data.  
Model Gerak Brown Fraksional Geometrik dalam Peramalan Harga Saham PT Indofood Sukses Makmur Tbk Menggunakan Pemrograman Python Nurhadini Putri; Firdaniza Firdaniza; Nurul Gusriani
SisInfo Vol 6 No 1 (2024): SisInfo
Publisher : Universitas Informatika dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37278/sisinfo.v6i1.798

Abstract

Peramalan harga saham yang tepat diperlukan oleh para investor. Beberapa metode dapat dilakukan dalam peramalan harga saham, seperti model trend, Autoregressive Integrated Moving Average, Double Moving Average, dan Exponential Smoothing. Selain itu, terdapat pula model yang lebih kompleks, seperti model Gerak Brown Geometrik (GBG) dan model Gerak Brown Fraksional Geometrik (GBFG). Model GBG dan GBFG memiliki beberapa keunggulan, diantaranya dapat meramalkan harga saham dengan periode waktu pendek, kesesuaian model dengan pergerakan harga saham yang selalu bernilai positif dan tidak memerlukan banyak pengujian data. Selain itu, model GBFG juga dapat mengatasi masalah data aktual saham yang sebagian besar tidak saling bebas. Penelitian ini bertujuan melakukan peramalan harga saham PT Indofood Sukses Makmur Tbk (INDF) mengunakan model Gerak Brown Fraksional Geometrik (GBFG). Indeks Hurst pada model GBFG diestimasi menggunakan Rescaled Range (R/S) dengan bantuan pemrograman Python. Hasil dari peramalan pergerakan harga saham PT Indofood Sukses Makmur Tbk (INDF) menggunakan model GBFG memberikan nilai yang sangat akurat berdasarkan nilai MAPE.
Formulasi Infinitesimal Generators Grup Lie Satu Parameter dari Transformasi Translasi dan Scaling Kurniadi, Edi; Badrulfalah, Badrulfalah; Gusriani, Nurul
Leibniz: Jurnal Matematika Vol. 5 No. 02 (2025): Leibniz: Jurnal Matematika
Publisher : Program Studi Matematika - Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas San Pedro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59632/leibniz.v5i02.496

Abstract

Grup Lie transformasi dapat dikarakterisasi melalui infinitesimal generators yang membentuk aljabar Lie. Infinitesimal generators dapat diaplikasikan untuk menyelesaikan persamaan diferensial biasa (PDB) maupun persamaan diferensial parsial (PDP) baik yang linear maupun nonlinear. Tujuan penelitian ini adalah untuk memberikan rumus ekplisit infinitesimal generators berkenaan dengan transformasi grup Lie satu parameter. Metode penelitian yang digunakan merupakan kombinasi dari metode kualitiatif berupa studi literatur khususnya transformasi translasi dan scaling dan metode kuantitatif dengan menentukan rumus eksplisit infinitesimal generators dan analisisnya. Hasil yang diperoleh adalah bentuk rumus eksplisit infinitesimal generators yang bersesuaian dengan jenis transformasi yang digunakan. Hasil ini bisa digunakan untuk penelitian selanjutnya dalam menyelesaikan model matematika reaksi difusi konveksi (RDK) dalam PDB maupun PDP sebagai salah satu langkah dalam aplikasi simetri Lie.  
Markov average-based weighted fuzzy time series model to predict PT Kimia farma Tbk stock price Azzahra, Rediva; Firdaniza, Firdaniza; Gusriani, Nurul
Desimal: Jurnal Matematika Vol. 4 No. 3 (2021): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v4i3.9675

Abstract

The COVID-19 pandemic impacted various activities in Indonesia, including the stock market. Despite the declining economic condition, people are increasingly interested in investing. Among other companies available on the Indonesia Stock Exchange, companies in the health sector have a particular appeal to potential investors, one of which is pharmaceutical companies. This research used a Markov Average-Based Weighted Fuzzy Time Series model applied to PT Kimia Farma Tbk stock price data. This model develops the previous Markov chain–Fuzzy Time Series model, which has not calculated the weights for recurring events and used the Sturgess rule to determine the interval length. In this research, each recurring event has given a different weight that provides different probability values for transitions from one state to another. The Average-Based method is used to determine the interval length that can reflect the fluctuation of the data used. The stock price prediction of PT Kimia Farma Tbk using this model is categorized as very accurate with a MAPE of 2.632%.
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.
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 Dianne Amor Kusuma Edi Kurniadi Edi Kurniadi Endang Rusyaman Fatahillah Akmal Ferdian Agustiana Firdaniza Firdaniza Ghiffari, Alif Muhamad Gumgum Darmawan Hasan, Nabila Nurmala Herlina Napitupulu Iin Irianingsih Irmansyah, Athaya Zahrani Jaka Nazarudin Kaerudin, Nandira Putri Kankan Parmikanti Kartika Dewi Luthfi Ghiffari Melda Noereast Talytha Michelle Selina Buntara Milani Destriana Mohamad Nurzaman Muslihin, Khoirunnisa R A Nabila Putri Nasri, Ramadhoni Natalia Syafitri Kustanto Nurhadini Putri Nursanti Anggriani Ramdhani, Muhammad Dhafin Qinthar Rediva Azzahra Reffa Ayu Anggraeni Reffa Ayu Anggraeni Riaman Ricardo, Dimitri Salsabila Fakhriyah Rizal Amegia Saputra Ruslan Ruslan Samantha Surya Saprilian Hidayat Satyaputra, Ida Bagus Wira Krishna Sri Rahayu Sunarta Susanto Sussy Susanti Syifana, Hani Tiara Aprilia K Tiara Aprilia K, Tiara Tomy Perdana Wahyu, Azkanul