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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA
Ridwan Ridwan
STMIK Nusa Mandiri, Jakarta
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Implementasi Algoritma Neural Network dalam Memprediksi Tingkat Kelulusan Mahasiswa Ridwan Ridwan; Hendarman Lubis; Prio Kustanto
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 4, No 2 (2020): April 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v4i2.2035

Abstract

Higher education institutions are demanded to be quality education providers. One of the instruments used by the government to measure the quality of education providers is the number of graduates. The higher the graduation level, the better the quality of education and this good quality will positively influence the value of accreditation given by BAN-PT. Therefore, in this study the researchers provided input for research conducted at Bhayangkara Jakarta Raya University to predict student graduation rates using the Neural Network algorithm. Neural Network is one method in machine learning developed from Multi Layer Perceptron (MLP) which is designed to process two-dimensional data. Neural Network is included in the Deep Neural Network type because of its deep network level and is widely implemented in image data. Neural Network has two methods; namely classification using feedforward and learning stages using backpropagation. The way Neural Network works is similar to MLP, but in Neural Network each neuron is presented in two dimensions, unlike MLP where each neuron is only one dimensional in size. The prediction accuracy obtained is 98.27%.
Implementasi K-NN Dalam Analisa Sentimen Riba Pada Bunga Bank Berdasarkan Data Twitter Rasenda Rasenda; Hendarman Lubis; Ridwan Ridwan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 4, No 2 (2020): April 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v4i2.2051

Abstract

This study aims to formulate public opinion about bank interest included in the category of usury or not. The method used in this study is the analysis of usury sentiments on bank interest using Twitter data with the K-NN algorithm. Sentiment analysis using the K-NN algorithm gives good results. Evidenced by testing 170 twitter dataset using the K-NN algorithm obtained an accuracy of ± 70.59%. Assisted by the preprocessing process which aims to erase unnecessary parts and also change the form of documents in the form of tweets to a standard form so that classification can be carried out, so that the results of usury sentiment analysis on bank interest can clarify assumptions in the community and serve as a reference in determining appropriate banking products to the needs of customers
Co-Authors Hendarman Lubis Kustanto , Prio Rasenda Rasenda