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A'yunin Sofro
Universitas Negeri Surabaya, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics Social Sciences

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ESG Factors and Interest Rate Stability: A Comparative Analysis of Statistical and Machine Learning Approaches Khusnia Nurul Khikmah; A'yunin Sofro
Inferensi Vol 9 No 1 (2026)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v9i1.9511

Abstract

This study aims to compare the performance of basic statistical approaches and machine learning in identifying environmental, social, and governance (ESG) issues that influence the stability of the rupiah through the Indonesian interest Rate. This study proposes two approaches: machine learning analysis (long short-term memory (LSTM) and multiple long short-term memory (M-LSTM)) and fundamental statistical models (autoregressive integrated moving average (ARIMA), transfer function, and Koyck regression) to forecast the Indonesian interest rate time series data based on ESG factors. The analysis uses a completely randomized design to capture complex patterns, utilizing interest rate data, PM2.5 air quality data, and rainfall index data. This study provides novelty through data splitting scenarios in its empirical analysis and a comparative study of approaches to the model. The analysis results indicate that ESG factors, specifically air quality (PM2.5) and the rainfall index, have a significant influence on Indonesian interest rate values, as determined by the transfer function results, Koyck regression, and M-LSTM model analyses. All proposed model approaches demonstrate good forecasting accuracy, empirically proven by MAPE values with MAPE<10%. The MAPE values of the basic statistical models (Koyck regression, transfer function, and ARIMA) are 0.2842%, 1.0350%, and 2.1245%, outperforming machine learning models (LSTM and M-LSTM) with MAPE values of 4.660% and 7.7353%.
Performance Evaluation of Clustering Algorithms for Grouping Regencies and Cities in Kalimantan Based on Socio-Demographics Khusnia Nurul Khikmah; A&#039;yunin Sofro
Inferensi Vol 9 No 2 (2026)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v9i2.9512

Abstract

Cluster analysis is an important data exploration technique that aims to identify patterns or characteristics of data, which, in practice, are used to support evidence-based policy making, one of which is used in population and regional development. However, choosing the wrong clustering method can produce useless analysis results. Therefore, this study proposes a comparative study to determine the performance of various clustering algorithms, comparing the performance of hierarchical clustering algorithms (single linkage, complete linkage, average linkage, centroid, ward) and non-hierarchical algorithms (k-means) in regencies and cities in Kalimantan. The study uses secondary data taken from the official websites of the Central Statistics Agency of the five provinces in Kalimantan, where the data to be clustered consists of 56 regencies and cities with eight socio-demographic variables. The analysis results show that all hierarchical methods produce robust and consistent clusters compared to non-hierarchical methods. This decision is based on the silhouette score value 0.701, which is 0.308 better than k-means. This shows that hierarchical methods can clearly identify regencies and cities in Kalimantan based on their socio-demographic factors.
Co-Authors Khusnia Nurul Khikmah