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Design and Implementation of Embedded Water Quality Control and Monitoring System for Indoor Shrimp Cultivation Natan, Oskar; Gunawan, Agus Indra; Dewantara, Bima Sena Bayu
EMITTER International Journal of Engineering Technology Vol 7, No 1 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Maintaining the water quality of a pond is one of the main issues on aquaculture management. Water quality represents the condition of a pond based on several water parameters such as dissolved oxygen (DO), temperature, pH, and salinity. All of these parameters need to be strictly supervised since it affects the life-sustainability of cultivated organisms. However, DO is said to be the main parameter since it affects the growth and survival rate of the shrimp. Therefore, a water quality control and monitoring system is needed to maintain water parameters at acceptable value. The system is developed on a mini-PC and microcontroller which are integrated with several sensors and actuator forming an embedded system. Then, this system is used to collect water quality data that is consisting of several water parameters and control the DO as the main parameter. In accordance with the stability needs against the sensitive environment, a fuzzy logic-based controller is developed to maintain the DO rate in the water. This system is also equipped with SIM800 module to notice the farmer by SMS, built-in wifi module for web-based data logging, and improved with Android-based graphical user interface (GUI) to perform user-friendly monitoring. From the experiment results, a fuzzy controller that is attached to the system can control the DO at the acceptable value of 6 ppm. The controller is said to have high robustness since its deviation for long-time use is only 0.12 ppm. Another test shows that the controller is able to overcome the given disturbance and easily adapt when the DO’s set point is changed. Finally, the system is able to collect and store the data into cloud storage periodically and show the data on a website.

Design and Implementation of Embedded Water Quality Control and Monitoring System for Indoor Shrimp Cultivation Oskar Natan; Agus Indra Gunawan; Bima Sena Bayu Dewantara
EMITTER International Journal of Engineering Technology Vol 7 No 1 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Maintaining the water quality of a pond is one of the main issues on aquaculture management. Water quality represents the condition of a pond based on several water parameters such as dissolved oxygen (DO), temperature, pH, and salinity. All of these parameters need to be strictly supervised since it affects the life-sustainability of cultivated organisms. However, DO is said to be the main parameter since it affects the growth and survival rate of the shrimp. Therefore, a water quality control and monitoring system is needed to maintain water parameters at acceptable value. The system is developed on a mini-PC and microcontroller which are integrated with several sensors and actuator forming an embedded system. Then, this system is used to collect water quality data that is consisting of several water parameters and control the DO as the main parameter. In accordance with the stability needs against the sensitive environment, a fuzzy logic-based controller is developed to maintain the DO rate in the water. This system is also equipped with SIM800 module to notice the farmer by SMS, built-in wifi module for web-based data logging, and improved with Android-based graphical user interface (GUI) to perform user-friendly monitoring. From the experiment results, a fuzzy controller that is attached to the system can control the DO at the acceptable value of 6 ppm. The controller is said to have high robustness since its deviation for long-time use is only 0.12 ppm. Another test shows that the controller is able to overcome the given disturbance and easily adapt when the DOâ€™s set point is changed. Â Finally, the system is able to collect and store the data into cloud storage periodically and show the data on a website.

Grid SVM: Aplikasi Machine Learning dalam Pengolahan Data Akuakultur Oskar Natan; Agus Indra Gunawan; Bima Sena Bayu Dewantara
Jurnal Rekayasa Elektrika Vol 15, No 1 (2019)
Publisher : Universitas Syiah Kuala

Water condition is the main factor that affects the success rate of aquaculture, especially in shrimp cultivation. However, the farmer often experiences difficulties in determining the condition which is stated based on the measurement of various water parameter. Therefore, a proper classification model is needed to help the farmer in classifying the water condition in a pond. By knowing the condition, then proper and correct treatment can be given. In this research, a machine learning algorithm called SVM is used to make a model from an aquaculture dataset. Another processing technique like data normalization and the usage of optimization algorithm named grid search is also performed to improve the modelling result. Furthermore, a test scheme with using k-fold cross-validation is performed to know the performance of the model which is measured by the value of accuracy, precision, recall, f-measure, and AUROC. Then, the SVM model is compared with several models which are made by using another machine learning algorithm such as KNN, CNB, RF, MLP, and LR in order to know the best model to be implemented on cultivation process. From the experiment results, the model which is made with SVM and grid search optimization has the best performance in the validation process with the performance score of 3.54383.

Grid SVM: Aplikasi Machine Learning dalam Pengolahan Data Akuakultur Oskar Natan; Agus Indra Gunawan; Bima Sena Bayu Dewantara
Jurnal Rekayasa Elektrika Vol 15, No 1 (2019)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v15i1.13298

Water condition is the main factor that affects the success rate of aquaculture, especially in shrimp cultivation. However, the farmer often experiences difficulties in determining the condition which is stated based on the measurement of various water parameter. Therefore, a proper classification model is needed to help the farmer in classifying the water condition in a pond. By knowing the condition, then proper and correct treatment can be given. In this research, a machine learning algorithm called SVM is used to make a model from an aquaculture dataset. Another processing technique like data normalization and the usage of optimization algorithm named grid search is also performed to improve the modelling result. Furthermore, a test scheme with using k-fold cross-validation is performed to know the performance of the model which is measured by the value of accuracy, precision, recall, f-measure, and AUROC. Then, the SVM model is compared with several models which are made by using another machine learning algorithm such as KNN, CNB, RF, MLP, and LR in order to know the best model to be implemented on cultivation process. From the experiment results, the model which is made with SVM and grid search optimization has the best performance in the validation process with the performance score of 3.54383.

Sistem Klasifikasi Sampah Otomatis Berbasis Deteksi Objek Real-Time Pada Single Board Computer Dengan Algoritma YOLO Firdaus, Ahmad Zaki; Lelono, Danang; Natan, Oskar
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 15, No 1 (2025): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijeis.104520

The development of an automatic waste classification system based on real-time object detection using the YOLO (You Only Look Once) algorithm on a Raspberry Pi 5 Single Board Computer (SBC) is the main focus of this final project. The main issue addressed is the increasing accumulation of waste, particularly in Indonesia, which requires an effective solution for automatic waste sorting. The system is designed to detect and sort plastic and metal waste in real-time using deep learning and computer vision technologies.This research employs the YOLO11n model, trained on a dataset of plastic and metal waste. The training process involves data augmentation techniques such as rotation and grayscale to enhance dataset variability. The training results show a mean Average Precision (mAP) of 98.44% on testing data. The system is implemented on a Raspberry Pi 5, with the model converted to NCNN format to improve inference speed. Testing results indicate that the system can achieve a speed of 8.90 FPS with a latency of 110 ms, meeting the criteria for a real-time system.

Camera-based simultaneous localization and mapping: methods, camera types, and deep learning trends Dwimantara, Anak Agung Ngurah Bagus; Natan, Oskar; Indarto, Novelio Putra; Dharmawan, Andi
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i2.pp162-172

The development of simultaneous localization and mapping (SLAM) technology is crucial for advancing autonomous systems in robotics and navigation. However, camera-based SLAM systems face significant challenges in accuracy, robustness, and computational efficiency, particularly under conditions of environmental variability, dynamic scenes, and hardware limitations. This paper provides a comprehensive review of camera-based SLAM methodologies, focusing on their different approaches for pose estimation, map reconstruction, and camera type. The application of deep learning also will be discussed on how it is expected to improve performance. The objective of this paper is to advance the understanding of camera-based SLAM systems and to provide a foundation for future innovations in robust, efficient, and adaptable SLAM solutions. Additionally, it offers pertinent references and insights for the design and implementation of next-generation SLAM systems across various applications.

Edge-aware distilled segmentation with pseudo-label refinement for autonomous driving perception Indarto, Novelio Putra; Natan, Oskar; Dharmawan, Andi
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i3.pp376-386

Achieving precise semantic segmentation is essential for enabling real-time perception in autonomous systems, yet leading approaches typically require substantial annotated data and powerful hardware, restricting their use on devices with limited resources. This work introduces an efficient segmentation framework that integrates pseudo-label refinement, knowledge distillation, and entropy-based confidence filtering to train compact student networks suitable for edge deployment. High-quality pseudo-labels are first produced by a robust teacher network, then further improved using a dense conditional random field to boost spatial consistency. An entropy-based selection mechanism removes unreliable predictions, ensuring that only the most trustworthy labels guide the student model's training. The use of knowledge distillation effectively transfers detailed semantic understanding from the teacher to the student, enhancing accuracy without added computational overhead. Experimental results with multiple EfficientNet backbones reveal that this pipeline improves segmentation accuracy and output clarity, while also supporting real-time or near real-time inference on CPUs with limited processing power. Extensive ablation and qualitative studies further confirm the method's robustness and flexibility for real-world edge applications.

Humanoid robot balance control system during backward walking using linear quadratic regulator Arsyi, Muhammad; Dharmawan, Andi; Sumbodo, Bakhtiar Alldino Ardi; Auzan, Muhammad; Istiyanto, Jazi Eko; Natan, Oskar
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i3.pp320-330

Humanoid robots are designed to replicate human activities, including tasks in hazardous environments. However, maintaining balance during backward walking remains a significant challenge due to center of mass (CoM) shifts beyond the support polygon and limited knee joint motion. This study proposes a control strategy that integrates a linear quadratic regulator (LQR) with optimized walking patterns to enhance dynamic stability. The approach combines LQR-based control with CoM trajectory planning to ensure safe and stable backward walking. The methodology includes inverse kinematics for generating walking patterns and the use of Inertial Measurement Unit (IMU) sensors to estimate the CoM trajectory. LQR parameters were tuned through simulation to improve responsiveness to disturbances. Evaluation metrics focused on CoM deviation, rise time, settling time, and overshoot. Experimental results demonstrate that the proposed LQR system effectively maintains the CoM within 5% of the support polygon boundary. The system achieved rise times under one second and settling times below two seconds, while minimizing pitch and roll overshoots. Compared to proportional control, the proposed method significantly improves stability and reduces the risk of falling. This research advances control strategies for humanoid robots, contributing to improved mobility and operational safety. Moreover, it supports Sustainable Development Goal (SDG) 9 by promoting innovation in intelligent robotic systems that can assist in complex or high-risk environments.

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