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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
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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Studi Analisi Konsentrasi Warna Pada Cairan Pewarna Makanan Dengan Metode Pengukuran Optical Density Onie Meiyanto; Agus Indra Gunawan; Bima Sena Bayu Dewantara
BRILIANT: Jurnal Riset dan Konseptual Vol 6, No 4 (2021): Volume 6 Nomor 4, November 2021
Publisher : Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1307.714 KB) | DOI: 10.28926/briliant.v6i4.718

Abstract

Metode Image Processing banyak diimplementasikan untuk mengidetifikasi suatu bentuk atau perubahan pada gambar untuk mendapatkan hasil identifikasi suatu percobaan. Dalam penelitian ini perpaduan Image Processing, optical density(OD) dan sensor rgb untuk menentukan kualitas campuran air yang didapatkan nilai komposisi cairan warna. Karakteristik warna dari sampel air diperoleh dari histogram pada gambar yang tertangkap oleh mikroskop digital, dari histogram warna dapat diperoleh nilai max dan mean dan hasil gambar dari difraksi oleh kamera digital serta nilai output sensor rgb. Dengan metode tersebut diperoleh hasil setiap sampel yang telah di encerkan memiliki karakteristik warna yang berbeda-beda, hal ini dapat dilihat dari setiap kanal warna dari output sensor. Pengolahan data dengan metode histogram untuk dilakukan proses pengambilan nilai rata-rata(mean) dan nilai maksimum(Max) diperoleh model untuk memprediksi jenis dan konsentrasi dari sampel, pengujian yang telah dilakukan, didapatkan hasil grafik yang sigifikan sesuai dengan komposisi kualitas air dengan pewarna makanan
Algoritma Deep Learning-LSTM untuk Memprediksi Umur Transformator Ayu Ahadi Ningrum; Iwan Syarif; Agus Indra Gunawan; Edi Satriyanto; Rosmaliati Muchtar
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 3: Juni 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021834587

Abstract

Kualitas dan ketersediaan pasokan listrik menjadi hal yang sangat penting. Kegagalan pada transformator menyebabkan pemadaman listrik yang dapat menurunkan kualitas layanan kepada pelanggan. Oleh karena itu, pengetahuan tentang umur transformator sangat penting untuk menghindari terjadinya kerusakan transformator secara mendadak yang dapat mengurangi kualitas layanan pada pelanggan. Penelitian ini bertujuan untuk mengembangkan aplikasi yang dapat memprediksi umur transformator secara akurat menggunakan metode Deep Learning-LSTM. LSTM adalah metode yang dapat digunakan untuk mempelajari suatu pola pada data deret waktu. Data yang digunakan dalam penelitian ini bersumber dari 25 unit transformator yang meliputi data dari sensor arus, tegangan, dan suhu. Analisis performa yang digunakan untuk mengukur kinerja LSTM adalah Root Mean Squared Error (RMSE) dan Squared Correlation (SC). Selain LSTM, penelitian ini juga menerapkan algoritma Multilayer Perceptron, Linear Regression, dan Gradient Boosting Regressor sebagai algoritma pembanding.  Hasil eksperimen menunjukkan bahwa LSTM mempunyai kinerja yang sangat bagus setelah dilakukan pencarian komposisi data, seleksi fitur menggunakan algoritma KBest dan melakukan percobaan beberapa variasi parameter. Hasil penelitian menunjukkan bahwa metode Deep Learning-LSTM mempunyai kinerja yang lebih baik daripada 3 algoritma lain yaitu nilai RMSE= 0,0004 dan nilai Squared Correlation= 0,9690. AbstractThe quality and availability of the electricity supply is very important. Failures in the transformer cause power outages which can reduce the quality of service to customers. Therefore, knowledge of transformer life is very important to avoid sudden transformer damage which can reduce the quality of service to customers. This study aims to develop applications that can predict transformer life accurately using the Deep Learning-LSTM method. LSTM is a method that can be used to study a pattern in time series data. The data used in this research comes from 25 transformer units which include data from current, voltage, and temperature sensors. The performance analysis used to measure LSTM performance is Root Mean Squared Error (RMSE) and Squared Correlation (SC). Apart from LSTM, this research also applies the Multilayer Perceptron algorithm, Linear Regression, and Gradient Boosting Regressor as a comparison algorithm. The experimental results show that LSTM has a very good performance after searching for the composition of the data, selecting features using the KBest algorithm and experimenting with several parameter variations. The results showed that the Deep Learning-LSTM method had better performance than the other 3 algorithms, namely the value of RMSE = 0.0004 and the value of Squared Correlation = 0.9690.
Identifikasi Sinyal Elektromiografi Otot Vastus Medialis dan Erector Spinae dalam Transisi Gerakan untuk Kontrol Robot Kaki Farid Amrinsani; Zainal Arief; Agus Indra Gunawan
INOVTEK POLBENG Vol 9, No 2 (2019): INOVTEK VOL.9 NO 2 - 2019
Publisher : POLITEKNIK NEGERI BENGKALIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (305.664 KB) | DOI: 10.35314/ip.v9i2.1011

Abstract

Kehilangan beberapa bagian tubuh dan kelemahan otot akibat cedera adalah faktor yang mengganggu aktivitas manusia sehari-hari. Konsep exoskeleton adalah pendekatan yang sangat positif bagi manusia dalam hal kerusakan pada tungkai bawah. Dalam studi ini, ekstremitas bawah selama gerakan jongkok ke berdiri, berdiri ke duduk, duduk ke berdiri, dan berdiri ke jongkok menjadi fokus dalam penelitian ini. Sinyal elektromiografi terdeteksi dari vastus medialis dan erector spinae. Enam responden terlibat dalam melakukan percobaan ini. Ada 2 tahap dalam percobaan ini. Pada tahap pertama, gunakan fitur ekstraksi domain waktu seperti MAV, MAD, dan RMS. Latensi 500 ms dengan waktu tumpang tindih 10 ms digunakan. Ambang digunakan untuk mendeteksi awal kontraksi otot 0,002 mV dan bagian akhir kontraksi otot 0,0015 mV. Data dalam ambang batas digunakan sebagai input dari jaringan saraf tiruan. Penggunaan python 2.7 jaringan syaraf tiruan dibuat dengan 240 input node, 80 hidden node, dan 4 output node. Data pergerakan dengan total 556 digunakan untuk melatih jaringan. Data pergerakan dengan total 160 digunakan untuk menguji jaringan. Sistem ini mampu menginterpretasikan gerakan sebenarnya dengan nilai persentase 84% dan nilai kesalahan 16%. Pada tahap kedua menggunakan metode yang sama, sistem diuji dengan responden yang berbeda. Data pergerakan dengan total 104 digunakan untuk menguji jaringan. Persentase keberhasilan sistem dalam menafsirkan gerakan adalah 59% dan nilai kesalahan 41%.
Penerapan Machine Learning Pada Smart Socket Dengan Multi Sensor Untuk Adaptasi Pola Pemakaian Pengguna Muhammad Furqon; Agus Indra Gunawan; Bambang Sumantri; Ardik Wijayanto
SinarFe7 Vol. 1 No. 1 (2018): Sinarfe7-1A 2018
Publisher : FORTEI Regional VII Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (399.279 KB)

Abstract

Peralatan elektronik masa kini tidak hanya dituntut untuk mampu memenuhi kebutuhan manusia yang semakin kompleks dan mendetail, namun telah menghadapi tantangan baru, yaitu tuntutan kemampuan alat dalam beradaptasi dengan kebutuhan manusia itu sendiri. Oleh karena itu, pada penelitian ini akan dibuat sebuah prototype alat berupa stopkontak yang dilengkapi dengan multi sensor yang bertujuan untuk mendapatkan behavior dari user yang nantinya akan digunakan sebagai konfigurasi ON/OFF sistem pada stopkontak tersebut, yang kemudian disebut dengan smart socket. Terdapat beragam metode yang relevan telah diuji dan diaplikasikan pada penelitian-penelitian sebelumnya, salah satunya adalah stopkontak multi-fitur multi-sensor dengan kontroler kondisional namun, metode tersebut kurang efektif karena user diharuskan melakukan konfigurasi awal untuk menggunakan smart socket tersebut, dan sistemnya yang bersifat statis sehingga user diharuskan mengubah konfigurasi setiap kali kondisi yang diinginkan berubah. Adapun metode y ng digunakan pada penelitian ini adalah menggunakan KMeans clustering, dimana metode tersebut akan mengolah data respon user yang direkam oleh sistem menjadi sebuah konfigurasi ON/OFF otomatis yang dapat flexible berubah mengikuti behavior dari user tersebut. Hasil dari pengujian sistem terhadap dua subjek user didapatkan konfigurasi user1 (sistem akan menyala dari jam 12:21:37 hingga jam 17:53:18 dengan rata-rata konsumsi daya dalam satu hari adalah 165.61 Watt), user2 (sistem akan menyala dari jam 21:29:55 hingga jam 08:41:31, akan menyala lagi dari jam 17:40:13 hingga jam 18:14:51 dengan rata-rata konsumsi daya dalam satu hari adalah 543.14 Watt).
Veins projection performance based on ultrasonic distance sensor in various surface objects I Putu Adi Surya Gunawan; Riyanto Sigit; Agus Indra Gunawan
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 3: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i3.pp1362-1370

Abstract

Intravenous therapy aims to inject fluids such as medicine or nutritions into the body via vein vessel. This procedure is needed in various cases whether in an ordinary or emergency. Every person has a different difficulty level thus a nurse usually encountered a problem when locating the position of vein vessel. A visualization device that able to work in realtime and have high mobility is really necessary for an emergency situation to speed up the intravenous access. In this study, a stand-alone veins visualization system was developed. The back-projection method that can adjust based on distance was used to speed up the visualization process. The distance between the device and the object is obtained by an ultrasonic distance sensor. The results of this projection method with a flat surface have maximum shift of 0.48 mm. While on various surfaces, projection shifts under 0.9 mm reach 89% from 140 measurement points. Projection shifts that reach more than 0.9 mm occurred due to the sensor readings are on steep curvature or large angles between segments and sensors.
Salinity Sensor Development for Pond Water Utilizing Ultrasonic Wave Dananjaya Endi Pratama; Agus Indra Gunawan; Rusminto Tjatur Widodo; Akhmad Hendriawan
Jurnal Segara Vol 18, No 2 (2022): August
Publisher : Pusat Riset Kelautan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15578/segara.v18i2.10932

Abstract

Shrimp farming is one of the most popular aquaculture activities in Indonesia. This activity is carried out in a pond. Therefore, there are many ponds as a place for shrimp farming in Indonesia. Several factors affect the results of shrimp farming in ponds. One of the factors is water quality. Four parameters that are commonly used to indicate water quality i.e. dissolve oxygen, salinity, PH, and temperature. In this study, we discussed salinity measurement. Most salinity sensors use the probe principle in measurement. When the sensors are used to measure the water that contains mineral salts, the probe will be susceptible to rust and cause measurement errors. Based on these conditions, we conducted a study of salinity measurements by using the acoustic technique. The measurement was carried out by using an ultrasonic wave. The water salinity was determined based on the acoustic intensity and acoustic speed. In this research, we developed a conversion curve based on the measurement of acoustic intensity from NaCl, KCl, and MgCl2 saline solutions with certain concentrations. The conversion curve is used to measure salinity in pond water. We also calculated salinity based on the measurement result of acoustic speed. From the experiment, the NaCl conversion curve became the most suitable for salinity measurement. The measurement result of salinity in pond water from the NaCl saline solution conversion curve was very close to the results of Del Grosso formula, Chen Millero formula, and refractometer.
Penerapan Platform Fishtech Alat Monitoring dan Kontrol Otomatis Berbasis IoT untuk Budidaya Udang di Lamongan Agus Indra Gunawan; Ricky Afiful Maula; M. Udin Harun Al Rasyid; Ahmad Rifa’i
Journal of Applied Community Engagement Vol 2 No 1 (2022): Journal of Applied Community Engagement (JACE)
Publisher : ISAS (Indonesian Society of Applied Science)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1043.818 KB) | DOI: 10.52158/jace.v2i1.266

Abstract

Indonesia is an archipelagic country which has enormous potential in terms of fishery productivity. One type of fishery productivity in Indonesia is shrimp farming productivity. However, despite having high potential, not a few cultivators experience crop failure. This is due to several factors, including the lack of understanding of the cultivators who do not observe the important parameters of the pond, cultivation methods based on hereditary techniques that do not see the real condition of the pond, and so on. Therefore, to overcome these problems, this community service program aims to implement a technology utilization for monitoring, management and automatic control so that aquaculture ponds are always in optimal condition. This system is called the fishtech platform which implements Internet of Things technology. With Internet of Things (IoT) technology, it is possible for users (cultivators) to know in real time the condition and quality of aquaculture ponds.
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 | Full PDF (1335.261 KB) | 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.
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 | Full PDF (814.834 KB) | DOI: 10.17529/jre.v15i1.13298

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (779.394 KB) | DOI: 10.17529/jre.v19i1.28631

Abstract

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.
Co-Authors Ahmad Rifa’i Akhmad Hendriawan Ardik Wijayanto Ayu Ahadi Ningrum Bambang Sumantri Bima Sena Bayu Dewantara Dananjaya Endi Pratama Edi Satriyanto 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 Onie Meiyanto Oskar Natan Ricky Afiful Maula Ricky Afiful Maula Riyanto Sigit Rosmaliati, Rosmaliati Rusminto Tjatur Widodo Setiawardhana Setiawardhana Taufiqurrahman Taufiqurrahman Tri Budi Santoso Tri Budi Santoso Zainal Arief