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All Journal Jurnal Sarjana Teknik Informatika Jurnal IPTEK JOIV : International Journal on Informatics Visualization Jurnal Pendidikan Informatika dan Sains INTEGER: Journal of Information Technology JUTIS : Jurnal Teknik Informatika Jifosi
Rizky Parlika, Rizky
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Industrial & Manufacturing Engineering Mechanical Engineering Physics

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Journal : JOIV : International Journal on Informatics Visualization

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Prediction of ROI Achievements and Potential Maximum Profit on Spot Bitcoin Rupiah Trading Using K-means Clustering and Patterned Dataset Model Parlika, Rizky; Isnanto, R. Rizal; Rahmat, Basuki
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3120

Abstract

Since Satoshi Nakamoto first proposed the idea of bitcoin in 2009, the cryptocurrency and prediction methods for it have grown and changed exceptionally quickly. The Patterned Dataset Model was a valuable tool in earlier studies to explain how changes in the price of Bitcoin affect the movements of other cryptocurrencies in a digital trading market. Three different kinds of datasets are generated by this model: patterned datasets under full conditions, patterned datasets under dropping prices (Crash), and patterned datasets under rising prices (Moon). The K-means approach was then used to cluster these three datasets. Specifically, each dataset was split into two clusters, and the clustering score was determined by utilizing eight unique clustering metrics. Consequently, the best clustering score was found in the patterned dataset in the crash situation. Additionally, from 2022 to 2024, the raw data from this crash-condition-patterned dataset is used to determine the possibility of reaching maximum profit and return on investment (ROI) daily and monthly. According to the calculation results, the range computed over the course of a whole month (30 to 31 days) is significantly larger than the daily range (24 hours multiplied by one month), which represents the most significant profit and ROI attained before the emergence of the first diamond crash level. This research also covers the application of a deep learning model to forecast patterned datasets for crash scenarios that may occur many days in advance. The ConvLSTM2D Model performs better in predicting pattern dataset values for the subsequent crash scenario, according to the hyperparameter comparison between the Gated Recurrent Unit (GRU) Model and the 2D Convolutional Long Short-Term Memory Model.
Minimum, Maximum, and Average Implementation of Patterned Datasets in Mapping Cryptocurrency Fluctuation Patterns Parlika, Rizky; Mustafid, Mustafid; Rahmat, Basuki
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1543

Abstract

Cryptocurrency price fluctuations are increasingly interesting and are of concern to researchers around the world. Many ways have been proposed to predict the next price, whether it will go up or down. This research shows how to create a patterned dataset from an API connection shared by Indonesia's leading digital currency market, Indodax. From the data on the movement of all cryptocurrencies, the lowest price variable is taken for 24 hours, the latest price, the highest price for 24 hours, and the time of price movement, which is then programmed into a pattern dataset. This patterned dataset is then mined and stored continuously on the MySQL Server DBMS on the hosting service. The patterned dataset is then separated per month, and the data per day is calculated. The minimum, maximum, and average functions are then applied to form a graph that displays paired lines of the movement of the patterned dataset in Crash and Moon conditions. From the observations, the Patterned Graphical Pair dataset using the Average function provides the best potential for predicting future cryptocurrency price fluctuations with the Bitcoin case study. The novelty of this research is the development of patterned datasets for predicting cryptocurrency fluctuations based on the influence of bitcoin price movements on all currencies in the cryptocurrency trading market. This research also proved the truth of hypotheses a and b related to the start and end of fluctuations.
Co-Authors Achmad Heidhar Mubarok, Achmad Heidhar Akbar, Fawwaz Ali al hakim, Rais Alfajr, Achmad Yuneda Alwin, Muhammad Izdihar Anggreini, Diana Nur Ardisty Palvelus Jumala Arianto, Chakra Satrya Pradana Putra Aulia N, Rayhan Auliya, Rahmat Basuki Rahmat Masdi Siduppa Didik U Pribadi, Didik U faradilla, yolla Fedianto, Muhammad Helmi Satria Fernanda, Rifky Akhmad Firmansyah, Fahrul Hidayat, Mochammad Fikri Humania B, Nobel Ilham Pradika, Sunu Khariono, Heri Lesmana, Hilman Fadlilah Lintang P, Heldian M. Syahrul Munir, M. Syahrul Muhammad Agung Shobirin Muhammad Hakim, Amir Mustafid Mustafid Nurilhaq, Muhammad Sabilli Perdana, Fatwa Zuhri Diva Prabowo, Avrie Akbar Pralas, Faris Hirmar Prayoga, Julio Cahya Putra, Ahmad Dendy Prasongko Putra, Alif Ernanda Putra, Hadiansyah Rachmawan R. Rizal Isnanto Rachman N.M., Kholilul Retno Mumpuni Rivaldy Setiawan, Rivaldy Satria, Vinza Hedi Setia R, Ilham Setiawan, Rienaldi Shahab, Muhammad Syaugi Shodiq, Ja'far Siregar, Ilham Krisdianta Suriansyah, Muhammad Syafriansyah, Muhammad Waskito, Muhammad Rizal Wijaya, Devan Cakra Mudra Yulianto, Emmil