Article Per Year (5 Year)
p-Index From 2021 - 2026
1.633
P-Index
This Author published in this journals
All Journal Unisda Journal of Mathematics and Computer Science (UJMC) Transformasi : Jurnal Pendidikan Matematika dan Matematika Alifmatika: Jurnal Pendidikan dan Pembelajaran Matematika Journal of Applied Statistics, Mathematics, and Data Science
Sulantari Sulantari
Universitas PGRI Argopuro Jember
Religion Computer Science & IT Decision Sciences, Operations Research & Management Education Mathematics Physics Other

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search
Journal : Unisda Journal of Mathematics and Computer Science (UJMC)

 1 
Estimasi Model Regresi Binomial Negatif Bivariat (BNBR) Pada Penderita Kusta di Jawa Timur WIGID HARIADI; Sulantari Sulantari
Unisda Journal of Mathematics and Computer Science (UJMC) Vol 5 No 2 (2019): Unisda Journal of Mathematics and Computer science
Publisher : Mathematics Department of Mathematics and Natural Sciences Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (400.172 KB) | DOI: 10.52166/ujmc.v5i2.1696

Abstract

Abstract. One of the methods used to overcome overdispersion in poisson regression model is a bivariate negative binomial regression model also known as BNBR Model. Leprosy is a dangerous infectious disease, because it can cause paralysis. Leprosy is divided into 2 types, namely is a leprosy Pausibasilier(PB) type and leprosy Multibasilier (MB) type. Where PB type leprosy is a dry leprosy and MB type leprosy is a wet leprosy. Analysis of the data used to model the number of PB leprosy and MB leprosy cases and find out what factor influence it in East Java, the writer uses the BNBR models. Parameter estimation of the BNBR model uses to Maximum likelihood estimation (MLE) methods with Newton-Raphson iteration as well as testing the hypothesis using MLRT methods. After regression analysis, the results are obtained that of the 10 predictor variables tested, both in PB leprosy and MB leprosy, there are 3 predictor variables that are not significant in the model, namely are: variable percentage of poor population, variable ratio of population who did not graduated SMA, and variable ratio of health facilities. Abstrak. Salah satu metode yang digunakan untuk mengatasi overdispersi dalam regresi Poisson yakni dengan regresi binomial negatif bivariat atau dikenal juga dengan model regresi BNBR. Penyakit Kusta adalah salah satu penyakit menular yang berbahaya, karena dapat menyebabkan kelumpuhan. Jenis penyakit kusta terbagi menjadi 2, yakni Kusta tipe Pausibasiler (PB) dan tipe Multibasiler.(MB). Dimana kusta tipe PB merupakan Kusta kering, dan kusta tipe MB adalah kusta basah. Analisis data yang digunakan untuk memodelkan besarnya jumlah kasus kusta tipePB dan tipe MB, kemudian untuk mengetahui faktor apa saja yang mempengaruhinya di Jawa Timur, penulis menggunakan model BNBR. Penaksiran parameter model BNBR menggunakan Maximum Likelihood Estimation (MLE) dengan iterasi Newton-Raphson serta melakukan pengujian hipotesis menggunakan metode MLRT. Setelah dilakukan analisis regresi, diperoleh hasil bahwa dari 10 variabel prediktor yang diujikan, baik pada kusta tipe PB maupun tipe MB, terdapat 3 variabel prediktor yang tidak signifikan dalam model, yakni: variabel presentase penduduk miskin, variabel rasio penduduk yang tidak tamat SMA, dan variabel rasio sarana kesehatan.
Pemodelan Kasus Pasien Terkonfirmasi Positif Covid-19 Per-Hari Di Indonesia dengan Metode SARIMA Wigid Hariadi; Sulantari Sulantari
UJMC (Unisda Journal of Mathematics and Computer Science) Vol 7 No 2 (2021): Unisda Journal of Mathematics and Computer science
Publisher : Mathematics Department, Faculty of Mathematics and Sciences Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v7i2.2743

Abstract

The Seasonal Autoregressive Integrated Moving Average (SARIMA) model is a popular method for forecasting univariate time series data for data containing seasonality. This method consists of several stages, namely: identification, parameter assessment, diagnostic examination, and forecasting using the SARIMA (p,d,q)(P,D,Q)S model. The SARIMA model can be applied in various fields, one of which is the medical field. The number of patients infected with the CoVID-19 virus continues to grow every day. Indonesia is one of the countries experiencing the impact of the COVID-19 virus. On December 28, 2021, the number of positive Covid-19 patients in Indonesia was 4,262,157, with 4,113,472 patients recovering and 144,071 patients dying. Seeing the high number of positive cases of Covid-19 in Indonesia, the author wants to conduct research on modeling cases of patients who are confirmed to be positive for Covid-19 per day in Indonesia and then from this model, data forecasting will be carried out for the next 28 periods. The data collection period is from November 1, 2021 to December 28, 2021. And the results of a good model for predicting cases of confirmed positive COVID-19 patients per day in Indonesia are the SARIMA (2,1,2)(2,1,1)7 model, with The seasonal length is 7 periods, and the sum squared resid is 0.927619. Abstrak Model Seasonal Autoregressive Integrated Moving Average (SARIMA) adalah metode populer untuk meramalkan data deret waktu univariat untuk data yang mengadung musiman. Metode ini terdiri dari beberapa tahapan, yaitu: identifikasi, penilaian parameter, pemeriksaan diagnostik, dan peramalan menggunakan model SARIMA (p,d,q)(P,D,Q)S. Model SARIMA dapat diterapkan di berbagai bidang, salah satunya bidang medis. Jumlah pasien yang terinfeksi virus CoVID-19 terus bertambah setiap harinya. Negara Indonesia merupakan salah satu Negara yang mengalami dampak virus covid-19. pada 28 Desember 2021, jumlah pasien positif Covid-19 di Indonesia sebanyak 4.262.157 pasien, dengan 4.113.472 pasien sembuh dan 144.071 pasien meninggal dunia. Melihat tingginya kasus positif Covid-19 di Indonesia, maka penulis ingin melakukan penelitian tentang pemodelan kasus pasien terkonfirmasi positif covid-19 perhari di Indonesia untuk kemudian dari model tersebut akan dilakuakn peramalan data untuk 28 periode kedepan. Periode pendataan dari tanggal 1 November 2021 sampai dengan 28 Desember 2021. Dan hasil model yang baik untuk memprediksi kasus pasien terkonfirmasi positif covid-19 perhari di Indonesia adalah model SARIMA (2,1,2)(2,1,1)7, dengan panjang musiman nya 7 periode, dan nilai sum squared resid sebesar 0.927619.
Analisis Survival Model Regresi Cox Pada Lama Waktu Sembuh Pasien Gejala Sedang Covid-19 Sulantari Sulantari; Wigid Hariadi
UJMC (Unisda Journal of Mathematics and Computer Science) Vol 8 No 1 (2022): Unisda Journal of Mathematics and Computer science
Publisher : Mathematics Department, Faculty of Mathematics and Sciences Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v8i1.3070

Abstract

Survival analysis Model is one of the statistical analysis that can be used to test the survival function of an event. Survival analysis can be applied to health cases. The Kaplan-Meier test and cox proportional hazard regression analysis are part of the survival analysis. One of the health problems that Indonesia and the world are facing today is the virus covid-19 case. The country of Indonesia is one of the countries that experienced the impact of the virus covid-19. In Jember Regency, as of October 23, 2020, the number of patients who were confirmed positive was 1,098 patients with 85.52% having recovered, and 5.01% having died. Seeing the high number of positive cases of Covid-19 in Jember Regency, the authors want to conduct a study on the survival analysis of cox regression model on the long time of recovery of patients with moderate symptoms of covid-19 in the Jember region, especially in RSD Dr. Soebandi Jember. The results of this analysis are: (1) survival function of patients with moderate symptoms of Covid-19 at RSD dr Soebandi Jember, for female patients, it has a faster recovery time than patients with male sex. (2) survival function of patients with moderate symptoms of Covid-19 at RSD dr Soebandi Jember, for patients who do not have comorbid have a longer recovery time faster than patients who have comorbid. And (3) the variables of sex and comorbid status affect the length of time to recover patients with moderate symptoms of covid-19 at RSD Dr. Soebandi Jember. With the likelihood ratio (LRT) is 3.5255 for gender variables, and 16.5221 for comorbid status variables Abstrak Model analisis survival adalah salah satu analisis statistik yang dapat digunakan untuk menguji fungsi survival suatu kejadian. Analisis survival dapat dapat diterapkan pada kasus kesehatan. Uji Kaplan-Meier dan analisis regresi cox proporsional hazard merupakan bagian dari analisis survival. Salah satu permasalahan kesehatan yang sedang dihadapi Indonesia dan Dunia saat ini adalah kasus virus covid-19. Negara Indonesia merupakan salah satu Negara yang mengalami dampak virus covid-19. Di Kabupaten Jember, per-tanggal 23 Oktober 2020, jumlah pasien yang terkonfirmasi positif sebanyak 1.098 pasien dengan 85,52% telah sembuh, dan 5,01% meninggal dunia. Melihat tingginya kasus positif Covid-19 di Kabupaten Jember, maka penulis ingin melakukan penelitian tentang analisis survival model regresi cox pada lama waktu sembuh pasien gejala sedang covid-19 di Wilayah Jember, khususnya di RSD dr Soebandi Jember. Hasil dari analisis ini yakni: (1) fungsi survival pasien gejala sedang Covid-19 di RSD dr Soebandi Jember, untuk pasien jenis kelamin perempuan memiliki lama waktu sembuh yang lebih cepat dibandingkan pasien dengan jenis kelamin laki-laki. (2) fungsi survival pasien gejala sedang Covid-19 di RSD dr Soebandi Jember, untuk pasien yang tidak memiliki comorbid mempunyai lama waktu sembuh yang lebih cepat dibandingkan pasien yang memiliki comorbid. Dan (3) variabel jenis kelamin dan status comorbid berpengaruh terhadap lama waktu sembuh pasien gejala sedang covid-19 di RSD dr Soebandi Jember. Dengan nilai likelihood rationya (LRT) adalah sebesar 3.5255 untuk variabel jenis kelamin, dan 16.5221 untuk variabel status comorbid.
Forecasting Tingkat Inflasi Year-on-Year Indonesia Dengan Metode Weighted Moving Average (WMA) Wigid Hariadi; Sulantari Sulantari
UJMC (Unisda Journal of Mathematics and Computer Science) Vol 8 No 2 (2022): Unisda Journal of Mathematics and Computer science
Publisher : Mathematics Department, Faculty of Mathematics and Sciences Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v8i2.3576

Abstract

Abstract. The weighted moving average (WMA) method is a method of calculating data forecasting values through moving average values that are given different weights for each time period. The Covid-19 pandemic has had a considerable impact on the State of Indonesia. Not only from the health sector of the residents, but also the economic sector of the residents was also quite badly affected. This economic impact can be felt from the rising prices of goods needed by the community and in addition to the increasingly deteriorating financial conditions of the community. Not yet recovered due to the Covid-19 pandemic, there was a war between Russia and Ukraine in 2022 which helped balance the inflation rate in Indonesia. This is because the war made the price of fuel and wheat commodities increase more and more expensive. This inflation rate is one indicator of economic growth in a country, and is very influential on the economic growth of a country. Because of this, it is important for us to be able to know the projection (data forecast) of the inflation rate for the next several periods. This is intended so that related parties can prepare the right strategy in dealing with the inflation rate. From the results of the study, it was concluded that a good Weighted Moving Average model for predicting Indonesia's year on year inflation rate is a 3rd order WMA model with a weight of 0.65; 0.2; 0.15, with an MSE value of 0.2632, MAD of 0.3549, and a MAPE value of 0.1114 or (11.14%). Indonesia's YoY inflation forecast for the next 4 months, namely: In December 2022 it was 5.56, January 2023 it was 5.55, February 2023 it was 5.53, and in March 2023 it was 5.54. Keywords: Weighted Moving Average, WMA, Inflation. Abstrak. Metode weighted moving average (WMA) adalah metode menghitung nilai peramalan data melalui nilai rata-rata bergerak yang diberikan bobot yang berbeda untuk tiap-tiap periode waktunya. Pandemi covid-19 memberikan dampak yang cukup besar bagi Negara Indonesia. Tidak hanya dari sektor kesehatan warganya, namun juga sektor perekonomian warga juga ikut terdampak cukup parah. Dampak ekonomi ini dapat dirasakan dari naiknya harga barang-barang kebutuhan masyarakat dan di tambah lagi kondisi keuangan masyrakat yang semakin memburuk. Belum pulih akibat pandemic covid-19, terjadi peristiwa perang antara Rusia dengan Ukraina pada tahun 2022 yang ikut menymbang Tingkat Inflasi di Indonesia. Hal ini disebabkan karena perang tersebut membuat harga BBM dan komoditas gandum meningkat semakin mahal. Tingkat inflasi ini menjadi salah satu indicator pertumbuhan ekonomi di suatu Negara, dan sangat berpengaruh terhadap pertumbuhan ekonomi suatu Negara. Karena hal itu, penting kiranya kita untuk dapat mengetahui proyeksi (peramalan data) tingkat inflasi untuk beberapa periode kedepan. Hal ini bertujuan agar pihak terkait dapat mempersiapakan strategi yang tepat dalam menangani laju tingkat inflasi. Dari hasil penelitian, diperoleh kesimpulan bahwa Model Weighted Moving Average yang baik untuk memprediksi nilai inflasi year on year Indonesia adalah model WMA orde 3 dengan Bobot 0.65; 0.2; 0.15, dengan nilai MSE sebesar 0.2632, MAD sebesar 0.3549, dan nilai MAPE sebesar 0.1114 atau (11.14%). Forecasting inflasi YoY Indonesia selama 4 bulan berikutnya, yaitu: Pada bulan Desember 2022 sebesar 5.56, Januari 2023 sebesar 5.55, Februari 2023 sebesar 5.53, dan pada Maret 2023 sebesar 5.54. Kata kunci: Weighted Moving Average, WMA, inflasi.
Analisis Regresi Linier Berganda Untuk Memodelkan Faktor yang Mempengaruhi Nilai Penambahan Utang Tahunan Negara Indonesia Sulantari, Sulantari; Hariadi, Wigid; Putra, Eric Dwi; Anas, Aswar
UJMC (Unisda Journal of Mathematics and Computer Science) Vol 10 No 1 (2024): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department, Faculty of Mathematics and Sciences Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v10i1.6631

Abstract

Indonesia is a one of developing country. After 79 years of independence, Indonesia is not yet free from debt. In 2023, Indonesia's total national debt will reach Rp. 8,144.69 trillion. The debt can cause economic problems in many debtor countries. Regression analysis is a statistical analysis that explains the relationship between the dependent variable (Y) and the independent variable (X). The author is interested in analyzing what factors influence the amount of the annual increase in Indonesia's debt. Using multiple linear regression analysis, the results show that the variables that influence the amount of the increase in Indonesia's national is a variable debt are the GDP growth (X1), variable the GDP value 1 year ago (X2), and variable the debt to GDP ratio 1 year ago (X3). The coefficient of determination (R2) value is 0.89, which means that 89% of the increase in Indonesia's national debt is influenced by variables variable debt are the GDP growth, variable the GDP value 1 year ago, and variable the debt to GDP ratio 1 year ago. The regression model formed is: Y = -60.489 (X1) + 0.046 (X2) + 592.738 (X3) + e. Keywords: Weighted Moving Average, WMA, Inflation. Abstrak. Negara Indonesia merupakan salah satu negara berkembang. setelah 79 tahun merdeka, Indonesia belum lepas dari hutang. Tahun 2023, total utang negara Indonesia mencapai Rp. 8.144,69 triliun. Utang dapat menimbulkan permasalahan ekonomi pada banyak negara debitur. Analisis regresi adalah suatu analisa statistika yang menjelaskan hubungan antara variabel terikat (Y) dan variabel bebas (X). penulis tertarik untuk menganlisis faktor apa saja yang mempengaruhi besaran nilai penambahan hutang tahunan negara Indonesia. Menggunakan analsisi regresi linier berganda, diperoleh hasil bahwa Variabel yang berpengaruh terhadap besar penambahan hutang Negara Indonesia adalah variabel pertumbuhan PDB (X1), variabel nilai PDB 1 tahun yang lalu (X2), dan variabel rasio utang terhadap PDB 1 tahun yang lalu (X3). Nilai koefisien determinasi (R2) sebesar 0.89 yang berarti bahwa sebesar 89% besar penambahan hutang Negara Indonesia dipengaruhi oleh variabel X1, X2, dan X3. Model regresi yang terbentuk adalah: Y = -60.489 (X1) + 0.046 (X2) + 592.738 (X3) + e . Kata kunci: Analisis Regresi, Berganda, Utang Indonesia.
Peramalan Produksi Gula Indonesia dengan Model ARIMA(2,2,0) Serta Bagaimana Strategi Swasembada Gula Terjadi Pada tahun 2030 Wigid Hariadi; Sulantari Sulantari; Adi Mustika
UJMC (Unisda Journal of Mathematics and Computer Science) Vol 11 No 2 (2025): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department, Faculty of Sciences and Technology Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v11i2.11865

Abstract

Indonesia is an agrarian country with the agricultural sector playing a vital role in meeting basic needs and boosting the social, economic, and trade sectors. One of the widely cultivated crops is sugar cane, which is the raw material for sugar. Sugar is a strategic commodity and plays a vital role in the economy and community food security. The demand for sugar is enormous, but sugar production is still unable to meet all of this demand. Indonesia continues to strive to achieve sugar self-sufficiency. Therefore, the author is interested in conducting research related to how to forecast Indonesian sugar production for 2025-2030 using the ARIMA method? And what strategies are there to achieve Indonesian sugar self-sufficiency in 2030? The results obtained are: forecasting Indonesian sugar production can be done using the ARIMA (2,2,0), with an Sun Square Residuals value of 0.210136. Indonesia's sugar production forecasts from 2025 to 2030 are: 2.497 million tons, 2.450 million tons, 2.601 million tons, 2.635 million tons, 2.646 million tons, and 2.755 million tons, respectively. To achieve sugar self-sufficiency in Indonesia by 2030, the Indonesian government needs to add 1.19 million hectares of new land for sugarcane farming.
Co-Authors Adi Mustika Aswar Anas Balqis Aabidah Islahi Fitriyatul Alifia Putra, Eric Dwi Wigid Hariadi