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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Aplikasi Statistika & Komputasi Statistik
Roosyidah, Nila Ayu Nur
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Modeling Multi-Output Back-Propagation DNN for Forecasting Indonesian Export-Import Maharsi, Rengganis Woro; Saputra, Wisnowan Hendy; Roosyidah, Nila Ayu Nur; Prastyo, Dedy Dwi; Rahayu, Santi Puteri
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 1 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i1.459

Abstract

Introduction/Main Objectives: International trade through the mechanisms of exports and imports plays a significant role in the Indonesian economy, making the timely availability of export and import value data crucial. Background Problems: Export and import values are influenced by inflation and exchange rate factors. Novelty: This study identifies two categories of variables, namely output (export value and import value) and input (inflation rate and the exchange rate of the Rupiah against the US Dollar). Research Methods: the research approach utilizes a Multi-output Deep Neural Network (DNN) with a Back-propagation algorithm to model the input-output relationship. The method can provide forecasting results for two or more bivariate or multivariate output variables. Finding/Results: The modeling analysis results indicate that the optimal model network structure is DNN (3.4). This model successfully predicts output 1 (export value) and output 2 (import value) with Mean Absolute Percentage Error (MAPE) rates of 13.76% and 13.63%, respectively. Additionally, the forecasting results show predicted export and import values for November to be US$ 16,208.13 billion and US$ 15,105.33 billion, respectively. These findings offer important insights into the direction of Indonesia's international trade movement, which can serve as a basis for future economic decision-making.
FUZZY GEOGRAPHICALLY WEIGHTED CLUSTERING WITH OPTIMIZATION ALGORITHMS FOR SOCIAL VULNERABILITY ANALYSIS IN JAVA ISLAND Fadlurohman, Alwan; Utami, Tiani Wahyu; Amrullah, Setiawan; Roosyidah, Nila Ayu Nur; Dhani, Oktaviana Rahma
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1841-1852

Abstract

The Social Vulnerability Index (SoVI) measurement assesses social vulnerability. However, the measurement of SoVI can only describe the general conditions without being able to show which factors dominate. Therefore, a clustering approach has been proposed to characterise the dominant social vulnerability factors. Fuzzy Geographically Weighted Clustering (FGWC) is a method that works for this purpose. FGWC is an extension of the Fuzzy C-Means algorithm, which involves geographical influences in calculating membership values. However, the FGWC method is sensitive because the initial initialisation to determine the centroid is randomised, and it will affect the cluster quality. This research uses a metaheuristic approach to overcome the weakness of FGWC by using Particle Swarm Optimisation (PSO) and Artificial Bee Colony (ABC). This study aims to cluster districts/cities in Java Island using the PSO-FGWC and ABC-FGWC methods based on social vulnerability variables and determine the dominant factors of social vulnerability in each region. Optimum cluster selection uses the index of the largest Partition Coefficient (PC) and the smallest Classification Entropy (CE). Clustering social vulnerability in Java Island resulted in the best clustering using the ABC-FGWC method with 5 optimum clusters based on the PC and CE index values of 0.343 and 1.298, respectively. This research found that social vulnerability exists in each region of Java Island. Cluster 1, consisting of 19 districts/cities, is characterized by vulnerabilities in demography and education. Cluster 2, consisting of 33 districts/cities, is characterized by demographic and health vulnerabilities. Cluster 3, which consists of 24 districts/cities, is dominated by education and economic vulnerability factors. Cluster 4, consisting of 14 districts/cities, has the highest social vulnerability characteristics on the unemployment rate and the proportion of house rent. The last one, cluster 5, consists of 29 districts/cities and has a vulnerability problem in the population growth variable.
Co-Authors Alwan Fadlurohman Amrullah, Setiawan Dedy Dwi Prastyo Dhani, Oktaviana Rahma Maharsi, Rengganis Woro Santi Puteri Rahayu Saputra, Wisnowan Hendy Tiani Wahyu Utami