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All Journal CommIT (Communication & Information Technology) Al-Jabar : Jurnal Pendidikan Matematika Prosiding Seminar Nasional Teknik Mesin Recent in Engineering Science and Technology
Abdillah, Abdul Azis
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Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering

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Journal : Recent in Engineering Science and Technology

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Self-Protection Equipment Detection System in Heavy Weight Workshop of Politeknik Negeri Jakarta Using Artificial Intel-ligence Rezakusuma, Muhammad; Abdillah, Abdul Azis; Liliana, Dewi Yanti; Edistria, Ega; Arifin, Samsul; Muzakki, Zahran
Recent in Engineering Science and Technology Vol. 1 No. 01 (2023): RiESTech Volume 01 No. 01 Years 2023
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v1i01.4

Abstract

The creating process, how it works and the performance of the detection system using Artificial Intelligence. The development of this innovation contributes to the Heavy Equipment Workshop of the Jakarta State Polytechnic to detect the early potential for work accidents. The methods are device tuning, inputs, training models, performance, trials and outputs. The creating process and how the detection system works using Artificial Intelligence each has 3 steps and accuracy using 3 cameras, namely the internal webcam (1MP), the JETE external webcam (720P) and the Samsung Galaxy A22 mobile phone camera (13MP). The process of making this innovation has 3 steps, namely data input, export, file grouping. There are 3 steps to work, namely open the file, run and output. The result of the accuracy of the internal webcam is very low, the JETE external webcam is better than the internal webcam and the mobile phone camera is better than the JETE external webcam.
Machine Predictive Maintenance by Using Support Vector Machines Assagaf , Idrus; Sukandi, Agus; Abdillah, Abdul Azis; Arifin, Samsul; Ga, Jonri Lomi
Recent in Engineering Science and Technology Vol. 1 No. 01 (2023): RiESTech Volume 01 No. 01 Years 2023
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v1i01.6

Abstract

Predictive Maintenance (PdM) is an adoptable worth strategy when we deal with the maintenance business, due to a necessity of minimizing stop time into a minimum and reduce expenses.  Recently, the research of PdM is now begin in utilizing the artificial intelligence by using the machine data itself and sensors. Data collected then analyzed and modelled so that the decision can be made for the near and next future. One of the popular artificial intelligences in handling such classification problem is Support Vector Machines (SVM). The purpose of the study is to detect machine failure by using the SVM model. The study is using database approach from the model of Machine Learning. The data collection comes from the sensors installed on the machine itself, so that it can predict the failure of machine function. The study also to test the performance and seek for the best parameter value for building a detection model of machine predictive maintenance The result shows based on dataset AI4I 2020 Predictive Maintenance, SVM is able to detect machine failure with the accuracy of 80%.
Machine Failure Detection using Deep Learning Assagaf, Idrus; Sukandi, Agus; Abdillah, Abdul Azis
Recent in Engineering Science and Technology Vol. 1 No. 03 (2023): RiESTech Volume 01 No. 03 Years 2023
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v1i03.21

Abstract

This article focuses on the application of deep learning methods for failure prediction. Failure prediction plays a crucial role in various industries to prevent unexpected equipment failures, minimize downtime, and improve maintenance strategies. Deep learning techniques, known for their ability to capture complex patterns and dependencies in data, are explored in this study. The research employs Multi-Layer Perceptron as deep learning architectures. This model is trained on AI4I 2020 Predictive Maintenance data to develop accurate failure prediction models. Data preprocessing involves cleaning, feature engineering, and normalization to ensure the quality and suitability of the data for deep learning models. The dataset is split into training and testing sets for model development and evaluation. Performance evaluation metrics such as accuracy, ROC, and AUC are utilized to assess the models' effectiveness in predicting failures. The experimental results demonstrate the effectiveness of deep learning methods in failure prediction. The models showcase high accuracy and outperform SVM approaches, particularly in capturing intricate patterns and temporal dependencies within the data. The utilization of Multi-Layer Perceptron architecture further enhances the models' ability to capture long-term dependencies. However, challenges such as the availability of diverse and high-quality data, the selection of appropriate architecture and hyperparameters, and the interpretability of deep learning models remain significant considerations. Interpretability remains a challenge due to the inherent complexity and black-box nature of deep learning models. In conclusion, deep learning method offer significant potential for accurate failure prediction. Their ability to capture complex patterns and temporal dependencies makes them well-suited for analyzing operational and sensor data. Future research should focus on addressing challenges related to data quality, interpretability, and model optimization to further enhance the application of deep learning in failure prediction.
The Development of an Ergonomic-Based Roadmap for Improving Passenger Mobility Onboard Intercity Trains in Indonesia Sihman, Lukman Septaekwara; Hitchcock, David; Harding, Kimberley; Abdillah, Abdul Azis
Recent in Engineering Science and Technology Vol. 2 No. 01 (2024): RiESTech Volume 02 No. 01 Years 2024
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v2i01.42

Abstract

Today in Indonesia, intercity railway service has become an essential part of human mobility and can carry more than 298 million passengers in 2022. It is a reliable service since it can carry many passengers and is a form of time-efficient mode of transport. More and more people are using this service including disabled passengers. They are using the railway to travel between the cities. The railway industry has changed a lot in recent years. If we look at the 90s and before, traveling by train was almost entirely used by non-disabled people and very few of the passengers are people with disabilities. It has now changed while some disabled passengers used the rail, and it created some issues for the industry. This service for disabled passengers is a part of Equality, Diversity, and Inclusion (EDI) and the Author found a mismatch between what the disabled passenger wanted and what the industry responded to. This research will try to understand what is happening in the Indonesian railway industry, compare it with the same issue in another country like the United Kingdom (UK), and what or how to get the ideal design and service for intercity onboard service for disabled passengers. This research uses the triangulation ergonomic principle which consists of measurement, observation, and consultation to combine a literature study and interviews with representatives of 4 stakeholders in the Indonesian railway: 1) A regular user of disabled passenger; 2) Executive Director of one of the Indonesian disability passengers Organization; 3) Traffic coordinator of the Directorate General of Railway (DGR) in the Indonesian Ministry of Transport; 4) A Vice President of the Passenger Division from PT. Kereta Api Indonesia (Persero)/KAI, an intercity railway operator. This interview is then combined with the data from the literature study then analyzed with several methods like Quality Function Deployment (QFD) to get the ideal coach design, Communication-Persuasion Matrix theory to address the communication gap between the parties and make an ideal roadmap solution with phasing approach.
Comparative Analysis of Regression Methods for Estimation of Remaining Useful Life of Lithium Ion Battery Assagaf, Idrus; Abdillah, Abdul Azis; Edistria, Ega; Sukandi, Agus; Prasetya, Sonki; Apriana, Asep; Nugroho; Kamil, Raihan
Recent in Engineering Science and Technology Vol. 3 No. 01 (2025): RiESTech Volume 03 No. 01 Years 2025
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v3i01.93

Abstract

Lithium batteries play a critical role in modern technological applications, including electric vehicles and portable electronic devices. Ensuring accurate estimation of their remaining useful life is essential to improve system efficiency and reliability. This study focuses on predicting the remaining useful life of lithium batteries using advanced regression methods. Data were collected from lithium battery charge-discharge cycles, encompassing key operational parameters such as voltage, current, and temperature. The analysis employed several regression models, including linear regression, lasso regression, and Ridge regression, to identify relationships between these parameters and battery life. The models were evaluated based on estimation accuracy, with Root Mean Square Error (RMSE) as the primary performance metric. The findings demonstrate that regression methods can effectively capture non-linear relationships between input variables and the remaining useful life, with lasso and Ridge regression showing superior performance in reducing prediction errors. These results underscore the potential of regression-based approaches in providing robust and reliable estimations of battery life. The conclusions highlight the importance of these models for developing predictive battery management systems, which can optimize battery performance and extend their operational lifespan across various applications. This research establishes a solid foundation for future studies on intelligent battery health monitoring and management.
Comparing MLP and 1D-CNN Architectures for Accurate RUL Forecasting in Lithium Batteries Assagaf, Idrus; Sukandi, Agus; Jannus, Parulian; Prasetya, Sonki; Apriana, Asep; Edistria, Ega; Abdillah, Abdul Azis
Recent in Engineering Science and Technology Vol. 3 No. 04 (2025): RiESTech Volume 03 No. 04 Years 2025
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v3i04.127

Abstract

Accurately forecasting the Remaining Useful Life (RUL) of lithium-ion batteries is critical for optimizing battery management and ensuring operational reliability. This study compares the performance of two deep learning architectures—a Multilayer Perceptron (MLP) and a one-dimensional Convolutional Neural Network (1D-CNN)—in predicting RUL using datasets from CALCE batteries B35, B36, and B37. Data preprocessing involved outlier removal, missing value handling, and feature normalization, with key features extracted including Resistance, Constant Voltage Charging Time (CVCT), and Constant Current Charging Time (CCCT). Correlation analyses confirmed strong relationships between these features and RUL. Both models were trained and validated on preprocessed data, and their predictive accuracies were assessed using Root Mean Square Error (RMSE) and coefficient of determination (R2). Results indicated that while both architectures effectively captured battery degradation patterns, the MLP consistently outperformed the 1D-CNN, achieving on average 5% lower RMSE and 1.5% higher R2 across all tested batteries. These findings suggest that simpler fully connected networks may suffice for this forecasting task under the given feature set and preprocessing conditions. This work provides valuable insights into neural network model selection for battery health prognostics, guiding the development of efficient and accurate predictive maintenance strategies.
Co-Authors A.A. Ketut Agung Cahyawan W Agus Sukandi Asep Apriana, Asep Assagaf, Idrus Christy, Nazha Ali Dewi Yanti Liliana Edistria, Ega Farhan Farhan Ga, Jonri Lomi Harding, Kimberley Hitchcock, David I Gusti Agung Anom Yudistira Jannus, Parulian Kamil, Raihan Muktyas, Indra Bayu Muzakki, Zahran Nugroho Puguh Wahyu Prasetyo, Puguh Rezakusuma, Muhammad Rinda Nariswari Samsul Arifin Samsul Arifin Sihman, Lukman Septaekwara Sonki Prasetya Susyanto, Nanang