Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal Jurnal Sains dan Teknologi: Jurnal Keilmuan dan Aplikasi Teknologi Industri Jurnal Sisfotek Global
Indrawan Indrawan
Akademi Komunitas Toyota Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
STANDARISASI PENCEGAHAN BODY MENABRAK SLIDING DOOR MENGGUNAKAN METODE POKA YOKE Indrawan Indrawan; Muhammad Rusydi; Akhmad Satria Daris Jaya
Jurnal Sains dan Teknologi: Jurnal Keilmuan dan Aplikasi Teknologi Industri Vol 21, No 2 (2021): JURNAL SAINS DAN TEKNOLOGI
Publisher : Sekolah Tinggi Teknologi Industri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36275/stsp.v21i2.406

Abstract

Kualitas adalah kesamaan dalam penggunaan barang guna untuk memenuhi kecukupan konsumen. Definisi dari kualitas sebagai pemenuhan kebutuhan konsumen, tanpa adanya cacat (ketidaksesuaian). Penelitian dan pengembangan ini dilakukan pada area kerja, bagian input surfacer, dan masalah yang terjadi adalah body menabrak sliding door. Teknik analisis data dilakukan dengan metode poka yoke pada wilayah berlangsungnya kecelakaan pada proses produksi. Metode tersebut direkomendasikan melalui penelitian dan pengembangan ini guna memperingati kepada pegawai (operator) untuk terhindar dalam ketidaksesuaian proses produksi. Pada tahap selanjutnya, adalah control dan monitor terhadap hasil analisis. Dengan tujuan proses body menabrak sliding door tidak terulang kembali. Maka ketika proses control dan monitor dilakukan dengan benar oleh pihak manajemen perusahaan maupun pegawai (operator) yang terintegrasi dengan masalah tersebut. Untuk itu penelitian atau pengembangan ini merekomendasikan guna dibuatkan lembar pemeriksaan pada periode pelaksanaan control dan monitor di tahap input surfacer.
IDENTIFIKASI PENYELESAIAN LINE STOP A&B CYLINDER HEAD PADA ROUGH RAW MATERIAL MENGGUNAKAN POKA YOKE Indrawan Indrawan; Muhammad Rusydi; Rizki Dwi Afrianto
Jurnal Sains dan Teknologi: Jurnal Keilmuan dan Aplikasi Teknologi Industri Vol 22, No 2 (2022): JURNAL SAINS DAN TEKNOLOGI
Publisher : Sekolah Tinggi Teknologi Industri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36275/stsp.v22i2.495

Abstract

In solving problems in this study background using the Poka Yoke method. PT. XYZ is the object of research this time and focuses on solving the Line Stop problem on Line A & B Cylinder Head on Rough Raw Materials. The purpose of the research for possible failure that occurs or has happened. After the failure is known, the next alternative problem solving is to prevent errors that will occur and/or activities/actions using the Poka Yoke method. The discovery in this study is known that the problem process when the spare part runs from the stopper at the shutter input rough raw material line A & B. So that it results in a spare part and exceeding work in procces (WIP) which can cause the IMSP-0011 engine to become a fault and line stop average 10 minutes per day. The results of this study using the Poka Yoke method through specific, measurable, achievable, reasonable and timephase (SMART) approaches which in essence are by adding Poka Yoke barrier to the stopper and changing ordinary nuts into nut lock. So the conclusion results of the implementation show the stopper functioning maximally, then the spare part according to the WIP.
Multi-Sensor Based Remaining Useful Life Prediction of Bearing Motors: A Comparative Study of LSTM and CNN Models Yani Koerniawan; Indrawan Indrawan; Raynaldi Yudha Prasetya; Wingky Kurniawan
JURNAL SISFOTEK GLOBAL Vol 16, No 1 (2026): JURNAL SISFOTEK GLOBAL
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/sisfotek.v16i2.16236

Abstract

Accurate Remaining Useful Life (RUL) prediction is essential for implementing effective predictive maintenance strategies in industrial rotating machinery. Bearing motors are particularly critical components whose unexpected failure may cause severe production losses and safety risks. This study presents a comparative investigation of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures for RUL prediction using multi-sensor monitoring data. The dataset consists of 1000 days of simulated operational data from three bearing motors under varying degradation conditions. Five sensor parameters are considered: vibration (RMS), acoustic emission, temperature, stator current, and rotational speed (RPM). After preprocessing and sliding-window segmentation, 2910 time-series sequences were generated and divided into training, validation, and test sets. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²). Experimental results show that LSTM significantly outperforms CNN, achieving an R² of 0.9877 on the test dataset, while CNN achieved R² below 0.34. The findings confirm the importance of temporal dependency modeling in long-horizon degradation prediction and provide guidance for selecting deep learning architectures in predictive maintenance applications.
Co-Authors Akhmad Satria Daris Jaya Muhammad Rusydi Raynaldi Yudha Prasetya Rizki Dwi Afrianto Wingky Kurniawan Yani Koerniawan