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All Journal Journal of Mechatronics and Artificial Intelligence
Muhamad Ajis
Universitas Pendidikan Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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Air Quality Classification System using Random Forest Algorithm using MQ-7 and MQ-135 Sensors with IoT-based Aisyah Aira Putri Maharani; Rizky Hamdani Sakti; Muhamad Fajar Imanul Haq; Muhamad Ajis; Abdu Malikh Silaban
Journal of Mechatronics and Artificial Intelligence Vol 1, No 2 (2024): JMAI: December 2024
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jmai.v1i2.75591

Abstract

Air is one of the essential elements in human life besides water and soil. However, currently the air quality in Indonesia is getting worse. Therefore, an air quality classification system using Random Forest algorithm based on CO and CO2 levels using IoT-based MQ7 and MQ13 sensors is needed as a smart solution. The workflow of this system begins with detecting air quality using the MQ-7 sensor for CO gas and the MQ-135 sensor for CO2 gas. Then, the classification process is carried out with the Machine Learning Random Forest algorithm by utilizing a number of training data that has been stored in the program to classify the gas sensor detection results into three types of classes, namely “Good”, “Bad”, or “Toxic”. The final output of this system is a website display that can be accessed on a PC/Laptop monitor in real time. From the results of the Random Forest machine learning algorithm classification testing process, 1 unsuitable data was found from a total of 100 trials that have been carried out. Therefore, the Random Forest machine learning algorithm can be said to be successful in detecting air levels in the surrounding environment well because it provides an accuracy value of 99%.
Performance Improvement of Hydraulic Excavator Efficiency: A Literature Review Geralda Livia Nugraha; Muhamad Ajis; Himmawan Sapta Adhi; Diky Zakaria
Journal of Mechatronics and Artificial Intelligence Vol 1, No 1 (2024): JMAI: June 2024
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jmai.v1i1.69947

Abstract

Excavators dominate heavy-duty jobs worldwide.  As a major construction machine, their enhanced productivity in work has led to a strong demand for them. Concerns for the environment, increased efficiency, and energy conservation all reflect this goal. Several studies on these matters Automation in construction equipment, particularly hydraulic excavators, has gained popularity among producers and academics. This article examines articles about environmental concerns, efficiency enhancements, and energy (storage and evolving) issues in hydraulic excavators from a number of databases. This article reviews the technology of hydraulic excavators, covering their performance, related components, energy use, efficiency, and future opportunities. Research questions addressed include: How do hydraulic excavators work, what are the components, what is the role of maintenance in maintaining the performance of hydraulic excavator systems, what are the latest innovations in development for hydraulic excavator systems that can improve efficiency and reliability, and how can new technologies help reduce the impact on the environment. The method used to answer the research questions is SLR. The results of this article illustrate that excavator efficiency and performance depend on the architecture of the component layout, technology, systems and operational machinery used. The energy regeneration system serves to capture and store the potential energy generated during excavator operation. This stored energy can be reused to help power the hydraulic system, reducing the need for additional energy input. 
Co-Authors Abdu Malikh Silaban Aisyah Aira Putri Maharani Diky Zakaria Geralda Livia Nugraha Himmawan Sapta Adhi Muhamad Fajar Imanul Haq Rizky Hamdani Sakti