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Meiliyani Siringoringo
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Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Mulawarman Jl. Barong Tongkok, Kampus Gunung Kelua Kota Samarinda, Provinsi Kalimantan Timur 75123
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INDONESIA
Eksponensial
Published by Universitas Mulawarman
ISSN : 20857829     EISSN : 27983455     DOI : https://doi.org/10.30872/
Core Subject : Economy, Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other
Jurnal Eksponensial is a scientific journal that publishes articles of statistics and its application. This journal This journal is intended for researchers and readers who are interested of statistics and its applications.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 205 Documents
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Model Geographically Weighted Poisson Regression (GWPR) dengan Fungsi Pembobot Adaptive Gaussian: (Studi Kasus : Angka Kematian Ibu (AKI) di 24 Kab/Kota Kalimantan Timur dan Kalimantan Barat Tahun 2017) Ridhawati, Ridhawati; Suyitno, Suyitno; Wasono, Wasono
EKSPONENSIAL Vol. 12 No. 2 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v12i2.807

Abstract

The Geographically Weighted Poisson Regression (GWPR) Model is a regression model developed from Poisson regression or a local form of Poisson regression. The GWPR model generates a local model parameter estimator at each observation location where the data is collected and assumes the data is Poisson distributed. The estimation of GWPR model parameters uses the Adaptive Gaussian weighting function by determining the optimum bandwidth using GCV criteria. Based on the GWPR model, it is found that the factors that influence the maternal mortality rate (MMR) data in 24 districts (cities) of East Kalimantan and West Kalimantan are the percentage of pregnant women receiving Fe3 tablets, pregnant women with obstetric complications and the number of hospitals. These three variables produce four groups of GWPR model. Based on the GCV value, it is obtained that the best model is the GWPR model because it has the smallest GCV value.
Analisis Spasial Persebaran Jumlah Kasus Malaria di Kalimantan Timur Menggunakan Indeks Moran dan Local Indicator Spatial of Autocorrelation Hadisti, Zahrah Dhafina; Hayati, Memi Nor; Fauziyah, Meirinda
EKSPONENSIAL Vol. 15 No. 1 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i1.1232

Abstract

Spatial analysis is an analysis that considers the location and distance of an object in the research data. Moran’s index is one of the spatial methods used to analyze spatial autocorrelation globally. Furthermore, there is the Local Indicator of Spatial Autocorrelation (LISA) method which is used to analyze spatial autocorrelation locally. This study aims to determine whether there is spatial autocorrelation and determine the distribution pattern formed in the data on the average number of malaria cases in East Kalimantan based on the regency/city during 2018-2022. The results showed that based on the Moran index globally, there was no spatial autocorrelation in the average number of malaria cases in East Kalimantan in 2018-2022. The type of spatial pattern in the distribution of malaria cases in East Kalimantan is a clustering pattern indicated by the clustering of malaria cases in each district/city in East Kalimantan. Furthermore, the results of spatial autocorrelation using LISA show that locally there is spatial autocorrelation in several districts/cities in East Kalimantan, namely Paser, Kutai Timur, Kutai Barat and Penajam Paser Utara.
Klasifikasi Status Hipertensi Pasien UPTD Puskesmas Sempaja, Kota Samarinda Menggunakan Metode K-Nearest Neighbor Soraya, Raihana; Hayati, Memi Nor; Goejantoro, Rito
EKSPONENSIAL Vol. 14 No. 2 (2023)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v14i2.1009

Abstract

Data mining is a method of selecting, exploring and modeling large amount of data to find knowledge and clear patterns or interesting relation of the data and useful in the process of data analysis. In data mining there are several techniques that have different function and one of them is classification tehcnique. The classification process itself is the process of finding patterns or differences between classes or data that can be used to predict object classes whose class labels are unknown. K-nearest neighbor (K-NN) is one of the methods in classification algorithm. This study discusses the classification using K-NN algorithm which is applied to the data hypertension status. The aim is to find out the optimal neighborliness value (K) accuracy value and the best propotion of the data hypertension status. The data used is the data of patients UPTD health center Sempaja, Samarinda city from February to May 2022 with dependent variabel is hypertension status and uses 4 independent variables, age, gender, diabetes mellitus and heart disease. Based on the research that has been done, obtained an accuracy value of 62,60% with K = 5 in the best proportion of the data is 70%:30%.
Klasifikasi Tingkat Keparahan Korban Kecelakaan Lalu Lintas Di Kota Samarinda Menggunakan Algoritma K-Nearest Neighbor dan Naive Bayes Salsabila, Nabila Abda
EKSPONENSIAL Vol. 14 No. 2 (2023)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v14i2.1085

Abstract

Classification is the process of evaluating data objects to be included in a particular class from a number of available classes. The K-Nearest Neighbor algorithm is one of the algorithms used to classify an object against a new object based on its K nearest neighbors. Naive Bayes is a classification of data using probability based on the Bayes theorem with strong independence assumptions. This study aims to compare the accuracy of the classification results on traffic accident victim data in Samarinda City using the K-Nearest Neighbor algorithm and the Naive Bayes algorithm. The data used is data on the severity of traffic accident victims in Samarinda City from 2020 to 2021 with death and non-death classes and uses 6 independent variables, namely age, gender, victim's role, victim's vehicle, road status, and condition weather. The measurement of accuracy in classifying the K-Nearest Neighbor algorithm and the Naive Bayes algorithm uses a classification performance matrix. Based on the results of the study, the accuracy of the classification results of the K-Nearest Neighbor algorithm was obtained at 75.86%, while the Naive Bayes algorithm obtained an accuracy rate of 79.31%. From the results of this analysis, it can be concluded that the Naive Bayes algorithm works better than the K-Nearest Neighbor algorithm in classifying the severity of traffic accident victims in Samarinda City.
Pengelompokan Kabupaten dan Kota di Jawa Timur berdasarkan Percepatan Pemulihan Ekonomi Menggunakan Pendekatan Hirearki Mahadesyawardani, Arinda; Zhafirab, Azizah Atsariyyah; Ariyawan, Jovansha; Humaira, Edla Putri; Mardianto, M. Fariz Fadillah; Amelia, Dita; Ana, Elly
EKSPONENSIAL Vol. 15 No. 1 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i1.1273

Abstract

The Covid-19 pandemic's diverse impact on Indonesia's economy, particularly in East Java, spurred the government to formulate a comprehensive work plan targeting three key objectives, one of which is to expedite economic recovery. This plan focuses on three key indicators: economic growth, open unemployment rate (TPT), and the gini ratio. It is known that during the pandemic, East Java initially experienced economic growth that contracted until eventually showing positive growth in the second quarter of 2021, which has been supported by national policies. This study explores district and city classification in East Java based on economic recovery indicators through hierarchical clustering. The analysis identifies Ward's linkage as the most effective model, with a cophenetic correlation coefficient of 0.9311. Internal clustering validation tests reveal two optimal clusters. Cluster 1 is characterized by a notably high average acceleration of economic recovery across all three indicators. The findings suggest that the government should optimize the economic stimulus program for cluster 2 and focus on enhancing income redistribution and job opportunities for cluster 1.
Peramalan Harga Minyak Goreng di Kalimantan Timur Menggunakan Model Hybrid Time Series Regression Quadratic – Neural Network Wahyuni, Risa Kristia; Wahyuningsih, Sri; Siringoringo, Meiliyani
EKSPONENSIAL Vol. 14 No. 2 (2023)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v14i2.1123

Abstract

A hybrid model is a combination of two or more forecasting methods. One of hybrid model that can be used in forecasting is Time Series Regression (TSR) Quadratic – Neural Network (NN). TSR Quadratic can be used in time series data that contains quadratic trend patterns, namely an increase or decrease that forms a curved or parabolic line NN is a method that has characteristics similar to biological neural networks in conducting data pattern recognition. This study was aimed to obtain a hybrid model of TSR quadratic-NN to forecast cooking oil prices in East Kalimantan and obtain forecasting results based on the best model. The results showed that the TSR Quadratic-NN hybrid model with 3 neurons in the hidden layer was the best model with a MAPE of 2.51368%. The forecasting results based on this model showed that cooking oil prices in East Kalimantan from January to December 2023 showed an increase
Indonesia Gold Price Forecasting Using ARIMA Model (0,1,1) - GARCH (1,0) Sari, Hafivah Rosvita; Wahyuningsih, Sri; Siringoringo, Meiliyani
EKSPONENSIAL Vol. 15 No. 1 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i1.1265

Abstract

A frequently employed time series model is the Autoregressive Integrated Moving Average (ARIMA) model. In highly volatile data, ARIMA models sometimes produce residual variances that are heteroscedasticity. One method that can overcome the problem of residual variance heteroscedasticity is the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) method. The purpose of this study is to obtain the ARIMA-GARCH model for daily gold price data in Indonesia for the period 1 January 2022 to 31 December 2022, and to obtain daily gold price forecasting results in Indonesia. The daily gold price forecasting model obtained for Indonesia is ARIMA (0,1,1) - GARCH (1,0) with a MAPE value of 0.5745% which shows that the model is very good because the MAPE value is less than 10%. The results of Indonesia's daily gold price forecast from January 1st, 2023 to January 3rd, 2023 remain stable.
Pemodelan Jumlah Kasus Tuberkulosis Paru di Indonesia dengan Geographically Weighted Negative Binomial Regression Putri, Ditha Reginna; Fathurahman, M.; Suyitno, Suyitno
EKSPONENSIAL Vol. 15 No. 1 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i1.1303

Abstract

Geographically Weighted Negative Binomial Regression (GWNBR) model is the development of Negative Binomial Regression (NBR) model applied to spatial data. The parameter estimation of GWNBR model is performed at each observation location using spatial weighting. The purpose of this study is to determine the GWNBR model of the number of pulmonary tuberculosis cases in Indonesia in 2021 and determine the factors that influence pulmonary tuberculosis cases in Indonesia in 2021. The research data are secondary data obtained from the Indonesian Ministry of Health and Indonesian Central Agency on Statistics. Parameter estimation method is Maximum Likelihood Estimation (MLE). Spatial weighting is calculated by using the Adaptive bi-square weighting function and the optimum bandwidth is determined by using the Cross-Validation (CV). The research results showed that the exact Maximum Likelihood (ML) estimator could not be obtained analytically and the approximation of ML estimator was obtained by using the Newton-Raphson iterative method. Based on the results of the parameter testing of GWNBR model, it was concluded that the factors affecting the number of tuberculosis cases were local and varied in 34 provinces. The factor affecting locally are population density, the percentage of districts/cities implementing GERMAS, and number of hospitals.
Penerapan Algoritma K-Medoids pada Pengelompokan Wilayah Provinsi di Indonesia Berdasarkan Indikator Pendidikan Septian, Rama; Darnah, Darnah
EKSPONENSIAL Vol. 14 No. 2 (2023)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v14i2.1150

Abstract

Cluster analysis has the goal of grouping data that has the same characteristics into the same cluster and data that has different properties will enter into different clusters. K-Medoids is a grouping method using a representative object as the center point (medoids). The k-medoids method was developed to overcome the weakness of the k-means method which is sensitive to outliers because an object with a large value allows it to deviate from the data distribution in size. After grouping using k-medoids, the results of the grouping were validated. The cluster validation method using the Silhoutte Coefficient (SC) is a method that can be used to see the quality and strength of clusters that combine cohesion and separation methods. This study aims to obtain the optimal cluster from the largest SC value and determine the grouping results of the optimal clusters that are formed. This grouping method is applied to data on education indicators in Indonesia in 2020. Based on the results of the analysis, it is found that the optimal cluster is 2 clusters with a SC value of 0.464, where cluster 1 has 14 provinces and cluster 2 has 20 provinces. Keywords: K-Medoids, Silhoutte Coefficient, Educational Indicators
Klasterisasi Kabupaten/Kota di Provinsi Papua Berdasarkan Indikator Pembangunan Manusia Tahun 2022 Karim, Abdul; Hanifatuzzuhra, Hanifatuzzuhra; Asteria, Monika
EKSPONENSIAL Vol. 15 No. 1 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i1.1267

Abstract

The Human Development Index (HDI) is a human development metric empowered by several main elements that reflect the quality of life, namely HLS, RLS, PKK, and UHH. High HDI is one of the basic capital for a country's sustainable development. The improvement of human development in Indonesia continues to increase every year. However, there is still uneven development between regions in Indonesia. The HDI of Papua Province has always been the lowest in Indonesia in the last 10 years and each indicator is still unevenly distributed between districts/cities. This needs to be a special concern by the government. Thus, this study was carried out with the aim of grouping districts / cities in Papua Province based on human development indicators with the application of the agglomerative hierarchy cluster method. Based on the highest correlation value, Average Linkage is the best method with the optimal number of clusters formed is five clusters (Very High, High, Medium, Low, and Very Low) according to the Calinski-Harabasz Index test conducted. The findings of this study can be the basis for designing equitable development policies in districts/cities in Papua Province.

Page 18 of 21 | Total Record : 205

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