Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal SAINTEK Review: Journal of Multidisciplinary in Social Sciences
Ahmad Syarifudin
Universitas Pelita Bangsa
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Social Sciences

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

Found 2 Documents
Search

 1 
Integration of Statistical Process Control and Artificial Intelligence to Improve Steering System Process Capability in the NANO Model Bagus Pamungkas; Nailla Dwi Rizkia; Ridzqi Alfauji; Ahmad Syarifudin; Yudi Prastyo
Review: Journal of Multidisciplinary in Social Sciences Vol. 3 No. 05 (2026): May 2026
Publisher : Lentera Ilmu Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59422/rjmss.v3i05.1355

Abstract

The high Defects per Unit (DPU) in the NANO model, mainly caused by handlebar effort problems, indicates process variation in the steering system that affects product quality. This study aims to evaluate the stability and capability of the steering system production process and identify process parameters that contribute to handlebar effort through the integration of Statistical Process Control (SPC) and Artificial Intelligence (AI). Data were collected from measurements of steering system component dimensions and assembly process parameters on the production line. The analysis used X̄-R control charts, process capability indices, and a machine learning-based AI approach to detect abnormal patterns among process variables. The results show that the process is generally close to statistical control, but special cause variations are still found. Process capability analysis indicates that the process has not consistently met specifications, as shown by Cp = 0.87 and Cpk = 0.74. The main parameters affecting process performance are steering stem height 1 and head pipe height, both of which show high variation and low process capability. AI analysis also identified abnormal multivariate patterns related to these parameters. Therefore, improvements should focus on standardizing work procedures, calibrating measuring instruments, controlling materials, maintaining jig fixtures, and improving operator competence. The integration of SPC and AI supports more predictive quality control and helps reduce defects in the steering system production process.
Kajian Penerapan Artificial Intelligence Pada Prediksi Cuaca Ahmad Syarifudin
Prosiding Sains dan Teknologi Vol. 4 No. 1 (2025): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 4 - Februari 2025
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Perubahan iklim global akibat pemanasan global telah menyebabkan kondisi cuaca yang semakin sulit diprediksi. Penelitian ini membahas implementasi Artificial Intelligence (AI) sebagai solusi inovatif untuk meningkatkan akurasi prediksi cuaca. Studi literatur dilakukan dengan mengkaji metode AI, termasuk algoritma machine learning seperti Levenberg-Marquardt dan Long Short-Term Memory (LSTM), serta membandingkannya dengan metode tradisional seperti ARIMA. Hasil penelitian menunjukkan bahwa pendekatan berbasis AI mampu memberikan prediksi yang lebih akurat dan efisien dengan memanfaatkan pola data yang kompleks. Selain itu, kemampuan AI untuk memproses data tanpa intervensi manusia mempercepat analisis dan validasi data. Temuan ini menegaskan bahwa pemanfaatan AI dalam prediksi cuaca dapat mendukung kebutuhan masyarakat modern dalam menghadapi tantangan perubahan iklim.
Co-Authors Bagus Pamungkas Nailla Dwi Rizkia Ridzqi Alfauji Yudi Prastyo