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Combination of 3des with dct algorithm in message security

IOP Conference Series: Materials science and Engineering 2020年第1期725卷

作者： S. Guntur MKM Nasution RW Nasution Master Program (S2) of Informatics Engineering Faculty of Computer Science and Information Technology University of Sumatera Utara Medan Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences University of Sumatera Utara Medan Indonesia

In the digital era, communication through computer networks plays an important role. Through electronic communication, a person can make transactions or communication very quickly and practically. Sending data / messages from one place to another is much constrained by the issue of confidentiality. There are many ways you can hide data / messages that will be sent. First, using cryptographic techniques, namely by encoding data / messages using certain algorithms. Another technique is to insert a message that will be sent to other media, so that the message will be hidden and what will appear is other media used to insert *** will be performed a Performance Analysis of the Combination of 3DES and DCT Algorithms in Securing Message Files into the Image.

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Feature Selection in Cross-Project Software Defect Prediction

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Journal of Physics: Conference Series 2020年第2期1569卷

作者： A Saifudin A Trisetyarso W Suparta C H Kang B S Abbas Y Heryadi Informatics Engineering Pamulang University Jalan Raya Puspitek 46 Banten 15310 Indonesia Doctor of Computer Science Program Bina Nusantara University Jalan Kebon Jeruk Raya 27 Jakarta 11530 Indonesia Informatics Department Pembangunan Jaya University Banten Indonesia Department of Electronics and Communication Engineering Kwangwoon University South Korea

Advances in technology have increased the use and complexity of software. The complexity of the software can increase the possibility of defects. Defective software can cause high losses. Fixing defective software requires a high cost because it can spend up 50% of the project schedule. Most software developers don't document their work properly so that making it difficult to analyse software development history data. Software metrics which use in cross-project software defects prediction have many features. Software metrics usually consist of various measurement techniques, so there are possibilities for their features to be similar. It is possible that these features are similar or irrelevant so that they can cause a decrease in the performance of classifiers. In this study, several feature selection techniques were proposed to select the relevant features. The classification algorithm used is Naive Bayes. Based on the analysis using ANOVA, the SBS and SBFS models can significantly improve the performance of the Naïve Bayes model.

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Retracted: Sylabus and Matrials Design for Vocational Student and Its Dissemination Using Website

Retracted: Sylabus and Matrials Design for Vocational Studen...

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2018 Workshop on Multidisciplinary and Its Applications: Applied Mathematics, computer science, Information Systems, and Information Technology, WMA-Mathcomtech 2018

作者： Harahap, Astro Julida Ongko, Erianto Manurung, Hotler Kurnia, Dian Hartono, H. Abdullah, Dahlan Erliana, Cut Ita Djanggih, Hardianto Saddhono, Kundharu Rianita, Dian Setiawan, Muhammad Ikhsan Setiawan, Yuliyanto Budi Manurung, Rosida Tiurma Sriadhi, S. Sinurat, Mangasi Siregar, Aisyah Soni Hutauruk, Rapat Piter Sibarani, Nico Benjamain Department of Informatics Akademi Teknologi Industri Immanuel Medan Indonesia Department of Computer Systems Universitas Pancabudi Medan Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Law Universitas Tompotika Luwuk Banggai Indonesia Graduate Program Universitas Sebelas Maret Surakarta Indonesia Department of Administration University of Lancang Kuning Pekanbaru Indonesia Department of Civil Engineering Narotama University Surabaya Indonesia Study Program of Communication Studies Universitas Semarang Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia STIE Bina Karya Tebing Tinggi Indonesia

The learners' needs are an important factor in designing syllabus and materials design, this research deals with the syllabus and material design based on the professional's needs. It is expected that the syllabus and materials designes are communicatively applicable to the professional academy. Descriptive method is applied in this research. The sample of this research is 30 students of ATII Immanuel Academy Medan. They were selected by random sampling to get the data, the questioners were administered to students. the questioners consisted of 54 items and the semi structured interview consisted of 5 questions, the finding indicated that learners' needs were focused on reading and speaking skills. With reference appropriately and proportionally derived for students of the Professional Academy. Further on the basis of the syllabus, materials are designed in which the skills of using language become a priority. The results of this research will be disseminated using the website. © Published under licence by IOP Publishing Ltd.

关键词： Information systems

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Reporting of Hospital Facility on Smartphone

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Journal of Physics: Conference Series 2021年第1期1807卷

作者： Jack Febrian Rusdi Sazilah Salam Nur Azman Abu Budi Sunaryo Nova Agustina Harya Gusdevi Hermansah Ari Hadhiwibowo Tuty Heryati Richki Hardi Rohmat Taufiq Muhammad Taher Jufri Ignatius Sudarsono Eko Wahyu Utomo Frans Richard Kodong Puteri Fannya Informatics Engineering Sekolah Tinggi Teknologi Bandung Bandung Indonesia. Information and Communication Technology Faculty Universiti Teknikal Malaysia Melaka Melaka Malaysia. Department of Electrical Engineering Andalas University Padang Indonesia. Rumah Sakit Umum Daerah Majalaya Kabupaten Bandung Majalaya Indonesia. Informatics Engineering Computer Science Faculty Universitas Mulya Balikpapan Indonesia. Department of Informatics Engineering Engineering Faculty University Muhammadiyah of Tangerang Tangerang 15117 Indonesia Program Studi Sistem Informasi Fakultas Teknik dan Sistem Informasi Universitas Yapis Papua Civil Engineering Department Engineering Faculty Universitas Langlangbuana Bandung Bandung Indonesia. Department of Informatics Engineering UPN 'Veteran' Yogyakarta Yogyakarta Indonesia. Department of Medical Record and Health Information Faculty of Health Universitas Esa Unggul Jakarta Indonesia.

In the context of achieving Good Corporate Governance (GCG) in hospitals, among others, it is carried out with control and supervision, including in the case of hospital facility maintenance installations. Besides that, the hospital needs to improve its services. The asset reporting system is one of the high needs in this field, so it requires a solution that can deal with existing problems. Unfortunately, the current reporting system so far is more likely to be conventional, such as by telephone. This study proposes a new solution to the hospital facility maintenance reporting process, namely by utilising smartphone-based applications that directly connected to the server. So, the information distributed to the competent parties. This solution can speed up the reporting process and actions. Also, save the historical equipment that is available from each service room — the results of this study used as a hospital facility maintenance reporting system.

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DEA Model with Hesitant Fuzzy Polyhedral Set in Benchmarking 1

DEA Model with Hesitant Fuzzy Polyhedral Set in Benchmarking

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1st International Conference of SNIKOM 2018, ICoSNIKOM 2018

作者： Abdullah, Dahlan Hartono, H. Erliana, Cut Ita Irwansyah, Defi Siregar, Nurintan Asyiah Sari, Ayu Esteka Hasibuan, Abdurrozzaq Abdussakir Kurniasih, Nuning Mas'Ut Rangkuti, Irma Yanti Nurmawati Buston, Erni Abdurrahman, Apridar Handayani, Tita Septi Hadikurniawati, Wiwien Winarno, Edy Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Management STIE Labuhanbatu Sumatera Utara Indonesia Department of Management STIE Sakti Alam Kerinci Jambi Indonesia Faculty of Engineering Universitas Islam Sumatera Utara Medan Indonesia Department of Mathematics Education State Islamic University of Malang Malang Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Faculty of Economic Universitas Islam Sumatera Utara Medan Indonesia Faculty of Medical Universitas Islam Sumatera Utara Medan Indonesia Faculty of Industry Technique University of 45 Surabaya Indonesia Departement of Nursing Politeknik Kesehatan Kementerian Bengkulu Indonesia Department of Management Universitas Malikussaleh Aceh Indonesia Departemen of Nursing Universitas Dehasen Bengkulu Indonesia Faculty of Information Technology Universitas Stikubank Semarang Indonesia

Data Envelopment Analysis (DEA) method is a linear programming approach that has been widely used as a framework for evaluating efficiency and measurement. DEA decision making is often faced with situations where the existing DMU has input and output that contains fuzzy and hesitant elements so that it is difficult to make efficiency measurements. This study will design a DEA model in the state of inputs and outputs that contain hesitant elements by using polyhedral uncertainty sets. The results of this study are expected to produce a benchmarking model with DEA that can overcome the hesitant element and have the advantage of using a polyhedral uncertainty set that can measure the linearity of the nominal problem. © Published under licence by IOP Publishing Ltd.

关键词： Data envelopment analysis

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Measurement of activeness lecturers in SmartLecturer based learning using fuzzy Mamdani and Sugeno

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IOP Conference Series: Materials science and Engineering 2020年第1期725卷

作者： Sfenrianto Sfenrianto Muhammad Yasin Kaman Nainggolan Mesra Betty Yel Information Systems Management Department BINUS Graduate Program – Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 Informatics Management Politeknik LP3I Jakarta Indonesia 10450 Master of Computer Science - Postgraduate Programs STMIK Nusa Mandiri Faculty of Computer Science STIKOM CKI Jakarta Indonesia

The success of learning in a classroom that uses supporting media demands lecturer activity. The level of activity of lecturers in class and using learning media such as Smart lecturer can be measured using a fuzzy logic approach. This study aims to measure the level of active lecturers using the approach, namely: Mamdani and Sugeno method. Stages of lecturer activity measurement by forming a fuzzy set, composition rules as many as 24 rules and the defuzzification process using the centroid method that produces the level of activity of each is low, medium and high. Based on 173 lecturer activity data, the results of the Mamdani method indicates low 66%, medium 31%, and high 3%. While the Sugeno method produces a level of activity low 75%, moderate 17%, and high 8%. Therefore, Mamdani method is more suitable for the calculation because the spread of results is relatively evenly distributed at each level.

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Retracted: Implementation Method CUSUM to Determine the Accident Prone Areas in Web Based

Retracted: Implementation Method CUSUM to Determine the Acci...

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2018 Workshop on Multidisciplinary and Its Applications: Applied Mathematics, computer science, Information Systems, and Information Technology, WMA-Mathcomtech 2018

作者： Dinata, Rozzi Kesuma Abdullah, Dahlan Hartono, H. Erliana, Cut Ita Riasti, Berliana Kusuma Iskandar, Akbar Sari, Ifit Novita Nurmawati, N. Nanuru, Ricardo Freedom Putri, Lusi Dwi Manurung, Rosida Tiurma Sriadhi, S. Saleh, A.A. Astari, T. Khaddafi, Muammar Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Informatics Universitas Sebelas Maret Surakarta Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Departement Of: Social Science Education Program Post Graduate Program Universitas Kanjuruhan Malang Malang Indonesia Faculty of Industry Technique University of 45 Surabaya Indonesia Universitas Kristen Indonesia Maluku Ambon Indonesia Department of Civil Engineering Universitas Lancang Kuning Pekanbaru Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Statistics Financial and Social Sciences Research Group Indonesia Department of Elementary School Teacher Education STKIP Citra Bangsa Aceh Utara Indonesia Department of Accounting Universitas Malikussaleh Aceh Indonesia

At the Unit Laka Lantas Polres Lhokseumawe determining accident-prone areas on roads in Lhokseumawe still using manual systems. Determination of accident-prone areas are less effective because the police Unit Laka Lantas Polres Lhokseumawe just using the data of the last year and had to repeatedly calculate manually accident-prone areas based on the number of human casualties. In this study the system implementation CUSUM method (Cummulative Summary) to determine the accident-prone areas designed using the web-based programming language PHP. In this system there are three processes to determine areas prone to accidents, the accident rate calculations based on the weighted severity, blacksite calculations using the Z-Score method for determining areas prone to accident blackspots and calculations using the CUSUM method to determine the critical points of an accident. The data used are secondary data obtained from the Police Unit Laka Lantas Polres Lhokseumawe years 2009-2013. © Published under licence by IOP Publishing Ltd.

关键词： Accidents

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The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

arXiv

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arXiv 2023年

作者： Moawad, Ahmed W. Janas, Anastasia Baid, Ujjwal Ramakrishnan, Divya Saluja, Rachit Ashraf, Nader Jekel, Leon Amiruddin, Raisa Adewole, Maruf Albrecht, Jake Anazodo, Udunna Aneja, Sanjay Anwar, Syed Muhammad Bergquist, Timothy Calabrese, Evan Chiang, Veronica Chung, Verena Conte, Gian Marco Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Kazerooni, Anahita Fathi Kofler, Florian Krantchev, Kiril LaBella, Dominic Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Link, Katherine E. Liu, Xinyang Maleki, Nazanin Meier, Zeke Menze, Bjoern H. Moy, Harrison Osenberg, Klara Piraud, Marie Reitman, Zachary Shinohara, Russel Takeshi Tahon, Nourel hoda Nada, Ayman Velichko, Yuri S. Wang, Chunhao Wiestler, Benedikt Wiggins, Walter Shafique, Umber Willms, Klara Avesta, Arman Bousabarah, Khaled Chakrabarty, Satrajit Gennaro, Nicolo Holler, Wolfgang Kaur, Manpreet LaMontagne, Pamela Lin, MingDe Lost, Jan Marcus, Daniel S. Maresca, Ryan Merkaj, Sarah Nada, Ayaman Pedersen, Gabriel Cassinelli von Reppert, Marc Sotiras, Aristeidis Teytelboym, Oleg Tillmans, Niklas Westerhoff, Malte Youssef, Ayda Godfrey, Devon Floyd, Scott Rauschecker, Andreas Villanueva-Meyer, Javier Pflüger, Irada Cho, Jaeyoung Bendszus, Martin Brugnara, Gianluca Cramer, Justin Perez-Carillo, Gloria Guzman J. Johnson, Derek R. Kam, Anthony Kwan, Benjamin Yin Ming Lai, Lillian Lall, Neil U. Memon, Fatima Patro, Satya Narayana Petrovic, Bojan So, Tiffany Y. Thompson, Gerard Wu, Lei Schrickel, E. Brooke Bansal, Anu Barkhof, Frederik Besada, Cristina Chu, Sammy Druzgal, Jason Dusoi, Alexandru Farage, Luciano Feltrin, Fabricio Fong, Amy Fung, Steve H. Gray, R. Ian Ikuta, Ichiro Iv, Michael Postma, Alida A. Mahajan, Amit Joyner, David Krumpelman, Chase Letourneau-Guillon, Laurent Lincoln, Christie M. Maros, Mate E. Miller, Elka Morón, Fanny Nimchinsky, Esther A. Ozsarlak, Ozkan Patel, Uresh Rohatgi, Saurabh Saha, Atin Sayah, Anousheh Schwartz, Eric D. Shih, Robert Shiroishi, Mark Trinity health Mid Atlantic Hosiptals DarbyPA United States Department of Radiology and Biomedical Imaging Yale School of Medicine New HavenCT United States Division of Computational Pathology Department of Pathology and Laboratory Medicine School of Medicine Indiana University IndianapolisIN United States Department of Electical and Computer Engineering Cornell University Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medicine New YorkNY United States ImagineQuant Children’s Hospital of Philadelphia PhiladelphiaPA United States College of Medicine Alfaisal University Riyadh Saudi Arabia DKFZ Division of Translational Neurooncology The WTZ German Cancer Consortium DKTK Partner Site University Hospital Essen Essen Germany Medical Artificial Intelligence Lab Crestview Radiology Lagos Nigeria Sage Bionetworks SeattleWA United States Montreal Neurological Institute McGill University Montreal Canada lab Crestview Radiology Lagos Nigeria Department of Therapeutic Radiology Yale School of Medicine New HavenCT United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Radiology Mayo Clinic RochesterMN United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Neurosurgery Yale School of Medicine New HavenCT United States Center for Global Health Perelman School of Medicine University of Pennsylvania PA United States Department of Informatics Technical University Munich Germany Children’s Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Children’s National Hospital WashingtonDC United States PrecisionFDA U.S. Food and Drug Administration S

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse d

关键词： Image segmentation

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A proposed framework in an intelligent recommender system for the college student

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Journal of Physics: Conference Series 2019年第6期1402卷

作者： D Kurniadi E Abdurachman H L H S Warnars W Suparta Department of Computer Science BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Department of Informatics Sekolah Tinggi Teknologi Garut Garut Indonesia

This article aims to proposed framework an Intelligent Recommender System (IRS) for students in higher education institutions. This conceptual framework includes problems in predicting student performance, the possibility of graduating on time, and recommends choosing subjects according to performance, and career interests, which are useful for assisting pedagogical interventions in future student development. The success in the development and implementation of the proposed IRS framework is inseparable from using data mining and machine learning techniques in predicting and providing recommendations. Data analysis consisted of clustering techniques, association rules, and classification using Support Vector Machine (SVM), Naïve Bayes, and k-Nearest Neighbour (k-NN). These techniques are used to solve problems related to students and to provide appropriate recommendations. The result is an IRS conceptual framework for the college student that can be used as smart agents to provide student guidance and suggestions to support the process of education in higher education.

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Correction to: A multi-step genomic approach prioritized TBKBP1 gene as relevant for multiple sclerosis susceptibility

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Journal of neurology 2022年第8期269卷 4523-4524页

作者： Melissa Sorosina Nadia Barizzone Ferdinando Clarelli Santosh Anand Sara Lupoli Erika Salvi Eleonora Mangano Roberta Bordoni Tina Roostaei Elisabetta Mascia Miriam Zuccalà Domizia Vecchio Paola Cavalla Silvia Santoro Laura Ferrè Alen Zollo Cristina Barlassina Daniele Cusi Vittorio Martinelli Giancarlo Comi Maurizio Leone Massimo Filippi Nikolaos A Patsopoulos Philip L De Jager Gianluca De Bellis Federica Esposito Sandra D'Alfonso Filippo Martinelli Boneschi Laboratory of Human Genetics of Neurological Disorders Division of Neuroscience IRCCS San Raffaele Scientific Institute 20132 Milan Italy. Department of Health Sciences Interdisciplinary Research Center of Autoimmune Diseases (IRCAD) University of Eastern Piedmont Avogadro University 28100 Novara Italy. Department of Informatics Systems and Communications (DISCo) University of Milano-Bicocca Milan Italy. Department of Health Sciences University of Milan 20139 Milan Italy. Neuroalgology Unit Fondazione IRCCS Istituto Neurologico "Carlo Besta" 20133 Milan Italy. National Research Council of Italy Institute for Biomedical Technologies Segrate 20090 Milan Italy. Center for Translational and Computational Neuroimmunology Department of Neurology and the Taub Institute for Research On Alzheimer's Disease and the Aging Brain Columbia University Irving Medical Center New York NY 10032 USA. MS Centre SCDU Neurology AOU Maggiore Della Carità Department of Translational Medicine Interdisciplinary Research Center of Autoimmune Diseases (IRCAD) University of Eastern Piedmont Avogadro 28100 Novara Italy. MS Center Department of Neuroscience and Mental Health City of Health and Science University Hospital of Torino 10126 Turin Italy. Neurology Unit IRCCS San Raffaele Scientific Institute 20132 Milan Italy. Department of Pathophysiology and Transplantation (DEPT) Dino Ferrari Centre Neuroscience Section University of Milan 20122 Milan Italy. Bio4Dreams Business Nursery for Life Sciences Piazzale Principessa Clotilde 4/A 20121 Milan Italy. SC Neurologia Dipartimento Di Scienze Mediche IRCCS Casa Sollievo Della Sofferenza San Giovanni Rotondo Italy. Vita-Salute San Raffaele University 20132 Milan Italy. Neuroimaging Research Unit Division of Neuroscience IRCCS San Raffaele Scientific Institute 20132 Milan Italy. Neurophysiology Unit IRCCS San Raffaele Scientific Institute IRCCS San Raffaele Scientific Institute 20132 Milan Italy. Systems Biology and Comp

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