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Rancang Bangun Sistem Multipoint Transmitter – Receiver untuk Inspeksi Bawah Air Berbasis Ultrasonik Frekuensi Rendah Muhammad Edy Hidayat; Agus Indra Gunawan; Tri Budi Santoso
Jurnal Rekayasa Elektrika Vol 16, No 3 (2020)
Publisher : Universitas Syiah Kuala

Non-destructive testing and evaluation are testing techniques that test and evaluate the properties of a material, component, or system without causing any damage caused by the testing and evaluation process. Ultrasonic sensors are devices with minimal risk in their use and are quite often used in non-destructive testing and evaluation processes. Low frequency ultrasonic (200kHz) has been used in the testing and evaluation process in several scientific fields. Improving the test capability of low-frequency ultrasonic measurement instruments while remaining efficient and affordable is the core of this research. Increasing test capability and efficiency by adding five test points to a low-frequency ultrasonic measurement instrument for underwater inspections have been carried out by engineering a trigger signal generator that transmits 35kHz signals at 50V voltage proven to improve the quality of the echo signal received when compared to using trigger signal sourced directly from the wave generator device, the use of a pre-amplifier module on the receiver side of the echo signal is proven to be able to increase the voltage level of the echo signal and improve the reading value of the received echo signal, as well as the signal coupling mechanism built in this study, proved to be adequate to increase efficiency multipoint testing using one ultrasonicbased testing instrument.

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.

Water Quality Level for Shrimp Pond at Probolinggo Area Based on Fuzzy Classification System Fithrotul Irda Amaliah; Agus Indra Gunawan; Taufiqurrahman Taufiqurrahman; Bima Sena Bayu Dewantara; Ferry Astika Saputra
Jurnal Rekayasa Elektrika Vol 19, No 1 (2023)
Publisher : Universitas Syiah Kuala

Since several years ago, vaname shrimp (Litopenaeus vannamei) has been extensively cultivated in Indonesia because it has good business opportunities. In aquaculture activities, water quality is an important factor that dramatically impacts the survival and quality of shrimp in the pond. Therefore, information of water quality must be known by the farmer for obtaining a satisfactory harvest. This study aims to develop a water quality monitoring system based on information of temperature, pH, salinity, and dissolved oxygen. The data from sensors are sent to the cloud utilizing Internet of Things (IoT) technology and then classified by a fuzzy logic system. In order to help farmers easily know the water quality of their shrimp pond, four sensor data including the result of classification from fuzzy logic are sent to the phone. After a trial of the system, 100% of the data are successfully sent to the cloud (google spreadsheet). The system also successfully classified the level of water quality as the expectation of the farmer. With this system, it is hoped that it can assist farmers in monitoring the water quality of shrimp pond to improve the quality and quantity of shrimp.

Handling Missing Value dengan Pendekatan Regresi pada Dataset Akuakultur Berukuran Kecil Ricky Afiful Maula; Agus Indra Gunawan; Bima Sena Bayu Dewantara; M. Udin Harun Al Rasyid; Setiawardhana Setiawardhana; Ferry Astika Saputra; Junaedi Ispianto
Jurnal Rekayasa Elektrika Vol 18, No 3 (2022)
Publisher : Universitas Syiah Kuala

Shrimp cultivation is strongly influenced by pond water quality conditions. Farmers must know the appropriate action in regulating water quality that is suitable for shrimp survival. The state of water quality can be understood by measuring pond parameters using various sensors. Installing sensors equipped with artificial intelligence modules to inform water quality conditions is the right action. However, the sensor cannot be separated from errors, so it results in not being able to get data or missing data. In this case, the approach of 5 parameters of pond water quality from 13 available parameters is carried out. This paper proposes a technique to obtain lost data caused by sensor error and looks for the best model. A simple approach can be taken, such as the Handling Missing Value (HMV), which is commonly used, namely the mean, with the K-Nearest Neighbors (KNN) classifier optimized using a grid search. However, the accuracy of this technique is still low, reaching 0.739 at 20-fold cross-validation. Calculations were carried out with other methods to further improve the prediction accuracy. It was found that Linear Regression (LR) can increase accuracy up to 0.757, which outperforms different approaches such as the statistical approach to mean 0.739, mode 0.716, median 0.734, and regression approach KNN 0.742, Lasso 0.751, Passive Aggressive Regressor (PAR) 0.737, Support Vector Regression (SVR) 0.739, Kernel Ridge (KR) 0.731, and Stochastic Gradient Descent (SGD) 0.734.

Pengukuran Speed dan Impedansi Akustik pada Tanah Liat dengan Memanfaatkan Sinyal Echo Ultrasonik Lusiana Lusiana; Agus Indra Gunawan; Bima Sena Bayu Dewantara
Jurnal Rekayasa Elektrika Vol 15, No 2 (2019)
Publisher : Universitas Syiah Kuala

Each material has its own characteristics, which are represented by the value of speed/ultrasonic wave propagation speed (C) and acoustic impedance/material resistance (Ztl). One technique that can be used to obtain these characteristics is by applying ultrasonic testing. This technique utilizes two ultrasonic sensors as transmitter (UST) and receiver (USR) to get signal properties from each material. The measurement mechanism is nondestructive testing (NDT) where the material tested is not damaged so it does not change the character of the sample. In this research, material characteristics are represented by reflected signals from material (echo). To process the echo signal data and get the characteristics of the sample, we need a number of data processing algorithms such as Fast Fourier Transform (FFT), Peak Detection, and Grid Search. By processing echo from reflected signals, C and Ztl values can be obtained. From the experimental results obtained, the values of C and Ztl in sample 1 with a density of 1856.97573 g/m3 are C = 636 m/s and Ztl = 474640 Ns/m3, samples 2 with a density of 1792.94208 g/m3 of C = 491 m/s and Ztl = 408080 Ns/m3, while the sample 3 with a density of 1663.85025 g/m3 is C = 434 m/s and Ztl = 405639 Ns/m3. The value of material characterization shown that a dense clay also has higher C and Ztl.

Karakterisasi dari Properti Larutan Garam dengan Range Finder Ultrasonik Menggunakan Metode Transformasi Fourier Ihwan Dwi Wicaksono; Agus Indra Gunawan; Bima Sena Bayu Dewantara
Jurnal Rekayasa Elektrika Vol 16, No 2 (2020)
Publisher : Universitas Syiah Kuala

In this paper we characterize the saline solution using Range Finder Ultrasonic (RFU). RFU is one kind of ultrasonic transducer that requires air as a transmission medium and commonly are used to determine distances. The advantages of this transducer are cheap and common in local market. Since it uses air as medium, the signal which is produced by transducer are easy to shape shift and has a very long noise tail wave. This phenomenon was seen in previous studies, when the transducer position was slightly shifted, the shape of the echo signal became very different. In this paper, we modified the input signal from the technique in the previous paper to improve the echo signal. Some modification of trigger signal from transmitter models were done, then calculate the echo signal to ensure the signal have smallest Signal to Noise Ratio (SNR) and noise tail wave. Furthermore, we did filtering process from echo signal and calculating using Fourier Transform which are performed to obtain accurate echo signal information of 40 KHz frequency. The results of this experiment is an improvement in the average error of calibration curve 0.1224221 (Vrms) and 0.14383881 (Vpeak). While the average error of the results of the normalization of the magnitude Fourier Transform of 40 KHz frequency is equal to 0.096973114.

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.

Karakterisasi dari Properti Larutan Garam dengan Range Finder Ultrasonik Menggunakan Metode Transformasi Fourier Ihwan Dwi Wicaksono; Agus Indra Gunawan; Bima Sena Bayu Dewantara
Jurnal Rekayasa Elektrika Vol 16, No 2 (2020)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v16i2.15371

In this paper we characterize the saline solution using Range Finder Ultrasonic (RFU). RFU is one kind of ultrasonic transducer that requires air as a transmission medium and commonly are used to determine distances. The advantages of this transducer are cheap and common in local market. Since it uses air as medium, the signal which is produced by transducer are easy to shape shift and has a very long noise tail wave. This phenomenon was seen in previous studies, when the transducer position was slightly shifted, the shape of the echo signal became very different. In this paper, we modified the input signal from the technique in the previous paper to improve the echo signal. Some modification of trigger signal from transmitter models were done, then calculate the echo signal to ensure the signal have smallest Signal to Noise Ratio (SNR) and noise tail wave. Furthermore, we did filtering process from echo signal and calculating using Fourier Transform which are performed to obtain accurate echo signal information of 40 KHz frequency. The results of this experiment is an improvement in the average error of calibration curve 0.1224221 (Vrms) and 0.14383881 (Vpeak). While the average error of the results of the normalization of the magnitude Fourier Transform of 40 KHz frequency is equal to 0.096973114.

Water Quality Level for Shrimp Pond at Probolinggo Area Based on Fuzzy Classification System Fithrotul Irda Amaliah; Agus Indra Gunawan; Taufiqurrahman Taufiqurrahman; Bima Sena Bayu Dewantara; Ferry Astika Saputra
Jurnal Rekayasa Elektrika Vol 19, No 1 (2023)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v19i1.28631

Since several years ago, vaname shrimp (Litopenaeus vannamei) has been extensively cultivated in Indonesia because it has good business opportunities. In aquaculture activities, water quality is an important factor that dramatically impacts the survival and quality of shrimp in the pond. Therefore, information of water quality must be known by the farmer for obtaining a satisfactory harvest. This study aims to develop a water quality monitoring system based on information of temperature, pH, salinity, and dissolved oxygen. The data from sensors are sent to the cloud utilizing Internet of Things (IoT) technology and then classified by a fuzzy logic system. In order to help farmers easily know the water quality of their shrimp pond, four sensor data including the result of classification from fuzzy logic are sent to the phone. After a trial of the system, 100% of the data are successfully sent to the cloud (google spreadsheet). The system also successfully classified the level of water quality as the expectation of the farmer. With this system, it is hoped that it can assist farmers in monitoring the water quality of shrimp pond to improve the quality and quantity of shrimp.

Pengukuran Speed dan Impedansi Akustik pada Tanah Liat dengan Memanfaatkan Sinyal Echo Ultrasonik Lusiana Lusiana; Agus Indra Gunawan; Bima Sena Bayu Dewantara
Jurnal Rekayasa Elektrika Vol 15, No 2 (2019)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v15i2.13815

Each material has its own characteristics, which are represented by the value of speed/ultrasonic wave propagation speed (C) and acoustic impedance/material resistance (Ztl). One technique that can be used to obtain these characteristics is by applying ultrasonic testing. This technique utilizes two ultrasonic sensors as transmitter (UST) and receiver (USR) to get signal properties from each material. The measurement mechanism is nondestructive testing (NDT) where the material tested is not damaged so it does not change the character of the sample. In this research, material characteristics are represented by reflected signals from material (echo). To process the echo signal data and get the characteristics of the sample, we need a number of data processing algorithms such as Fast Fourier Transform (FFT), Peak Detection, and Grid Search. By processing echo from reflected signals, C and Ztl values can be obtained. From the experimental results obtained, the values of C and Ztl in sample 1 with a density of 1856.97573 g/m3 are C = 636 m/s and Ztl = 474640 Ns/m3, samples 2 with a density of 1792.94208 g/m3 of C = 491 m/s and Ztl = 408080 Ns/m3, while the sample 3 with a density of 1663.85025 g/m3 is C = 434 m/s and Ztl = 405639 Ns/m3. The value of material characterization shown that a dense clay also has higher C and Ztl.

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