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All Journal Jurnal Ilmiah Wahana Pendidikan
Felix Andreas
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Implementasi Algoritma Support Vector Classifier (SVC) dengan Data Training Numerik dan Teks untuk Mengklasifikasi SMS Spam Thomas Reizaldi Sanusi; Felix Andreas; Betha Nurina Sari
Jurnal Ilmiah Wahana Pendidikan Vol 8 No 14 (2022): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (287.549 KB) | DOI: 10.5281/zenodo.6994895

Abstract

Short Message Service (SMS) is a service that resembles correspondence found on mobile phones. The reason why SMS is massively used is because of its low cost and instant. However, with the advancement of this technology, SMS is often misused by many people. Often people send messages that are meaningless. This message called “spam”. Many people deal with spam messages by blocking the sender of the message. However, this method is less effective. So the solution to the problem solving for spam messages is to classify messages that are categorized as spam and not spam (ham). In this research we use Support Vector Classifier (SVC) algorithm to classified spam, SMS spam was classified in two ways, one with training data in the form of numeric and the other with training data in the form of text. This research conclude that the classification of spam messages will have the highest accuracy if the training data is in the form of text rather than in the form of numeric.
Perbandingan Algoritma Backpropagation Neural Network dan Long Short-Term Memory dalam Memprediksi Harga Bitcoin Felix Andreas; Mikhael Mikhael; Ultach Enri
Jurnal Ilmiah Wahana Pendidikan Vol 8 No 12 (2022): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (256.127 KB) | DOI: 10.5281/zenodo.7009768

Abstract

In actual practice, Bitcoin is the decentralized currency that allows two individuals to transact without third-party intervention. However, due to its high volatility, it has been such an attraction to investors to gain profit. But, that also mean that high volatility can also bring disadvantage if someone predicts the increase or decrease of the price of Bitcoin incorrectly. The technical analysis which is often used to predict Bitcoin prices has a weakness, that is specifically depends on the users of technical indicators. Therefore, it is necessary to use the Data Mining algorithm as an alternative solution to predict Bitcoin prices. In this paper, the implemented algorithms to predict Bitcoin prices are Long Short-Term Memory (LSTM) and Backpropagation Neural Network. The final results using T-Test showed there is no significant difference between LSTM and Backpropagation in predicting the data test with an average RMSE value of 661.580 and 1.812.503, respectively. However Backpropagation has the advantage to predict new data (outside of the dataset) with an average RMSE value of 629.545, while the average RMSE value of the LSTM is 2.818.248.
Co-Authors Mikhael Mikhael Thomas Reizaldi Sanusi Ultach Enri