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Parlika, Rizky
Department Of Informatics System, Faculty Of Computer Science, Universitas Pembangunan Nasional “Veteran” Surabaya, East Java
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Journal : joiv international journal on informatics visualization

 1 
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.
Patterned Dataset Model Optimization to Predict Bitcoin IDR Price using Long Short Term Memory Parlika, Rizky; Isnanto, R Rizal; Rahmat, Basuki
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.4036

Abstract

The goal of this study was to determine the optimal combination for optimizing the Patterned Dataset Model, particularly in patterned datasets during periods of price decline (crash).  In previous research, the Crash Patterned Dataset has been shown to predict the next Bitcoin price. In this study, an experiment was conducted using a combination of prediction models, including ARIMA, machine learning, and deep learning. This research was conducted in 3 stages. The first stage is to compare the error results from the Bitcoin pair IDR crypto asset prediction process, which are part of the stored data from the patterned dataset under crash conditions. This dataset was tested with several prediction models, and the LSTM model with 60 seconds of resampling produced the best results, with an MAPE of 0.19%. In the second stage, BTCIDR, as part of the data from the patterned dataset in crash conditions, was resampled with variants 1D, 2D, 3D, 4D, 5D, 6D, 7D, 1H, 2H, 3H, 4H, 5H, 6H, 7H, 8H, 9H, 10H, 11H, and 12H. The result is that BTCIDR with a 3H resample has the lowest MAPE, at 1.3%. In the third stage, the prediction process is carried out using the LSTM model on the BTC IDR test dataset (as part of the Patterned Dataset in crash conditions) with a 3H resample. The dataset range is from May 2022 to 2025-01-23 11:05:48. This test predicts the Bitcoin IDR price series for the next 30 days, calculates the MAPE between the predicted series and the actual BTC IDR dataset 30 days later, and evaluates the results. The MAPE value for the Bitcoin IDR price prediction was 9.27%. This indicates that the average prediction error against the actual price is around 9.27%. The main objective of this research is to more accurately predict the price of the Bitcoin-IDR pair, providing additional helpful information for trading cryptocurrencies.
Co-Authors Abidin Sulaiman Abrori, Merdin Risalul Achmad Heidhar Mubarok, Achmad Heidhar Achmad Yuneda Alfajr Agung Wibowo, Mochamad Ahmad Dendy Prasongko Putra Ahmad Maghfur’ Ali Akbar, Fawwaz Ali Akhlis Munazilin, Akhlis al hakim, Rais Alfajr, Achmad Yuneda Alif Ernanda Putra Alwin, Muhammad Izdihar Amir Muhammad Hakim Andreas Nugroho Sihananto Andry S, Firdaus Anggoro Cahyo Nugroho Anggreini, Diana Nur Anita Nusari Ardiana Deka Maharani Ardika, Rendra Ardisty Palvelus Jumala Arianto, Chakra Satrya Pradana Putra Aris Pratama Arista Pratama Arzaki, Muhammad Ilham Asif Faroqi Atmaja, Pratama Wirya Aulia N, Rayhan Auliya, Rahmat Avrie Akbar Prabowo Ayu Ithriah, Syurfah Azaidane, Dandi Basuki Rahmat Masdi Siduppa Benny Danendra Hadi Bregsi Atingsari Julastri Chakra Satrya Pradana Putra Arianto Devan Cakra Mudra Wijaya Devi Anugrah Putri Dewi Azizah Dhany Satya Hutama Didik U Pribadi, Didik U Didik Utomo Pribadi Dimas Rizward Hikmah Utomo Dino Rosanilo Yuswanto Dwi Rahmadewi, Cynthia Emmil Yulianto Erayanti, Aninda Elsa faradilla, yolla Faris Hirmar Pralas Fatwa Zuhri Diva Perdana Fedianto, Muhammad Helmi Satria Fernanda, Rifky Akhmad Fernanda, Rifky Akhmad Firmansyah, Fahrul Firza Prima Aditiawan Hadiansyah Rachmawan Putra Haidar Ananta Kusuma Hakim, Arif Rahman Hamdi Indra Hanafi, Agus Heldian Lintang P Heri Khariono Heri Khariono Hermawan, Riky Hidayat, Mochammad Fikri Hilman Fadlilah Lesmana Humam Maulana Tsubasanofa Ramadhan Humam Maulana Tsubasanofa Ramadhan Humania B, Nobel Ilham Asy’ari Ilham Krisdianta Siregar Ilham Pradika, Sunu Ilham Setia R Isfan Rachmad Ja'far Shodiq Kartini Kartini Kartini Kartini Khariono, Heri Kholilul Rachman Nur Manab Kumala, Yudhistira Nanda Lesmana, Hilman Fadlilah Lintang P, Heldian Luthfiyatul ‘Azizah M. Syahrul Munir, M. Syahrul Malik, Gamar Ramadhani Maulana, Hendra Melinda Shilatil Fauziyah Merdin Risalul Abrori Miftakhoneki, Sufi Misbahul Munir Mochammad Fikri Hidayat Mochammad Zayyan Ramadhan Moh. Ainur Rofik Mohammad Idhom Muhammad Agung Shobirin Muhammad Ghifari Alifian Muhammad Hakim, Amir Muhammad Helmi Satria Fedianto Muhammad Izdihar Alwin Muhammad Rafli Aulia Rojani Lutfi Muhammad Rizal Waskito Muhammad Romi Nasution Muhammad Suriansyah Munir, M Syahrul Mustafid Mustafid Nafa Nabila El Indri Nizam, M Miftahul Nobel Humania B Nur Cahyo, Arif Nur Manab, Kholilul Rachman Nurilhaq, Muhammad Sabilli Olivia i Anggun Permatasar Orissa, Dendy Fektor Parlika, Anjaya Perdana, Fatwa Zuhri Diva Prabowo, Avrie Akbar Pralas, Faris Hirmar Prasurya, Bima Rizqy Prayoga, Julio Cahya Pribadi, Didik U Putra Dwi Wira Gardha Yuniahans Putra, Ahmad Dendy Prasongko Putra, Alif Ernanda Putra, Hadiansyah Rachmawan Qonitah Jihan Nabilah R Rizal Isnanto R. Rizal Isnanto Rachman N.M., Kholilul Rahmat Auliya Ramadhan, Ferry Dzaky Rayhan Aulia N Rayhan Rizal Mahendra Rayhan Saneval Arhinza Retno Mumpuni Reza Achmad Gallanta Rifardi Taufiq Yufananda Rifky Akhmad Fernanda Rifky Akhmad Fernanda Riky Hermawan Rivaldy Setiawan, Rivaldy Rizky Ananda Ramadhan Rizqy Khoirul Waritsin Roiqoh, Aprinia Salsabila S. Gama, Nemicio de Sarirotul Latifah Satria, Vinza Hedi Setia R, Ilham Setiawan, Rienaldi Shahab, Muhammad Syaugi Shodiq, Ja'far Siregar, Ilham Krisdianta Steffanuel Pranatalie Krispriyanto Stevanus Frangky Handono Sunu Ilham Pradika Suriansyah, Muhammad Susy Rahmawati Syafriansyah, Muhammad Syahrul Munir Syalum Marsya Pruista Tasya Ardhian Nisaa’ Tauhid, Hidayat Nur Tegar Satria Kirana Ummam, Mohamad Arel Intidhofatul Vito Fausta Majid Waskito, Muhammad Rizal Wifaqul Azmi, Muhammad Wijaya, Devan Cakra Mudra Wijaya, Devan Cakra Mudra Wiratama, Fadhli Shidqi Yoga Ari Tofan Yulianto, Emmil