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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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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 Regresi Nonparametrik Spline Truncated Pada Indeks Pembangunan Manusia di Indonesia Ramadhan, M. Rizky; Darnah, Darnah; Wahyuningsih, Sri
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.v14i2.1090

Abstract

Truncated spline nonparametric regression is a nonparametric regression analysis using a segmented polynomial model. This segmented nature provides flexibility so that the regression model can adapt more effectively to the local characteristics of the data. The purpose of this study was to obtain a regression model and determine the factors that influence the Human Development Index (HDI) in all provinces in Indonesia using multivariable truncated spline nonparametric regression. The Human Development Index is an important indicator in measuring success in efforts to build the quality of human life. The Human Development Index can determine the rank or level of development of a region and a country. In development, a high Human Development Index is something that is expected to be achieved, especially for developing countries. The Human Development Index data used in this study is based on BPS data published in 2020 from all provinces in Indonesia. In this study, based on the results of the analysis, the best nonparametric truncated spline regression was obtained using 1 knot point, 2 knot point and 3 knot point. Based on the minimum Generalized Cross Validation (GCV) value, the best truncated spline regression model is 3 knots with an R2 value of 83.70%. The factors that influence the Human Development Index are the variables expected length of schooling, life expectancy at birth, and population
Penerapan Metode Geographically Weighted Logistic Regression Untuk Memodelkan Pencemaran Air Sungai Mahakam Berdasarkan Data Dissolved Oxygen Widyaningsih, Wiwit; Suyitno, Suyitno; A'yun, Qonita Qurrota
EKSPONENSIAL Vol. 15 No. 2 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i2.1365

Abstract

The Geographically Weighted Logistic Regression (GWLR) model is a local model of logistic regression applied to spatial heterogenity data. Parameter estimation of the GWLR model is conducted at each observation location using spatial weighting. The aim of this research is to obtain the GWLR model on the Dissolved Oxygen (DO) data of Mahakam River in 2022, and to identify the factors affecting the probability of Mahakam River water is polluted. The research data is secondary data obtained from Environmental Department of East Kalimantan Province. Spatial weight is calculated using the adaptive bisquare weighting function, and the optimal bandwidth is determined using the Generalized Cross Validation (GCV) criterion. Parameter estimation method is Maximum Likelihood Estimation (MLE), and Maximum Likelihood (ML) estimator was obtained using the iterative Newton-Raphson method. Based on the result of the GWLR model parameter testing, it was concluded that locally influential factors on the probability of Mahakam River water pollution are nitrate concentration and iron concentration, and globally influential factor is nitrate concentration.
Penerapan Regresi Linear Pada Minat Berwirausaha Mahasiswa Telkom University Surabaya Nafi’atus Sa’idah, Rizqy Athiyya; Zahra, Aisyah Nabila; Siahaan, Fitri Rayani; Hidayati, Sri
EKSPONENSIAL Vol. 15 No. 2 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i2.1199

Abstract

Entrepreneurship is an effort to reduce the problem of unemployment. To build interest in entrepreneurship, it is necessary to analyze factors related to entrepreneurship. In this study, respondents were taken from students of Telkom Institute of Technology Surabaya Information Systems and Digital Business Study Program with factors taken from income expectations, family environment, and entrepreneurship education. This research has important implications in equipping students with entrepreneurial skills and increasing their interest in entrepreneurship. Thus, the results of this study provide a foundation for the development of programs and policies that support the development of entrepreneurship among students. The method used in this study is linear regression with the results showing that income expectation factors, family environment and entrepreneurship education have a positive influence on student entrepreneurial interest both partially and simultaneously
Prediksi Ketepatan Klasifikasi Status Predikat Lulusan Program Sarjana FMIPA Universitas Mulawarman Menggunakan Regresi Logistik Biner dan Neural Networks Khasanah, Lisa Dwi Nurul; Fathurahman, M.; Hayati, Memi Nor
EKSPONENSIAL Vol. 15 No. 2 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i2.1301

Abstract

Classification is a learning technique for identifying categorical groups from a data set whose group member categories are known. Several methods that can be used in classification include binary logistic regression and neural networks. This research aims to compare the prediction results for the accuracy of the classification of predicate status for graduates of the FMIPA Mulawarman University undergraduate program in 2021. In the binary logistic regression method, the model parameters are estimated using the maximum likelihood estimation and Fisher scoring iteration methods. The neural networks used the backpropagation algorithm. The results of the research show that the classification accuracy using the confusion matrix obtained with binary logistic regression and neural networks is the same, namely 87.5%.
Penerapan Automatic Clustering pada Fuzzy Time series pada Data Wisatawan Mancanegara Kalimantan Timur Juliartha, Made Angga; Purnamasari, Ika; Goejantoro, Rito
EKSPONENSIAL Vol. 15 No. 2 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i2.1326

Abstract

Tourism played a significant role in national foreign exchange earnings and Regional Original Revenue (PAD), therefore accurate statistical analysis was needed as a preventive measure. Forecasting was one of the accurate statistical analyses that could assist the government in determining more effective policies in the future. The method used was the Automatic Clustering Fuzzy Logical Relationship (ACFLR) for time series data. Automatic Clustering was used to determine the length of data intervals, while the Fuzzy Logical Relationship was used to obtain forecasting results. The research objective was to forecast the number of foreign tourist visits for the next period using data on the number of foreign tourist visits to East Kalimantan from January 2023 to March 2024. The accuracy of the forecast was measured using the Mean Absolute Percentage Error (MAPE). The research findings indicated that the forecast for April 2024 was 261 visits with a MAPE value of 7.72%, indicating a very good level of accuracy. The conclusion of this research showed that the ACFLR method was effective in forecasting the number of foreign tourists, thus it could be used as a decision-making tool by local governments.
Clustering Titik Panas Bumi Pada Potensi Kebakaran Hutan Menggunakan K-Affinity Propagation Primantoro, Sudhan; Goejantoro, Rito; Prangga, Surya
EKSPONENSIAL Vol. 15 No. 2 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i2.1299

Abstract

K-Affinity Propagation is a development of affinity propagation from Brendan J. Frey and Delbert Dueck. The purpose of this research is to cluster geothermal hotspots on potential forest fires in Indonesia using K-Affinity Propagation for the period July 2022 and obtain optimal cluster results using standard deviation with ratio calculations. The optimal cluster results are 4 clusters, with the number of members in cluster 1 being 12 members with copies in West Sumatera Province, the number of members in cluster 2 being 12 members with copies in Southeast Sulawesi Province, the number of members in cluster 3 being 4 members with copies in Central Sulawesi Province, the number of members in cluster 4 being 1 member with copies in North Sulawesi Province. The optimal cluster results using standard deviation with the smallest ratio value is cluster 4 with a ratio value of 0.057.
Penerapan Algoritma Divisive Analysis dalam Pengelompokan Provinsi di Indonesia Berdasarkan Prevalensi Stunting Suyono, Ari Krisna; Hayati, Memi Nor; Siringoringo, Meiliyani; Prangga, Surya; Fathurahman, M.
EKSPONENSIAL Vol. 15 No. 2 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i2.1341

Abstract

Cluster analysis is an analysis that aims to group data (objects) based only on the information contained in the data that describes objects and the relationships between the objects. Divisive analysis is a clustering method using a top-down approach which starts by placing all objects into one cluster or what is called a hierarchical root and then dividing the cluster root into several smaller clusters. This research aimed to group 34 provinces in Indonesia into 2,3, and 4 clusters based on stunting prevalence data and factors causing stunting in 2022 using a divisive analysis algorithm. The results showed that for 2 clusters, cluster 1 consisted of 32 provinces with low stunting prevalence, and cluster 2 consisted of 2 provinces with high stunting prevalence. For 3 clusters, cluster 1 consisted of 26 provinces with moderate stunting prevalence, cluster 2 consisted of 6 provinces with low stunting prevalence, and cluster 3 consisted of 2 provinces with high stunting prevalence. For 4 clusters, cluster 1 consisted of 21 provinces with moderate stunting prevalence, cluster 2 consisted of 5 provinces with low stunting prevalence, cluster 3 consisted of 6 provinces with high stunting prevalence, and cluster 4 consisted of 2 provinces with very high stunting prevalence.
Selection of Optimum Exponential Smoothing Parameters with Golden Section to Forecast Rainfall in East Kutai Regency Sa’diyah, Lita Vindiyatus; Wahyuningsih, Sri; Hayati, Memi Nor
EKSPONENSIAL Vol. 15 No. 2 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i2.1269

Abstract

The exponential smoothing method is one method that can be used to forecast time series data by smoothing the data. In this research, the method used is exponential smoothing with one smoothing parameter from Brown. The data used is the amount of rainfall in East Kutai for the period January 2017 to December 2021. The purpose of this study was to obtain the optimum parameter value of the exponential smoothing method using the golden section method to obtain MAPE values and obtain forecasting results for the amount of rainfall in East Kutai Regency for the period January to March 2022. From the results of the analysis, smoothing parameters was obtained optimum in Double Exponential Smoothing (DES) of 0.3924052 and Triple Exponential Smoothing (TES) of 0.1995108. The results showed that forecasting the amount of rainfall with the DES method had a MAPE of 37.9061200% and the TES method had a MAPE of 39.4323800%. The DES method is a better method than the TES method to forecast the amount of rainfall in East Kutai Regency.
Pengelompokan Kabupaten/Kota di Kalimantan Berdasarkan Indikator Pendidikan Menggunakan Metode K-Means dengan Optimasi Principal Component Analysis Putri, Nurlia Sucianti; Hayati, Memi Nor; Goejantoro, Rito
EKSPONENSIAL Vol. 15 No. 2 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i2.1373

Abstract

Cluster analysis is used to group several objects based on similarities within the group. There are many methods included in cluster analysis, including k-means. K-means is a non-hierarchical cluster analysis method. The assumption that needs to be considered in cluster analysis is that there is no strong correlation between research variables. An alternative that can be done to deal with variables that are strongly correlated is to use Principal Component Analysis (PCA). This research aims to group districts/cities in Kalimantan based on education indicators in 2022 using k-means with PCA optimization, as well as finding out the optimal cluster based on the smallest Davies Bouldin Index (DBI) value. Based on the results of the analysis, from 11 research variables two main components were formed. From these two main components, new data transformations are produced which are then used in grouping districts/cities in Kalimantan based on education indicators using the k-means methods. The analysis results, it was found that the optimal cluster with k-means grouping was 5 clusters with a DBI value of 0.835. Cluster 1 has 8 regencies/cities, cluster 2 has 16 regencies/cities, cluster 6 has 5 regencies/cities, cluster 4 has 21 regencies/cities, and cluster 5 has 5 regencies/cities.
Analisis Regresi Linier Berganda Dalam Estimasi Indeks Pembangunan Manusia di Indonesia Khotimah, Ariska Khusnul; Rahman, Athaya Azahra; Alam, Muhammad Zainul; Adawiyah, Rabiatul; Nur, Yumi Handayani; Aufi, Tresna Restu; Sifriyani, Sifriyani
EKSPONENSIAL Vol. 15 No. 2 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i2.1318

Abstract

The human development index has an important role in determining and measuring achievements in developing the quality of life and development ranking of a country. By increasing the level of the part forming the human development index, it will greatly influence various aspects in terms of health, longevity, quality of life, and improving the quality of human resources. Therefore, this research aims to determine the influence of the percentage of young people who have never attended school, the percentage of the population with higher education, the minimum wage, the percentage of young people who are married under age, the average per capita food expenditure, the number of people receiving 4G LTE signals, and the level of open unemployment on the index. Human Development . This research uses a Multiple Linear Regression analysis method which can be used to look for patterns of relationship between one response variable and only one predictor variable. The data in this study is secondary data obtained from the Central Agency covering 34 provinces in Indonesia in 2023. In the test results using multiple linear regression, a p-value coefficient of determination was obtained of 0,8073, indicating that there was 80,73% variation what occurs in the Human Development Index is caused by the variables Percentage of Youth Never Attending School, Percentage of Population Having Higher Education, Minimum Wage, Percentage of Youth Married Under Age, Average Per Capita Food Expenditure, Number of Receive 4G LTE Signals, and Open Unemployment Rate. This indicates that there are around 19,27% other variables that influence the Human Development Index.

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