Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Sistem Cerdas Jurnal Polimesin
Dani Hari Tunggal Prasetiyo
University of Jember
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Integration of taguchi method and digital metrology for precision fused deposition modelling of PLA-based vibration-damping components Danang Yudistiro; Salahuddin Junus; Dani Hari Tunggal Prasetiyo; Istiqomah Rahmawati; Helda Wika Amini; Meta Fitri Rizkiana
Jurnal Polimesin Vol 24, No 1 (2026): February
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v24i1.8352

Abstract

This study integrates the Taguchi method and 3D digital metrology to improve dimensional accuracy in the Polylactic Acid (PLA)-based Fused Deposition Modelling (FDM) process. The research focuses on the fabrication of vibration-damping roller components in a Continuously Variable Transmission (CVT) system that demands high geometric precision. A Taguchi L9 experimental design was used to analyze the effects of layer height, extrusion temperature, and filling density on the volume deviation of the printed product. The evaluation was conducted using 3D scanning and analyzed using the Signal-to-Noise (S/N) ratio and Analysis of Variance (ANOVA). The optimal parameter combination was obtained at a layer height of 0.25 mm and an extrusion temperature of 220 °C, resulting in the minimum volume deviation and the highest process stability. The ANOVA results identified layer height as the most dominant factor, followed by extrusion temperature, while filling density had a relatively small effect. Validation tests showed good agreement between the predictions and the experimental results. These findings confirm the effectiveness of integrating the Taguchi method with digital metrology in supporting the development of PLA-based precision additive manufacturing for sustainable automotive component applications.This study integrates the Taguchi method and 3D digital metrology to improve dimensional accuracy in the Polylactic Acid (PLA)-based Fused Deposition Modelling (FDM) process. The research focuses on the fabrication of vibration-damping roller components in a Continuously Variable Transmission (CVT) system that demands high geometric precision. A Taguchi L9 experimental design was used to analyze the effects of layer height, extrusion temperature, and filling density on the volume deviation of the printed product. The evaluation was conducted using 3D scanning and analyzed using the Signal-to-Noise (S/N) ratio and Analysis of Variance (ANOVA). The optimal parameter combination was obtained at a layer height of 0.25 mm and an extrusion temperature of 220 °C, resulting in the minimum volume deviation and the highest process stability. The ANOVA results identified layer height as the most dominant factor, followed by extrusion temperature, while filling density had a relatively small effect. Validation tests showed good agreement between the predictions and the experimental results. These findings confirm the effectiveness of integrating the Taguchi method with digital metrology in supporting the development of PLA-based precision additive manufacturing for sustainable automotive component applications.
A Multivariate LSTM Approach for Monthly Rice Production Forecasting in East Java Hasanur Mohammad Firdausi; Satryo Budi Utomo; Gamma Aditya Rahardi; Dani Hari Tunggal Prasetiyo
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.595

Abstract

Accurate forecasting of rice output is essential for improving regional food security planning, particularly in East Java Province, which serves as a major national rice granary. This study develops a Long Short-Term Memory (LSTM) model to predict rice production using monthly data on production and harvested area from 2018 to 2024. The methodology includes data preprocessing, normalization, sequence construction with a sliding window, training of a multivariate LSTM model, and performance evaluation using mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Results show that the LSTM model achieves superior predictive accuracy, with an MAE of 95,030.16, RMSE of 120,229.01, and MAPE of 16.64%, significantly outperforming baseline Moving Average and Linear Regression models. While the model effectively captures seasonal production trends, some inaccuracies remain during periods of anomalous production values. These findings suggest that the LSTM model is effective for projecting rice production and may provide a foundation for early warning systems and regional food distribution strategies. Further improvements could be realized by integrating climate variables or adopting a hybrid model architecture to enhance predictive precision.
Co-Authors Danang Yudistiro Gamma Aditya Rahardi Hasanur Mohammad Firdausi Helda Wika Amini Istiqomah Rahmawati Meta Fitri Rizkiana Salahuddin Junus Satryo Budi Utomo