Garuda - Garba Rujukan Digital

p-Index From 2020 - 2025

4.497

P-Index

This Author published in this journals

All Journal Jurnal Varian ARRUS Journal of Mathematics and Applied Science VARIANSI: Journal of Statistics and Its Application on Teaching and Research Journal of Research Applications in Community Services Eksponensial IJASDS: Indonesian Journal of Applied Statistics and Data Science

Siringoringo, Meiliyani

Unknown Affiliation

Author-ID : 1153728

Religion Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

Published : 28 Documents Claim Missing Document

Claim Missing Document

Articles

1 2 3

Peramalan Jumlah Kunjungan Wisatawan Mancanegara Ke Indonesia Menggunakan Autoregressive Integrated Moving Average (ARIMA) Ramadhani, Adelia; Wahyuningsih, Sri; Siringoringo, Meiliyani
EKSPONENSIAL Vol. 13 No. 2 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Autoregressive Moving Average (ARIMA) is a general model that is often used in time series modeling. One application of ARIMA can be used on the data foreign tourist visits to Indonesia. The tourism sector is one of the priority sectors in Indonesia's economic development. One of the determining factors in the tourism sector is the number of foreign tourist visits. Therefore, forecasting the number of foreign tourist visits is very necessary. The purpose of this study was to obtain a model and forecast results for the number of foreign tourist visits from March 2020 to October 2021 using the ARIMA model. The results of the analysis showed that the ARIMA model (0,1,1) was the best model with a MAPE of 6.23%. The forecasting results with the best model showed that the highest number of foreign tourist visits is in Agustus 2021 and the lowest is in December 2020.

Model-Model Regresi Weibull Univariat pada Indikator Pencemaran Air Dissolved Oxygen di Daerah Aliran Sungai Lingkungan Hutan Hujan Tropis Kalimantan Timur Chairina, Puspa; Suyitno, Suyitno; Siringoringo, Meiliyani
EKSPONENSIAL Vol. 11 No. 1 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

A Univariate Weibull Regression is a model of regression developed from univariate Weibull distribution with the parameter scale is stated in parameter regression. There are some of univariate Weibull regression model, namely Weibull survival regression, Weibull hazard regression and mean model. Univariate Weibull regression model in this research is applied to the water pollution indicator dissolved oxygen (DO data at Mahakam river in East Kalimantan. The purpose of this study is to find out the model of univariate Weibull regression based on the parameter estimation by using maximum likelihood estimation method (MLE) and to find out the factors which affect to univariate Weibull regression in Mahakam river. The result shows that the closed form of the maximum likelihood estimator can not be found analytically, and it can be approximed by using the Newton-Raphson iterative method. Based on the result of partial hypothesis test for all the parameter regression, it was found that detergent concentration and nitrate concentration had significant influence to the DO in the water of Mahakam river.

Analisis Regresi Logistik Multinomial Bayes untuk Pemodelan Minat Peserta Didik MAN 2 Samarinda Tahun Ajaran 2018/2019 Cahyani, Era Tri; Goejantoro, Rito; Siringoringo, Meiliyani
EKSPONENSIAL Vol. 13 No. 1 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Currently, Senior High School and Madrasah Aliyah have implemented student specialization. The specialization includes Natural Science, Social Science and Language. There are several criteria for determining interest in Senior High School and Madrasah Aliyah which include academic scores, student interests and IQ. The multinomial logistic regression model is used to examine these factors because the dependent variable has more than 2 categories. Bayes method is used to estimate the parameters of the multinomial logistic regression. The Bayesian method is a parameter estimation technique that combines the likelihood and prior distribution function. The estimation with the Bayesian method was solved using Markov Chain Monte Carlo simulation (MCMC) with the Gibbs Sampler algorithm. The data used were new students at MAN 2 Samarinda on 2018/2019 with the results of interest namely Natural Science, Social Science and Language. Independent variables were used, namely the score of the Junior High School in subjects Natural Science, Social Science, Language and the rate of National Test. The results of modeling and analysis showed that the factors that significantly influenced were the score of the junior high school in the subject of Natural Science and the rate of National Test. The classification accuracy of the model was 63,10%.

Model Geographically Weighted Weibull Regression Pada Indikator Pencemaran Air COD di Daerah Aliran Sungai Mahakam Kalimantan Timur Primadigna, Ullimaz Sam; Suyitno, Suyitno; Siringoringo, Meiliyani
EKSPONENSIAL Vol. 13 No. 2 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

The Geographically Weighted Weibull Regression (GWWR) model is a Weibull regression model applied to spatial data. Parameter estimation is carried out at each observation location using spatial weighting. This study aimed to determine the GWWR model on the Chemical Oxygen Demand (COD) water pollution indicator data and to obtain the factors that influence COD in the Mahakam watershed. The parameter estimation method was Maximum Likelihood Estimation (MLE). Spatial weighting in parameter estimation has been determined using the adaptive tricube weighting function and the criteria for determining the optimum bandwidth was Generalized Cross-Validation (GCV). The research sample was 20 location points of the Mahakam river determined by the Environmental Department of East Kalimantan Province. The results showed that the factors that influence COD locally was temperature, while the factors that influence globally were temperature, Total Suspended Solids (TSS), and Fecal Coli.

Pemodelan Regresi Weibull Pada Data Kontinu Yang Diklasifikasikan (Studi kasus: Data Indikator Pencemaran Air Dissolved Oxygen Pada DAS Mahakam Kalimantan Timur Tahun 2020) Sudarman, Alfiannur Rizki; Suyitno, Suyitno; Siringoringo, Meiliyani
EKSPONENSIAL Vol. 14 No. 1 (2023)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v14i1.993

Weibull regression model is a Weibull distribution that is directly influenced by covariates. Weibull regression models discussed in this study are Weibull survival regression model, Weibull hazard regression, and Weibull mean regression. The Weibull regression model in this study was applied to water pollution indicator of dissolved oxygen (DO) data in the Mahakam watershed of East Kalimantan in 2020. The purpose of this study was to obtain a Weibull regression model for water pollution indicator of DO data, to obtain the factors that influence the Weibull regression model, and to interpretation the Weibull regression model of water pollution indicator of DO data. The study’s result is that the Newton-Raphson iterative approach was used to find the approximate of maximum likelihood estimator. Based on the hypothesis testing, it is concluded the factors that influence the water pollution indicator of DO data the Mahakam watershed in 2020 are total suspended solid (TSS), total dissolved solid (TDS), nitrate and ammonia.

Peramalan Produksi Kelapa Sawit Menggunakan Metode Pegel’s Exponential Smoothing Sinaga, Yetty Veronica Lestari; Wahyuningsih, Sri; Siringoringo, Meiliyani
EKSPONENSIAL Vol. 12 No. 2 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Time series data analysis using Pegel's exponential smoothing method are an analysis of time series that is influenced by trend and seasonal data patterns. The data used in this study was oil palm production in East Kalimantan Province from January 2014 until December 2018. This study aims to predict oil palm production for January, February, March in 2019. Forecasting results were verified based on the MAPE value and monitoring signal tracking method. The results showed that in the Pegel method, the exponential smoothing model without a multiplicative seasonal trend with a MAPE value of 7.84% had better forecasting accuracy than the other methods. The forecast results of the Pegel's exponential smoothing method without a multiplicative seasonal trend can be used to predict the next 3 periods, namely January, February and March 2019. The forecast results for the next 3 periods have increased in succession.

Peramalan Jumlah Titik Panas Provinsi Kalimantan Timur Menggunakan Analisis Intervensi Fungsi Pulse Saputra, Ahmad Ronaldy; Wahyuningsih, Sri; Siringoringo, Meiliyani
EKSPONENSIAL Vol. 12 No. 1 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Intervention analysis is a time series analysis that used to explain the influence of intervention caused by external and internal factors. As for the number of hotspot in East Borneo which was increased in 2015. The purpose of this study was to determine the best intervention model for forecasting the number of hotspots in East Borneo. In the initial stage of the intervention analysis is to divide the data into 2 parts, namely data before the intervention and data after the intervention occurred. The results of the analysis obtained the best model for the data before the intervention occurred were SARIMA (0,1,1)(0,1,1)12. The next step was identifying the intervention function by observing the residual graph of the SARIMA model and obtained the order b = 0, s = 0 and r = 0 with the AIC value of the intervention model of -143,16. Furthermore, based on the intervention model obtained forecasting results is increased from July to September 2019. The number of hotspots with the highest number of hotspots occurring on September 2019 with 249 hotspots. Then decreasing on October 2019 to 183 hotspots. On November 2019 it dropped significantly to 13 hotspots.

Peramalan Curah Hujan di Kota Samarinda Menggunakan Autoregressive Integrated Moving Average (ARIMA) Syawal, Al Fitri; Wahyuningsih, Sri; Siringoringo, Meiliyani
EKSPONENSIAL Vol. 13 No. 2 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Autoregressive Integrated Moving Average (ARIMA) is a forecasting model for time series data analysis. In this study, the modeling and forecasting of monthly rainfall in Samarinda City was carried out using the ARIMA model. The results showed that the ARIMA (6, 1, 1) model was the best model . The results of forecasting rainfall for the period January to December 2022 in Samarinda City using the ARIMA (6, 1, 1) model show that rainfall tends to be constant every month. The lowest level of rainfall occurred in January 2022, which was 210.3869 mm. The highest level of rainfall occurred in April 2022, which was 271.5705 mm.

Upaya Pencegahan Pencemaran Air Sungai Mahakam melalui Pemodelan Geographically Weighted Logistic Regression pada Data BOD Inayah, Ulfah Resti; Suyitno, Suyitno; Siringoringo, Meiliyani
EKSPONENSIAL Vol. 12 No. 1 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Since the early years, Mahakam River has important roles in supporting human needs in East Kalimantan province. Activities around Mahakam watershed such as restaurants, fishery, and industries were in the potential of generating waste around the flow area. The waste consisted of domestic and nondomestic waste. The waste was a threat to the Mahakam River water quality. Water pollution around the Mahakam River was a threat to public health, and therefore, there’s a need for precaution. One of the precautions is to give the public information regarding the factors that influence the chances of polluted water in the Mahakam River increased through logistic regression modeling. One way to detect water pollution is to indicate by using Biochemical Oxygen Demand (BOD). BOD data was suspected spatial, therefore the appropriate statistical modeling is Geographically Weighted Logistic Regression (GWLR). GWLR is a regression model that developed from a logistic regression in which parameter estimation is done locally at every observation location. The purpose of the research is to determine the GWLR model on the BOD data of Mahakam River and to find out the factors that influence water pollution at 27 observation points along with the Mahakam River flow. The parameter estimation method is the Maximum Likelihood Estimation (MLE). The spatial weighting is calculated by using the Adaptive Bisquare weighting function and the optimum bandwidth is determined by using Generalized Cross-Validation (GCV) criteria. Research shows that the closed-form of the Maximum Likelihood estimator can’t be obtained analytically and the approximation is obtained by using Newton-Raphson (N-R) iterative method. Based on parameter testing of the GWLR model result, it was concluded that the factors were influences the probability of Mahakam River water were polluted based on the BOD indicator was locally and different in each 27 observation locations. The factors that influence locally were water temperature, acidity, Total Dissolved Solids (TDS), ammonia concentration, and water debit, meanwhile, the factors which influence globally were acidity and TDS.

The Weibull Regression Model Analysis of Mahakam River Water Pollution Potential Pradipa, Zalva; Suyitno, Suyitno; Siringoringo, Meiliyani
Jurnal Varian Vol 8 No 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i1.3699

Mahakam River has a vital role in the lives of the people of the East Kalimantan province, includingproviding a raw source of clean water. The multi-activity of the Mahakam River watershed, as a watertraffic lane, mining, fisheries, hotels, restaurants, and resident houses, has the potential to produce wasteinto the water. Increasing waste in the water flow can increase the pollution potential of river water,threatening people’s health. Therefore, precaution is necessary. In this research, statistical preventionwas proposed, providing information to the East Kalimantan people regarding the factors affecting thepollution potential of the Mahakam River through Weibull regression (WR) modeling on dissolved oxygen (DO) data 2022. Research data was secondary data provided by the Life Environmental Departmentof East Kalimantan province. The WR model is a Weibull distribution that is directly influenced by covariates. WR model consists of Weibull survival regression, cumulative distribution regression, hazardregression, and Weibull mean regression. This research aims to obtain the factors affecting the pollution potential and to provide the pollution potential information of Mahakam River 2022. The researchconcluded that factors influencing the pollution potential of the Mahakam River were watercolor degreeand nitrate concentration. Applying the WR model to DO data 2022 was able to provide the pollutionpotential information of Mahakam River, namely the probability of river water isn’t polluted is 0.6555,or the probability of the polluted river water is 0.3445, the pollution rate is 6 locations are polluted forevery 10 mg/L DO, and the DO average of river water is 5.7450 mg/L. Increasing water color degreeand nitrate concentration will decrease the probability of the Mahakam River being polluted, increasethe probability of the Mahakam River being polluted, increase the pollution rate, and reduce the DO ofMahakam River water.

Co-Authors Alfian Hidayat Andini, Putri Dwiayu Aulia Arumsari, Melisa Astafira, Ilyas Cahyani, Era Tri Chairina, Puspa Dani, Andrea Tri Rian Darnah Fauziyah, Meirinda Goenjatoro, Rito Ika Purnamasari Inayah, Ulfah Resti Kesuma, Ahmad Rizky Krisna Rendi Awalludin Lestari, Vivi Dwi M. Fathurahman Mahmuda, Siti Memi Nor Hayati Messakh, Gerald Claudio Mochammad Imron Awalludin Nida, Khairun Octavianto, Thifan Oroh, Chiko Zet Pradipa, Zalva Pratiwi, Nadya Primadigna, Ullimaz Sam Rahmah, Siti Mahmudatur Ramadhani, Adelia Rito Goejantoro, Rito Saputra, Ahmad Ronaldy Sari, Hafivah Rosvita Sergio, Andrean Sinaga, Yetty Veronica Lestari Sri Wahyuningsih Sudarman, Alfiannur Rizki Surya Prangga Suyitno Suyitno Suyono, Ari Krisna Syawal, Al Fitri Wahyuni, Risa Kristia Yani, Tika Anggre Ria Yuniarti, Desi Yustiani, Iis

Title

Found 28 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 28 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 28 Documents
Search