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All Journal Epsilon: Jurnal Matematika Murni dan Terapan AKSIOMA: Jurnal Program Studi Pendidikan Matematika Jurnal Fisika FLUX Info-Teknik Jurnal Agro Industri Perkebunan Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Dinamisia: Jurnal Pengabdian Kepada Masyarakat Zero : Jurnal Sains, Matematika, dan Terapan Jurnal Pendidikan Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) VARIANSI: Journal of Statistics and Its Application on Teaching and Research Jurnal Sistem Informasi, Akuntansi dan Manajemen RAGAM: Journal of Statistics and Its Application
Dewi Sri Susanti
Department Of Statistics, Lambung Mangkurat University, Banjarbaru
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ANALISIS REGRESI LOGISTIK ORDINAL UNTUK MEMODELKAN TINGKAT.KEPARAHAN.PENYAKIT HIV/AIDS.DI RUMAH SAKIT DAERAH IDAMAN BANJARBARU Thaibatun Nissa; Dewi Sri Susanti
RAGAM: Journal of Statistics & Its Application Vol 3, No 1 (2024): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v3i1.12797

Abstract

HIV is a virus that infects the immune system cells, thereby damaging the human immune system. AIDS is a collection of symptoms that arise due to the compromised immune system of the human body as a result of a positive infection by the HIV virus. HIV/AIDS remains a complex and significant global health issue. Despite advancements in treatment and prevention, the severity of HIV/AIDS remains a primary focus in healthcare management efforts. This study aims to determine the factors influencing the severity of HIV/AIDS patients at the Regional Hospital of Idaman Banjarbaru using ordinal logistic regression analysis. Ordinal logistic regression is employed to understand the relationship between the dependent variable (severity of the disease) and independent variables, where the dependent variable is ordinal in scale. The data used for this analysis is secondary data extracted from the inpatient medical records of the Idaman Banjarbaru Regional Hospital, comprising a total of 68 cases of HIV/AIDS. Assumed factors influencing the severity of patients include gender, age, duration of hospitalization, education, employment status, marital status, and place of residence. The analysis results indicate a significant relationship between the severity of HIV/AIDS patients and marital status. The highest likelihood of patients experiencing HIV/AIDS is in the divorced response category with a stage 3 category, where the probability value is 0.943. Individuals in the married and divorced categories are 1.53 times more likely to experience HIV/AIDS with a stage 4 status and complications ranging from 3 to 5. Keywords:   Severity of Disease, HIV/AIDS, Ordinal Logistic Regression, Odds Ratio
PEMODELAN GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) PADA DATA INDEKS HARGA KONSUMEN (IHK) 5 IBUKOTA PROVINSI DI PULAU KALIMANTAN Muhammad Aldi Relawanto; Yuana Sukmawaty; Dewi Sri Susanti
RAGAM: Journal of Statistics & Its Application Vol 2, No 2 (2023): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v2i2.11427

Abstract

Generalized Space Time Autoregressive (GSTAR) model is a development model from the generalized STAR (Space Time Autoregressive) model. GSTAR model have autoregressive order to see the effect of the time element and location weighting matrix to see the effect of the location element. Unlike the STAR model, it can assume that each location research has different characteristics. The purpose of this research is to apply the Generalized Space Time Autoregressive (GSTAR) model to the Consumer Price Index (CPI) data in Kalimantan Island, especially in the capital city of each province in Kalimantan Island to find out the best estimation model with the best location weight. The location weights used the distance inverse location weights and the normalized cross-correlation location weights by estimating the parameters of the GSTAR model using the Ordinary Least Square (OLS) method. The best estimated model can be seen from the smallest Akaikae’s Information Criterion (AIC) and Root Mean Square Error (RMSE) value. From the research results, it was found that the best GSTAR prediction model for CPI data for 5 cities in Kalimantan Island was the GSTAR(1,1)-I(1). These results are based on the GSTAR prediction model with the smallest AIC value and the data is differencing 1 time. The best location weight based on the smallest RMSE value for the GSTAR(1,1)-I(1) model is the normalized cross-correlation location weight.
SPATIAL ANALYSIS OF THE RELATIONSHIP BETWEEN HUMAN DEVELOPMENT INDEXES AND ITS DETERMINANT FACTORS IN SOUTH KALIMANTAN PROVINCE: COMPARISON OF SPATIAL REGRESSION MODELING Juhar Latifah; Dewi Sri Susanti
RAGAM: Journal of Statistics & Its Application Vol 2, No 2 (2023): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v2i2.11608

Abstract

Indeks Pembangunan Manusia (IPM) berfungsi sebagai metrik penting yang mencerminkan tingkat kesejahteraan suatu wilayah melalui dimensi kesehatan, pendidikan, dan pendapatan. Dalam upaya untuk memahami lebih lanjut mengenai faktor-faktor yang mempengaruhi IPM, penelitian ini fokus pada Keparahan Tingkat Kemiskinan (X1), Kepadatan Manusia Penduduk (X2), dan Angka Partisipasi Kasar (X3) sebagai variabel kunci yang mungkin berdampak pada pembangunan, khususnya di provinsi Kalimantan Selatan. Metode yang digunakan meliputi regresi klasik, gabungan regresi spasial, dan model kesalahan spasial. Model ketiga ini akan dibandingkan dan ditentukan model dengan kinerja terbaik. Berdasarkan temuan penelitian, Structural Equation Model (SEM) muncul sebagai model yang paling efektif dalam menganalisis faktor-faktor yang mempengaruhi IPM di Kalimantan Selatan. Nilai R-square yang diperoleh sebesar 0,8946 menunjukkan tingkat daya penjelas yang tinggi, melampaui nilai R-square model lainnya.
PEMODELAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION (GWNBR) PADA KEJADIAN STUNTING DIiKABUPATEN BARITO KUALA TAHUN 2022 Azkia Azkia; Dewi Sri Susanti
RAGAM: Journal of Statistics & Its Application Vol 3, No 1 (2024): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v3i1.12796

Abstract

Stunting is a condition of malnutrition in toddlers that causes their height to be lower than other children their age. In 2022, South Kalimantan Province has a stunting prevalence of 24.6% and ranks fifteenth in Indonesia. Barito Kuala Regency, one of the regions in South Kalimantan Province, has the highest stunting rate at 33.6% which is included in the Chronic- Acute category (≥ 20%). This study uses the GWNBR model to characterize the factors that cause stunting in Barito Kuala Regency. The GWNBR model will make it easier for researchers to find out the factors that affect stunting in each sub-district. The weight matrix used is a fixed kernel function and an adaptive kernel function. The predictor variables used were the percentage of infant history of complete basic immunization, history of exclusive breastfeeding in infants <6 months, history of low birth weight babies, new visits to pregnant women (K1), sixth antenatal care (ANC) visit (K6), history of pregnant women who received blood supplement tablets, history of infants 6-11 months who received vitamin A, active posyandu and households with access to appropate sanitary facilities.(healthy latrines). The best model results obtained with adaptive gaussian weighting with an AIC value of 167.25. Keywords: Stunting Cases, GWNBR model.
PEMODELAN REGRESI SPASIAL PADA ANGKA PARTISIPASI MURNI JENJANG PENDIDIKAN SMA SEDERAJAT DI PROVINSI KALIMANTAN SELATAN Suci Anshari; Dewi Sri Susanti; Fuad Muhajirin Farid
RAGAM: Journal of Statistics & Its Application Vol 1, No 1 (2022): RAGAM: Journal of Statistics and Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v1i1.7318

Abstract

This research is done for modeling of the Pure Enrolment Rate (PER) at the senior high school level in South Kalimantan Province that uses analysis of spatial regression. The purpose of this analysis is to construct the modeling of spatial regression of the Pure Enrolment Rate (PER) at the senior high school level in South Kalimantan Province and to identify the significant factors that influent the pure enrollment rate (PER). The result of this research shows that the modeling of spasial regression is suitable for use in the Pure Enrolment Rate (PER) at the senior high school level in South Kalimantan Province in 2017 – 2019 is the Spatial Autoregressive Model (SAR). The model form can be seen that in 2017 there is no significant influence factors to the PER, in 2018 the ratio of the student number to the school number (X5) and the ratio of the student number to the teacher number (X6) that are the influence factors significantly to the Pure Enrollment Rate (PER), while in 2019 only the factor of the ratio of the students number to the schools number (X5 ) that influents significantly to the PER.Keywords:   PER, Education, and Spatial Regression Analysis
PEMODELAN GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) MENGGUNAKAN PEMBOBOT KERNEL PADA KASUS TINGKAT PENGANGGURAN TERBUKA DI KALIMANTAN Viona Oktafiani; Dewi Sri Susanti; Yeni Rahkmawati
RAGAM: Journal of Statistics & Its Application Vol 3, No 1 (2024): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v3i1.12822

Abstract

AbstractUnemployment is one of the serious problems in Indonesia's economic development. This unemployment describes human resources that have not been utilized optimally, as a result of which people's productivity and income have not been maximized, this can also be one of the causes of poverty and other social problems. This study aims to find out the general picture of the open unemployment rate in the Kalimantan region, get the best model and factors that influence the open unemployment rate and illustrate it through thematic maps. The study began with testing assumptions and spatial effects then continued with testing global regression modeling and Geographically Weighted Regression. The weighting function used in this study is adaptive gaussian kernel. The variable that has a positive effect on the open unemployment rate in the Kalimantan region is population density. While the variable that negatively affects the open unemployment rate is the Labor Force Participation Rate. Keywords:   Open Unemployment Rate, Kalimantan Island, Spatial, GWR
Co-Authors Adzim, Muhammad Fauzan Ahmad Yusuf Akbar, Arief Rahmad Maulana Al Hujjah Asianingrum Anggraini, Dewi Arfan Eko Fahrudin Arfan Ikhsan, Arfan Arifin, Samsul Az Zahra, Aisyah Nur Azizah, Rahma Dina Nur Azkia Azkia Badruzsaufari Badruzsaufari Bizaini Bizaini Budi Kristanto Chairil Fachrurazie Challen, Auliffi Ermian Dewi Anggraini DEWI ANGGRAINI Dewi Anggraini Dian Handiana Dian Handiana Dini Hidayati Eko Suhartono Elmanizar, Elmanizar Etza Budiarti Febriani, Arika Fitriadi Fitriadi Fuad Muhajirin Farid Fuad Muhajirin Farid Genardi, Angelina Ivanna Geofani Setiawan Geofani Setiawan Hijriati, Naimah Hindarto, Imam Izafera, Anis Huzaimanor Jainal Jainal Juhar Latifah Karim Karim Krisdianto Krisdianto Krisdianto Sugiyanto Lalu Rudyat Telly Savalas Lestia, Aprida Siska Maisarah Maisarah, Maisarah Manik, Tetti Novalina Muchamad Arief Soendjoto Muhammad Ahsar Karim Muhammad Aldi Relawanto Muhammad Meidy Maulana Muhammad Reza Faisal, Muhammad Reza Mustika Khadijah Noor Baitirahmah Noordyanti, Erna Nooriman, Raihan Nur Salam Nur Salam Nur Salam Nurul huda NURUL QOMARIYAH Oktaviani, Viona Oni Soesanto oni Soesanto Pamona Dwi Rahayu Prabowo, Sigit Dwi Rahmat Yunus Rahmi Hidayati Raihan Nooriman Raihan Nooriman Randy Toleka Ririhena Rasjava, Achmad Ramadhanna'il Riza, Yusi Rizki Fatriasi Rizqi Elmuna Hidayah Salsabilla, Rania Selvi Annisa Setiawan, Geofani Silvi Risaria Dewi Siti Nur Hamidah Soesanto, Oni Sri Cahyo Wahjono Sri Cahyo Wahyono Sri Mulyanie Hardiyanthy Suci Anshari Susilo, Tanto Budi Sutomo Sutomo Syamsiar, Syamsiar Thaibatun Nissa Thresye Thresye Thresye,, Thresye, Tiara Elma Uthami, Mariza Viona Oktafiani Winnugroho Wiratman, Manfaluthy Hakim, Tiara Aninditha, Aru W. Sudoyo, Joedo Prihartono Yeni Rahkmawati Yeni Rahkmawati Yuana Sukmawaty Yulian Firmana Arifin Yulianti, Irma Sari Yuni Yulida Yuyun Hidayat