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All Journal Lani: Jurnal Kajian Ilmu Sejarah dan Budaya PELS (Procedia of Engineering and Life Science)
Ahmad Saroji
Universitas Darussalam Gontor
Arts Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Social Sciences

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SEJARAH EVOLUSI GENERASI INTERNET Ahmad Saroji; Triana Harmini; Muhammad Taqiyuddin
Lani: Jurnal Kajian Ilmu Sejarah dan Budaya Vol 2 No 2 (2021): Lani: Jurnal Kajian Ilmu Sejarah dan Budaya
Publisher : Program Studi Pendidikan Sejarah Fakultas Keguruan dan Ilmu Pendidikan Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (390.893 KB) | DOI: 10.30598/Lanivol2iss2page65-75

Abstract

This study aims to determine the evolutionary history of the internet generation.The evolution of the internet to date has reached the fifth generation (5G).During the last decade, internet technology has experienced very rapiddevelopment. In the development of internet evolution, each internet generationhas advantages and disadvantages. In this article, we will explain the featuresand specifications of several internet generations, namely 1G, 2G, 3G, 4G, 5G.Then when the Indonesian state uses the internet. Who is the father of theinternet in Indonesia? The internet is a medium used by humans to getinformation from places they have never met. The internet has become acommunication medium that humans use to connect whenever and whereverthey are. Humans use the internet to make their work easier. Humans as socialbeings are very difficult to be separated from social media. A person's habit ofgetting information by browsing, chatting and studying online, all of whichrequire the internet.
Outlier Detection On Graduation Data Of Darussalam Gontor University Using One-Class Support Vector Machine Oddy Virgantara Putra; Triana Harmini; Ahmad Saroji
Procedia of Engineering and Life Science Vol 2 (2021): Proceedings of the 3rd Seminar Nasional Sains 2021
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (496.665 KB) | DOI: 10.21070/pels.v2i0.1139

Abstract

Outlier detection is an important field of study because it is able to detect abnormal data distribution from a set of data. In the student graduation data, there are students with high semester GPA but do not graduate on time but students with low semester GPA can graduate on time. This study aims to detect outlier values ​​in student graduation data for the 2020-2021 class. Factors (attributes) used in this study are Student Academic Support Credit Scores (AKPAM) and Social Studies from semester one to semester six. The dataset used is 1204 graduates. The outlier detection method used is One-Class Support Vector Machine (SVM). One-class SVM is a derivative of SVM method that detects outliers based on data outside the specified class. The results of outlier detection using One-Class SVM method with three types of kernels produce the following reference values: kernel 'rbf' n by 91.4%, kernel 'linear' by 90% and kernel 'poly' by 90%. After normalization using MinMaxScaler the reference value increased by 2% in each kernel. The results of testing the One-Class SVM method get an average 90.3%, thus it can be concluded that the One-Class SVM method is feasible to be used as an outlier detection method.
Co-Authors Muhammad Taqiyuddin Oddy Virgantara Putra Triana Harmini