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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Briliant: Jurnal Riset dan Konseptual INOVTEK POLBENG Jurnal Segara Indonesian Journal of Electrical Engineering and Computer Science SinarFe7 Journal of Applied Community Engagement (JACE) Jurnal Rekayasa elektrika The Indonesian Journal of Computer Science
Agus Indra Gunawan
Politeknik Elektronika Negeri Surabaya
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Materials Science & Nanotechnology

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Journal : Jurnal Rekayasa elektrika

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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

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v16i3.17512

Abstract

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.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v18i3.25903

Abstract

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.
Co-Authors Ahmad Rifa’i Akhmad Hendriawan Ardik Wijayanto Ayu Ahadi Ningrum Bambang Sumantri Bima Sena Bayu Dewantara Dananjaya Endi Pratama Edi Satriyanto Faizulddin Ebrahimi Farid Amrinsani Ferry Astika Saputra Fithrotul Irda Amaliah I Putu Adi Surya Gunawan Ihwan Dwi Wicaksono iwan Syarif Junaedi Ispianto Lusiana Lusiana M Udin Harun Al Rasyid, M Udin Harun Muhammad Edy Hidayat Muhammad Furqon Novie Ayub Windarko Onie Meiyanto Oskar Natan Ricky Afiful Maula Ricky Afiful Maula Riyanto Sigit Rosmaliati, Rosmaliati Rossi Arisdiawan Rusminto Tjatur Widodo Setiawardhana Setiawardhana Setiawardhana Taufiqurrahman Taufiqurrahman Tri Budi Santoso Tri Budi Santoso Zainal Arief