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10th National Physics Seminar, SNF 2021

作者： Sianipar, L.K. Sitompul, H. Sanjaya, L.A. Puspa, R.W.D. Pertiwi, W.A. Qoiriyah, W. Department of Physics Education Universitas Pelita Harapan Tangerang15811 Indonesia Department of Physics Faculty of Mathematics and Natural Science Universitas Negeri Jakarta Indonesia Kartika VIII-1 Senior High School Jakarta Indonesia Islamic Education Management Study Program Faculty Islamic of Education and Teacher Training Muhammad Azim Institute Jambi Indonesia Informatics Engineering Study Program Faculty of Engineering Sheikh Yusuf Islamic University Tangerang Indonesia

Based on the results of the questionnaire distributed to Primary Teacher Education Students, 96% of students said that Physics is difficult to understand because the material is very complex, many formulas, especially the learning process is done online. This study aims to describe the influence of providing feedback on the motivation of Primary Teacher Education Students in primary school physics courses. The method used in this research is descriptive qualitative. Meanwhile, the instrument used was a questionnaire which was distributed to 115 elementary school education students. The result of the research shows that giving feedback consistently is able to motivate students. This can be seen from the results of the questionnaires distributed, each indicator is of very good value. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Students

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Electrodermal Activity-Based Stress Measurement Using Continuous Deconvolution Analysis Method 1

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1st International Conference on Electronics, Biomedical engineering, and Health Informatics, ICEBEHI 2020

作者： Putri, Yang Sa’ada Kamila Ariyansah Rahma, Osmalina Nur Fajriaty, Nuzula Dwi Putra, Alfian Pramudita Rahmatillah, Akif Ain, Khusnul Biomedical Engineering Study Program Department of Physics Faculty of Science and Technology Universitas Airlangga Surabaya60115 Indonesia Biomedical Signals and Systems Research Group Faculty of Science and Technology Universitas Airlangga Surabaya60115 Indonesia

ISBN: (数字)9789813369269

ISBN: (纸本)9789813369252

Stress is a psychological response that must be handled properly, or it can affect psychology and trigger other complicated diseases. Nowadays, stress measured by filling out stress-related forms or interviews with a psychologist. This method still requires a willingness from the patient to consult their condition, which is often not giving the best result due to late treatment. Therefore, we need a system to monitor a person’s stress level conditions independently so they could be threatened quickly if their state gets worsen. One of the non-invasive methods that can be used to measure stress is an Electrodermal Activity (EDA) sensor. EDA is known to be associated with sweat gland activity regulated by SudoMotor Nerve Activity (SMNA), which response to a stressor. However, the SMNA need to decompose into the Skin Conductance Response (SCR) before it could represent stress condition. Hence, the decomposition stage is essential in measuring the quality of the stress condition. In this study, the EDA sensor placed in the palm to obtain the skin conductivity signal. Then, the skin conductivity signals were deconvolved using the Continuous Deconvolution Analysis (CDA) method to capture the SCR. CDA was chosen because it could produce a zero-based line SCR driver and could separate two overlapping peaks in SCR. The result, CDA is adequate to extract SCR, which is shown by the zero baseline of the SCR driver line with a clear peak. The clear SCR peaks are essential for identifying the responses related to the stimulus (stressor). © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Noninvasive medical procedures

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Monitoring Stress Level Through EDA by Using Convex Optimization 1

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1st International Conference on Electronics, Biomedical engineering, and Health Informatics, ICEBEHI 2020

作者： Fajriaty, Nuzula Dwi Rahma, Osmalina Nur Putri, Yang Sa’ada Kamila Ariyansah Putra, Alfian Pramudita Rahmatillah, Akif Ain, Khusnul Biomedical Engineering Study Program Department of Physics Faculty of Science and Technology Universitas Airlangga Surabaya60115 Indonesia Biomedical Signals and Systems Research Group Faculty of Science and Technology Universitas Airlangga Surabaya60115 Indonesia

ISBN: (数字)9789813369269

ISBN: (纸本)9789813369252

This study designed an effective system for monitoring stress levels based on Electrodermal Activity (EDA) sensors by knowing the signal patterns and characteristics using convex optimization decomposition. The EDA sensor considered accurate and sensitive for identifying stress by analyzing the skin conductivity (SC) due to the changes in sympathetic nerve activity. However, the SC signal consists of phasic and tonic components, which needed to decompose to analyze stress levels. The SC signals also followed by the white Gaussian noise, which represents the error value. Hence, deconvolution is a crucial stage for the further process because the quality of the measurement depends on this result. This study aims to deconvolve the SC signal using the convex optimization method (cvxEDA). This model is physiology inspired by EDA based on Bayesian statistics, convex mathematical optimization, and sparsity. This research conducted with 18 subjects through three sessions of measurement. The given stimuli arise in each session to increase the level of stress. The results showed that this method could separate and identify SC. The Phasic component shows an increase in the stimulus of each session, as seen from the number of peaks. In contrast, there were no significant differences in the tonic component. This study shows that the phasic component is closely related to changes in sudomotor nerve activity (SMNA) and response to a stressor, which could be useful to classify stress levels in the future study. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convex optimization

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Implementation of Malaria Detection Using MobileNetV2 Pretrained Model for Improved Deep Learning Based Diagnostics

Implementation of Malaria Detection Using MobileNetV2 Pretra...

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International Conference on Computing, engineering, and Design (ICCED)

作者： Rahmat Hidayat Achmad Solichin Adhitia Erfina Sudin Saepudin Muhamad Muslih Mohd Zainuri Saringat Master of Computer Science Budi Luhur University Jakarta Indonesia Information Systems Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Tun Hussein Onn Malaysia University

ISBN: (数字)9798331529376

ISBN: (纸本)9798331529383

The Plasmodium parasite, which causes malaria, is an acute fever illness that infects people when a female Anopheles mosquito bites them. It is predicted that malaria would claim 619,000 lives in 2021, with 96% of those deaths occurring in the African continent. We can achieve this by using a microscope to examine thick and thin blood smears. The proficiency of a microscope examiner is crucial for doing microscopic examinations. Consider how time-consuming, ineffective, and costly it would be to examine thousands of malaria cases. Consequently, Creating an automated method for detecting malaria parasites is the aim of this study. We employ a MobileNetV2 pretrained model with CNN technology. Because it has been trained on dozens or even millions of data points, this pretrained model is incredibly light but dependable. There are two main benefits of automatic malaria parasite detection: firstly, it can offer a more accurate diagnosis, particularly in locations with limited resources; secondly, it lowers diagnostic expenses. The optimizer utilizes Adam Weight, the criteria uses NLLLoss, and the model is trained using 32 for batch_size. In the fourteenth epoch, we obtained the maximum accuracy score of 96.26% based on the training data. The outcomes of the predictions demonstrate how excellent this score is. EfficienceNet, DenseNet, AlexNet, and other pretrained models are among the alternatives that scientists are advised to try training with.

关键词： Training Adaptation models Accuracy Costs Malaria Microscopy Data visualization Training data Cost function Data models

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Salt Hash Password Using MD5 Combination for Dictionary Attack Protection

Salt Hash Password Using MD5 Combination for Dictionary Atta...

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International Conference of Computer and Informatics engineering (IC2IE)

作者： Agung Nugroho Teddy Mantoro Cyber Defense Engineering Study Program Sains and Defense Technology Faculty The Republic of Indonesia Defense University Bogor Indonesia Departement of Computer Science Media-Tech Lab Sampoerna University Jakarta Indonesia

Using MD5 hashing of passwords in web logins is highly vulnerable to dictionary attacks. One online application for cracking hashed passwords using a dictionary attack that is free and easy to use is ***. Even so, the use of hashing passwords using MD5 which has a low level of security is still commonly found to secure web login passwords. This research presents an increase in security through a combination of salt hash passwords when using MD5. The methodology of this study uses a quantitative experimental method to apply hash salt passwords using the MD5 combination. The password cracking test was carried out using the *** online hash cracking application on weak passwords such as “admin” with the addition of a password hash salt using the MD5 combination. The results showed that Salt Password using the MD5 combination could not be cracked by *** using a dictionary attack.

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Updated Geologic Structures and Stratigraphy of the Darajat Geothermal Field in Indonesia 9

Updated Geologic Structures and Stratigraphy of the Darajat ...

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9th ITB International Geothermal Workshop 2020, IIGW 2020

作者： Intani, Rindu Grahabhakti Wicaksono, Satrio Golla, Glenn Suryantini Geothermal Engineering Master's Program Faculty of Mining and Petroleum Engineering Institut Teknologi Bandung Jl. Ganesha 10 Bandung40132 Indonesia Star Energy Geothermal Salak Ltd Sentral Senayan 2 Building Central Jakarta India

The main objectives of the evaluation of the geologic structures and stratigraphy of the Darajat geothermal field are to refine and update both the Darajat conceptual and geologic1 models. Re-interpretation of the borehole image logs, additional petrographic analysis of rock cuttings and cores, integration of subsurface data from the wells drilled during 2009-2011, and recent resource observations allowed definition of at least seven (7) lithologic units based on the dominant rock type. The updated stratigraphy of the reservoir was correlated with the surface rocks to develop a volcano-stratigraphy and geochronology of the Darajat geothermal system. The andesite-intrusive complex, which comprises the Darajat geothermal reservoir and the hypothesized sub-volcanic portion of an earlier liquid-dominated geothermal system, belongs to the Kendang Volcanics and makes up subsurface Units A and B. The interpretation of the major volcano-tectonic features and structures at Darajat was refined through the analysis of the Light Detection and Ranging (LIDAR) data/imagery that was acquired in 2014. The LIDAR Digital Elevation Model (DEM) hill-shade map shows several conspicuous volcanic vents in Puncak Cae, Gagak and Kiamis. The prominent Kendang Fault, which extends to the Kamojang Field in the northeast, might be a section of the ring structure of an earlier volcano, herein, called Kendang. Wellbore image data from several wells suggest that the Kendang Fault may dip at 70° eastward. Although needing further substantiation, we hypothesize that the decompression event that preceded the development of the steam-dominated geothermal reservoir at present might be related to the eruption of the Kendang volcano. The Gagak Fault is another prominent surface structure and believed to form during the eruption of the resurgent Gagak volcano after the eruption of the older Kendang volcano. © Published under licence by IOP Publishing Ltd.

关键词： Stratigraphy

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High Level Learning Approach for High Level Collision Avoidance Using Sensors 1

High Level Learning Approach for High Level Collision Avoida...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Naibaho, Lamhot Razauddin Vertivendan, L. Karunakaran, V. Oberoi, Ashish Vishwath, N.C. Ajay Christian University of Indonesia English Language Education Study Program Faculty of Letters and Languages Indonesia Gulf Colleges Saudia Arabia Department of Computer and Information Sciences Saudi Arabia Galgotias University Department of CSE Uttar Pradesh Greater Noida India Faculty of Engineering and Technology Department of CSE Bangalore India RIMT University Department of CSE Godindgarh Mandi India Parul Institute of Engineering and Technology Parul University Aeronautical Department India

ISBN: (纸本)9781665456876

Motion planning for many unmanned aircraft is challenging because they have a larger configuration space than self-driving automobile development (Automated guided vehicles). Additionally, there are more significant uncertainties and disruptions in UAV environments, which makes inter automatic navigation more difficult. In this letter, I proposed a 2 recurrent neural networks (RL) oriented multi-UAV collision avoidance technique by modeling the unpredictability or noise in the environment. Our objective is to create a strategy that can construct a path without clashing with anything using local noisy data. But unlike supervised algorithms, RL lacks a steady training data set with floor labels, thus its collision avoidance strategies often show significant fluctuation and are difficult to reproduce. To solve these issues, we created a two-stage training method for RL-based collision avoidance. We first optimize the policy, and then we utilize a supervised learning methodology with a loss function that encourages the agents to adopt the well-known reciprocity obstacle detection mechanism. In the second step, we use transmission to fine-tune the policy. The complete computer simulation findings demonstrate that this approach can handle noisy local views with erratic sound levels and can design moment & accident paths under inadequate sensing. We review the impact of our policies in a variety of ways. © 2022 IEEE.

关键词： Recurrent neural networks

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study of Increase Geothermal Well Production Rate by Downhole Pump Installation for Utilization in Power Plant 9

Study of Increase Geothermal Well Production Rate by Downhol...

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9th ITB International Geothermal Workshop 2020, IIGW 2020

作者： Prabowo, Ricky Saptadji, Nenny Miryani Maulana, Dimas Taha Nusiaputra, Yodha Yudhistra Geothermal Engineering Master's Program Faculty of Mining and Petroleum Engineering Institut Teknologi Bandung Jl. Ganesha 10 Bandung40132 Indonesia Jl. Trunojoyo no. 135 Kebayoran Baru Jakarta Selatan DKI Jakarta Indonesia

A geothermal well is one of the main components in the geothermal field. The well is essential for geothermal utilization and connecting between reservoir and surface. Based on the condition to discharge fluid toward the surface, wells are categorized as self-discharge wells (able to discharge naturally to surface) and non-self-discharge wells (not able to discharge naturally to surface). For wells categorized as non-self-discharge with low pressure and low-medium temperature, an artificial method needs to be implemented, which is the use of downhole pump technology, namely electrical submersible pump (ESP) or line shaft pump (LSP). This condition occurs in well in Tulehu geothermal field (X-1). Downhole pump technology has been developed in several geothermal fields in other countries and has been proven to increase geothermal well production. The purpose of this research is to determine the appropriate engineering design for the implementation of a downhole pump in X-1 well and the net power output that can be produced. The research method was carried out analytically from evaluating and analyzing well-testing results, including completion test and production test to determine the condition and characteristics of the well, performing technical design calculation for downhole pump implementation, and calculating the binary cycle analysis of power plant obtain net power generated. Optimum pump setting depth in 600 meters depth that can produce mass flow rate 48.28 kg/s, and optimum model is ORC power plant with Butane working fluid that can produce Net Power Output 1.382 MW for ORC power plant model combined with ESP-2. © Published under licence by IOP Publishing Ltd.

关键词： Well testing

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Catalytic cracking of polyethylene terephthalate (PET) plastic waste and palm fibre mixtures using Ni-USY zeolite catalyst 6

Catalytic cracking of polyethylene terephthalate (PET) plast...

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6th International Conference on Mathematics, Science, and Education, ICMSE 2019

作者： Nazarudin Jayanti, N. Alfernando, O. Prabasari, I.G. Ulyarti Sarip, R. Chemistry Engineering Study Program Faculty of Engineering Universitas Jambi Indonesia Center of Energy and Nanomaterial Study Universitas Jambi Indonesia Agricultural Product Technology Study Program Faculty of Agricultural Technology Universitas Jambi Indonesia Chemistry Department Faculty of Science University of Malaya Malaysia

This research was carried out to produce fuel (Oil Liquid Poduct/OLP) from catalytic cracking of a mixture of Polyethylene Terephthalate (PET) and fiber waste from palm oil. USY zeolite was previously ion-exchanged by three levels of Ni-metal concentration (1%, 2%, 3%) before used as catalyst in the cacking process. The catalytic cracking was carried out at rasio of sample to catalyst was 6:1, three levels of temperature (400°C, 425°C and 450°C) and three levels of reaction time (10, 20 and 30 minutes).The SEM images and EDX spectra of Ni-USY zeolite catalysts showed that all catalysts were in crystalline form of FAU framework. The highest of OLP percentage, 24.5%, was produced by catalytic cracking at 450°C for 10 minutes using Ni-USY prepared by ion-exchange using 2% concentration of Nickel Nitrate solution. © Published under licence by IOP Publishing Ltd.

关键词： Catalysts

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Paracetamol degradation in a dual-chamber rectangular membrane bioreactor using microbial fuel cell system with a microbial consortium from sewage sludge

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Case Studies in Chemical and Environmental engineering 2024年 9卷

作者： Utami, Tania Surya Arbianti, Rita Hidayatullah, Ibnu Maulana Yusupandi, Fauzi Hamdan, Mukti Fadilah Putri, Najah Riyadi, Fatimah Azizah Boopathy, Ramaraj Department of Chemical Engineering Faculty of Engineering Universitas Indonesia West Java Depok 16424 Indonesia Bioprocess Engineering Study Program Faculty of Engineering Universitas Indonesia West Java Depok 16424 Indonesia Research Center for Biomass Valorization Universitas Indonesia West Java Depok 16424 Indonesia Department of Chemical Engineering Institut Teknologi Sumatera Lampung Selatan Lampung 35365 Indonesia Department of Biological Sciences Nicholls State University Thibodaux 70310 LA United States

The public widely uses Paracetamol (PCT) as an analgesic drug to reduce pain and fever symptoms in the human body. The COVID-19 pandemic has led to an increase in PCT consumption, resulting in its presence in water bodies and establishing it as a common aquatic environmental pollutant. Conventional methods for PCT removal rely on chemical treatments, which necessitate harsh reaction conditions and incur high operational costs. This study aims to investigate the potential for PCT degradation within the anode environment of the Microbial Fuel Cell (MFC) system, using a consortium of bacteria isolated from sewage sludge. The initial PCT concentration and the pH of the anode environment were varied. The results showed that PCT degradation occurred within 72 hours. The highest PCT removal rate was achieved at elevated concentrations, specifically 28.54 ± 18.84% for an initial PCT concentration of 31.45 mg/L. Furthermore, the pH levels of the anode environment were observed to influence PCT degradation, with the highest degradation rate recorded at 48.69 ± 0.86% at a pH of 8.2. In this study, the highest average power density reached was 1174.42 mW/m² with an external resistance of 1000 Ω at an initial PCT concentration of 31.45 mg/L. Microbial community analysis was conducted, revealing that Burkholderia sp. dominated the reactor containing PCT. These findings offer valuable insights for the development of more efficient technologies for the removal of PCT from pharmaceutical wastewater using natural microbial communities. © 2023 The Authors

关键词： COVID-19 Dual chamber microbial fuel cell Microbial consortium Paracetamol Pollutant degradation

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