Article Per Year (5 Year)
p-Index From 2020 - 2025
0.562
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Wahana Pendidikan
Mikhael Mikhael
Unknown Affiliation
Religion Education Social Sciences Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Analisis Perbandingan Algoritma Bubble Sort, Shell Sort, dan Quick Sort dalam Mengurutkan Baris Angka Acak menggunakan Bahasa Java Muhammad Luthfi Zulfa; Mikhael Mikhael; Betha Nurina Sari
Jurnal Ilmiah Wahana Pendidikan Vol 8 No 13 (2022): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (329.925 KB) | DOI: 10.5281/zenodo.6962346

Abstract

Sorting data is very useful because sorted data will be easier to check and correct if there are errors. There are various kinds of data sorting algorithms including bubble sort, merge sort, shell sort, radix sort, quicksort, and so on. Sorting data is the best treatment as it will be easier to check and correct if there are errors occurring. In this paper, bubble sort, shell sort, and quick sort are tested to sorting 100 lines of random integer with a value range of 0-100. The result shows that quick sort is the most efficient algorithm because it has the fewest steps, consume less memory, and take a little time while sorting.
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 Djunaid, Ika Suryono Felix Andreas Muhammad Luthfi Ultach Enri