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All Journal Trends in Mechanical Engineering Research Theta: Journal of Statistics
Agung Satrio Wicaksono
Department of Statistics, Faculty of Engineering, Universitas Sultan Ageng Tirtayasa.
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Comparative Analysis of Univariate and Spatio-Temporal Models for Forecasting Micro and Small Industries in Java Ferdian Bangkit Wijaya; Weksi Budiaji; Aulia Ikhsan; Agung Satrio Wicaksono; Aditya Rahadian Fachrur; Dinda Dwi Anugrah Pertiwi
Theta: Journal of Statistics Vol 1, No 2 (2025): Available Online in September 2025
Publisher : Faculty of Engineering, Univesitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62870/tjs.v1i2.37067

Abstract

Micro and Small Industries (MSEs) serve as the backbone of the regional economy in Java, Indonesia, characterized by high volatility and potential spatial interdependencies. While advanced spatio-temporal models like GSTARIMA are theoretically superior in capturing inter-regional spillover effects, their empirical effectiveness on limited annual aggregate data remains questionable. This study evaluates the forecasting performance of the univariate ARIMA model against the multivariate GSTARIMA model for predicting the number of MSE business units across six provinces in Java from 2013 to 2024. Using a Queen Contiguity spatial weight matrix and Root Mean Square Error (RMSE) as the evaluation metric, the study rigorously tests the Principle of Parsimony. Preliminary analysis using Moran’s I indicates non-significant spatial autocorrelation, suggesting that business growth is predominantly driven by internal temporal inertia rather than immediate spatial propagation. Consequently, the results demonstrate that the simpler ARIMA model outperforms the complex GSTARIMA model in 10 out of 12 testing scenarios (83.3%), with accuracy improvements reaching up to 94% in specific provinces. The study concludes that adding spatial complexity to short-term annual time series (N=12) leads to over-fitting without proportional gains in accuracy. The ARIMA-based forecast for 2025–2029 identifies three distinct regional growth typologies: expansive growth, market saturation, and structural correction, providing critical insights for differentiated regional policy planning.
STOCHASTIC MODELING OF BEARING FAILURE TIME USING THE WEIBULL DISTRIBUTION: A MONTE CARLO SIMULATION APPROACH Syarif Abdullah; Himmatul Mursyidah; Mekro Permana Pinem; Sri Istiyarti Uswatun Chasanah; Miftahul Huda; Fajri Ikhsan; Agung Satrio Wicaksono; Reka Pandu Anggara
Trends in Mechanical Engineering Research Vol 3, No 2 (2025): December
Publisher : Department of Mechanical Engineering, Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62870/timer.v3i2.37059

Abstract

This paper introduces a hagoslot stochastic model to investigate the failure times of bearings based on a two-parameter Weibull distribution utilizing Monte Carlo simulation. The failure times were fitted based on maximum likelihood estimation, and the parameters showed that the wear-out failure type with an increasing hazard rate (β>1) was corresponding to the fatigue-induced breakdown phenomenon in the rolling bearings. A Monte Carlo simulation with 1000 runs was performed to quantify the uncertainty of lifetime predictions, which have presented relatively high spreads but stable central tendencies in the Weibull parameter estimates. Survival analysis and hazard function showed increasing probability of failure with time, indicative of the need for prognosis-based maintenance. The findings demonstrate that the Weibull model is a reliable and interpretable paradigm that can be used to describe the probabilistic nature of mechanical component failure. The presented modeling strategy is appropriate for both engineering purposes and simulation-based reliability analyses, possibly evolved into a mixture-Weibull representation or data-driven parameter estimation.
Co-Authors Aditya Rahadian Fachrur Aulia Ikhsan Dinda Dwi Anugrah Pertiwi Fajri Ikhsan Ferdian Bangkit Wijaya Himmatul Mursyidah Mekro Permana Pinem Miftahul Huda Reka Pandu Anggara Sri Istiyarti Uswatun Chasanah Syarif Abdullah Weksi Budiaji