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1st International Conference on Advanced information Scientific Development, ICAISD 2020

作者： Sihotang, Hengki Tamando Efendi, Syahril Zamzami, Elvyawati M Mawengkang, Herman Doctoral Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Sumatera Utara Indonesia

Security of data text file on a computer can be done by utilizing encryption and decryption techniques. One technique is encryption and decryption of data encryption system text file with cryptography. Cryptography is the science or art to randomize and secure the message to avoid the manipulation of data by performing encryption and decryption on the data of the text file. Cryptographic algorithm consists of several types of one example is the algorithm Rivest Shamir Adleman (RSA). The RSA algorithm is one of asymmetric cryptography technique ie riptografi modern technique that hastwo keys of the public key used for encryption and a private key techniques to make the process deskripsi. The purpose of this research is to design and implement RSA cryptography algorithm on text file data security. The results of this design is a dekstop based software. This research is expected to provide benefits to the users, individually as well for other organizations. © 2020 Publishedunder licence by IOP Publishing Ltd.

关键词： Security of data

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Clustering Satellite Images of Forest Area Change in Nabire Regency with NDVI, SAVI, MSAVI, VCI, TCI, dan VHI Method

Clustering Satellite Images of Forest Area Change in Nabire ...

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International Seminar on Research of information technology and Intelligent Systems (ISRITI)

作者： Gunawan Prayitno Eko Sediyono Irwan Sembiring Sri Yulianto Prasetyo puput Dani Prasetyo Adi Doctoral Program of Computer Science Faculty of Information Technology Satya Wacana Christian University Indonesia Salatiga Indonesia Satya Wacana Christian University Indonesia Salatiga Indonesia National Research and Innovation Agency (BRIN) Bandung Indonesia

Based on data from the Forestry and Plantation Service of Nabire Regency, the forest area in Nabire Regency is 3.26% of the total forest area in Papua, which is 42,224,840 Ha. The existence of forests in Nabire Regency greatly supports the survival of unique animals such as cuscus, birds of paradise, cassowary, peacocks, and parrots, and there are also various types of reptiles, mammals, and insects. By looking at the diversity of animals and ornamental plants in the forests of Nabire Regency, further research is needed to see the development and change of forests as an effort to estimate forest destruction. This research focuses on developing a research model to classify forest areas as vulnerable to collapse. The methods used in this study used OLI Landsat 8 satellite imagery and advanced analysis methods to assess and predict potential forest destruction in Nabire District, Indonesia; using OLI Landsat 8 satellite imagery and multi-band sensors, the developed research model can present a systematic approach that understands forest dynamics. Research has several stages, such as data preparation, data classification, vegetation index analysis, interpolation, and grouping of vegetation index data. Applying vegetation indices such as NDVI, SAVI, MSAVI, VCI, TCI, and VHI yields insights into vegetation health and distribution, enabling comprehensive monitoring of ecosystem change.

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Rice Variety Identification Using Edge Feature Extraction and Texture-Based Segmentation

Rice Variety Identification Using Edge Feature Extraction an...

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International Seminar on Application for technology of information and Communication (ISemantic)

作者： Aji Setiawan Kusworo Adi Catur Edi Widodo Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Information Technology Faculty of Engineering Darma Persada University Jakarta Indonesia School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

This research proposes a methodology for identifying the optimal feature combination using Support Vector Machine (SVM) based on edge and texture features. Canny, Sobel, and Prewitt for edge detection and GLCM, Gabor, and LBP for texture analysis are popular feature extraction techniques widely employed in various cases. The approach combines edge and texture features with SVM to classify rice types into five categories: Ipsala, Jasmine, Karacadag, Arborio, and Basmati. Experimental results demonstrate that SVM using Canny, Sobel, and Prewitt combined Gabor texture features outperforms the comparative methods. A comparative analysis based on accuracy scores using confusion matrices is conducted. Combining all feature edge methods, Canny, Sobel, and Prewitt with Gabor on SVM classification is the best model for rice object classification, achieving an accuracy of 96%.

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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International Conference on Computer Applications and Industrial Electronics (ICCAIE)

作者： Wandy Wandy Kusworo Adi Media Anugerah Ayu Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University South Jakarta Indonesia

Sports Science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport Science recognizes and prevents injuries. Sensors and statistics formalize Sports Science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM 2.5 and PM 10 ). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM 2.5 and PM 10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results.

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An Analysis Air Traffic Prediction During a Pandemic

An Analysis Air Traffic Prediction During a Pandemic

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Darmeli Nasution Herman Mawengkang Fahmi Muhammad Zarlis Departmen Of Computer Science and Information Technology Doctoral Program Universitas Sumatera Utara Medan Indonesia Departmen Of Math and Science Universitas Sumatera Utara Medan Indonesia Departmen Of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Departmen Of Computer Science and Information Technology Universitas Bina Nusantara Jakarta Indonesia

ISBN: (纸本)9781665453967

Air transportation during the covid-19 pandemic experienced a very drastic decline. The decrease in the number of passengers was caused by national and international restrictions. The troublesome administration makes passengers discouraged from traveling using Air transportation. Based on the National Statistics Agency, air transportation experienced a decline from early 2020 to 2021. This study focuses on air traffic predictions, namely the number of aircraft passengers during the COVID-19 pandemic at Indonesia's main airports, namely Kuala Namu, Sukarno Hatta, and Juanda airports., Ngurah Rai and Hasanuddin. The method used to predict the number of airplane passengers during a pandemic is the backpropagation algorithm using the Fletcher Reeves method.

关键词： COVID-19 Analytical models Airplanes Pandemics Atmospheric modeling Backpropagation algorithms Predictive models

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Relational database reconstruction from SQL to Entity Relational Diagrams 4

Relational database reconstruction from SQL to Entity Relati...

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4th International Conference on Computing and Applied Informatics 2019, ICCAI 2019

作者： Hakim Lubis, Juanda Muisa Zamzami, Elviawaty Faculty of Computer Science and Information Technology Doctoral Program of Computer Science Studies Universitas Sumatera Utara Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Indonesia

The absence of requirement documents or incompatibility of the requirement documents with existing software can be a problem in developing or maintaining software. Without knowing things that become requirements can cause software as a product that fails because it does not meet customer desires. One of the main problems in developing or maintaining software is that developers do not know the entity relational schema of the software. So developers will prefer to rebuild from scratch. This paper discusses database reconstruction from SQL to entity relational diagrams by identifying the primary and foreign keys of SQL sampling. © Published under licence by IOP Publishing Ltd.

关键词： Condensed matter physics

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Modifying the Revised NIOSH Lifting Equation using Anthropometric Variables to Calculate Horizontal and Vertical Multipliers

Modifying the Revised NIOSH Lifting Equation using Anthropom...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Moehamad Adi Rochmat Sarifuddin Madenda Tri Handhika Ernastuti Ernastuti Department of Industrial Engineering Gunadarma University Depok Indonesia Doctoral Program in Information Technology Gunadarma University Depok Indonesia Centre for Computational Mathematics Studies Gunadarma University Depok Indonesia Faculty of Industrial Technology Gunadarma University Depok Indonesia

ISBN: (纸本)9781665453967

The Revised NIOSH Lifting Equation is a work system assessment tool which often used in industrial world. The calculation result in the form of Recommended Weight Limit (RWL) and the Lifting Index (LI) become the basis for improving the work system carried out. RNLE is an application developed by NIOSH and uses American anthropometry as a constant in its calculations. Some researchers, especially in Asia, consider that the application is less suitable for Asia people who have anthropometric differences with Americans. The assessment carried out by the RNLE application is based on work activities carried out without considering the worker's body posture. In particular, the calculate of HM and VM in this application uses a constant related to anthropometry. RNLE uses the constants based on the average anthropometry of workers in calculation. The constant anthropometry used in the equation led to the results of the risk calculation that does not correspond to physical aspects of workers. It takes an equation that consider anthropometry workers to get the calculation of the value of the appropriate work risk. The study aims to propose equations that consider anthropometry factors to calculate the risk factor. The development of the equation with Python programming language allows further development in the field of information and communication technology. The results of the study shows that the average results of the RWL and LI calculation uses anthropometry data not equal to the RNLE, thus required new equation in order to resulting more objective risk value.

关键词： Asia Anthropometry Mathematical models information and communication technology Indexes Intelligent systems Cybernetics

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DEVELOPMENT of GEOSPATIAL information INTEGRATED with BIG DATA to AGRICULTURAL HAZARD MONITORING in WEST JAVA

DEVELOPMENT of GEOSPATIAL INFORMATION INTEGRATED with BIG DA...

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2022 Annual Conference, ASPRS 2022

作者： Virtriana, R. Riqqi, A. Anggraini, T.S. Fauzan, K.N. Ihsan, K.T.N. Mustika, F.C. Atmaja, F.W. Suwardhi, D. Harto, A.B. Sakti, A.D. Deliar, A. Soeksmantono, B. Wikantika, K. Remote Sensing and Geographic Information Sciences Research Group Faculty of Earth Sciences and Technology Institut Teknologi Bandung Bandung40132 Indonesia Center for Remote Sensing Institut Teknologi Bandung Bandung40132 Indonesia Center for Spatial Data Infrastructure Institut Teknologi Bandung Bandung40132 Indonesia Master Program of Geodesy and Geomatics Engineering Faculty of Earth Sciences and Technology Institut Teknologi Bandung Jl. Ganesha No.10 Bandung40132 Indonesia Doctoral Program of Geodesy and Geomatics Engineering Faculty of Earth Sciences and Technology Institut Teknologi Bandung Jl. Ganesha No.10 Bandung40132 Indonesia

Food security is highly dependent on three aspects, namely food availability, food access, and food utilization. The availability aspect depends on food supply which is identical to agricultural productivity. West Java Province is the third national rice producer with 16.6%, but West Java Province is the most extensive rice consumer, around 21.1% of the total national rice consumption. Agricultural productivity can decline due to natural hazards such as floods and droughts. Monitoring floods and droughts in paddy fields are necessary to prevent decreased agricultural productivity. This study aims to monitor the rice fields from the dangers of flooding and drought every month. Agricultural hazard monitoring is divided into two parameters, namely static parameters and dynamic parameters. Dynamic parameters are observed every month so that the hazard index is generated on a monthly scale. GIS and Remote sensing data are integrated to perform agricultural hazard modelling. Furthermore, this agricultural hazard modelling results will be strengthened by using big to provide information about an almost real-Time event that can be accessed through the Application program Interface (API) service. This study uses a data mining system from Drone Emprit that performs data mining on Twitter and news portals with machine learning technology (probabilistic classifier) and Natural Learning Process. The results obtained are around 15,000 data from January 1 to November 1, 2021, and 37.9% of them are identified by location based on the city or district level in West Java Province. It is hoped that the policy-maker can consider the area of agricultural land that requires assistance to increase productivity and plan a policy to support agriculture in West Java in the future. © 2022 R. Virtriana et al.

关键词： Agriculture

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Integrating Noise Classification and Speech Enhancement Model for Hearing Aids

Integrating Noise Classification and Speech Enhancement Mode...

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International SoC Design Conference, ISOCC

作者： Ying-Hsiu Hung Yen-Ching Chang Shin-Chi Lai Wen-Ho Juang Ming-Hwa Sheu Jeng-Dao Lee Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan Dept. Computer Science and Information Engineering National Formosa University Huwei Taiwan Dept. Automation Engineering National Formosa University Huwei Taiwan

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

As the global population ages, the number of elderly people needing hearing aids is increasing. This paper proposed an architecture designed to enhance speech based on noise conditions. After noise types are identified by a Convolutional Neural Network (CNN) noise classification model, these noise conditions are combined with the noisy speech and input into a conditional Generative Adversarial Network (cGAN) for speech enhancement. The CNN noise classification model achieves an average recognition accuracy of nearly 92%, with a Kappa value as high as 0.88. The cGAN speech enhancement achieves lower error values in the Mel-Cepstral Distortion (MCD) measure. Although some parts of the human voice may be filtered out, the results demonstrate the potential development of the proposed algorithm.

关键词： Accuracy Noise Noise reduction Speech recognition Speech enhancement Hearing aids Classification algorithms Convolutional neural networks Human voice Older adults

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Analysis of the quantum perceptron algorithm for classification of bank marketing data

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AIP Conference Proceedings 2023年第1期2714卷

作者： Solikhun Syahril Efendi Muhammad Zarlis Poltak Sihombing Doctoral Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

Quantum computing is a technology that takes advantage of quantum effects, which operates using quantum computing, resulting in increased algorithm speed compared to the speed of algorithms that use classical computing. This study aims to classify bank marketing data in determining the eligibility of customers who are entitled to become depositors with the quantum perceptron algorithm using calculations with 5 variables, namely employment, status, education, home credit, and bank loans. The result of this research is the analysis of bank marketing classification data to determine the eligibility of customers who are entitled to become depositors using the quantum perceptron algorithm with quantum computation.

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