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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer INTEK: Informatika dan Teknologi Informasi
Akmal Setiawan Wijaya
Universitas Islam Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Other

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Analisis Exploratory Kata “donasi” Akibat Pandemi Covid-19 Pada Media Sosial Twitter Akmal Setiawan Wijaya
INTEK : Jurnal Informatika dan Teknologi Informasi Vol. 5 No. 1 (2022)
Publisher : Universitas Muhammadiyah Purworejo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37729/intek.v5i1.1954

Abstract

Pandemi Covid telah memberikan banyak dampak negatif bagi umat manusia. Banyaknya dampak negatif tersebut telah mendorong masyarakat untuk saling membantu sesamanya. Tujuan yang ingin dicapai dalam penelitian ini melakukan analisis eksplaratori kata “donasi” sebagai representasi dari saling membantu di Tweeter. Analisis dilakukan dengan membandingkan tweet “donasi” 2 tahun sebelum pandemi dan 2 tahun saat pandemi. Selain tweet dalam penelitian ini juga akan dilakukan analisis terhadap replies, Like, dan Retweet dari kata “donasi” tersebut. Setelah dilakukan analisis di dapat hasil bahwa tweet, replies, Like, dan Retweet sebelum masa pandemi cenderung rendah dan stabil, sedangkan pada masa pandemi terjadi loncakan saat awal pandemi yaitu pada bulan Maret 2020 dan Mei 2021 saat puncak pandemi gelombang ke dua.
The Improvement of Artificial Neural Network Accuracy Using Principle Component Analysis Approach Arief Hermawan; Adityo Permana Wibowo; Akmal Setiawan Wijaya
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 1 (2022)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.1880

Abstract

An important problem in a classification system is how to get good accuracy results. A way to increase the accuracy of a classifier system is to improve the number of input data attributes. Improving the number of input data attributes can be done using the Principal Component Analysis (PCA) method. The aim of this research is to reduce the number of input data attributes to increase the accuracy in a mushroom classification system. The research method used in this study started from collecting datasets from Kaggle.com related to mushroom-classification, then the data visualization process was carried out using pie charts then a dimension reduction process was carried out to reduce the number of variables using the PCA method. The next step is the training and testing of the artificial neural network. The architecture of artificial neural network used is backward error propagation with the number of hidden layers as much as 2 layers with the number of cells as many as 3 and 2. The training data used is 80%, while the testing data is 20%. Based on the test results, obtained an accuracy of 100% with 150,000 iterations and using 11 input variables from 22 existing input variables. By adding Principal Component Analysis part of the development that can improve the accuracy and performance of Artificial Neural Networks
Co-Authors Adityo Permana Wibowo Arief Hermawan