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7th International Conference on ICT for Smart Society, ICISS 2020

作者： Prasetyo, Hoedi Nugroho, Adi Politeknik ATMI Surakarta Mechatronics Study Program Surakarta Indonesia Politeknik ATMI Surakarta Manufacture Design Study Program Surakarta Indonesia

ISBN: (纸本)9781665404228

This research proposes the on-grid photovoltaic system power monitoring with a responsive dashboard design. The power monitoring utilizes open source and low-cost resources namely NodeMCU, MQTT, Node-RED, and the Raspberry Pi as a local server. A CT (Current Transformers) is used as an electric current sensor. The monitoring dashboard is created using the Node-RED UIbuilder node instead of using the default dashboard nodes module. There are eight displays of information presented on the dashboard, namely Real-Time Power, Runtime, Highest Power Record, Total Energy Savings, Total Cost Savings, Daily Savings Rate, Summary Last Update, and Power Monitoring Log. The power monitoring dashboard performs well on the widescreen and the mobile screen devices. The accuracy of power readings is tolerable. © 2020 IEEE.

关键词： Internet of things

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Composite adsorbent from sugarcane (Saccharum officinarum) bagasse biochar generated from atmospheric pressure microwave plasma pyrolysis process and nano zero valent iron (nZVI) for rapid and highly efficient Cr(VI) adsorption

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Case Studies in Chemical and Environmental Engineering 2025年 11卷

作者： Dermawan, Denny Satriavi, Aulia Diva Nurhidayati, Dyah Isna Firnandi, Rahmad Mayangsari, Novi Eka Ramadani, Tarikh Azis Widiana, Dika Rahayu Juniani, Anda Iviana Mujiyanti, Dwi Rasy Wang, Ya-Fen Waste Treatment Engineering Study Program Shipbuilding Institute of Polytechnic Surabaya Jalan Teknik Kimia Kampus ITS Sukolilo Surabaya Indonesia Safety and Risk Engineering Study Program Shipbuilding Institute of Polytechnic Surabaya Jalan Teknik Kimia Kampus ITS Sukolilo Surabaya Indonesia Waste Treatment Engineering Laboratory Shipbuilding Institute of Polytechnic Surabaya Jalan Teknik Kimia Kampus ITS Sukolilo Surabaya Indonesia Design and Manufacture Study Program Shipbuilding Institute of Polytechnic Surabaya Jalan Teknik Kimia Kampus ITS Sukolilo Surabaya Indonesia Department of Environmental Engineering Chung Yuan Christian University Chung-Li 320 Taiwan Center for Environmental Risk Management Chung Yuan Christian University Chung-Li 32023 Taiwan

Sugarcane bagasse, with a 25–28 % lignin content, can be converted into biochar, serving as a promising adsorbent due to its high surface area. Nano-zero-valent iron (nZVI) is known for its strong magnetization and ability to bind heavy metals. In this research, biochar derived from sugarcane bagasse was composited with nZVI at varying ratios (1:1, 2:1, and 3:1) to improve adsorption efficiency for Cr (VI) removal. The composites were synthesized using the biochar from the plasma pyrolysis method, followed by nZVI incorporation. Batch adsorption tests were conducted with different Cr (VI) concentrations, contact times, pH levels, and adsorbent doses to determine the optimum conditions for each ratio. Characterization of the adsorbent included XRD, BET, FTIR, SEM, VSM, and Zeta potential analysis. XRD analysis of sugarcane bagasse and biochar demonstrated crystallinity and particle size improvements post-pyrolysis. BET results showed that sugarcane bagasse biomass had a surface area of 0.061 m²/g, which increased to 87.50 m²/g after conversion to biochar. However, once composited with nZVI, the surface area decreased to 37.44 m²/g (1:1), 49.26 m²/g (2:1), and 62.37 m²/g (3:1). FTIR and SEM analyses revealed the interactions between biochar and nZVI, as well as the binding of Cr (VI) to the composite surfaces. VSM showed a reduction in magnetization after adsorption, confirming the oxidation of nZVI to various iron oxides (e.g., FeO, Fe₂O₃, Fe₃O₄), which are less magnetic. The adsorption tests indicated that the adsorption capacity increased with a higher SBB/nZVI ratio. The biochar alone had an adsorption capacity of 77.82 mg/g. In comparison, the composites achieved 86.47 mg/g (1:1), 95.12 mg/g (2:1), and 112.41 mg/g (3:1). Optimal removal was achieved at an initial Cr (VI) concentration of 175 mg/L, a contact time of 180 minutes, and a pH of 2. The Langmuir isotherm model best described the adsorption behavior,

关键词： Atmospheric pressure microwave plasma Cr (VI) removal Nano zero valent iron Pyrolysis Sugarcane bagasse biochar

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On-Grid Photovoltaic System Power Monitoring with A Responsive Dashboard design

On-Grid Photovoltaic System Power Monitoring with A Responsi...

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International Conference on ICT For Smart Society (ICISS)

作者： Hoedi Prasetyo Adi Nugroho Mechatronics Study Program Politeknik ATMI Surakarta Surakarta Indonesia Manufacture Design Study Program Politeknik ATMI Surakarta Surakarta Indonesia

ISBN: (数字)9781665404228

ISBN: (纸本)9781665404235

关键词： Monitoring Real-time systems Internet of Things Databases Current transformers Servers Arrays

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Identification of Epilepsy Phase Based on Time Domain Feature Using ECG Signals

Identification of Epilepsy Phase Based on Time Domain Featur...

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2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020

作者： Islamiyah, Wardah R. Wulandari, Diah P. Sarasati, Nadhira N. Suprapto, Yoyon K. Purnami, Santi W. Juniani, Anda I. Universitas Airlangga Neurology Department Indonesia Institut Teknologi Sepuluh Nopember Computer Engineering Department Indonesia Institut Teknologi Sepuluh Nopember Computer Engineering Department Indonesia Design and Manufacture Study Program Politenik Perkapalan Negeri Surabaya Indonesia

ISBN: (纸本)9781728182834

Epilepsy is a neural disease caused by brain signal abnormalities. There are three phases in epilepsy, the pre-ictal, ictal, and post-ictal phase. To distinguish those phases, usually EEG signal is used. However, there is a study mentioning the connection between epilepsy and heart signals, so there is a probability to distinguish those phases using ECG. This study is made for distinguish the three phases in epilepsy and the normal condition of epilepsy patient using K Nearest Neighbors (KNN) algorithm. Dataset used in this study was from PhysioNet, obtained from long-Term EEG and ECG record of epileptic patient without history of cardiac disease. With the ability to do the identification of epilepsy phase, it is expected to help doctors and medical staffs to differ epileptic ECG signals for every different phases in epilepsy, and to prove the hypothesis whether the three phases in epilepsy can be distinguished from the heart signal. © 2020 IEEE.

关键词： Electrocardiograms

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Identification of Epilepsy Phase Based on Time Domain Feature Using ECG Signals

Identification of Epilepsy Phase Based on Time Domain Featur...

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International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM)

作者： Wardah R. Islamiyah Diah P. Wulandari Nadhira N. Sarasati Yoyon K. Suprapto Santi W. Purnami Anda I. Juniani Universitas Airlangga Institut Teknologi Sepuluh Nopember Design and Manufacture Study Program Politenik Perkapalan Negeri Surabaya

ISBN: (数字)9781728182834

ISBN: (纸本)9781728182841

Epilepsy is a neural disease caused by brain signal abnormalities. There are three phases in epilepsy, the pre-ictal, ictal, and post-ictal phase. To distinguish those phases, usually EEG signal is used. However, there is a study mentioning the connection between epilepsy and heart signals, so there is a probability to distinguish those phases using ECG. This study is made for distinguish the three phases in epilepsy and the normal condition of epilepsy patient using K Nearest Neighbors (KNN) algorithm. Dataset used in this study was from PhysioNet, obtained from long-term EEG and ECG record of epileptic patient without history of cardiac disease. With the ability to do the identification of epilepsy phase, it is expected to help doctors and medical staffs to differ epileptic ECG signals for every different phases in epilepsy, and to prove the hypothesis whether the three phases in epilepsy can be distinguished from the heart signal.

关键词： Epilepsy Feature extraction Electrocardiography Testing Tuning Heart Entropy

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Epileptic Seizure Detection Based on Bandwidth Features of EEG Signals 5

Epileptic Seizure Detection Based on Bandwidth Features of E...

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5th Information Systems International Conference (ISICO) on Role of Information Systems in Industry 4.0

作者： Wulandari, Diah P. Putriz, Nomala G. P. Suprapto, Yoyon K. Purnami, Santi W. Juniani, Anda, I Islamiyah, Wardah R. Inst Teknol Sepuluh Nopember Surabaya Dept Comp Engn Surabaya Indonesia Inst Teknol Sepuluh Nopember Surabaya Dept Stat Surabaya Indonesia Politekn Perkapalan Negeri Surabaya Design & Manufacture Study Program Surabaya Indonesia Univ Airlangga Surabaya Dept Neurol Surabaya Indonesia

Detection of seizure from EEG signals may help neurologist analyze the condition of epileptic patients. Variance in types of epileptic seizures provides challenge in recognizing the pattern of seizure signals from normal ones. In this paper, we discuss the classification of seizure and non-seizure conditions in epilepsy based on bandwidth features of EEG signals. These features were extracted using Empirical Mode Decomposition (EMD). This method decomposes signal into eight Intrinsic Mode Functions (IMFs) and its residue. The IMFs is then analyzed by Hilbert transform to obtain amplitude modulation bandwidth (BAM) and frequency modulation bandwidth (BFM). These features are fed as input to Support Vector Machine (SVM). We propose to combine the first four IMFs to extract bandwidth features and we implement this method on two data sets, public data set that contains signals from both extracranial and intracranial EEG, and data set OF Rumah Sakit Universitas Airlangga (RSUA) that contains signals from extracranial EEG only. The results showed that in general, the values of accuracy and specificity using combined features of first four IMFs outperformed those using features of single IMF, for all SVM kernel functions. The best accuracy and specificity on public data set were 97.3% and 98.25% respectively using RBF kernel, while the best sensitivity was gained using polynomial kernel based on only IMF1 bandwidth features by 99.0%. The experiments on RSUA data showed that the best accuracy was 96.5% and the best specificity was 99.38% when using Mexican Hat kernel based on combined features of first four IMFs, while the best sensitivity was 94% when using polynomial kernel based on only IMF1 bandwidth features. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： EEG Empirical Mode Decomposition epilepsy machine learning

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Epileptic Seizure Detection Based on Bandwidth Features of EEG Signals

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Procedia Computer Science 2019年 161卷 568-576页

作者： Diah P. Wulandari Nomala G.P. Putriz Yoyon K. Suprapto Santi W. Purnami Anda I. Juniani Wardah R. Islamiyah Dept. of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Statistics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Design and Manufacture Study Program Politeknik Perkapalan Negeri Surabaya Indonesia Dept. of Neurology Universitas Airlangga Surabaya Indonesia

关键词： EEG Empirical Mode Decomposition epilepsy machine learning

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Visualization of Epilepsy Patient's Brain Condition based on Spectral Analysis of EEG Signals using Topographic Mapping

Visualization of Epilepsy Patient's Brain Condition based on...

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Joint International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM) / 11th AUN/SEED-Net Regional Conference on Computer and Information Engineering (RCCIE)

作者： Wulandari, Diah P. Suprapto, Yoyon K. Juniani, Anda I. Elyantono, Thalia F. Purnami, Santi W. Islamiyah, Wardah R. Inst Teknol Sepuluh Nopember Dept Comp Engn Surabaya Indonesia Politekn Perkapalan Negeri Surabaya Design & Manufacture Study Program Surabaya Indonesia Inst Teknol Sepuluh Nopember Dept Stat Surabaya Indonesia Univ Airlangga Dept Neurol Surabaya Indonesia

ISBN: (纸本)9781538675090

In analyzing the epilepsy patient data it is necessary for a doctor to know where the epileptic focus are located. This information is related to the determination of the type of drug delivery and the therapeutic measures that will be performed for the epilepsy patient. In this research, a system that can visualize the state of the brains of epilepsy patients by performing an analysis of brain wave signals or electroencephalography (EEG) is built. Visualization is performed using topographic mapping techniques on each EEG signal sub-band, namely delta, theta, alpha, beta and gamma sub-bands. The feature extraction process is conducted using Fast Fourier Transform (FFT) to transform signals from time domain into frequency domain. The absolute power and relative power data of EEG signals are used as input values in the visualization process. The results are compared to neurologist analysis and they indicate that this system can visualize the brain condition of epileptic patients to find out where the epileptogenic zone is located by adjusting data cutting process to the duration of the seizure wave that occurs.

关键词： Epilepsy EEG FFT relative power topographic mapping

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Risk Analysis of Musculoskeletal Complaints with Rula Method in Chemical Company

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IOP Conference Series: Materials Science and Engineering 2019年第1期462卷

作者： B A Aziz L Handoko A I Juniani Study Program of Safety Engineering Marine Engineering Department Shipbuilding Institute of Polytechnic Surabaya Jl. Teknik Kimia Kampus ITS Sukolilo Surabaya 60111 Indonesia Study Program of Design Manufacture Marine Engineering Department Ship building Institute of Polytechnic Surabaya Jl. Teknik Kimia Kampus ITS Sukolilo Surabaya 60111 Indonesia

Office workers seem trivial, not many people have aware of occupational diseases risk, office workers who most are computer users in their daily work. Often the symptoms are ignored, most people assume that it is due to ordinary fatigue whereas things it is the beginning of the onset of occupational diseases. Muscle receive static load repeatedly in a long time, will be able to cause complaints of damage to joints, ligaments, and tendons. In this study, the method is RULA that shows a very high and moderate risk level. From the results, such assessments are used as a basis for applying improvements facilities at work. design through to a table and a chair by analysing anthropometry, it is anticipated that analysis ergonomics and analysis of the redesigning. They used this application for reasonable enough CATIA. That is in pop up based on observation, so it can be an alternative redesigning and possible to reduce the potential of any disease, from behind their backs and purchased operator local control room, publicly listed heavy duty equipment operators affected by the policy and office workers.

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Visualization of Epilepsy Patient’s Brain Condition based on Spectral Analysis of EEG Signals using Topographic Mapping

Visualization of Epilepsy Patient’s Brain Condition based o...

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International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM)

作者： Diah P. Wulandari Yoyon K. Suprapto Anda I. Juniani Thalia F. Elyantono Santi W. Purnami Wardah R. Islamiyah Dept. of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Design and Manufacture Study Program Politeknik Perkapalan Negeri Surabaya Indonesia Dept. of Statistics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Neurology Univesitas Airlangga Surabaya Indonesia

ISBN: (纸本)9781538675106;9781538675090

关键词： Electroencephalography Epilepsy Electrodes Data visualization Feature extraction Tumors Brain mapping

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