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11th National Physics Seminar, SNF 2022

作者： Fitri, U.R. Ziveria, M. Muhara, I. Department of Physics Education Universitas Negeri Jakarta Jl. Rawamangun Muka Jakarta13220 Indonesia Information Systems Study Program Kalbis Institute of Technology and Business Jl. Pulomas Selatan No.22 Jakarta Indonesia Chemical Engineering Study Program Faculty of Industrial Technology Institut Teknologi Bandung West Java Bandung Indonesia

Planck's constant experiment has been carried out to determine the value of Planck's constant using the regression analysis method. The steps are carried out using the scientific process outlined in the practical work steps. The specification of the experimental equipment uses the Planck constant experimental set with 12v/35w tungsten halogen light, 15v output, +-0.2% accuracy, 220V power requirement, 0.5A fuse rating and red, yellow 1, yellow 2, and red color filters, green and blue. Experiments using the concept of the photoelectric effect phenomenon began by irradiating metallic materials with light using a filter. The wavelength (635, 570, 540, 500, 460) nm is the light filter variation. The wavelength that hits the metal material is selected by placing a light filter on the light propagation path toward the metal material. So the light that passes through the filter is only light with a certain wavelength. After the metal material is irradiated by light with a certain wavelength, then the stopping potential voltage measurement is carried out on the metal material irradiated by light. From the variation of the light filter used, it is possible to calculate Planck's constant using the regression method with the value obtained 6.26x1034. This value is very close to the actual Planck's constant value which is 6.62x1034 so an error value is obtained 4.23%. With 95.77% accuracy, Planck's constant value obtained from the calculation using the linear regression method can be said to be very close to the actual Planck's constant value. © Published under licence by IOP Publishing Ltd.

关键词： Photoelectricity

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Performance of GNSS positioning in PPP mode using MADOCA precise products

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Geodesy and Geodynamics 2024年第6期15卷 642-651页

作者： Brian Bramanto Rachel Theresia Irwan Gumilar Sidik T.Wibowo Geodetic Science Engineeringand Innovation Research GroupFaculty of Earth Sciences and TechnologyInstitut Teknologi BandungBandung 40135Indonesia Geodesy and Geomatics Engineering Study Program Faculty of Earth Sciences and TechnologyInstitut Teknologi BandungBandung 40135Indonesia Geodetic and Geodynamic Control Network Center Geospatial Information Agency of Indonesia(BIG)Cibinong 16911Indonesia

The Global Navigation Satellite System (GNSS) is widely utilized for accurate *** commonly applied method to obtain precise coordinate estimates is by implementing the relative positioning in network ***,this approach can be complex and ***,The Japan Aerospace Exploration Agency (JAXA) offers freely available satellite orbit and clock correction products called Multi-GNSS Advanced Demonstration Tool for Orbit and Clock Analysis (MADOCA),which can enhance positioning accuracy through the precise point positioning (PPP) *** study focuses on evaluating PPP static mode positioning using MADOCA products and comparing the results with the highly precise relative positioning *** analyzing a network of 20 GNSS stations in Indonesia,we found that the PPP method using MADOCA products provided favorable positioning *** median discrepancies and the corresponding median absolute deviation (MAD) for easting,northing,and up components were estimated as 9±18 mm,10±9 mm,and 3±40 mm,*** results indicate that PPP with MADOCA products can be a reliable alternative for establishing Indonesia's horizontal control networks,particularly for orders 0,1,2,and 3,and for a broad spectrum of geoscience monitoring ***,considerations such as epoch transformations and seismic activities should be taken into account for accurate positioning applications that comply with the definition of the national reference framework.

关键词： Global Navigation Satellite System(GNSS) Precise point positioning(PPP) MADOCA Positioning evaluation

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Optimization of hyper parameter bandwidth on naïve Bayes kernel density estimation for the breast cancer classification 2

Optimization of hyper parameter bandwidth on naïve Bayes ke...

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2nd International Conference on information and Communications technology, ICOIACT 2019

作者： Sasongko, Theopilus Bayu Arifin, Oki Al Fatta, Hanif Information Technology Department Faculty of Computer Science Universitas Amikom Yogyakarta Yogyakarta Indonesia Management of Informatics Study Program Economy and Business Departement Politeknik Negeri Lampung Lampung Indonesia

ISBN: (纸本)9781728116556

Cancer is viewed as the most harmful disease in the world. One of the most deadly cancer diseases attacking the women is breast cancer. In this paper, used Naïve Bayes classifier Kernel Density Estimation in breast cancer Wisconsin dataset take from UCI data repository. Naïve Bayes with Kernel Density Estimation was used as it could enable the Naïve Bayes to process the quantitative data. However, the performance of KDE (kernel density estimation) highly depends upon the size of the bandwidth parameter used to control the curve. The problem an accurate choice of parameter bandwidth (h) using trial and error technique requires a long process. A large bandwidth (h) will over-smooth the density and mask the structure in the data, a small bandwidth (h) will yield a density estimate that is spiky and very hard to interpret. The aim of this research is to compare the optimization of grid search parameter with the genetic algorithm to determine the bandwidth parameter (h) on the Naïve Bayes Kernel Density Estimator algorithm. The parameters used to compare the two optimization methods included accuracy, AUC (Area under Curve), and computation time. The experiment results on the test of the significance value on the comparison of grid search and algorithm in determination of the bandwidth parameter (h) naïve Bayes kernel density used Mann-Whitney Test. The result of the significance ranking showed that mean rank of the genetic algorithm accuracy was 489.95 better than the mean value of the rank of grid search accuracy at only 189.05, mean rank of AUC genetic algorithm was 491.82 better than mean rank of AUC grid search only at 187.18. The mean rank of computation time grid search was 243.37 faster than mean rank of computation time genetic algorithm at 435.63. © 2019 IEEE

关键词： Classification (of information)

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Web Browser Extension Development of Structured Query Language Injection Vulnerability Detection Using Long Short-Term Memory Algorithm

Web Browser Extension Development of Structured Query Langua...

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International Conference of Computer Science and information technology

作者： Togu Novriansyah Turnip Hotma Aruan Anita Lasmaria Siagian Leonardo Siagian Information Technology Study Program Institut Teknologi Del Toba Indonesia

ISBN: (纸本)9798350399080

Structured Query Language Injection (SQLi) is a client-side application vulnerability that allows attackers to inject malicious SQL queries with harmful intents, including stealing sensitive information, bypassing authentication, and even executing illegal operations to cause more catastrophic damage to users on the web application. According to OWASP, the top 10 harmful attacks against web applications are SQL Injection attacks. Moreover, based on data reports from the UK's National Fraud Authority, SQL Injection is responsible for 97% of data exposures. Therefore, in order to prevent the SQL Injection attack, detection SQLi system is essential. The contribution of this research is securing web applications by developing a browser extension for Google Chrome using Long Short-Term Memory (LSTM), which is a unique kind of RNN algorithm capable of learning long-term dependencies like SQL Injection attacks. The results of the model will be deployed in static analysis in a browser extension, and the LSTM algorithm will learn to identify the URL that has to be injected into Damn Vulnerable Web Application (DVWA) as a sample-tested web application. Experimental results show that the proposed SQLi detection model based on the LSTM algorithm achieves an accuracy rate of 99.97%, which means that a reliable client-side can effectively detect whether the URL being accessed contains a SQLi attack or not.

关键词： Uniform resource locators Structured Query Language Web pages Static analysis SQL injection Browsers Classification algorithms

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Application of System Usability Scale (SUS) method in testing the usefulness of information system Student Creativity program (PKM) based on website

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AIP Conference Proceedings 2022年第1期2658卷

作者： Silvia Oktaviani Citra Wiguna Agus Priyanto 1Information System Study Program Informatics Faculty Telkom Institute of Technology Purwokerto Indonesia 2Informatics Study Program Informatics Faculty Telkom Institute of Technology Purwokerto Indonesia

One of the significant indicators in the Education field is having a good experience from outside the campus. The student experience is reflected in three main factors: thoughts, feelings, and skills. Students Creativity program (PKM) is a form of the implementation of the Threefold Mission of Higher Education (Tri Dharma Perguruan Tinggi). PKM has an effort to grow, accommodate, and realize students' creative and innovative ideas. The use of information technology that is said to be a supporting factor in running business processes can improve effective and efficient performance, one of which is the use of computerized websites in PKM activities at Telkom Institute of technology Purwokerto. The use of a website that is new still needs a lot of development. There are still some functions that have not run as they should be, such as the assessment process on the website that is still semi-manual. Based on these problems, so that the website can run optimally, it is necessary to evaluate the feasibility of the PKM system used by end users at Telkom Institute of technology Purwokerto. The test was conducted using the System Usability Scale (SUS) method consisting of 10 statement items in the form of questionnaires with the main focus on measuring the feasibility level of the website involving respondents as end-users. The results showed that the validity test is said to be valid. Meanwhile, the reliability test is said to be reliable with a score of 63,308. This result can be categorized OK with grade scale D. The result shows that the research can be developed by updating the content or interface of the system to be more consistent and conducting demonstrations or socialization on how to use the system before users access the system.

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Feature Learning using Deep Variational Autoencoder for Prediction of Defects in Car Engine

Feature Learning using Deep Variational Autoencoder for Pred...

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2024 International Conference on information technology Research and Innovation, ICITRI 2024

作者： Susanto, Nanang Pardede, Hilman Ferdinandus Universitas Nusa Mandiri Computer Science Study Program Jakarta Indonesia National Research and Innovation Agency Indonesia Universitas Nusa Mandiri Computer Science Faculty of Information Technology Jakarta Indonesia

ISBN: (纸本)9798350376210

Machine learning has been employed to automatically detect the defects on car engines in several studies. One of crucial challenges on applying machine learning is on the amount of defect data collected is often large with high dimensional data, making manual detection inefficient and inaccurate. The other problem is on the missing data as oftentimes the collected data are incomplete. In this paper, we employ machine learning frameworks for engine defect detection. It comprises the data pre-processing stage which includes imputing missing value data, then performing feature correlation using the Pearson method, and selecting the features to use. After that, the label encoder and standard scaler are carried out. The experimental process begins with creating a baseline, then continues with improving imbalance data using SMOTE, and feature reconstruction using variational autoencoder (VAE). Afterwards, for classification, we employ convolutional neural networks (CNN). The proposed method achieved precision 99.63%. We collect engine quality dataset of 224,239 data with 90 features from major automobile manufacturing in Indonesia. This showed that SMOTE and Variational Autoencoder dimensional reconstruction method can overcome defect predictions in car engine defect data with data imbalance conditions. This novel methodology distinguished our study from prior methods and shows considerable increases in precision and recall matrix. © 2024 IEEE.

关键词： Convolutional neural networks

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The implementation of supply chain management in the n95 respirator product by using blockchain technology 6

The implementation of supply chain management in the n95 res...

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6th International Conference on information Management and technology, ICIMTech 2021

作者： Halim, Erwin Subagio, Michael Adrian Halim, Davine Dorothy Hebrard, Marylise Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Sepuluh Nopember Institute of Technology Chemical Engineering Department Surabaya Indonesia Institut des Usages Enterprise Law Program Study Montpellier France

ISBN: (纸本)9781665449373

As it starts at the end of the year 2019, the Covid-19 pandemic has already disturbed the condition of human life, socio-culture, and economics globally. As the virus is spreading from human to human, the need for a respirator mask is critical. One very well-known respirator mask is N95. The huge demand and high price of the N95 respirator effect create a bogus mask. This study proposes using blockchain in a global implementation of supply chain management of N95 respirator supply chain activity. Blockchain systems may help to reduce people's worldwide anxiety about the spread of N95 respirator counterfeits. The method used in this research is a systematic literature review and proposing a blockchain framework base on blockchain components. The implementation can use RFID at the level of manufacturers, distributors, and sellers. Using QR code or RFID stickers are possible in the level of customers or buyers. The result of this research is a proposal of blockchain technology for N95 respirator supply chain management systems, N95 respirator supply chain activities workflow, and a use case diagram. © 2021 IEEE.

关键词： Supply chain management

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Multidocument Summarization using GloVe Word Embedding and Agglomerative Cluster Methods

Multidocument Summarization using GloVe Word Embedding and A...

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International Conference on Sustainable Engineering and Creative Computing (ICSECC)

作者： Rosalina Rosalina Rafiqul Huda Genta Sahuri Faculty of Computing Information Technology Study Program President University Bekasi Indonesia

ISBN: (纸本)9781728175898

This paper explores the method of extracting multi-document summary terminology that enhances single document summary methods using the information related to the document and perhaps even the relation between the documents. In the problems of fragmentation, density, duplication and selection of passages, the summarization of several documents ranges from the summarization of single documents to the creation of efficient summaries. Our approach addresses these issues by using Agglomerative cluster sentence, GloVe, TextRank, Cosine Similarity. In addition, this research also use NLTK as library filter word such as stopwords, numeric, punctuation, multiple_whitespaces, short_words in order Vectorizing the sentence when using GloVe. The result of this paper was evaluated using ROUGE; 41% for unigram, 17% for bigram, 57% for trigram.

关键词： Terminology Conferences Libraries Generators Filtering theory Internet Object recognition

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Finding the Best Techniques for Predicting Term Deposit Subscriptions (Case study UCI Machine Learning Dataset)

Finding the Best Techniques for Predicting Term Deposit Subs...

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International Conference on Sustainable Engineering and Creative Computing (ICSECC)

作者： Lila Setiyani Ayu Indahsari Rosalina Tjong Wansen Master of Science Information Technology study program President University Bekasi Indonesia Informatics Study Program President University Bekasi Indonesia

Knowing a lot about attributes, such as bank client data, marketing campaigns, social and economic context, and so on, is necessary to predict which bank customers are likely to sign up for time deposits. The direct marketing campaigns dataset for the bank institution was made available by the UCI machine learning repository. This dataset had previously been used in studies to forecast customers who would sign up for time deposits, but the outcomes of the two studies were different. The purpose of this study is to determine which machine learning methods are most effective for predicting customers who will sign up for time deposits. In this study, researchers used machine learning methods like random forests, K-nearest neighbors, decision trees, naive bayes, logistic regression, SVM, and XGBoost to uncover the truth. Results showed that XGBoost had the highest accuracy (91.73%), followed by the DT classifier (88.79%), LR classifier (91.2%), NB classifier (84.78%), KNN classifier (90.6%), SVM (89.6%), and XGBoost (91.44%).

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Monitoring System of Greenhouse Based on WSN and Auto Flushing Sensor Mechanism

Monitoring System of Greenhouse Based on WSN and Auto Flushi...

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International Conference on information technology, information Systems and Electrical Engineering (ICITISEE)

作者： Herman Yuliandoko Farisqi Panduardi Abdul Holik Information Technology Program Study Politeknik Negeri Banyuwangi Banyuwangi Indonesia Agrobusiness Program Study Politeknik Negeri Banyuwangi Banyuwangi Indonesia

Indonesia is an agricultural country with agriculture as one of the mainstays of state income. However, the competition for agricultural production is currently very high, so it requires innovations in agricultural technology to increase the competitiveness of Indonesian food production. One of the latest agriculture technologies is Greenhouse. With Greenhouse, it is possible to adjust temperature conditions, room humidity and other plant parameters. In Indonesia, this technology has begun to be widely used but is still very limited due to technological constraints and high costs. In a greenhouse, some tools monitor plant parameters to support plant growth, such as water pumps, sensors, dynamos, and sprays. However, currently, there are still many greenhouses that operate these tools conventionally. This causes greenhouse operations to take a long time and a lot of labor, so greenhouse operations are expensive and complicated. Therefore, Greenhouse innovation is needed with a flexible, easy and efficient system. In this research, innovation will be carried out with a Greenhouse monitoring system by implementing the WSN (Wireless Sensor Network). In addition, the use of sensors with auto flushing is done to prevent encrustation on the sensor surface. Using WSN will increase the flexibility of data transmission and minimize energy use so that it can be applied in remote areas. Room temperature is also connected with a blower as an automation mechanism to maintain wind circulation in Greenhouse. The monitoring of plan nutrition in the Greenhouse is very important that why this research also study it.

关键词： Temperature sensors Temperature measurement Wireless sensor networks Technological innovation Production Agriculture Water pumps

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