cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Meiliyani Siringoringo
Contact Email
meiliyanisiringoringo@fmipa.unmul.ac.id
Phone
+6285250326564
Journal Mail Official
eksponensial@fmipa.unmul.ac.id
Editorial Address
Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Mulawarman Jl. Barong Tongkok, Kampus Gunung Kelua Kota Samarinda, Provinsi Kalimantan Timur 75123
Location
Kota samarinda,
Kalimantan timur
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 6 Documents
 1 
Search results for , issue "Vol. 14 No. 2 (2023)" : 6 Documents clear
Penerapan Metode K-Means Dalam Pengelompokan Kabupaten/Kota Di Kalimantan Berdasarkan Indikator Pendidikan Messakh, Gerald Claudio; Hayati, Memi Nor; Sifriyani, Sifriyani
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.v14i2.1103

Abstract

Cluster analysis is an analysis that aims to classify data based on the similarity of spesific characteristics. Based on the structure, cluster analysis is divided into two, namely hierarchical and non-hierarchical methods. One of the non-hierarchical methods used in this study is K-Means. K-Means is a partition-based non-hierarchical data grouping method. This purpose of this study is to obtain the best results of grouping regencies/cities on the island of Kalimantan based on education indicators using the K-Means method based on the smallest ratio of standard deviation. Based on the results of the analysis, it can be concluded that the best grouping results based on the smallest ratio of standard deviation is 0.6052 which produces optimal clusters of 2 clusters with the first cluster consisting of 14 Regencies/Cities while the second cluster consists of 42 Regencies/Cities on Kalimantan Island
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.
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
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
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

Page 1 of 1 | Total Record : 6

 1 

Filter by Year

2023 2023


Reset
Filter By Issues
All Issue Vol. 16 No. 2 (2025): Jurnal Eksponensial Vol. 16 No. 1 (2025): Jurnal Eksponensial Vol. 15 No. 2 (2024): Jurnal Eksponensial Vol. 15 No. 1 (2024): Jurnal Eksponensial Vol. 14 No. 2 (2023) Vol. 14 No. 1 (2023) Vol 14 No 1 (2023): Jurnal Eksponensial Vol. 13 No. 2 (2022) Vol. 13 No. 1 (2022) Vol. 12 No. 2 (2021) Vol. 12 No. 1 (2021) Vol. 11 No. 2 (2020) Vol. 11 No. 1 (2020) Vol 10 No 2 (2019) Vol 10 No 2 (2019): Jurnal Eksponensial Vol 10 No 1 (2019) Vol 9 No 2 (2018) Vol 9 No 1 (2018) Vol 8 No 2 (2017) Vol 8 No 1 (2017) Vol 7 No 2 (2016) Vol 7 No 1 (2016) More Issue