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International Conference on science and Technology-computer (ICST)

作者： Ramos Somya Edi Winarko Sigit Priyanta Doctoral Program in Computer Science FMIPA Universitas Gadjah Mada Yogyakarta Indonesia Department of Computer Science and Electronics FMIPA Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9781728123691

ISBN: (纸本)9781728123707

Online buying and selling activities in Indonesia continue to develop every year. In 2018 there were approximately 106.96 million users of online-based transactions and the number is predicted to continue to grow. This is caused by a shift from conventional shopping method to online shopping method, as online shopping method is more practical. At present, online shops are starting to analyze the data of online sales and purchases, generated from the websites, for the purpose of compiling a sales strategy. One type of data that can be analyzed is customer behavior data. This data can be recorded based on customer browsing activities when they are doing online shopping, resulting in clickstream data. In this study, a data collection architecture for online shops will be proposed, which will provide a module for recording clickstream data for online shops' users. This module will be designed on web and mobile applications, considering that currently customers can also access online shops through mobile commerce applications on smartphones that are used. The clickstream data that has been collected, will be processed further for the purpose of making decisions related to the sales strategy, such as to identify potential customers and determine recommendations for customers.

关键词： Business Mobile applications Data mining Servers Databases Systems architecture Sensors

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Analysis of Application of the SAW, WP and TOPSIS Methods in Decision Support System Determining Scholarship Recipients at University

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Journal of Physics: Conference Series 2021年第1期1830卷

作者： Murni Marbun Muhammad Zarlis Zulkifli Nasution Doctoral Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Teknik Informatika STMIK Pelita Nusantara Medan Indonesia

This research is an analysis of the application of the SAW, WP and TOPSIS methods to the support system for decision making at university. The types of scholarships provided are PPA education scholarships given by the Kementerian Riset, Teknologi dan Pendidikan Tinggi. The research variables are the attributes or criteria specified by the Directorate General of Learning and Student Affairs as contained in the 2018 Academic Achievement Improvement (PPA) guidebook. Decision Support Systems apply the classic FMADM method SAW, WP and TOPSIS. The results of this study are the application of the both methods of SAW and WP produce the same ranking and TOPSIS giving different rankings.

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Improving Random Forest Method to Detect Hatespeech and Offensive Word

Improving Random Forest Method to Detect Hatespeech and Offe...

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International Conference on Information and Communications Technology (ICOIACT)

作者： Kristiawan Nugroho Edy Noersasongko Purwanto Muljono Ahmad Zainul Fanani Affandy Ruri Suko Basuki Doctoral Program Dian Nuswantoro University Semarang Indonesia Faculty of Computer Science Dian Nuswantoro University Semarang Indonesia

Hate Speech is a problem that often occurs when someone communicates with each other using social media on the Internet. Research on hate speech is generally done by exploring datasets in the form of text comments on social media such as Twitter, Facebook and MySpace. This study aims to improve the performance of the Random Forest method in detecting hatespeech and crude speech. In this paper the researcher uses a twitter hate speech and offensive identification dataset that is classified using the Random Forest method which will be compared with the results of its accuracy with AdaBoost and Neural Network to detect hatespeech and crude speech. The detection results of hatespeech and crude speech identification resulted in an accuracy of 0.722 for the Random Forest method and 0.708 using AdaBoost and 0.596 using Neural Network method.

关键词： Neural networks Random forests Vegetation computer science Twitter Information and communication technology

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Treatment Algorithm for the Resorption of Calcific Tendinitis Using Extracorporeal Shockwave Therapy: A Data Mining Study

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Orthopaedic Journal of Sports Medicine 2024年第3期12卷 23259671241231609页

作者： Chou, Wen-Yi Cheng, Jai-Hong Lien, Yu-Jui Huang, Tian-Hsiang Ho, Wen-Hsien Chou, Paul Pei-Hsi Doctoral Degree Program in Biomedical Engineering College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Orthopedic Surgery Kaohsiung Chang Gung Memorial Hospital Chang Gung University College of Medicine Kaohsiung Taiwan Department of Leisure and Sport Management Cheng Shiu University Kaohsiung Taiwan Center for Shockwave Medicine and Tissue Engineering Department of Medical Research Kaohsiung Chang Gung Memorial Hospital Chang Gung University College of Medicine Kaohsiung Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung Taiwan Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan College of Professional Studies National Pingtung University of Science and Technology Pingtung Taiwan Department of Sports Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Division of Sports Medicine Department of Orthopaedic Surgery Kaohsiung Medical University Hospital Kaohsiung Taiwan Department of Orthopaedics School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Orthopaedic Surgery Kaohsiung Municipal Hsiao-Kang Hospital Kaohsiung Taiwan

Background: Although evidence indicates that extracorporeal shockwave therapy (ESWT) is effective in treating calcifying shoulder tendinitis, incomplete resorption and dissatisfactory results are still reported in many cases. Data mining techniques have been applied in health care in the past decade to predict outcomes of disease and treatment. Purpose: To identify the ideal data mining technique for the prediction of ESWT-induced shoulder calcification resorption and the most accurate algorithm for use in the clinical setting. Study Design: Case-control study. Methods: Patients with painful calcified shoulder tendinitis treated by ESWT were enrolled. Seven clinical factors related to shoulder calcification were adopted as the input attributes: sex, age, side affected, symptom duration, pretreatment Constant-Murley score, and calcification size and type. The 5 data mining techniques assessed were multilayer perceptron (neural network), naïve Bayes, sequential minimal optimization, logistic regression, and the J48 decision tree classifier. Results: A total of 248 patients with calcified shoulder tendinitis were enrolled in this study. Shorter symptom duration yielded the highest gain ratio (0.374), followed by smaller calcification size (0.336) and calcification type (0.253). With the J48 decision tree method, the accuracy of 3 input attributes was 89.5% by 10-fold cross-validation, indicating satisfactory accuracy. A treatment algorithm using the J48 decision tree indicated that a symptom duration of ≤10 months was the most positive indicator of calcification resorption, followed by a calcification size of ≤10.82 mm. Conclusion: The J48 decision tree method demonstrated the highest precision and accuracy in the prediction of shoulder calcification resorption by ESWT. A symptom duration of ≤10 months or calcification size of ≤10.82 mm represented the clinical scenarios most likely to show resorption after ESWT. © The Author(s) 2024.

关键词： calcific tendinitis data mining extracorporeal shockwave therapy J48 decision tree shoulder

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Identification of Sumatera Butterfly species by using deep learning with data augmentation

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AIP Conference Proceedings 2024年第1期2970卷

作者： Rico Andrian Admi Syarif Favorisen Rosyking Lumbanraja Emantis Rosa Doctoral Program of Mathematics and Natural Sciences Lampung University Jl. Prof. Dr. Sumantri Brojonegoro No. 1 Bandar Lampung 35145 Indonesia Department of Computer Science Lampung University Jl. Prof. Dr. Sumantri Brojonegoro No. 1 Bandar Lampung 35145 Indonesia Department of Biology Lampung University Jl. Prof. Dr. Sumantri Brojonegoro No. 1 Bandar Lampung 35145 Indonesia

Butterflies are members of the Lepidoptera order. The complexity of the Sumatera Butterfly’s patterns and hues makes it difficult to identify with the naked eye. Gita Persada Butterfly Park is always crowded with people who want to see and learn about the different species of Sumatera butterfly. The Sumatera Butterfly, which humans can see with the naked eye, is quite difficult to identify without the assistance of certain technologies due to its diverse patterns and colors and the fact that there are nearly several species. AlexNet is one of the deep learning architectures that can detect images of the Sumatera Butterfly, functioning similarly to the human brain in object recognition. Deep learning employs artificial neural networks for learning. The first stage of this research consisted of collecting the Sumatera Butterfly dataset and identifying the butterfly species with AlexNet. The second step is identifying Sumatera butterfly species using AlexNet with additional data augmentation, flip data augmentation, and rotate data augmentation. The comparison between pre- and post-augmentation performance will reveal the difference. AlexNet’s accuracy without boost is 98.12 percent. After adding data augmentation to AlexNet architecture, the accuracy ratio is 99.06 percent, while AlexNet’s highest accuracy is 99.37 percent with flip augmentation.

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Relational database reconstruction from SQL to Entity Relational Diagrams

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Journal of Physics: Conference Series 2020年第1期1566卷

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

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.

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Design and Implementation of Rivest Shamir Adleman's (RSA) Cryptography Algorithm in Text File Data Security

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Journal of Physics: Conference Series 2020年第1期1641卷

作者： Hengki Tamando Sihotang Syahril Efendi Elvyawati M Zamzami Herman Mawengkang 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 has two 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.

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Soft and Hard Clustering for Abstract Scientific Paper in Indonesian

Soft and Hard Clustering for Abstract Scientific Paper in In...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Johannes Petrus Ermatita Sukemi Teknik Informatika Universitas Sriwijaya Doctoral program student (lecturer at STMIK GI MDP) Palembang Indonesia Computer Science Faculty Universitas Sriwijaya Palembang Indonesia

ISBN: (纸本)9781728129310

For ease in grouping research papers is by doing clustering. Clustering is a method to classify the objects into subsets with similar attributes. Clustering method divided into two categories ie hard and soft clustering. Hard clustering is method to grouping the data items such that each item is only assigned to one cluster, K-Means is one of them. While Soft clustering is method to grouping the data items such that an item can exist in multiple clusters, Fuzzy C-Means (FCM) is an example. Most research papers are documented into groups that are associated with the area of expertise of the researcher, even though there is also research whose contents relate to other fields outside the area of expertise of the researcher, so it should also be documented in the group of other fields so that its contribution in other fields can be known. Here in this paper we analyse the abstract of papers written in Indonesian as data set. Data samples were taken from 3 fields, namely information technology, health and economics. Clustering process using k-means and FCM to find out whether scientific paper's abstracts from different fields of research can be in the same group / cluster as a whole, not whole or different groups. As unstructured data, abstracts must be processed through a text mining procedure first to become vector data.

关键词： data mining fuzzy set theory natural language processing pattern clustering text analysis Cluster Analysis text processing Natural Language Processing Indonesian Fuzzy Set Theory data mining paper field surveys Data sampling Fields vector data

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The design of an online information system of the check stage in plan-do-check-act cycle for evaluation of student learning in Taiwan Preschools 19

The design of an online information system of the check stag...

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3rd International Conference on E-Education, E-Business and E-Technology, ICEBT 2019

作者： Huang, Huei-Ting Wongthai, Winai Doctoral Program of Educational Leadership and Technology Management Tamkang University No.151 Yingzhuan Rd. Tamsui Dist. New Taipei City25137 Taiwan Department of Computer Science and Information Technology Naresuan University 99 Moo 9 Phitsanulok-Nakhonsawan Road Tambon Tapho Muang Phitsanulok65000 Thailand

ISBN: (纸本)9781450372565

It is a hard job for two teachers to deal with 30 students in a preschool classroom in Taiwan. Teachers need to take photos and manage paper documents related to each student's activities and assessments, then print all of them to show to their parents. This is not an efficient and effective working style. In order to support e-education and mitigate these issues in the education domain via application of the Plan-Do-Check-Act (PDCA) cycle, this paper thoroughly investigates appropriate features of previous studies related to e-education and the PDCA computing system. It then proposes the design of an online information system for the "check" stage in the Plan-Do-Check-Act cycle or OISC for evaluating Taiwanese preschool students' learning. The design is simple and equipped with current technologies such as mobile applications. We provided the details of all components and technologies in this design and how the design works, which is arranged for participants to use as a blueprint of an e-education system such as OISC that can be built upon the participants' requirements. We believe that a system based on our design can help teachers to work effectively and efficiently. Thus, this paper can be regarded as an example of adopting an e-education approach to evaluating and enhancing students' learning performance. © 2019 ACM.

关键词： HTTP

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Probing time series multiplicative model and fuzzy relation methodologies

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Journal of Physics: Conference Series 2021年第1期1869卷

作者： H I Pratiwi Y Heriadi L Lukas A Wibowo Doctoral Program Department of Computer Science Bina Nusantara University Jl. Anggrek Kemanggisan Jakarta Indonesia Cognitive Engineering Research Group (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia

Probing of a question between two methodologies of the multiplicative model of Time Series and Fuzzy Relation is challenging to answer, without exploring each of both theories. This paper aims to discern any discrepancies or similarities in them, since each has detailed items that could lead to practical advantages to proceed on forecasting or decision making. Pseudo coding is a chance to explore theories and to surface those practical advantages. The finding states that the multiplicative model of time series factually has components that could have dependencies one to another, and the fuzzy relations help to map each point along their processes to determine how much dependency contained in the system that could conclude of a decision making.

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