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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 211 Documents
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Prediksi Curah Hujan di Kabupaten Berau Menggunakan Support Vector Regression Patiallo, Monalisa Randang; Fathurahman, M.; Prangga, Surya; Nadhilah Widyaningrum, Erlyne
EKSPONENSIAL Vol. 16 No. 2 (2025): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/v16i2.1508

Abstract

Machine learning is an analytical approach that is able to predict the output of a system based on patterns that have been formed from previous data. One of the machine learning methods used in this research is Support Vector Regression (SVR). SVR is the application of the support vector machine method in the case of regression. The concept of the SVR algorithm is to obtain a function with the minimum error rate so as to produce a good predictive value. The advantage of SVR lies in its ability to handle nonlinear data using the kernel functions. This study aims to determine the results of rainfall prediction in Berau Regency using the SVR method. The data used is rainfall data in Berau Regency from January 2014 to December 2023 as much as 120 data, and uses five predictor variables namely temperature, humidity, air pressure, wind speed, and solar irradiation. The kernel function used is a polynomial kernel with parameter values  and . The results showed that the best SVR model to predict rainfall in Berau Regency is the SVR model with parameter values  and . This model provides good prediction performance, with an RMSE value of 0,1786.
Pengelompokan Provinsi di Indonesia berdasarkan Ketimpangan Akses Layanan Kesehatan Tahun 2024 Menggunakan Pendekatan Cluster Hirarki Nabila Rahma Na’ifa, Ariza; Rohayah, Dewi; Yuliati, Intan; Tsabita Amalia Shofa, Nayla; Pusporani, Elly; Amelia, Dita
EKSPONENSIAL Vol. 16 No. 2 (2025): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/v16i2.1560

Abstract

Health disparities remain a major challenge in Indonesia, particularly in terms of access to healthcare services across provinces. This study aims to classify 38 Indonesian provinces based on inequality in healthcare access in 2024 using a hierarchical clustering approach. Three key indicators were used: the number of hospitals, the number of medical personnel, and the percentage of people experiencing health complaints who opted for self-medication. The analysis identified the average linkage method as the most suitable model, supported by the highest cophenetic correlation coefficient (0,911). The results revealed two distinct clusters. The first cluster includes most provinces outside Java Island, characterized by limited healthcare infrastructure and personnel. The second cluster comprises four provinces on Java Island with advanced healthcare facilities but a high rate of self-medication. These findings suggest that healthcare access inequality is influenced not only by infrastructure but also by social and behavioral factors. Therefore, policy recommendations should be tailored accordingly: infrastructure improvement and equitable distribution of medical personnel for the first cluster, and health education interventions for the second. This study contributes to evidence-based policy design in line with the Sustainable Development Goals (SDGs), particularly the goal of ensuring equitable healthcare access for all.
Pengelompokan Kabupaten/Kota di Pulau Kalimantan Berdasarkan Indeks kemahalan Konstruksi Tahun 2020-2024  Menggunakan Algoritma Spatio Temporal-DBSCAN Irfan, Muh.; Suyitno, Suyitno; Fathurahman, M.
EKSPONENSIAL Vol. 16 No. 2 (2025): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/76nh3g16

Abstract

The Construction Cost Index (CCI) is an indicator that describes the level of cost of construction in a region compared to the national average. The CCI between districts/cities in Kalimantan Island in 2024 still shows a considerable difference. To understand the pattern and similarity of CCI values between districts/cities, a clustering approach is needed. Clustering is a data analysis technique to group data based on similarity. The clustering algorithm used in this research is the Spatio Temporal Density Based on Spatial Clustering of Application with Noise (Spatio Temporal-DBSCAN) algorithm which forms clusters based on density in spatial and temporal aspects simultaneously. The purpose of this study is to obtain the optimal cluster in clustering districts/cities on Kalimantan Island based on spatial aspects (longitude and latitude data) and temporal aspects (IKK value from 2020-2024) based on the Silhouette Coefficient (SC) value of the Eps and MinPts combinations that were tried. Based on the clustering results, 2 clusters and also noise were obtained from the combination of Eps1=2, Eps2=13 and MinPts=8 with an SC value of 0.3179 which means that the optimal cluster formed has a weak structure.
Pengaruh Harga Pelayanan Dan Keselamatan Terhadap Tingkat Kepuasan Mahasiswa Dalam Menggunakan Ojek Online Aulia, Nabila; Afif Nurdiansyah, Mochamad; Shalihatunnisa, Shalihatunnisa; Christian, Diego; Angeline Seru, Indra; Sifriyani, Sifriyani; Wanti Wulan Sari, Nariza; Yuniarti, Desi; Atarezcha Pangruruk, Thesya; Nadhilah Widyaningrum, Erlyne
EKSPONENSIAL Vol. 16 No. 2 (2025): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/fp39ng21

Abstract

This study aims to analyze the influence of price, service, and safety on the level of satisfaction of FMIPA students at Mulawarman University in using online motorcycle taxi services (Gojek). This study uses a quantitative approach with the Structural Equation Modeling (SEM) method based on Partial Least Square (PLS). A sample of 100 students was obtained through accidental sampling. The variables studied include price, service, safety, and customer satisfaction, each measured by several indicators. The analysis results indicate that the three independent variables (price, service, and safety) have a positive and significant effect on student satisfaction, with safety being the most dominant factor. The R-square value of 0.669 indicates that the model explains 66.9% of the variability in student satisfaction. Validity and reliability tests show that all constructs meet the model's validity criteria. This study suggests that online ride-hailing service providers should prioritize safety aspects, accompanied by improvements in service quality and reasonable price adjustments to enhance user satisfaction.
Penerapan Metode SEM-PLS pada Kepuasan Pengguna Aplikasi Instagram Tamba, Felicia Joy Rotua; Nasywa, Syarifah; Salsabila, Adellia; Khoiruddin, Ahmad Zulfikar; Tandi Kala, Ezra Alfrianto; Sifriyani, Sifriyani; Sari, Nariza Wanti Wulan; Yuniarti, Desi; Nadhilah Widyaningrum, Erlyne; Atarezcha Pangruruk, Thesya
EKSPONENSIAL Vol. 16 No. 2 (2025): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/a8agna87

Abstract

Social media platforms, particularly Instagram, have emerged as widely utilized channels among diverse user groups, including university students, for information sharing, social interaction, and entertainment purposes. The study seeks to analyze how Instagram quality, perceived benefits, and social interaction contribute to user satisfaction and loyalty within the FMIPA community at Mulawarman University.  The SmartPLS 3.0 software facilitates the use of the Structural Equation Modelling technique based on Partial Least Squares (SEM-PLS) in this investigation.  The results show that each of the three independent factors significantly and favourably affects user pleasure, which in turn significantly boosts customer loyalty. The R-square values of 0.685 for satisfaction and 0.655 for loyalty suggest that the proposed model adequately explains the relationships among the variables. Furthermore, all measurement indicators were confirmed to be both valid and reliable. In conclusion, the study demonstrates that users’ positive perceptions of Instagram’s quality, benefits, and social interaction contribute to enhanced satisfaction and foster greater loyalty toward the platform.
Penerapan CRISP-DM Model dengan Algoritma Decision Tree, Naive Bayes, SVM, dan KNN untuk Klasifikasi Curah Hujan di Kabupaten Malang Arizki Dwi Cahyo; Arie Wahyu Wijayanto
EKSPONENSIAL Vol. 17 No. 1 (2026): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v17i1.1649

Abstract

Rainfall refers to the amount of rain that falls over a specific period and is measured on a flat surface that does not absorb or channel the water. Rainfall is classified into six categories: cloudy, light rain, moderate rain, heavy rain, very heavy rain, and extreme rain. The objective of this study is to compare several classification methods in data mining, namely Decision Tree, Naïve Bayes, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) to determine the method with the highest accuracy for classifying rainfall in Malang Regency. To achieve this objective, the procedure used refers to CRISP-DM, which is a methodology for planning data mining projects. The data used in this study were obtained from NASA and consist of daily records collected from June 1, 2019, to June 1, 2025. The total number of observations in this study is 2,193. Data mining classification was employed to identify patterns and similarities in rainfall characteristics in the area. Based on the analysis results, the KNN method demonstrated the best performance with an accuracy of 87.3% and is therefore recommended as the most effective method for rainfall classification in Malang Regency.
Penerapan Metode K-Harmonic Means pada Pengelompokan Kabupaten/Kota di Pulau Kalimantan Berdasarkan Indikator Perumahan dan Kesehatan Lingkungan Fany Novianti; Rito Goejantoro; Memi Nor Hayati
EKSPONENSIAL Vol. 17 No. 1 (2026): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v17i1.1586

Abstract

Cluster analysis is grouping data into subsets whose members have a high degree of similarity. One commonly used clustering technique is the K-Harmonic Means, an enhancement of the K-Means method, which assigns data to clusters by calculating the harmonic mean of distances from each point to the cluster centers. This study aims to determine the optimal grouping of Regencies/Cities in Kalimantan Island based on housing and environmental health indicators and to identify the best Dunn index value. Based on the grouping results, 5 groups were obtained with parameters (p) = 2. The optimal Dunn index value is 0.2171. Based on the description of the housing and environmental health indicators, it can be seen that there are still disparities between groups in terms of access to basic services, such as the percentage of clean drinking water source services, the percentage of lighting sources from PLN electricity, the percentage of proper sanitation services, and the percentage of home ownership status. Group 4 and group 2 are groups that need special attention because they have the lowest achievements in several important indicators
Perbandingan Metode Imputasi Data Hilang untuk Peramalan Curah Hujan dengan ARIMA di Palangka Raya Khusnia Nurul Khikmah
EKSPONENSIAL Vol. 17 No. 1 (2026): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v17i1.1611

Abstract

Rainfall is one of the fundamental factors influencing various aspects of a region, including the city of Palangka Raya. Therefore, statistical tools are needed to forecast rainfall for mitigation and planning related to its effects. The objective of this study is to evaluate the performance of the ARIMA model in forecasting rainfall and to analyze the impact of three methods of missing data imputation on forecast accuracy. Therefore, this study proposes an evaluation of the autoregressive integrated moving average (ARIMA) model's performance for rainfall forecasting and the handling of missing data through three imputation methods: mean imputation, forward filling, and backward filling. The data used in this study consists of historical rainfall data from 2013 to 2022. The forecasting results indicate that rainfall data modelled using ARIMA and treated for missing data via mean imputation demonstrated the best forecasting accuracy in the validation test, as rainfall data in Palangka Raya City is fluctuating without a consistent seasonal pattern. Thus, the historical average value is more representative than the preceding or subsequent values, which could reinforce local bias. This is demonstrated by the smallest RMSE, MAE, and MAPE values, which are 124,17%, 103,57%, and 40,32%, respectively.
Klasterisasi Rumah Tangga Miskin di Kota Samarinda Menggunakan Algoritma K-Modes dengan Validasi Klaster Davies Bouldin Index Memi Nor Hayati; Nihayatul Khoiriyah; Nariza Wanti Wulan Sari; Aji Syarif Hidayatullah
EKSPONENSIAL Vol. 17 No. 1 (2026): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v17i1.1627

Abstract

Poverty alleviation is one of goals at Sustainable Development Goals (SDGs) that can be supported by grouping poor households based on similar characteristics, enabling social assistance programs to be delivered more effectively and accurately. This study aims to cluster poor households in Samarinda City using the K-Modes algorithm with validation through the Davies-Bouldin Index (DBI). The data were obtained from the 2023 Samarinda Poverty Survey and consist of 16 poor households indicators. The analysis was conducted by testing the number of clusters (K) from 2 to 10. The results indicate that the optimal number of clusters is 3, with a DBI value of 1.6997. Cluster 1 consists of 11,237 households, Cluster 2 consists of 2,327 households, and Cluster 3 consists of 1,451 households. The distinct characteristics of each cluster suggest that the clustering results can serve as a basis for designing more targeted social assistance programs. Future research is recommended to consider alternative clustering methods for categorical data, such as the ROCK algorithm, which utilizes link-based similarity by considering the number of common neighbors between objects, allowing it to better capture the inherent structure of categorical data compared to distance-based methods.
Pengelompokan Tren Kunjungan Wisatawan Mancanegara ke Indonesia Menggunakan Metode Hierarchical Clustering Ward Linkage Hardianti Hafid; Annisa Syalsabila
EKSPONENSIAL Vol. 17 No. 1 (2026): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/cgfhj284

Abstract

This study aims to cluster the trends of international tourist arrivals to Indonesia based on entry points using the Hierarchical Clustering Ward Linkage method. The research employs secondary data on monthly tourist arrivals by entry points from 2008 to 2024, obtained from BPS. Data analysis was conducted through preprocessing, distance measurement using Euclidean distance, clustering with Ward’s method, validation using the Dunn Index, and visualization through dendrograms. The results show that the optimal cluster structure is obtained when the data are grouped into four clusters, with the highest Dunn Index value of 3.201. The clustering reveals distinct patterns: Ngurah Rai International Airport and Soekarno-Hatta International Airport form separate clusters due to their dominant role in international arrivals, while Batam Port constitutes a single cluster driven by cross-border tourism with Singapore. Other entry points are grouped into a homogeneous cluster with relatively smaller volumes of arrivals. These findings highlight the importance of segmented tourism development strategies. Entry points with high tourist volumes require enhanced international capacity and services, while cross-border hubs such as Batam necessitate strengthened regional cooperation. The results provide valuable insights for policymakers in formulating more targeted and effective tourism strategies to enhance Indonesia’s global competitiveness.

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