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All Journal Journal of Applied Geospatial Information Jurnal Mantik Penusa JOURNAL OF SCIENCE AND SOCIAL RESEARCH CSRID (Computer Science Research and Its Development Journal) Jurnal Mantik Jusikom: Jurnal Sistem Informasi Ilmu Komputer International Journal of Economics Development Research (IJEDR) Journal of Intelligent Decision Support System (IDSS) Jurnal Teknik Informatika (JUTIF) International Journal of Basic and Applied Science Jurnal Mandiri IT Jurnal Minfo Polgan (JMP) Jurnal Pengabdian Kepada Masyarakat Transformasi Masyarakat : Jurnal Inovasi Sosial dan Pengabdian Venus: Jurnal Publikasi Rumpun Ilmu Teknik Indonesia Bergerak: Jurnal Hasil Kegiatan Pengabdian Masyarakat Jurnal Bisnis Digital Global Science: Journal of Information Technology and Computer Science Jurnal Teknik Informatika dan Teknologi Informasi
Mutiara S. Simanjuntak
Politeknik Negeri Lhokseumawe
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Journal : international journal of basic and applied science

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Performance Analysis Of Support Vector Machine In Identifying Comments And Ratings On E-Commerce Mutiara S. Simanjuntak; Nurafni Damanik; Allwine
International Journal of Basic and Applied Science Vol. 11 No. 1 (2022): June: Basic and Applied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v11i1.79

Abstract

Consumers who have shopped at E-Commerce will provide reviews/comments on products that have been purchased. Customer confidence in the rating is hampered due to inconsistency of answers such as reviews that have negative text with a positive rating value. For this reason, a technique is needed to adjust the rating with comments or reviews of purchased goods to make it easier for consumers when shopping to see the rating directly without reading the reviews/comments of previous buyers. purpose of this study is to classify comments and ratings and then obtain the results of the accuracy of the classification system so that the above problems can be answered.This study uses Support Vector Machine classification technique because this algorithm is better in classification’s terms. Data used are 1044 comment data and 1044 rating. Data are grouped into Good, Neutral, Less good categories using Python by Google Colab and divided into training and test data. To test capability of system, data that has been classified then analyzed using Confusion matrix. Results showed that SVM Algorithm was able to classify with an accuracy rate of 71.14%, 88% precision, and 79% recall.SVM algorithm is able to formulate training data with an accuracy of 91.3%.
Epoch in a neural network for brain stroke Mutiara Simanjuntak; Juanto Simangunsong; Hongjie Dai
International Journal of Basic and Applied Science Vol. 11 No. 4 (2023): March: Basic and Applied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v11i4.156

Abstract

A neural network is a data processing system consisting of a large number of simple and highly interconnected processing elements in an architecture inspired by the structure of the cortical regions of the brain. Therefore, neural networks can often do things that humans or animals can do, but traditional computers are often lousy. This research discusses brain tumors that can be detected by artificial intelligence. Stroke includes the sudden death of brain cells due to lack of oxygen, blockage of the circulatory system, or severance of flexible pathways to the brain. Therefore the need for action that must be faster to be able to detect this deadly disease. The method used is a Neural Network which can collect knowledge by detecting patterns and relationships between data and learning experiences. So that the detection process is carried out more quickly and the patient can be given medical action as soon as possible. In the study I conducted brain stroke from the number of strokes with a value of 0 4733 and 1 out of 248. This research has a test conducted by conducting epoch training from 1 to 300, the highest score accuracy is in epoch 1 and 2 with more high scores.
Co-Authors Aji Priyambodo Allwine Arnold Pakpahan Baehaqi Chairil Umri Elshad Yusifov Ginting, Syahril Hongjie Dai IRNA TRIANNUR LUBIS Iskandar Iskandar Joel Panjaitan Juanto Simangunsong Juanto Simangunsong Junifa, Dira Manalu, Pius Deski Manullang, Evi Yanti Nissa, Fachrun Nurafni Damanik Pieter Leuvanggi Hutagalung Pius Deski Manalu Pulungan, Wahyu Aji Regina Sirait - Rico Wijaya Riduan, Suwardy Rika Rosnelly Sairun, Aryani Selvi Aristantya Simangunsong, Juanto Simanjuntak, Andrean Simanjuntak, Nurmala Dewi Siti Aisyah Syofyan Anwar Syahputra Syofyan Anwar Syahputra Teddy Surya Gunawan Wanayumini