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Diky Zakaria
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INDONESIA
Journal of Mechatronics and Artificial Intelligence
Published by Universitas Pendidikan Indonesia
ISSN : 3062729X     EISSN : 30484227     DOI : https://doi.org/10.17509/jmai.v1i1
Core Subject :
The Journal of Mechatronics and Artificial Intelligence (JMAI) (E-ISSN 3048-4227 P-ISSN 3062-729X) serves as a platform for disseminating scholarly research related to the fields of mechatronics and artificial intelligence, as well as related sub-disciplines. We extend an invitation to researchers, engineers, senior researchers, lecturers, and students from Indonesia as well as countries across the globe to disseminate their research findings through our journal platform. A comprehensive and thorough review process will be implemented to guarantee the production of high-quality articles. The types of research that can be published on JMAI are Literature review articles, Empirical studies, Case studies, Theoretical articles. The scope of the journal are Mechatronics, Industrial automation, Robotics, Control and Systems, Sensors, Electronics, Electrical Machines, Image processing and pattern recognition, Artificial Intelligence, Machine learning, Instrumentation and Measurement, Agents and multi-agent systems, Natural language and Energy.
Arjuna Subject : -
Articles 26 Documents
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Designing Wheeled Robot with Rocker Bogie System for Precision Farming Muhammad Hafizh Aditya; M Bima Nugraha
Journal of Mechatronics and Artificial Intelligence Vol. 2 No. 2 (2025): JMAI: December 2025
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jmai.v2i2.91141

Abstract

This study presents the development of a small autonomous robot designed to support precision farming through improved mobility and control efficiency. The robot employs a Rocker-Bogie suspension system to maintain stability and traction across varied terrain, ensuring even load distribution. Six DC motors are controlled using a discrete PID system optimized through MATLAB simulations to achieve precise speed and position control. By integrating mechanical design and control optimization, the proposed system enhances operational efficiency, reduces environmental impact, and contributes to sustainable and intelligent agricultural practices.
Development and Evaluation of an IoT-Based Smart Manufacturing Framework for SMEs Amenda Tarigan; Ayu Nova Rahmawati; Andicho Haryus Wirasapta
Journal of Mechatronics and Artificial Intelligence Vol. 3 No. 1 (2026): JMAI: June 2026
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jmai.v3i1.122

Abstract

Small and Medium Enterprises (SMEs) play an important role in economic growth; however, many SMEs still face challenges related to productivity, operational efficiency, and technological adoption. The development of Internet of Things (IoT) technologies provides opportunities for SMEs to improve operational visibility and automation toward smart manufacturing systems. This study aims to develop a simplified IoT-based smart manufacturing framework for SMEs and validate the framework through laboratory-scale implementation. The research employed a qualitative and experimental approach consisting of literature analysis, framework development, laboratory-scale implementation, and system evaluation. The literature analysis identified that most IoT implementations in SMEs focus on real-time monitoring and basic automatic control using low-cost technologies such as sensors, microcontrollers, and wireless communication platforms. Based on these findings, a five-layer framework consisting of the Data Acquisition Layer, Monitoring Layer, Control Layer, Integration Layer, and Smart Decision Layer was proposed. To validate the framework, a laboratory-scale IoT monitoring and control system was developed using NodeMCU ESP8266, DHT11 sensor, MQ-series gas sensor, ultrasonic sensor, relay module, and IoT dashboard platform. The developed system successfully demonstrated real-time monitoring and automatic control capabilities under laboratory conditions. The results indicate that affordable IoT technologies are feasible for supporting the gradual adoption of smart manufacturing systems in SMEs. The proposed framework provides practical guidance for SMEs to progressively adopt IoT technologies according to their operational capabilities, with future integration of AI/ML-based decision support envisioned to enhance predictive and autonomous functionalities.
Smart Nutrition Monitoring: IoT-Based Rice Soil Health Classification Using Naive Bayes and Fuzzy Expert System Abi Bayu Perkasa; Aisyah Faradila Fatah; Istikomah; Abdul Rafi; Rifa’a Aemelia Kartika; Aslam Syahid Majid
Journal of Mechatronics and Artificial Intelligence Vol. 3 No. 1 (2026): JMAI: June 2026
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jmai.v3i1.134

Abstract

The implementation of Machine Learning (ML) in precision agriculture often stops at probabilistic outputs without producing directly executable (actionable) agronomic guidance. In response to this functional gap, this study created an IoT proof of Concept (PoC) system architecture design that integrates Machine Learning with the Mamdani Fuzzy Expert System. To validate the software before entering the implementation phase, the system was trained and tested using 5000 synthetic datasets. This synthetic data was generated using a Multimodal Gaussian distribution to represent real-world conditions in the field (TDS, pH, humidity, and temperature). The evaluation results obtained proved that the Naive Bayes classification was very efficient for use in system architectures with minimal resources, with an accuracy of 96.65% and a latency of 0.054.  In the final stage of the system, the Fuzzy Logic integration successfully produced output in the form of solutions and recommendations based on the values generated by the Machine Learning system. The integration of these three systems successfully became a unified, complete architectural system. This system has proven to be able to bridge the gap between the complexity of the system and the needs of farmers, and is ready to be implemented in the field using real-time data.
Mamdani Fuzzy-Based Soil Fertility Detection Using Moisture and Color Sensors Fikri Arif Wicaksana; Trisiani Dewi Hendrawati; Panji Narputro; M Farhan
Journal of Mechatronics and Artificial Intelligence Vol. 3 No. 1 (2026): JMAI: June 2026
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jmai.v3i1.135

Abstract

Soil fertility plays an important role in supporting agricultural productivity and sustainable farming practices. Conventional methods for determining soil fertility, such as visual observation of soil color and manual inspection of soil moisture, are often subjective, inefficient, and less accurate. This study proposes an Internet of Things (IoT)-based soil fertility detection system using a soil moisture sensor and a TCS3200 color sensor to provide real-time and objective soil condition monitoring. The system employs a NodeMCU ESP8266 microcontroller for data acquisition and wireless communication. Sensor data are processed using the Mamdani Fuzzy Inference System (FIS) to classify soil fertility into three categories: fertile, moderately fertile, and infertile. The developed system displays monitoring results locally through an OLED display and remotely through Google Spreadsheet integration for real-time observation. Sensor calibration and field testing were conducted using several soil samples with different moisture and color characteristics. Experimental results showed that the soil moisture sensor achieved an average error rate of 1.57%, indicating good measurement accuracy. Furthermore, the fuzzy-based classification successfully identified soil fertility levels according to the measured parameters. The integration of IoT technology and fuzzy logic provides an effective low-cost solution for precision agriculture applications, particularly for small-scale farming environments. The proposed system is expected to assist farmers in monitoring soil conditions more efficiently, accurately, and continuously.
Savonius Turbine Suitability Analysis Based on Low Wind Speed Characteristics Adi Nugraha; Fajar Ramadhan; Muhammad Fathurrizki; Reza Abdillah Prastian; Muhamad Khadavy; Muhammad Ilham Daifullah
Journal of Mechatronics and Artificial Intelligence Vol. 3 No. 1 (2026): JMAI: June 2026
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jmai.v3i1.137

Abstract

Wind energy is one of the renewable energy sources with significant potential to be developed as an environmentally friendly power generation alternative. However, the selection of wind turbine types must be adjusted to the wind speed characteristics of the implementation area to ensure optimal performance. This study aims to analyze the suitability of the Savonius turbine based on low wind speed characteristics. The research was conducted at the Faculty of Engineering, Universitas Sultan Ageng Tirtayasa (FT UNTIRTA), by measuring wind speed at three different locations using an anemometer. The measurement locations included an open sports field, a building rooftop, and a green open space area. The measurement results showed that the wind speed ranged from 1.5 m/s to 3.2 m/s, categorized as low wind speed below 4 m/s. The obtained data were analyzed and compared with the wind turbine performance characteristic graph based on the relationship between power coefficient (Cp) and turbine tip speed ratio (TSR). The analysis results indicate that the Savonius turbine has the most suitable characteristics for low wind speed conditions because it can operate at low TSR values and has good self-starting capability and high starting torque. In addition, the Savonius turbine offers simple construction, ease of manufacturing, and the ability to operate under varying wind directions. Based on the research results, the Savonius turbine is recommended as an alternative small-scale wind power generation system for areas with low wind speed characteristics.
Efficiency Evaluation of a DC Generator Using Experimental Loss Measurements Elysa Nensy Irawan; Chisaki Osumi
Journal of Mechatronics and Artificial Intelligence Vol. 3 No. 1 (2026): JMAI: June 2026
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jmai.v3i1.142

Abstract

This study evaluates the efficiency of a DC generator using experimental loss measurements based on a two-identical-machine method. Two identical DC machines were mechanically coupled, with one machine operated as the driving motor and the other as the generator under test. The total loss was determined from the difference between the motor input power and the generator output power, while the copper loss was calculated using the armature resistance obtained from a locked-rotor test. Since the generator did not have a field winding, only armature copper loss was considered. The friction loss was estimated from the no-load test, and the iron loss was determined using a residual loss method. The experimental results showed that the generator output power increased from 0.0064 W at 1.5 V to 1.8032 W at 5.0 V. The generator efficiency also increased with operating voltage, reaching a maximum value of 63.96% at 5.0 V. Copper loss increased significantly with load current and became a dominant loss component at higher operating conditions. The residual iron loss showed positive values at low voltage levels but became negative at higher voltages, indicating limitations in separating friction and iron losses using the current method. Overall, the proposed experimental approach was effective for evaluating the efficiency trend and identifying the dominant loss behavior of the DC generator, although further refinement is required to separate mechanical and magnetic losses more accurately.

Page 3 of 3 | Total Record : 26

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