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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal INKOM TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JETT (Jurnal Elektro dan Telekomunikasi Terapan) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Building of Informatics, Technology and Science Journal of Electronics, Electromedical Engineering, and Medical Informatics IJAIT (International Journal of Applied Information Technology) Journal of Applied Engineering and Technological Science (JAETS) Jurnal Abdi Insani Madani : Indonesian Journal of Civil Society JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Charity : Jurnal Pengabdian Masyarakat JURNAL ILMIAH GLOBAL EDUCATION Prosiding Konferensi Nasional PKM-CSR Jurnal Nasional Teknik Elektro dan Teknologi Informasi eProceedings of Applied Science eProceedings of Engineering Abdibaraya: Jurnal Pengabdian Masyarakat Jurnal Rekayasa elektrika Jurnal INFOTEL Journal of Applied Engineering and Social Science Proceeding of Community Service and Engagement
Sugondo Hadiyoso
Universitas Telkom
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Nursing Public Health Social Sciences Other

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Analisis Perbandingan Pola Sinyal Alpha Dan Beta Eeg Dalam Kondisi Trypophobia Dengan Metode Wavelet Jehan Pratama Herdaning; Inung Wijayanto; Sugondo Hadiyoso
eProceedings of Engineering Vol 6, No 1 (2019): April 2019
Publisher : eProceedings of Engineering

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Abstrak Phobia merupakan rasa takut manusia akan hal-hal yang sangat sepele bagi mayoritas orang. Salah satu phobia yaitu Trypophobia merupakan rasa takut akan visual lubang-lubang yang kecil. Pengaruh dari efek trypophobia itupun bisa kita lihat gelombang otaknya dengan alat bernama EEG atau disebut Electroencephalograph, sehingga kita bisa mengetahui seseorang itu benar-benar mengalami Trypophobia atau tidak. Pada tugas akhir ini dibangun sistem untuk mengklasifikasikan kondisi seseorang tidak merasa takut, dan kondisi seseorang merasa takut akan Trypophobia berdasarkan analisi sinyal alpha dan beta EEG. Artificial Neural Network (ANN) digunakan untuk pengklasifikasian kondisi. Untuk ekstra ciri datanya digunakan Discrete Wavelete Transform (DWT) agar performansi sistem bisa ditingkatkan dan melakukan reduksi dimensi dataset EEG. Hasil pengujianya menunjukan bahwa performa terbaik didapatkan pada sinyal beta yang memiliki akurasi parameter ciri tertinggi yaitu Maksimum, Standar Deviasi dan Variansi dengan nilai akurasi 100%, dengan waktu komputasi 0.027 dan 0.037 detik. Sedangkan untuk sinyal alfa didapat dengan parameter Variansi dan Interquartile Range sebesar 96.42% dengan waktu 0.03 dan 0.032 detik. Meskipun akurasinya sama, namun rata-rata akurasi berdasarkan neuronnya, beta lebih tinggi dari pada alfa, sehingga dapat disimpulkan sinyal beta lebih peka terhadap ketakutan seperti Trypophobia dan channel AF7 baik dalam menangkap sinyal EEG yang terstimulus Trypophobia. Kata Kunci : Phobia, Trypophobia, Electroencephalograph, Artificial Neural Network, Discrete Wavelete Transform. Abstract A phobia is a human fear of things that are very trivial for people. One phobia, Trypophobia, is the fear of visual small holes. The effect of the trypophobia effect can we see its brain waves with a device called EEG or called Electroencephalograph, so we can understand who really improved Trypophobia or not. In this final project a system was developed to classify the condition of someone who is not afraid, and the condition of someone who is afraid of Trypophobia is based on alpha signal analysis and EEG beta. Artificial Neural Networks (ANN) are used for classifying conditions. For the extra features of the data Discrete Wavelete Transform (DWT) is used so that system performance can be improved and reduce the EEG dataset dimensions. The test results show that the best performance is obtained in beta signals which have the highest characteristic parameter accuracy are Maksimum, Standard Deviation and Variance with an accuracy value of 100%, with a calculation time of 0.027 and 0.037 seconds. While for alpha signals obtained with Variance and Interquartile Range parameters of 96.42% with a time of 0.03 and 0.032 seconds. Although the accuracy is the same, but the average is resolved based on the neurons, beta is higher than alpha, so it can told that beta signals more than sensitive to such as Trypophobia and AF7 channels good in catching EEG signals of Trypophobia stimulated condition. Keywords: Phobia, Trypophobia, Electroencephalograph, Artificial Neural Network, Discrete Wavelete Transform.
Klasifikasi Kenyenyakan Tidur Berdasarkan Umur Pada Sinyal Electroencephalograph Dengan Melihat Kondisi Non Rapid Eye Movement : Classification Depth Of Sleep Based On Age By Using Electroencephalograpgh Wave With See Non Rapid Eye Movement Condition Naufal Rizky Pratama; Raditiana Patmasari; Sugondo Hadiyoso
eProceedings of Engineering Vol 6, No 1 (2019): April 2019
Publisher : eProceedings of Engineering

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Abstrak Manusia membutuhkan tidur untuk mengekang stress di dalam diri. Kurang tidur membuat mudah stress, cemas, dan juga tegang. Maka dari itu tidur yang cukup sangatlah penting. Saat tidur, otak beraktivitas, merespon, dan menghasilkan brainwave atau sinyal otak. Dalam tidur terbagi dua metode kondisi mata yaitu Rapid Eye Movement (REM) dan Non Rapid Eye Movement (NREM). Salah satu cara untuk mendeteksi dan merekam sinyal otak yang disebabkan oleh aktivitas neuron pada otak manusia adalah Electroencephalography (EEG). Oleh karena itu penelitian ini mengklasifikasikan kondisi kenyenyakan tidur pada sinyal EEG yang di ekstraksi ciri dengan HJORTH Descriptor. Setelah itu akan diklasifikasikan menggunakan Support Vector Machine. Dalam mengklasifikasikannya penelitian ini mengambil data dari penelitian Analysis of a SleepDependent Neuronal Feedback Loop: The Slow-Wave Microcontinuity of the EEG. Data ini sudah melalui tahap proses pre-procesing data yang ada di database, setelah itu menggunakan metode Hjorth Descriptor untuk mengekstraksi ciri fitur sinyal EEG dan diklasifikasi menggunakan SVM untuk melihat kondisi tidur tersebut termasuk dalam kategori nyenyak, kurang nyenyak, atau bahkan tidak nyenyak. Dalam penelitian ini hanya mengambil 39 data yang terdiri dari 20 correspondent dan dalam 2 kondisi malam yang berbeda. Malam pertama perekaman tidur normal. Malam kedua perekaman tidur dengan diberikan obat tidur kepada correspondent. Penelitian ini memperoleh parameter keberhasilan 100% menggunakan kernel Linear SVM, menghasilkan keluaran kondisi tidur yang terdiri dari tidur nyenyak pada saat lampu dimatikan, tidur kurang nyenyak pada saat mau terbangun, dan tidur tidak nyenyak pada saat awal tidur. Kata kunci : EEG, NREM, REM, HJROTH Descriptor, SVM Abstract Human need sleep to curb stress. Lack of sleep make easy stress, worried, and uptight. Therefore enough sleep is more important. At sleep, the brain moves, respond, and generate brainwave. In sleep has divided two condition method eye there is Rapid Eye Movement (REM) and Non Rapid Eye Movement (NREM). Either way to detect and record brainwave is Electroencephalography (EEG). Therefore this research will classify depth of sleep in EEG signal using HJORTH Descriptor to extraction the feature of data. After that will classify using Support Vector Machine. In classifying it, this research take data from research Analysis of a Sleep-Dependent Neuronal Feedback Loop: The Slow-Wave Microcontinuity of the EEG. The data has been pre-processing, after that using HJORTH Descriptor to extract characterstic feature signal of EEG and classify using SVM too see the sleep condition included in the category depth of sleep, well sleep, or not well sleep. In this research just took 39 data consisting of 20 correspondent in two night difference condition. The first night normal sleep recorded. The second night Temazepam has given to correspondent. This research has been reach 100% using Linier kernel SVM, produce output condition of sleep consisting of depth of sleep when the lights off, well sleep when before waking up, not well sleep when the lights on. Keywords: EEG, NREM, REM, HJROTH Descriptor, SVM
Klasifikasi Efek Familiarity Pada Sinyal Eeg Manusia Menggunakan Metode Hjorth Descriptor : Classification Of Familiarity Effects In Human Eeg Signal Using Hjorth Descriptor Method Hannissa Sanggarini; Rita Purnamasari; Sugondo Hadiyoso
eProceedings of Engineering Vol 6, No 1 (2019): April 2019
Publisher : eProceedings of Engineering

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Abstrak Dalam Human-Computer Interaction, audiovisual sangat berpengaruh bagi kondisi fisiologis yang mempengaruhi perasaan manusia. Hal ini dapat dilihat dari kemampuan manusia yang mampu merasakan perasaan yang berbeda-beda saat melihat tayangan video musik. Perasaan ini muncul akibat stimulus yang dihasilkan dari tayangan video musik tersebut sehingga terjadi fluktuasi aktifitas otak dan menghasilkan karakteristik sinyal otak tertentu. Dengan menggunakan Electroencephalogram (EEG), dilakukan klasifikasi karakteristik sinyal otak pada kategori familiarity. Familiarity adalah keadaan saat manusia mengenali sesuatu. Penelitian ini menggunakan data sekunder yang diambil dari DEAP: A Database for Emotion Analysis using Physiological Signals. Data yang diambil dari DEAP berjumlah 32 data yang telah melalui beberapa tahap pre-processing, maka data dapat langsung diproses dengan menggunakan metode Hjorth Descriptor untuk ekstraksi ciri dan metode Multilayer Perceptron (MLP) untuk klasifikasi. Pengujian dilakukan dengan skenario dimana data dari 29 data yang digunakan, 15 data digunakan sebagai data latih dan 14 data digunakan sebagai data uji. Dari hasil pengujian yang dilakukan, didapatkan akurasi terbaik pada kondisi balance class sebesar 78.57% pada percobaan 1, 2 dan 27 dengan kombinasi ciri Hjorth Descriptor activity, mobility dan complexity. Digunakan juga dua hidden layer dengan 12 neurons pada tiap hidden layer serta epoch berjumlah 1.000 epochs pada MLP. Kata Kunci: EEG, familiar, Hjorth Descriptor, Multilayer Perceptron. Abstract In Human-Computer Interaction, audiovisual is very influential for physiological condition that affects human’s feelings. This can be seen from human ability to feel different feelings while watching music video. This feeling occured because of the stimulus elicited from the music video, so that brain activity fluctuation happened and obtained certain brain signals characteristics. By using Electroencephalogram (EEG), we did a classification of brain signal characteristics in familiarity category. Familiarity is a state when human recognize something. This research is using secondary data taken from DEAP: A Database for Emotion Analysis using Physiological Signals. Data taken from deap is the amount of 32 and has been through several pre-processing methods, so data can go straight to be processed using Hjorth Descriptor as the feature extraction method and Multilayer Perceptron (MLP) as the classifier method. The test is done with scenario where from 29 data used, 15 data is used as training data and 14 data is used as testing data. From the test result, the best accuracy is gained in balance class is 78.57% in trial 1, 2 and 27 with Hjorth Descriptor feature combinations of activity, mobility and complexity. Two hidden layers with 12 neurons in each hidden layer and epoch with the amount of 1000 is also used in MLP. Keywords: EEG, familiar, Hjorth Descriptor, Multilayer Perceptron
Klasifikasi Emosi Berdasarkan Sinyal Eeg Dengan Menggunakan Metode Algoritma Genetika Dan Independent Component Analysis Bimo Rian Tri Nugroho; Rita Purnamasari; Sugondo Hadiyoso
eProceedings of Engineering Vol 6, No 2 (2019): Agustus 2019
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Abstrak Dalam mengambil keputusan, emosi mempengaruhi hasil keputusan tersebut. Contoh saat senang, penilaian suatu hal akan cenderung baik karena menyukai hal tersebut, sebaliknya jika sedih, penilaian suatu hal akan cenderung kurang baik. Pada penelitian sebelumnya emosi dinilai dari sumber fisiologis yaitu sinyal Electroencephalographic (EEG) dari otak. EEG memperoleh sinyal yang berasal dari neuron-neuron yang bekerja pada otak. Rekaman EEG timbul saat terjadi aktivitas listrik pada otak. Data diperoleh melalui media video yang diberikan kepada peserta untuk mengetahui emosi yang terjadi pada peserta. Dalam penelitian ini sinyal EEG diambil dari penelitian DEAP : A database for Emotion Analysis using physiological Signals dan diproses oleh Independent Component Analysis (ICA). Data yang digunakan sudah melalui tahap pre-processing yang berasal dari database. Data dari database mempunyai beberapa tingkatan yaitu arousal, valence, liking, dominance, dan familiarity. Tingkat yang diambil hanya dari valence. Dengan menggunakan ICA untuk mendapatkan matriks setiap percobaan, kemudian dari matriks tersebut diambil ekstraksi fitur yang kemudian digunakan sebagai data latih dan data uji. Hasil fitur yang didapat diklasifikasikan oleh Support Vector Machine (SVM) dan Genetic Algorithm (GA) agar memperoleh akurasi serta kondisi emosi yang dialami saat senang atau sedih. Dalam penelitian yang dilakukan, hasil klasifikasi hanya menggunakan SVM memperoleh akurasi sebesar 56.25% dan klasifikasi menggunakan SVM yang dioptimalisasi oleh GA memperoleh akurasi sebesar 77.2727%. Hal ini menunjukan bahwa klasifikasi SVM yang dioptimalisasi oleh GA memberikan hasil akurasi yang lebih baik dibandingkan klasifikasi jika hanya menggunakan SVM. Hasil akurasi yang dapatkan menunjukan hasil klasifikasi emosi antara senang dan sedih. Kata Kunci : EEG, DEAP, ICA, GA, SVM Abstract In making decisions, emotions influence the outcome of the decision. For example, when feels happy, evaluating something can be tend to be good, on the contrary when feels sad, the assessment of something can be tend to be bad. In previous studies, emotions were assessed from physiological sources is Electroencephalographic (EEG) signals from the brain. EEGs get signals that come from neurons that work in the brain. EEG footage appears when electrical activity occurs in the brain. Data is obtained through video media given to participants to find out the emotions that occur in participants. In this study EEG signals were taken from the DEAP study: Database for Emotion Analysis using physiological signals and processed by Independent Component Analysis (ICA). The data used has been preprocessing originating from the database. Data from the database has several levels of arousal, valence, likes, domination, and familiarity. The level taken is only from valence. By using ICA to get the matrix of each experiment, then the feature extraction is taken from the matrix which is then used as training data and test data. The results of the features obtained are classified by Support Vector Machine (SVM) and Genetic Algorithm (GA) in order to obtain the accuracy and emotional conditions experienced when happy or sad. In the research conducted, the classification results using only SVM obtained an accuracy of 56.25% and the classification using SVM optimized by GA obtained an accuracy of 77.2727%. This shows that SVM classification optimized by GA provides better accuracy results than classification only using SVM. The accuracy results obtained show the classification of emotions between happy and sad. Keyword : EEG, DEAP, ICA, GA, SVM
Coarse Grained Lyapunov Exponent Sebagai Ekstaksi Fitur Pada Klasifikasi Sinyal Elektroensefalogram Imaginasi Gerak Nadya Silva Arline; Inung Wijayanto; Sugondo Hadiyoso
eProceedings of Engineering Vol 8, No 5 (2021): Oktober 2021
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Motor imagery merupakan suatu kondisi dimana seseorang sedang dalam keadaan secara mental mensimulasikan suatu tindakan, atau dalam kata lain orang tersebut sudah merasa melakukan suatu tindakan tetapi pada kenyataannya orang tersebut belum atau tidak dapat melakukan tindakan tersebut. Hal ini biasanya dialami oleh penderita cacat motorik atau lumpuh. Untuk mengukur adanya ketidaknormalan atau gangguan pada motor imagery dapat melakukan pemeriksaan aktivitas kelistrikan otak menggunakan elektroensefalogram (EEG). EEG akan menangkap aktivitas listrik seseorang saat otak menerima atau menanggapi stimulus. Dengan demikian aktivitas motor imagery dapat diamati. Pada Tugas Akhir ini dilakukan klasfikasi motor imagery untuk memprediksi gerakan motoric seseorang berdasarkan sinyal EEG. Motor imagery yang disimulasikan terdiri dari dua isyarat meliputi gerkaan tangan kanan dan gerakan tangan kiri. Lalu dilakukan proses multiscale menggunakan Coerse Grained Procedure. Sinyal EEG diekstraksi menggunakan metode Largest Lyapunov Exponent (LLE) untuk mendapatkan set fitur dalam numerik. Setelah itu dilakukan klasifikasi sinyal berdasarkan nilai LLE tersebut menggunakan K-Nearest Neighbor (kNN). Proses pengklasifikasian menggunakan metode cosine similarity untuk mengukur jarak data latih yang paling dekat dengan objek. Dari simulasi yang telah dilakukan, akurasi maksimum yang dapat dicapai adalah 60%. Diharapkan penelitian ini dapat membantu dalam analisis motor imagery EEG sehingga dapat mengidentifikasi jika terdapat ketidaknormalan syaraf motoric khusunya bagian otak. Kata Kunci: Motor Imagery, Elektroensephalograph, Lyapunov Exponent, K-Nearest Neighbor.
Classification of Koilonychia, Beaus Lines, and Leukonychia based on Nail Image using Transfer Learning VGG-16 Sugondo Hadiyoso; Suci Aulia
Jurnal Rekayasa Elektrika Vol 18, No 2 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (594.161 KB) | DOI: 10.17529/jre.v18i2.25694

Abstract

Human nail disease is usually ignored since it does not reveal clinical signs that are harmful to one's health. Nail disease, on the other hand, can be an early sign of a health issue. Some types of nail disease can cause infection, injury, or even the loss of the nail itself. It can reduce a person's aesthetics and beauty. Nail disease is very varied, so it is often difficult for clinicians to diagnose because several types have high similarities. Therefore, an automatic nail disease classification method based on nail photos was proposed in this study. The proposed method was based on the VGG-16 neural network architecture with an Adam optimizer. Nail diseases including Koilonychia, Beaus Lines, Leukonychia have been classified in this study. The model in this study is simulated in Python programming. The simulation results show that the highest classification accuracy is 96%, achieved with epoch-10. The transfer learning method based on a neural network simulated in this study is expected to support the clinical diagnosis of nail disease.
Abnormal ECG Classification using Empirical Mode Decomposition and Entropy Suci Aulia; Sugondo Hadiyoso
Jurnal Rekayasa Elektrika Vol 17, No 3 (2021)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (946.152 KB) | DOI: 10.17529/jre.v17i3.22070

Abstract

Heart disease is one of the leading causes of death in the world. Early detection followed by therapy is one of the efforts to reduce the mortality rate of this disease. One of the leading medical instruments for diagnosing heart disorders is the electrocardiogram (ECG). The shape of the ECG signal represents normal or abnormal heart conditions. Some of the most common heart defects are atrial fibrillation and left bundle branch block. Detection or classification can be difficult if performed visually. Therefore in this study, we propose a method for the automatic classification of ECG signals. This method generally consists of feature extraction and classification. The feature extraction used is based on information theory, namely Fuzzy entropy and Shannon entropy, which is calculated on the decomposed signal. The simulated ECG signals are of three types: normal sinus rhythm, atrial fibrillation, and left bundle branch block. Support vector machine and k-Nearest Neighbor algorithms were employed for the validation performance of the proposed method. From the test results obtained, the highest accuracy is 81.1%. With specificity and sensitivity of 79.4% and 89.8%, respectively. It is hoped that this proposed method can be further developed to assist clinical diagnosis.
Multi-Class Heart Abnormalities Detection Based on ECG Graph Using Transfer Learning Method Sugondo Hadiyoso; Suci Aulia; Indrarini Dyah Irawati
Jurnal Rekayasa Elektrika Vol 19, No 1 (2023)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (951.797 KB) | DOI: 10.17529/jre.v19i1.28637

Abstract

The heart is one of the vital organs in the circulatory system. Regular checkups are very important to prevent heart disease. The most basic examination is blood pressure then further examination is related to the evaluation of the electrical activity of the heart using an electrocardiogram (ECG). The ECG carries important information regarding various abnormalities of heart function. Several automated classification techniques have been proposed to facilitate diagnosis. However, not all digital ECG devices provide raw data for analysis. ECG classification method based on images can be an alternative in classification. Therefore, in this study, it is proposed to classify ECG based on signal images. The proposed classification method uses transfer learning with VGG, AlexNet, and DenseNet architectures. The method used for the classification of multi-class ECG consists of normal, PVC, Atrial Fibrilation, AFL, Bigeminy, LBBB, and APB. The simulation results generate the best accuracy of 92% and F1-score of 92%. Best performance is achieved using DenseNet architecture at 60 epochs. This study is expected to be a new reference technique in the classification of ECG signals.
Deteksi Kantuk pada Pengemudi Berdasarkan Penginderaan Wajah Menggunakan PCA dan SVM Nur Ramadhani; Suci Aulia; Efri Suhartono; Sugondo Hadiyoso
Jurnal Rekayasa Elektrika Vol 17, No 2 (2021)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (709.161 KB) | DOI: 10.17529/jre.v17i2.19884

Abstract

Drowsiness while driving is one of the main causes of traffic accidents it affects the level of focus of the driver. Therefore, we need an automatic drowsiness detection mechanism for the driver to provide a warning or alarm so that an accident can be avoided. In this study, we design and simulate a system to detect drowsiness through the driver’s yawn expression. The acquisition is made by recording the face from two shooting points including the dashboard and front mirrors in the car. From the video recording, then it is taken into several images with a size of 128x82 pixels which are used as training and testing data. This image is then processed using Principal Component Analysis (PCA) for feature extraction and classified using a Support Vector Machine (SVM). From the tests carried out, the system generates the highest accuracy of 98%. This best performance is obtained by SVM with polynomial kernel in the camera position on the dashboard. Meanwhile, based on compression testing, the image that can still meet system requirements is 25% of the original size. It is hoped that the proposed drowsiness detection method in this study can be applied for real-time drowsiness detection in vehicles. 
Web-based Water Quality Parameter Monitoring for Bok Coy Hydroponics using Multi Sensors Indrarini Dyah Irawati; Dadan Nur Ramadan; Sugondo Hadiyoso
Jurnal Rekayasa Elektrika Vol 18, No 3 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (834.701 KB) | DOI: 10.17529/jre.v18i3.26017

Abstract

The hydroponic planting method is one solution for supplying vegetable needs where agricultural land is limited. Hydroponics allows the growing of vegetables in stages in a limited area by utilizing water as a growing medium. Water quality greatly determines plant fertility, so monitoring must be carried out regularly. Currently, the agricultural sector in Sukabumi has a large potential for the economy of the community. Farmers develop hydroponic farming but monitoring of water quality is still done traditionally. Therefore, in this study, a water quality monitoring system is proposed including pH, turbidity, and temperature. Another parameter that is observed is the water level in the reservoir which is useful for maintaining water circulation. This system works online through the internet network, both the sensing process, data transmission, and data display using the Internet of Things (IoT) platform. The measured parameters can be observed via a web application. Performance evaluation of sensor devices is carried out by comparing the measurement values of standard devices. The test results on the system that has been implemented show that the system has high accuracy, and all parameters are successfully displayed on the web page. The applied systems can increase the fertility of vegetables on hydroponic land so that it can improve the quality of production.
Co-Authors -, Suryatiningsih A. V. Senthil Kumar A.A. Ketut Agung Cahyawan W Aaron Abel Abi Hakim Amanullah Achmad Rizal Achmad Rizal ADIANGGIALI, ANYELIA Adisaputra, Rangga Adiwijaya, Agustinus Aldian Adjie Gery Ramadhan Adnan Azhary Afandi, Mas Aly Agung Muliawan Ahmad Hilmi Ahmad Muammar Agusti Akhmad Alfaruq Akhmad Alfaruq Alfaruq, Akhmad Alfaruq, Akhmad Aliffansyah, Lingga Alvinas Deva Sih Illahi Ana Durrotul Isma Anatasya Bella Andhita Nurul Khasanah Andri Juli Setiawan Andro Harjanto Anggit Syorgaffi Anggun Fitrian Isnawati ANGGUNMEKA LUHUR PRASASTI Arfianto Fahmi Arif Indra Irawan ARIS HARTAMAN Ashshiddiqqi, Muhammad Arhizal Asma Zahira Asril Ibrahim Astri Wulandari Audry Stevany Aulia Ayu Dyah Lestari Ayu Chellsya, Ananda Ayu Tuty Utami Azahra, Yasmin Azriel Gilbert Samuel Rogito Azzahra, Salwa Bagus Tri Astadi Balova , Fathrurrizqa Bambang Hidayat Bandiyah Sri Aprillia Barus, Exal Deo Jayata Bayu Erviga Yulanda Setiawan Bayuaji Kurniadhani Bimo Rian Tri Nugroho Budhi Irawan Budi Prasetya Budi Prasetya Budiyawan Naztin Burhanuddin D. Burhanuddin Dirgantoro Cucu Fitri Dadan Nur Ramadan Dadan Nur Ramadhan Dadan Nur Ramadhan Denny Darlis Dewi Budiwati, Sari Dewi Rahmaniar, Thalita Dharu Arseno Didin Bramastya Dieny Rofiatul Mardiyah Diliana, Faizza Haya Efri Suhartono Ema ERVIN MASITA DEWI Exal Deo Jayata Barus Ezi Rohmat Fadiaga Omar Michlas Fairuz Azmi FAJRI, SETIO EKA FARDAN FARDAN Farhan Alghifari Chaniago Saputro, Muhammad Farrel Fahrozi Fathrurrizqa Balova FATURRAHMAN, RAIHAN Fauzia Anis Sekar Ningrum Fony Ferliana Widianingrum Gadama, Melsan Gartina Husein, Inne Gelar Budiman Ghilman Hafizhan Gifari, Rizqi Al Goldfried Manuel Lbn Tobing Habib, Arrijal Hadjwan, Razel Hannissa Sanggarini Hariyani , Yuli Sun Hasanah Putri Hengky Yudha Bintara Heru Nugroho Hilman Fauzi, Hilman HUMAIRANI, ANNISA Hurianti Vidyaningtyas HW, EVA AISAH Ilham Edwian Berliandhy Ilmi, M. Bahrul Indrarini Dyah Irawati Inung Wijayanto Irsyad Abdul Basit Istikmal Ivany Sesa Rehadi Ivosierra Andrea Larasaty Jannah, Firna Noor Jannah, Sabila Hayyinun Jasmine, Diva Dhila Jauhari, Muhammad I Javani Sekar Larasati Jehan Pratama Herdaning Jondri Jondri Koredianto Usman Kridanto Surendro Kris Sujatmoko Kurnia Ismanto, Rima Ananda Larasaty, Ivosierra Andrea Lata Tripathi, Suman LATIP, ROHAYA Ledya Novamizanti Lurina, Manda Luthfi Muhammad Pahlevi Lutvi Murdiansyah Murdiansyah M. Nur Imam DJ Mahmud Dwi Sulistiyo Manda Lurina Meidatomo , Muhammad Haykal Milan Adila Amalia Mohamad Ramdhani Muh. Kurniawan, A. Muhamad Roihan Muhammad Adnan Muhammad Afif Ridwansyah Muhammad Alfachri Akbar Muhammad Arhizal Ashshiddiqqi Muhammad Farhan Alghifari Chaniago Saputro Muhammad Iqbal MUHAMMAD JULIAN, MUHAMMAD Nadya Silva Arline Nasution, Muhammad Ilham Kurniawan Nasution, Seri Wahyuni Naufal Juhaidi Jafal Naufal Rizky Pratama Nur Arviah Sofyan Nur Pratama, Yohanes Juan Nur Ramadhani Nursanto Nursanto NURSANTO NURSANTO, NURSANTO Nurwan Reza Fachrurrozi Okki Rahmalisty, Fiona Pahira, Ela Diranda Patricia Lovenia Garcia Periyadi Permana, Andri Satia Prahara, Dzakwan Bahar Prajna Deshanta Ibnugraha Putra, I Gusti Ngurah R. A. Putri Fatoni, Salwa Berliana Putri, Athaliqa Ananda Putri, Silvi Dahlia R. Dhenake Aghni Bunga R. Yunendah Nur Fu’adah Radial Anwar, Radial Radian Sigit Raditiana Patmasari Rahmaniar, Thalita Dewi Rahmat Widadi Ramdani, Ahmad Zaky Ratna Mayasari Reivind P. Persada RENALDI, LUKY RENALDI, LUKY RENDIKA, ANANDA Rendy Munadi Reni Dyah Wahyuningrum Reny Yuliani Arnis Ridha Muldina Negara Rina Pudji Astuti Riska Aprilina Rita Magdalena Rita Purnamasari Rizal Fachrudin Maulana Rizky Aulia Rahman Robinzon Pakpahan Rogito, Azriel Gilbert Samuel ROHMAT TULLOH Rosmiati, Mia Ruli Pandapotan, Bagas Ryan Bagus Wicaksono Safitri, Ayu Sekar Said, Ziani Sania Marcellina Bryan Sasmi Hidayatul Yulianing Tyas Sa’idah, Sofia Sekar Safitri, Ayu Septiansyah, Rizky SETIAWAN, AWAN WAHYU Sianturi, Kristian Fery Sidqi, Anka Sigit, Radian Siti Sarah Maidin Siti Zahrotul Fajriyah Sofia Naning Hertiana Suci Aulia Sugeng Santoso Sulistyo, Tobias Mikha Surya Putra Agung Saragih Suyatno Suyatno Syifa Nurgaida Yutia Tasya Chairunnisa Tati Latifah Erawati Rajab Teguh Musaharpa Gunawan Thomhert Suprapto Siadari Tita Haryanti Tobing, Goldfried Manuel Lbn Tri Nopiani Damayanti Triadi Triadi Unang Sunarya Untari Novia Wisesty Vany Octaviany Vera Suryani Wahyu Hauzan Rafi Wibowo, Raiyan Adi Wirakusuma, Muhammad P. Yasmin Azahra Yoza Radyaputra Yudha Purwanto Yudiansyah Yudiansyah YULI SUN HARIYANI YUYUN SITI ROHMAH Zahrah, Nasywa Nur Zhillan Al Rashif, Mohammad Zulfikar F.M. Ramli