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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Media Statistika Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) Jurnal Sains dan Seni ITS IPTEK Journal of Proceedings Series CAUCHY: Jurnal Matematika Murni dan Aplikasi Journal of Natural A Jurnal Tata Kota dan Daerah International Journal of Advances in Intelligent Informatics Prosiding Seminar Matematika dan Pendidikan Matematik ELINVO (Electronics, Informatics, and Vocational Education) QALAMUNA: Jurnal Pendidikan, Sosial, dan Agama BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal of Islamic Monetary Economics and Finance JPEK (Jurnal Pendidikan Ekonomi dan Kewirausahaan) Musamus Journal of Physical Education and Sport (MJPES) Infotekmesin Inferensi Infotek : Jurnal Informatika dan Teknologi Afeksi: Jurnal Penelitian dan Evaluasi Pendidikan Proceeding ISETH (International Summit on Science, Technology, and Humanity) Malcom: Indonesian Journal of Machine Learning and Computer Science Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Sewagati PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Riyahuna: Jurnal Pendidikan Bahasa Arab Jurnal Terapan Pendidikan Dasar dan Menengah
Heri Kuswanto
Departemen Statistika, Institut Teknologi Sepuluh Nopember (ITS)
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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Journal : International Journal of Advances in Intelligent Informatics

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Hybrid SSA-TSR-ARIMA for water demand forecasting Suhartono Suhartono; Salafiyah Isnawati; Novi Ajeng Salehah; Dedy Dwi Prastyo; Heri Kuswanto; Muhammad Hisyam Lee
International Journal of Advances in Intelligent Informatics Vol 4, No 3 (2018): November 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v4i3.275

Abstract

Water supply management effectively becomes challenging due to the human population and their needs have been growing rapidly. The aim of this research is to propose hybrid methods based on Singular Spectrum Analysis (SSA) decomposition, Time Series Regression (TSR), and Automatic Autoregressive Integrated Moving Average (ARIMA), known as hybrid SSA-TSR-ARIMA, for water demand forecasting. Monthly water demand data frequently contain trend and seasonal patterns. In this research, two groups of different hybrid methods were developed and proposed, i.e. hybrid methods for individual SSA components and for aggregate SSA components. TSR was used for modeling aggregate trend component and Automatic ARIMA for modeling aggregate seasonal and noise components separately. Firstly, simulation study was conducted for evaluating the performance of the proposed methods. Then, the best hybrid method was applied to real data sample. The simulation showed that hybrid SSA-TSR-ARIMA for aggregate components yielded more accurate forecast than other hybrid methods. Moreover, the comparison of forecast accuracy in real data also showed that hybrid SSA-TSR-ARIMA for aggregate components could improve the forecast accuracy of ARIMA model and yielded better forecast than other hybrid methods. In general, it could be concluded that the hybrid model tends to give more accurate forecast than the individual methods. Thus, this research in line with the third result of the M3 competition that stated the accuracy of hybrid method outperformed, on average, the individual methods being combined and did very well in comparison to other methods.
Computation of spatial error model with matrix exponential spatial specification approach Marsono, Marsono; Setiawan, Setiawan; Kuswanto, Heri
International Journal of Advances in Intelligent Informatics Vol 10, No 3 (2024): August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i3.1506

Abstract

In spatial regression analysis, we not only consider the internal factors of a location, but also take into account the spatial factors that may affect the relationship. The model of spatial dependence between regions caused by unknown factors or errors is known as the Spatial Error Model (SEM). In its application to large datasets, SEM suffers from several problems in parameter estimation and computational time. One of the methods to solve this problem is to use Matrix Exponential Spatial Specification (MESS). The purpose of this research is to find another alternative to modeling data containing spatial autocorrelation errors as a substitute for SEM. MESS(0,1) is named as an alternative model to SEM. With the advantage of MESS features, the MESS(0,1) model is expected to be faster in analytics and computation compared to SEM when using Maximum Likelihood Estimation (MLE). The purpose of this study was to evaluate the effectiveness of the MESS (0,1) model as an alternative to SEM using MLE based on simulation studies and real data analysis. Simulation studies were conducted by generating data from small samples to large samples and then estimating parameters with the MESS(0,1) and SEM models. Then we compared the performance of the two models with the time used during estimation and the root mean square error (RMSE). In addition, it is applied to real data, namely Gross Regional Domestic Product (GRDP) data. The real data used is the GRDP of the construction category on Java Island in 2021. This is in line with the massive infrastructure development as a government program. The independent variables used and considered influential on the GRDP of the construction sector are domestic investment, foreign investment, labor, and wages. Based on the simulation study results, the computation time for estimating the parameters of MESS(0,1) is faster than the SEM model. In addition, in terms of accuracy, the RMSE indicator shows MESS(0,1) is more accurate than the SEM. In addition, the MESS(0,1) and SEM models were applied to the real data. The modeling real data results show that all variables have a significant positive effect on GRDP in the construction category.
Co-Authors A.A. Ketut Agung Cahyawan W Adatul Mukarromah Ahmad Fathoni Ahmad Shofiyuddin Ichsan Ani Budi Astuti Ary Miftakhul Huda Bani Unggul, Didik Beben Sutara, Beben Dea Astri Titi Dedy Dwi Prastyo Dwi Rantini Effendi, Magdalena Eka Supriatna Ekarini, Fitria Faiqah, Elok Farida Ariany Fatul, Fitasari Fithriasari, Kartika Fitri Ayu Kusumawati, Fitri Ayu Hady Suryono Hariadi Hariadi Hia, Supriadi Idrus Syahzaqi Indah Idamawarti, Itamara Irhamah Irhamah - Jamaluddin Jamaluddin Kinanti Hanugera Gusti Koesniawanto, Moch. Made Ayu Dwi Octavanny Marsono Marsono Moch. Koesniawanto Mohammad Hatta Rafsanjani Muhammad Hisyam Lee Munjahid Nitivijaya, Maulidiah Noorihsan, Sandrian Yulian Firmansyah Novi Ajeng Salehah Nur Iriawan Paramita, NLP Satyaning Pradnya Pramono Dwi Utomo Puspita Kartikasari Qori’atunnadyah, Marita Raditya Sukmana Rahmawati, Hasri Rouhan, Asva Abadila Rumaisa Kruba S Suhartono, S Salafiyah Isnawati Sediono, Sediono Setiawan Setiawan Sri Aryani, Sri Suhartono Suhartono Suhartono Suhartono Suhartono Suhartono Suhartono Suhartono Suryansah Susila, Muktar Redy Sutikno Sutikno Ulfa Siti Nuraini Widhianingsih, Tintrim Dwi Ary Wiwiek Setya Winahju