Article Per Year (5 Year)
p-Index From 2020 - 2025
1.066
P-Index
This Author published in this journals
All Journal The Indonesian Journal of Computer Science
Aye, Maung
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
Weather Monitoring and Prediction System for Rice Cultivation in Mandalay Using IoT and Machine Learning Khaing Zar Zar Myint; Aye, Maung; Hla, Tin Tin
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4749

Abstract

The purpose of this research is to monitor and predict temperature, humidity and carbon-dioxide with the objective of increasing rice yields in the rice fields located east of Mandalay. This research focuses on monitoring the temperature and humidity of rice fields near MTU. The data are displayed on a LCD and uploaded to a server to ensure timely access for farmers. Monthly weather forecasts are provided to assist farmers in making advance preparations. The energy generated by the solar system is sufficient to meet the system’s low power consumption requirements. An ESP32 collects weather data from DHT11 sensor. CO2 data from the DM118 sensor is sent to the ESP32 via Arduino UNO using the UART protocol. These data are uploaded to the AWS Lightsail server. LSTM well-suited for time-series and sequence prediction tasks. Additionally, the data is presented in the farmers’ native language to ensure readability for non-English speakers.
Comparative Evaluation of Inception V3 and YOLOv8 for Strawberry Plant Diseases Classification Using Deep Learning Models Tin Tin Wai; Aye, Maung; Hla, Tin Tin
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4750

Abstract

Plant diseases and pests threaten agricultural productivity, with leaf diseases causing major crop losses. Early detection is essential to mitigate these impacts. This study presents a system for detecting strawberry leaf diseases using deep learning-based Convolutional Neural Networks (CNNs) by utilizing two pre-trained models, Inception V3 and YOLOv8, to classify leaves as healthy or diseased. A custom dataset of 5,192 images, comprising one healthy class and four disease-infected categories (leaf blight, blotch, scorch, and spot), is used. Inception V3 achieved 93.8% accuracy, while YOLOv8 outperformed it with 95.4% accuracy, a mAP of 78.6%, and precision, recall, and F1-scores of 89%, 88%, and 89%, respectively. With a compact size of 12 MB and a rapid inference time of 10 ms per image, YOLOv8 is highly suitable for real-time applications. These findings highlight YOLOv8's potential to improve agricultural productivity and food security through precise and efficient disease detection.
Development and Performance Analysis of a Human Detection Robot Using YOLOv8 and PWM-Based Speed Control Ni Ni Htay Lwin; Aye, Maung; Tin Tin Hla
The Indonesian Journal of Computer Science Vol. 14 No. 3 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i3.4896

Abstract

This paper presents the design and performance evaluation of a human detection robot using the YOLOv8 model and the COCO dataset for object recognition. The robot is equipped with a Pi camera, Raspberry Pi, four GM25 13CPR motors, an L298 motor driver, and a buck converter, ensuring efficient operation in real-time environments. The human detection accuracy was evaluated at different distances, achieving 99% at 2 feet, 98% at 15 feet, and 96% at 25 feet, demonstrating the effectiveness of the YOLOv8 model in varying conditions.The robot's movement is controlled using a PWM-based speed control technique, where the DC motors operate at different duty cycles. Experimental results show variations in speed accuracy, with error percentages of 7.6% at 20% duty cycle, 5.8% at 40%, 5.1% at 60%, 4.8% at 80%, and 3.8% at 100% duty cycle. These results indicate that higher duty cycles lead to improved speed accuracy, minimizing the deviation from the desired speed. The study highlights the integration of YOLOv8 for object detection and PWM for precise motor control, making the system suitable for applications in autonomous navigation, surveillance, and security.
Enhancing Agricultural Efficiency: Deep Learning-Based Soil Crack Detection for Water Irrigation Myint, Khin Moe; Aye, Maung; Hla, Tin Tin
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3979

Abstract

The escalating demand for agricultural precision and environmental monitoring underscores the necessity for effective soil crack detection methods. This study explores the feasibility of employing a Raspberry Pi-powered camera system and deep learning image recognition to detect soil cracks and control agricultural irrigation. The purpose is to develop a soil crack detection system using deep learning techniques, sustain plant growth process, increase productivity, and optimize water irrigation practice. Our approach leverages TensorFlow to craft a convolutional neural network tailored specifically for execution on a Raspberry Pi 3B+. A dataset comprises manually captured images and is trained with the InceptionV3 model categorized into crack or nocrack classes. The accuracy is achieved ranging from 97% to 99%. These results underscore deep learning image recognition models on Raspberry Pi for cost-effective soil crack monitoring and controlling the plants watering system.
Performance Evaluation of Physical Properties on Zinc Sulfide (ZnS)-based Field Effect Transistor Mya Su Kyi; Aye, Maung; Hla, Tin Tin
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3968

Abstract

The paper presents the performance evaluation of physical properties on Zinc Sulfide (ZnS)-based Field Effect Transistor. The most famous III-V compound-based semiconductor devices have several affected to the environment and the toxic contents are directly responded to the society. Due to the lack of technology on nontoxic compound-based semiconductor device fabrications, the novel device with II-VI compound materials are challenging issues for the environments. The specific objectives of doing research on fabrication of II-VI compound-based semiconductor devices in advanced laboratories are to emphasize the numerical modeling of the device structure and designing the FET based on ZnS material, to contribute the mathematical model for physical characteristics of the FET structure and the modification of the device structure will be easily established by using numerical simulation. The mathematical analyses on physical properties of device structure with ZnS material are confirmed and observed the several properties of electrical and electronic characteristics. The detailed implementations for ZnS-based FET devices are performed and evaluated the performance of the developed FET devices. There are two steps analyses in physical properties of ZnS-based FET devices with numerical implementation by MATLAB languages. The results observed in this study could be confirmed with the recent works from several research laboratories and the developed ZnS-based FET devices could be utilized in high performance wide band applications on switching in the power electronics and amplification purposes in modern amplifier design in real world applications.
Analysis on Heterostructure Band Diagram Designs and Electrical Characteristics of Light Emitting Diode (LED) Lin, Khin Ohmar; Aye, Maung; Hla, Tin Tin
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4353

Abstract

This paper presents the analysis on hetero-structure band diagram designs and electrical characteristics of light emitting diode (LED). This work is based on physical parameters of ZnCdSe, a ternary alloy semiconductor composed of Zinc (Zn), Cadmium (Cd), and Selenium (Se). In this work, the band diagram design results for n-ZnCdSe/p-Si heterojunction leds are approved by the parameters of the material such as such as effective masses of the materials, doing concentrations, electron and hole hall mobilities, bandgap bowing and so on. The electrical characteristics (energy dispersion with wave vector, intrinsic carrier concentration with temperature, intensity with wavelengths) of LED based on ZnCdSe material are also analyzed in this paper. This simulation curves are done by computer simulation. The band layer design for n-ZnCdSe/p-Si LED confirmed the high quality LED for real applications. This research gives information for the researchers who study in electronic and optoelectronic semiconductor devices.
Co-Authors Hla, Tin Tin Khaing Zar Zar Myint Lin, Khin Ohmar Mya Su Kyi Myint, Khin Moe Ni Ni Htay Lwin Tin Tin Hla Tin Tin Wai