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 12 Documents
 1   2 
Search results for , issue "Vol. 11 No. 1 (2020)" : 12 Documents clear
Pengujian Hipotesis Parameter Model Mixed Geographically Weighted Regression Data Indeks Pembangunan Manusia di Kalimantan Tahun 2016 Utami, Riska Putri; Suyitno, Suyitno; Hayati, Memi Nor
EKSPONENSIAL Vol. 11 No. 1 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (688.592 KB) | DOI: 10.30872/eksponensial.v11i1.640

Abstract

Mixed Geographically Weighted Regression (MGWR) model is a Geographically Weighted Regression (GWR) model with some parameters are global (have the same value) and several other parameters are local (have different values) for each observation location. The purpose of this study is to obtain a MGWR model on the Human Development Index (HDI) data and find out the factors that influence the HDI of each district (city) in the provinces of East Kalimantan, Central Kalimantan and South Kalimantan in 2016. The parameter estimation method is carried out through two stages (backshift), namely local parameter estimation by using the Weighted Least Square (WLS) method and global parameter estimation by using the Ordinary Least Square (OLS) method. Spatial weighting on local parameter estimation is obtained by using an adaptive Bisquare weighting functions, where optimum bandwidth determination uses Generalized Cross-Validation (GCV) criterion. Based on the result of MGWR parameter testing, it was concluded that the school enrollment rates (SMP) affected the HDI of all districts (cities) in East Kalimantan, Central Kalimantan and South Kalimantan, while the population density affects the HDI only in a few districts (cities), namely East Kutai, Balikpapan, Samarinda and Bontang.
Analisis Data Ketinggian Permukaan Air Sungai Mahakam Daerah Kutai Kartanegara Tahun 2010-2016 Menggunakan Model Autoregressive Integrated Moving Average (ARIMA) Dengan Efek Outlier: Studi Kasus: Data Rata-rata Ketinggian Tiap Bulan Permukaan Air Sungai Mahakam, Tenggarong, Kalimantan Timur Agustianto, Rezky; Purnamasari, Ika; Suyitno, Suyitno
EKSPONENSIAL Vol. 11 No. 1 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v11i1.643

Abstract

Measurement of water level is useful as a guide for flood events in an area. As a result of global warming it is predicted that rainfall will increase and the water level will be high, so that the chances of flooding will increase. The method often used in forecasting is the method of Autoregressive Integrated Moving Average (ARIMA). ARIMA is one of the time series forecasting methods that has been studied in depth by Box and Jenkins. ARIMA's basic concepts include,identification of models, parameter estimation, diagnostic checks and forecasting. Forecasting results with the ARIMA method are inaccurate, on data that contains outliers. The weakness of the ARIMA method can be overcome using the ARIMA method with outlier detection. The type of outlier detection in this study is additive outlier (AO). The purpose of this study was to determine the ARIMA forecasting model with an outlier effect on the average water level data of the Mahakam River in the Kutai Kartanegara Region in front of the Tenggarong Museum Building from January 2010 - December 2016. The results showed that the best forecasting model was the river Mahakam Kutai Kartanegara Region is ARIMA ([12], 1,0) with the addition of 4 outlier effects and measure of goodness is AIC with a value of 250,0776.

Page 2 of 2 | Total Record : 12

 1   2 

Filter by Year

2020 2020


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): Jurnal Eksponensial Vol. 14 No. 1 (2023) 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