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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 Jurnal Sisfokom (Sistem Informasi dan Komputer) JUTIS : Jurnal Teknik Informatika Jifosi
Rizky Parlika, Rizky
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering Mechanical Engineering Physics

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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.
Mapping Bitcoin Research in Information Systems: A Comprehensive Bibliometric Analysis (2008–2025) Munazilin, Akhlis; Agung Wibowo, Mochamad; Parlika, Rizky
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2538

Abstract

Bitcoin has been a major focus of interdisciplinary research in information systems, finance, and economics since its emergence in 2008. Despite the extensive literature on Bitcoin, patterns of intellectual collaboration, the evolution of research themes, and research gaps have not been comprehensively mapped. This study presents a bibliometric analysis of 3,312 scientific articles indexed by Scopus from 2008 to May 2025, using a quantitative approach based on Bibliometrix. The analysis includes publication trends, author and institutional collaboration networks, co-citation mapping, and thematic clusters based on keywords. The results reveal five dominant themes: (1) blockchain development beyond crypto, (2) regulatory challenges and global adoption, (3) Bitcoin price volatility, (4) impacts on the global financial system, and (5) social implications in developing countries. The study also identifies an epistemological fragmentation between technical and policy approaches. These findings reinforce the need for an integrated multimodal approach that combines market data, sentiment analysis, and regulatory context to develop more robust predictive models. This study is the first comprehensive bibliometric review of Bitcoin in global scope that explicitly links findings to information systems research opportunities.
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 Achmad Heidhar Mubarok, Achmad Heidhar Agung Wibowo, Mochamad Akbar, Fawwaz Ali Akhlis Munazilin, Akhlis 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 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