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检索条件"机构=Study Program in Informatics Engineering Faculty of Computer Science"

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Identification of Beef Freshness Using Linear Discriminant Analysis (LDA) Algorithm and Extraction of HSV Values

Identification of Beef Freshness Using Linear Discriminant A...

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Electronic and Electrical engineering and Intelligent System (ICE3IS), International Conference on

作者： Riduwan Napianto Agung Tri Prastowo Agung Deni Wahyudi Wajiran Yohanes Egi Pratama Yudoutomo Arico Ayani Suparto Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandarlampung Indonesia Information Technology Education Study Program STKIP PGRI Situbondo Situbondo Indonesia

ISBN: (纸本)9781665465427

To determine the quality of beef, it is usually seen using a flashlight and matching it with a predetermined color standard. However, this method is time consuming and the results are inconsistent due to human visual limitations. This study aims to identify the freshness of beef based on its color characteristics using feature extraction method with HSV and LDA method for identification. The identification process begins with the transformation process from RGB images to L*a*b, then image segmentation serves to separate one object from another and image identification uses the LDA method with feature extraction using HSV. The use of color feature extraction with the HSV algorithm to assist in obtaining information from the colors contained in the image for easy identification. Furthermore, the LDA algorithm has a function to obtain the maximum projection to obtain a smaller dimensional space so that the desired patterns can be separated through the boundary line of a linear equation so as to form a certain class or group. Based on the test results using the confusion matrix, it produces a precision value of 80%, recall of 84%, and accuracy of 82%.

关键词： Visualization Image segmentation Image color analysis Shape Feature extraction Mathematical models Linear discriminant analysis

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A Comparison of Multi Class Support Vector Machine vs Deep Convolutional Neural Network for Brain Tumor Classification

A Comparison of Multi Class Support Vector Machine vs Deep C...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Nur Ryan Dwi Cahyo Christy Atika Sari Eko Hari Rachmawanto Cahaya Jatmoko Rabei Raad Ali Al-Jawry Mohamed Ayad Alkhafaji Department of Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department Computer Science Northern Technical University Semarang Indonesia Department of Computer Engineering Technology Northern Technical University Mosul Iraq Department of Computer Sciece National University of Science and Technology DhiQar Iraq

Multi SVM has long been one of the popular methods in classification, while DCNN has recently gained significant attention in image processing and pattern recognition. This research evaluates the effectiveness of Multi Class Support Vector Machine (M-SVM) and Deep Convolutional Neural Network (DCNN) techniques in classifying brain tumors. A dataset of 2660 3D medical images with dimensions 227 x 227 x 3; including Glioma, Meningioma, and Pituitary tumors, has been partitioned into distinct sets for both training and testing purposes. DCNN approach achieves excellent accuracy in identifying tumor names, with a training accuracy of 97.8% and 100% success rate in 9 experiments. The Multi SVM method demonstrates relatively good accuracy, with training accuracies ranging from 70% to 90% based on different kernel functions. These findings provide valuable insights for selecting appropriate methods in brain tumor classification and encourage further exploration of hybrid Multi SVM-DCNN approaches to enhance accuracy and reliability.

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Group-in-a-box layout for multi-faceted analysis of communities

Group-in-a-box layout for multi-faceted analysis of communit...

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2011 IEEE International Conference on Privacy, Security, Risk and Trust, PASSAT 2011 and 2011 IEEE International Conference on Social Computing, SocialCom 2011

作者： Rodrigues, Eduarda Mendes Milic-Frayling, Natasa Smith, Marc Shneiderman, Ben Hansen, Derek Dept. of Informatics Engineering Faculty of Engineering University of Porto Portugal Microsoft Research Cambridge United Kingdom Connected Action Consulting Group Belmont CA United States Dept. of Computer Science Human-Computer Interaction Lab University of Maryland College Park MD United States College of Information Studies Center for Advanced Study of Communities and Information University of Maryland College Park MD United States

ISBN: (纸本)9780769545783

Communities in social networks emerge from interactions among individuals and can be analyzed through a combination of clustering and graph layout algorithms. These approaches result in 2D or 3D visualizations of clustered graphs, with groups of vertices representing individuals that form a community. However, in many instances the vertices have attributes that divide individuals into distinct categories such as gender, profession, geographic location, and similar. It is often important to investigate what categories of individuals comprise each community and vice-versa, how the community structures associate the individuals from the same category. Currently, there are no effective methods for analyzing both the community structure and the category-based partitions of social graphs. We propose Group-In-a-Box (GIB), a metalayout for clustered graphs that enables multi-faceted analysis of networks. It uses the treemap space filling technique to display each graph cluster or category group within its own box, sized according to the number of vertices therein. GIB optimizes visualization of the network sub-graphs, providing a semantic substrate for category-based and cluster-based partitions of social graphs. We illustrate the application of GIB to multi-faceted analysis of real social networks and discuss desirable properties of GIB using synthetic datasets. © 2011 IEEE.

关键词： Visualization

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Comparison Performance of Decision Tree Classification Model for Spam Filtering with or without the Recursive Feature Elimination (RFE) Approach

Comparison Performance of Decision Tree Classification Model...

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International Conference on Information Technology, Information Systems and Electrical engineering (ICITISEE)

作者： Ahmad Fikri Zulfikar Dede Supriyadi Yaya Heryadi Lukas Faculty of Technique Program Study of Informatics University of Pamulang South Tangerang Indonesian Program Study of Informatics STMIK Eresha South Tangerang Indonesian Dept. of Computer Science Bina Nusantara University West Jakarta Indonesian Cognitive Enginnering Research Group (CERG) Faculty of Enginnering University of Katolik Indonesia Atma Jaya Jakarta Indonesia

ISBN: (数字)9781728151182

ISBN: (纸本)9781728151199

Spam filters have become an important tool for ISP(Internet service Provider) to address the growing spam in cyberspace. Spam filtering is a program used to detect spam, prevent it and enter users' mailboxes. With the rapid growth of Internet technology, email communication is becoming increasingly important in people's daily lives. In addition, the amount of spam contained in emails has increased considerably in recent years. In this study, the authors will compare the performance of the decision tree classification models using or not using Recursive Feature Elimination (RFE) in spam filtering. The results of this test will be compared with the results of level accuracy, recall, precision, and F 1 Score using or not using the RFE method. where in the results of this level measurement, the model used can be a reference or solution to filter spam correctly, especially through the use of supervised learning data.

关键词： Unsolicited e-mail Decision trees Machine learning Classification algorithms Information filtering

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Early Detection and Classification of Melanoma Based on Android MobileNet V2 Convolutional Neural Network

Early Detection and Classification of Melanoma Based on Andr...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Jasmine Iswarini Dyah Prabandari Christy Atika Sari Eko Hari Rachmawanto Candra Irawan Bilal R. Altamer Mohamed Ayad Alkhafaji Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Study Program in Information Systems University of Dian Nuswantoro Semarang Indonesia Mosul University Presidency University of Mosul Mosul Iraq Department of Computer Science National University of Science and Technology DhiQar Iraq

Melanoma is a malignant form of cancer that affects the skin and has a particularly high mortality rate, so it requires early detection to increase the level of safety for users. Diagnosis and detection of skin cancer are usually done through manual screening and visual inspection. This process requires a long time, has high complexity, is subjective, and is prone to errors. CNN is one of the algorithms with advantages in accurate classification. In this research, early detection and classification of melanoma cancer were carried out based on two classes, namely benign and malignant using the Convolutional Neural Network method. Our proposed method yields an accuracy of 81.11% for the validation data. The accuracy results obtained can be improved by using more datasets and increasing the number of layers used. This study uses the CNN method using MobileNet V2 architecture to detect melanoma skin cancer. The class used is benign and malignant.

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Improvement Suggestion Performance of Blowing Machine Line 4 with Total Productive Maintenance (TPM) Method at PT. Coca-Cola Amatil Indonesia MedanUnit 1

Improvement Suggestion Performance of Blowing Machine Line 4...

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1st International Conference of SNIKOM 2018, ICoSNIKOM 2018

作者： Irwansyah, Defi Harahap, M.R.F. Erliana, Cut Ita Abdullah, Dahlan Sari, Ayuesteka Siregar, Nurintanasyiah Achmaddaengs, G.S. Indahingwati, Asmara Sumartono, Eko Wilujeng, Sri Nurmawati Subekti, Purwo Kurniasih, Nuning Rosalina, Febrianti Hartono, H. Departement of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Departement of Informatics Universitas Malikussaleh Aceh Indonesia Department of Management STIE Sakti AlamKerinci Jambi Indonesia Department of Management STIE Labuhanbatu Sumatera Utara Indonesia Department of Management Universitas 45 Surabaya Indonesia Department of Management School of Economics Indonesia STIESIA Surabaya Indonesia Faculty of Agriculture Universitas Bengkulu Bengkulu Indonesia Department of Management Faculty of Economy Universitas Kanjuruhan Malang Indonesia Program Study of Industrial Engineering University of 45 Surabaya Indonesia Mechanical Engineering Pasir Pengaraian University Riau Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Department of Agrotechnology Universitas Muhammadiyah Sorong Sorong Indonesia Department of Computer Science STMIK IBBI Medan Indonesia

Maintenance is an activity to maintain facilities or equipment of the factory and make repairs or adjustments to the necessary replacement to satisfactory condition of production operations in accordance with what is planned. Companies often experience damage to the Blowing machine so that interconnected machines cannot be operated. The purpose of this study was to improve the performance of Blowing line 4 engines with the Total Productive Maintenance (TPM) method at PT. Coca-Cola Amatil Indonesia Medan Unit, also to determine the level of Blowing machine performance based on the analysis of overall equipment effectiveness (OEE) at PT. Coca-Cola Amatil Indonesia Medan Unit. The method used in this study is the overall equipment effectiveness (OEE) method to measure Ishikawa's treatments and diagrams to compare and propose low engine performance. The results of data processing shows machine maintenance has not comply the OEE standards in July 81.48%, August 81.48%, September 78.07%, October 81.22%, November 78.08%, and those that fill the standards in June amount to 86.48%, with losses that most affect the effectiveness of equipment in line 4 blowroom machine is Equipment Failure Losses of 11.42% based on the results of the study, the proposed improvement in engine performance is to provide training to employees, and increase operator awareness. against engine damage, thus the production process will run smoothly. © Published under licence by IOP Publishing Ltd.

关键词： Data handling

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Diabetes Prediction Model for Better Clarification by using Machine Learning 6

Diabetes Prediction Model for Better Clarification by using ...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Eben, J. Lysa Jayasudha, R. Ramya, S. Kaliappan, S. Aswal, Shobha Al-Salehi, Khalid Ali Salem Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai India Department of Mathematics Dr.N.G.P.Institute of Technology Coimbatore India Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Erode India Division of Research and Development Lovely Professional University Punjab Phagwara144411 India School of Computing Graphic Era Hill University Dehradun India Biomedical Engineering Study Program Faculty of Science and Technology Airlangga University Surabaya Indonesia

ISBN: (纸本)9798350398496

Diabetes mellitus is one of the most pressing health concerns because so many people are afflicted by its disabling symptoms. Factors such as age, excess body fat, insufficient physical activity, a history of diabetes in one's family, a sedentary lifestyle, an unhealthy diet, hypertension, etc., all increase the likelihood of developing diabetes mellitus. Health complications are more common in people with diabetes, including cardiovascular disease, renal failure, stroke, blindness, and nerve injury. To validate a diagnosis of diabetes, hospitals typically perform a battery of procedures on the patient. Big data analytics has many vital applications in the healthcare sector. Numerous large computer systems are used in the healthcare sector. With the help of big data analytics, researchers can sift through mountains of data in search of previously unseen patterns and insights. Current techniques have a poor degree of precision in classification and forecast. While previous research has focused on factors such as glucose, body mass index, age, insulin, etc., the proposed model takes these into account and also the other factors that may be more relevant to the development of diabetes. The newer sample is superior to the older one based on categorization accuracy. A workflow algorithm for diabetes prognosis is also required to improve the accuracy. © 2023 IEEE.

关键词： Forecasting

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An Evaluation of Number of Pixels Change Rate (NPCR) in Symetric Cryptography Based on Data Encryption Standard (DES)

An Evaluation of Number of Pixels Change Rate (NPCR) in Syme...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Muhammad Rizky Naufal Christy Atika Sari Eko Hari Rachmawanto Lekso Budi Handoko Folasade Olubusola Isinkaye Wisam Subhi Talib Al-Dayyeni Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department of Computer Science Ekiti State University Ekiti Nigeria School of Information Technologies and Engineering ADA University Baku Azerbaijan

Data Encryption Standard (DES) is a symmetric encryption algorithm that uses a single key to encrypt and decrypt information. In addition, the cipher text will be hidden inside an image using Stepic. DES Encryption use symmetric encryption to encrypt and decryption to decrypt. It will secure the symmetric encryption and maintain the speed of the encryption and decryption. The result of this study is the lowest error that we got as the Mean Square Error (MSE) is 0.00% and is inversely proportional with the Peak Signal to Noise Ratio (PSNR) and Avalanche with 79.36% and 34.91% in order that is not stable for result of Avalanche Effect (AVA). Inversely proportional to the length of the text that is hidden in the image, the longest text that is hidden, the more changes that we get in the image with the highest Unified Average Changing Intensity (UACI) and Number of Pixels Change Rate (NPCR) with the biggest image size with 43.47% for UACI and 99.87% for the NPCR.

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An application program of fingerprint detection using wavelet transform for authentication 3

An application program of fingerprint detection using wavele...

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3rd Management and Innovation Technology International Conference, MITiCON 2016

作者： Kwaochai, Aunchisa Pongyupinpanich, Surapong Areekul, Pratya San-Um, Wimol Intelligent Electronic System Research Laboratory Faculty of Engineering Thai-Nichi Institute of Technology Pattanakarn 37 Saunlaung Bangkok10250 Thailand Master Program of Forensic Informatics Ramkhamhang University Bangkapi Bangkok10240 Thailand Department of Mathematics Computer Science Chulachomklao Royal Military Academy Province Nakornnayok26001 Thailand

ISBN: (纸本)9781509041053

This paper presents the application program of fingerprint detection using wavelet transform for authentication. Fingerprints are obtained from the site of crime, old documents and excavated things. This paper proposes a fingerprint recognition technique based on wavelet-based texture pattern recognition method. By collecting incomplete fingerprints of 2 people with 6 images each. The original seven of uncompleted fingerprint images were undetected and only one was detected. After enhancing image quality using wavelet transform method, it was found that those of seven images were completed. The comparison method was a matching technique. This work focuses on the ability of the program and knowledge of image processing by studying algorithms of image processing. In design algorithm process, different technique enhancements were employed as well as the study of different of filters and algorithms based on existing research results from literature. © 2016 IEEE.

关键词： Wavelet transforms

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A comparative evaluation of competitive learning algorithms for edge detection enhancement

A comparative evaluation of competitive learning algorithms ...

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European Signal Processing Conference (EUSIPCO)

作者： Tuba Sirin Mehmet Izzet Saglam Isin Erer Muhittin Gokmen Okan Ersoy Informatics Institute Computer Science and Engineering Informatics Institute Satellite Communication and Remote Sensing Program Faculty of Electrical and Electronic Engineering Electronics and Communication Department Faculty of Electrical and Electronic Engineering Istanbul Technical University Maslak Istanbul TURKEY Purdue University School of Electrical and Computer Engineering West Lafayette Indiana USA

Most edge detection algorithms include three main stages: smoothing, differentiation, and labeling. In this paper, we evaluate the performance of algorithms in which competitive learning is applied first to enhance edges, followed by an edge detector to locate the edges. In this way, more detailed and relatively more unbroken edges can be found as compared to the results when an edge detector is applied alone. The algorithms compared are K-Means, SOM and SOGR for clustering, and Canny and GED for edge detection. Perceptionally, best results were obtained with the GED-SOGR algorithm. The SOGR is also considerably simpler and faster than the SOM algorithm.

关键词： Image edge detection Image segmentation Vectors Abstracts Filtering algorithms Erbium Training

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