ABSTRAKPenelitian ini bertujuan untuk menentukan pengaruh pergerakan harga global komoditassektor energi, terutama Harga Minyak Mentah dan Harga Gas Alam terhadap pergerakan hargabitcoin. Penelitian ini lebih fokus pada Criptocurrency Bitcoin. Metode pengambilan data yangdigunakan dalam penelitian ini adalah metode kuantitatif dengan teknik purposive sampling, danpengumpulan data dilakukan dengan menggunakan data sekunder secara periode mulai dari Juni2019 sampai dengan Juli 2022. Teknik analisis data yang digunakan oleh penulis adalah AnalisisRegresi Linier Berganda. Hasil dari penelitian ini menunjukkan bahwa harga global komoditassektor energi, terutama Minyak Mentah dan Gas Alami, memiliki pengaruh positif terhadappergerakan harga Bitcoin.Kata kunci : Cryptocurrency, Bitcoin, Minyak Mentah, Gas AlamABSTRACTThe aim of this study is to determine the influence of global energy sector commodity pricemovements, especially Crude Oil and Natural Gas Prices, on the movement of Cryptocurrencyprices. This study focuses primarily on the Cryptocurrency Bitcoin. The data collection method inthis research is quantitative with purposive sampling technique. The researchers use secondarydata from June 2019 to July 2022 weekly. The sample size for this study consists of 165 samples.Secondary data sources are obtained through the finance.yahoo.com website. Technique analysisanalysis in this research is Multiple Linear Regression. The results of this study show that globalenergy sector commodity prices, especially Crude Oil and Natural Gas, have a positive influenceon the movement of Bitcoin prices.Keyword : Cryptocurrency, Bitcoin, Crude Oil, Natural Gas
Chang Chi Hung, Jacky Filbert Wijaya, Victor Victor, Irpan Adiputra Pardosi · 5 authors
Bitcoin merupakan salah satu cryptocurrency paling berharga di dunia dan diperdagangkan di lebih dari 40 bursa di seluruh dunia dan menerima lebih dari 30 mata uang berbeda dengan 250.000 transaksi per hari. Dalam perdagangannya, Bitcoin menunjukkan fluktuasi pada pasar yang diperdagangkan, dalam hal ini fluktuasinya dapat mencapai 10 kali lebih tinggi daripada fluktuasi nilai tukar mata uang asing. Karena fluktuasi harga bitcoin yang masif dan tinggi, prediksi fluktuasi harga sangat dibutuhkan, terutama karena harga bitcoin bergerak dengan sangat acak. Untuk melalukan prediksi flutuktuasi harga, Random Forest classifier merupakan salah satu algoritma machine learning yang sering digunakan untuk prediksi, kesehatan, artificial intelligence, dll. K-means clustering juga dipergunakan untuk membantu algoritma random forest classifier dalam hal mengkluster data. Hasil dari penelitian ini yaitu melakukan prediksi terhadap naik atau turunnya harga bitcoin dengan akurasi sebanyak 71% yang didapatkan dari perbandingan hasil prediksi dan data asli dengan bantuan algoritma confusion matrix.
Bitcoin is always interesting to keep predicting where the next price movement will go. Bitcoin is the first and most influential Cryptocurrency on cryptocurrency price movements. Bitcoin is traded in many markets, the largest in Indonesia is Indodax. Indodax provides a document sharing API so that third parties can build applications that are able to process data on bitcoin price movements in real-time and continuously. This research shows how patterned datasets can be applied to monitor bitcoin price movements from the indodax market and show their effects on other cryptocurrency assets besides bitcoin. This research shows how data that is patterned and then processed using the minimum and maximum functions can provide 2 important information, namely the potential position of bitcoin when it is at the maximum and minimum points. The results of the patterned dataset formula are then compared to the movements of the 2 cryptocurrencies with the largest capitalization, namely BTC (Bitcoin) and ETH (Ethereum). The results of the comparison show that the use of patterned dataset formulas successfully shows important points in cryptocurrency trading, with simpler instructions.
Cryptocurrency is a digital currency that can be used for transaction on an international scale and as an investment. The potential provided by cryptocurrency in the development of the digital economy in the world has become a special attraction for individuals, organizations, and government. Blockchain system that underlies cryptocurrencies has worked flawlessly in both the financial and non-financial worlds. This study uses Basic Risk Management Ishikawa Diagram and evaluated by ARIMA predictive algorithm in determining cause and effect of the development of cryptocurrencies. It was found that the development of cryptocurrency is very influential by the state of the world, especially countries that have great influence such as USA. USA inflation have a big influence, and the model can be used as a basis for a country's government in observing.
Muhammad Sahi, Muhammad Faisal, Yunifa Miftachul Arif, Cahyo Crysdian
Bitcoin is one of the fastest-growing digital currencies or cryptocurrencies in the world. However, the highly volatile Bitcoin price poses a very extreme risk for traders investing in cryptocurrencies, especially Bitcoin. To anticipate these risks, a prediction system is needed to predict the fluctuations in cryptocurrency prices. Artificial Neural Network (ANN) is a relatively new model discovered and can solve many complex problems because the way it works mimics human nerve cells. ANN has the advantage of being able to describe both linear and non-linear models with a fairly wide range. This research aims to determine the best performance and level of accuracy of the ANN model using the Back-Propagation Neural Network (BPNN) algorithm in predicting Bitcoin prices. This study uses Bitcoin price data for the period 2020 to 2023 taken from the CoinDesk market. The results of this study indicate that the ANN model produces the best performance in the form of four input nodes, 12 hidden nodes, and one output node (4-12-1) with an accuracy rate of around 3.0617175%.
The cryptographic virtual currency, bitcoin, is considered the main originator of cryptocurrencies that emerged due United States financial crisis in 2008. The idea was sparked by Nakamoto by introducing an alternative currency system that really refers to the strength of supply and demand. Based on INDODAX data, the bitcoin exchange rate during October 2020 to February 2021 is a condition of a large increase in a short time with a percentage increase of 450%. The increase in bitcoin prices can be modelled using the b-spline nonparametric regression method based on order and optimal knot points based on the smallest Generalized Cross Validation value. The resulting b-spline 4 degree and the number of knots points 5 as the best model with each bases described recursively.
Bitcoin price prediction involves analyzing a variety of factors, including market sentiment, trading volume, economic news, technological developments, and other factors that affect supply and demand. Both technical and fundamental analysis methods can be used to try to predict Bitcoin price movements. In this Bitcoin price prediction using a Deep Learning approach with the chosen method is LSTM. The LSTM (Long Short-Term Memory) method is a popular type of Recurrent Neural Network (RNN) model for predicting the price of Bitcoin and other financial assets. LSTM can solve the problem of price movements that have long-term dependencies, which traditional RNN models cannot handle well. LSTMs have the ability to "remember" information from longer periods of time, thereby recognizing complex patterns and trends in historical data. In this study the prediction period used a dataset from March 1 2016 to November 24 2018. This study used an epoch parameter of 10 with a learning rate of 0.001. In addition, the batch size parameter used is 25 with layers only. The evaluation results of this study resulted in an RMSE of 77.74 and an MAE of 278.33. This shows that the RMSE value is small because the Bitcoin price range is too far.
Cryptocurrency mining is a process carried out using a specialised computer network in order to obtain new crypto assets. The cryptocurrency mining business in today's digital era is increasingly in demand by netizens. Many netizens run cryptocurrency mining businesses to generate new cryptocurrency assets that can be traded on the cryptocurrency market to earn huge profits. In doing cryptocurrency mining itself, it is necessary to be careful in choosing the cryptocurrency mining machine used to get the maximum profit. In this study, researchers proposed the Multifactor Evaluation Process as a decision support system method used to simplify the process of selecting the best cryptocurrency mining machine. The results of this study show that the best cryptocurrency mining machine that is most recommended to use is Cheetah Miner F5I (0.2120), followed by the alternatives iBeLink DSM7T Miner (0.2072), Bitfury RD4 (0.2016), Aladdin T1 16T (0.2016), and Obelisk SC1 Dual (0.1800).
Ridwan Setiawan, Indri Tri Julianto, Fikri Fahru Roji
Cryptocurrency has become a phenomenon worldwide. Although not all countries have legalized it, it is considered a promising investment asset. Currently, there are three top-ranking cryptocurrencies: Bitcoin, Ethereum, and Tether. This research aims to compare the performance of five forecasting algorithms, namely Autoregressive Integrated Moving Average (ARIMA), Neural Network, Support Vector Machine, Linear Regression, and Generalized Linear Model, using the dataset of Bitcoin, Ethereum, and Tether cryptocurrencies. The research methodology employed is Knowledge Discovery In Databases (KDD). The technique involves assessing the performance based on the Root Mean Square Error (RMSE) and comparing the results to find the most optimal model performance. The research findings indicate that for Bitcoin cryptocurrency, the Neural Network algorithm produced the most optimal results with an RMSE of 9180.534. For Ethereum cryptocurrency, the Neural Network algorithm demonstrated the best performance with an RMSE value of 537.528. Furthermore, for Tether cryptocurrency, the ARIMA algorithm yielded the best performance with an RMSE value of 0.003. Keywords – bitcoin, cryptocurrency, ethereum, forecasting, tether
Cryptocurrency adalah mata uang digital terdesentralisasi yang diatur oleh pemerintah pusat. Karena cryptocurrency sangat fluktuatif, analisis diperlukan sebelum menggunakan cryptocurrency untuk meminimalkan kerugian. Penelitian ini melakukan perbandingan antara model Long Short Term Memory (LSTM) dan algoritma optimasi seperti Adam dan Root Mean Square Propagation (RMSProp) untuk melakukan prediksi terhadap nilai cryptocurrency. Metode LSTM dioptimasi menggunakan Adam Optimizer dan dievaluasi berdasarkan Root Mean Square Error (RMSE). Dengan demikian diperoleh prediksi nilai RMSE sebesar 0.08217562639465784 yang merupakan nilai error yang kecil sehingga mendekati nilai aktual. Sedangkan nilai RMSE 0.10699215580552895 menggunakan RMSProp mendapatkan nilai yang lebih besar yang berdampak terhadap akurasi hasil prediksi. Dengan demikian kombinasi antara algoritma LSTM dan Adam dapat melakukan prediksi dan mengoptimasi data dengan akurat.
Repi Septia Nugraha, Ariawan Djoko Rachmanto, Zen Munawar
Untuk mengetahui harga dari Bitcoin akan naik atau turun, bisa dilakukan dengan berbagai cara, salah satunya menggunakan data mining, yaitu proses pengumpulan informasi dan menyimpulkannya menjadi informasi-informasi yang bisa dipakai untuk berbagai hal, seperti meningkatkan keuntungan, memperkecil risiko, memprediksi suatu nilai, membantu membuat keputusan, dan lain sebagainya. Forecasting adalah salah satu fungsi dari data mining, yaitu proses memprediksi berdasarkan pola-pola dalam suatu data. Prediksi dilakukan agar kita bisa melakukan segala sesuatu dengan risiko sekecil mungkin pada masa yang akan datang. Regresi linier adalah salah algoritma yang ada pada data mining, yaitu alat yang dgunakan untuk mengetahui pengaruh satu atau beberapa variabel terhadap variabel lainnya. Penelitian ini bertujuan untuk mendapatkan gambaran tentang apa itu Bitcoin dan bagaimana nilai Bitcoin jika dianalisis menggunakan algoritma data mining, yaitu regresi linier dengan menggunakan aplikasi RapidMiner Studio Educational 9.9.002. Hasil dari penelitian ini menunjukkan bahwa keakuratan nilai Bitcoin berdasarkan data yang diambil dari coinmarketcap.com dan investing.com dengan menggunakan regresi linier mendapat rata-rata persentase sebesar 11%.
Cryptocurrency merupakan mata uang digital yang dapat digunakan untuk transaksi atau investasi. Investasi aset cryptocurrency saat ini semakin banyak diminati oleh masyarakat. Investasi ini memiliki resiko yang tinggi dikarenakan harganya dapat turun ataupun naik dalam waktu yang singkat. Karena keadaan naik turunnya harga cryptocurrency yang begitu drastis inilah membuat para investor yang berharap ingin mendapatkan keuntungan justru mengalami kerugian. Oleh karena itu, diperlukan sebuah sistem prediksi yang dapat membantu memberikan pertimbangan kepada investor dalam pembelian aset cryptocurrency. Pada penelitian ini menggunakan metode GRU untuk memprediksi harga cryptocurrency, yaitu bitcoin dan ethereum dari tahun 2018 sampai 2021. Data dilakukan pelatihan menggunakan varian nilai window size untuk mendapatkan model dengan window size yang optimal dari nilai error terkecil dengan perhitungan Mean Absolute Percentage Error (MAPE). Berdasarkan hasil pengujian, dengan menggunakan nilai window size sebanyak 2, sistem mendapatkan hasil error yang paling kecil. Perhitungan akurasi prediksi untuk 1, 6, dan 12 bulan berikutnya pada data uji bitcoin masing-masing sebesar 90.26%, 77.74%, dan 75.98%, sedangkan pada data uji ethereum masing-masing sebesar 90.15%, 76,88%, dan 66.09%. Dapat dikategorikan sistem prediksi harga cryptocurrency ini tergolong sangat baik untuk memprediksi 1 bulan berikutnya dan dikategorikan cukup untuk memprediksi 6 dan 12 bulan berikutnya.
Bitcoin is one of the digital payments that is currently booming, fast delivery makes bitcoin in great demand by many people, currently there are many digital currency exchanges that can be used, one of the well-known ones in Indonesia, namely Indodax. Indodax is a cryptocurrency exchange, not only an exchange, Indodax also provides a chat room containing investors' opinions. Opinions contained in the Indodax chat room can be used to determine whether comments are positive, neutral or negative, so that it can be an investor's decision to sell or buy bitcoin using sentiment analysis. The sentiment analysis process begins with collecting data using an instant data scraper on the Indodax website, data preprcoessing, labeling using vader lexicon, TF-IDF as word weighting, data splitting, naïve Bayes algorithm and support vector machine, feature selection xgboost and gradient boosting, model evaluation with confusion matrix, then comparing the results of the two algorithms. Based on the tests that have been carried out, naïve bayes obtained the best accuracy value of 70.7%, naïve bayes combined with XGBoost obtained the best accuracy value of 86.6%, while the Support vector machine obtained the best accuracy 86.1%, support vector machine combined with gradient boosting obtained the best accuracy value of 88%. Based on these results the use of feature selection can increase the accuracy value of the algorithm.
The title of this study is to analyze the effect of cryptocurrency returns and cryptocurrency volume on the stock price indices of Indonesia, Singapore and Thailand. This research was conducted using the panel data regression method by combining cross section data with time series data using Eviews software. This study uses cryptocurrency returns and volumes in a weekly period from 1 January 2018 to 31 December 2021 for 48 weeks for each country with a total of 144 data. The results of this study indicate that cryptocurrency returns have a significant positive effect on the stock price indexes of Indonesia, Singapore, and Thailand. and cryptocurrency volume has a significant positive effect on the stock price indices of Indonesia, Singapore and Thailand.
This research proposed machine learning forecasting models to support bitcoin investment decisions based on bitcoin price and trade volume from 2019 to 2021. The moving average crossovers of 5, 30, and 90 daily closing prices and their variances were inputs loaded into decision tree, random forest, and extreme gradient boosting (XGBoost) techniques to forecast bitcoin investment strategies, including market trends, actions, and holding amounts. The research also measured the models' performance based on accuracy, precision, recall, F1-score, and area under the curve-receiver operating characteristics (AUC-ROC). The results indicated that the XGBoost is the most efficient model: (1) trend (0.930 accuracy, 0.930 precision, 0.930 recall, 0.929 F1-score, and 0.983 AUC-ROC); (2) action (0.985 accuracy, 0.985 precision, 0.985 recall, 0.985 F1-score, and 0.998 AUC-ROC); and (3) amount (0.987 accuracy, 0.987 precision, 0.987 recall, 0.987 F1-score, and 0.997 AUC-ROC). The random forest achieved the second most efficient model, while the decision tree provided the lowest forecasting results. Since the bitcoin investment market in 2022 is significantly different from the previous two years due to several negative factors, the research further validated the models' performance with an unseen data set comprising 275 days of bitcoin market prices from January 1 to October 2, 2022. All the models suggested that investors hold with half the investment consistent with the investment market in 2022. Furthermore, although the decision tree and XGBoost models forecasted the investment trend for most days as up, the random forest forecasted the trend as sideway, consistent with the 2022 trend. Received: 23 January 2023 | Revised: 22 February 2023 | Accepted: 23 March 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in [The Securities and Exchange Commission] at https://www.sec.or.th/TH/Pages/WEEKLYREPORT-2564-12.aspx; in [Yahoo Finance] at https://finance.yahoo.com/.
The purpose of this study is to test the ability of the ARIMA model to predict the value of Ethereum, especially during economic shocks such as the current COVID-19 pandemic. The population in this study is Ethereum value weekly data for the period January 2017 to December 2020, so there are 208 samples in this study. The results showed that the use of the ARIMA method in predicting the value of Ethereum got poor results, where the forecast value was very much different from the actual value. This is evidenced from the results of the accuracy test using MAPE which got a result of 51.94%. On the other hand, the economic conditions that are experiencing uncertainty due to the COVID-19 pandemic and the emergence of deficit (decentralized finance) in early 2021 have pushed up a very significant increase in the value of Ethereum so that the error standard is higher and reduces the ability of the ARIMA model to predict the value of Ethereum. Further research is recommended to use a more advanced model such as the Autoregressive Fractionally Integrated Moving Average (AFRIMA) in order to obtain a better forecast value.
Indri Tri Julianto, Dede Kurniadi, Fathia Alisha Fauziah, Ricky Rohmanto
Cryptocurrency is a digital currency not managed by a state or central bank, and transactions are peer-to-peer. Cryptocurrency is still considered a speculative asset and its price volatility is relatively high, but it is also expected to become an efficient and secure transaction tool in the future. The purpose of this study is to compare and improve the performance of the Data Mining Algorithm model using the Feature Selection-Wrapper with the Binance Coin (BNB) cryptocurrency dataset. The Feature Selection-Wrapper approach used is Forward Selection and Backward Elimination. The algorithms used are Neural Networks, Deep Learning, Support Vector Machines, and Linear Regression. The methodology used is Knowledge Discovery in Databases. The results showed that from a comparison using K-Fold Cross Validation with a value of K=10, the Neural Network Algorithm has the best Root Mean Square Error value of 10,734 +/- 10,124 (micro average: 14,580 +/- 0,000). Then after improving performance using Forward Selection and Backward Elimination in the Neural Network Algorithm, the best performance improvement results are shown by using Backward Elimination with RMSE 5,302 +/- 2,647 (micro average: 5,805 +/- 0,000).
P Rachana, B. Rajlakshmi, P V Ajay, G A Achuth · 5 authors
Often, in today’s world, it is difficult to make new acquaintances if we discuss in our social group, and even for individuals, finding someone who has common interests can be challenging. Additionally, by utilizing web3, WebRTC, and machine learning, this project facilitates safe connections between individuals with shared interests located all over the world. Every person in this world has unique interests, preferences, and dislikes. Everyone wants to get in touch with someone who shares their interests so that they can communicate more effectively. We support the connection of all types of people in this project because some people need mentoring, others want to practice interviews, others enjoy listening to stories, and still, others want to perform stand-up comedy. People who enjoy learning about new cultures from various nations and languages can also connect.The entire user’s interest data, including age, favourite subject, learned programming languages, consulting interest, interview interest, current employment history, favourite Netflix shows, favourite movies, favourite hero, and favourite song playlist, will be collected for this project. We use all the data from the various individuals to match people using a machine learning algorithm, and then, based on the outcomes, we connect the people using WebRTC so that they can communicate face-to-face while sharing real-time audio and video. More user interest information will increase the precision of finding the ideal match. Our algorithm matches you with various people who can observe, suggest to you, and help you eliminate loneliness by talking to other people while people share their screens and work on tasks like studying and coding.
<p><em><span lang="EN-US">Cryptocurrencies like Bitcoin, Ethereum and are high volatility digital commodities, so using them as an alternative to trading investments is very dangerous. Volatility analysis, portfolio formation, and implementation forecasts need to be carried out to minimize risk levels and risk management to help investors/traders make decisions. If you estimate, its use is suitable for analyzing the returns and volatility of cryptocurrencies. The Metode used in this study are methods of converting data into returns, detecting constancy with the ADF test, and normality testing with the JarqueBera test. The result of this study is that there are differences in the rate of return on bitcoin and ethereum coins every year, in these two crypto coins have experienced a very significant increase from before the covid 19 pandemic to covid 19. The result of this study is that there are differences in the rate of return on bitcoin and ethereum coins every year, in these two crypto coins have experienced a very significant increase from before the covid 19 pandemic to covid 19.</span></em></p>
NFT or Non-Fungible Token is a unique token attached to a digital asset that is connected to the blockchain system. Various assets, digital art, music, tweeters, memes, sold as NFT, NFT has been widely discussed on various social media, one of which is Youtube. NFT has become a new trend for the Indonesian people, based on the fact that someone who sells selfie photos at the Open Sea is viral because people think it is a trivial thing but why do they produce it, but people actually accept the trend as a mistake, they intentionally upload their identity on the platform. This Open Sea, this happened because there was little information related to NFT and the public did not really understand that NFT could be a bridge for criminals. But in this case, many people as artists have been greatly helped in the marketing of their art. And even when the stock market is down, NFT remains one of the digital assets that attracts the attention of the world community, therefore this study was made to analyze the public's response with sentiment analysis, data obtained from Youtube content comments and then classified into Positive, negative, and neutral classes with TF IDF for the process of word weighting and classification using the Naïve Bayes Classifier algorithm. The test is carried out by calculating accuracy, precision, recall and F1-score, using a variety of training data and test data. And the accuracy results are 64%, for positive prediction class precision is 63%, neutral class precision is 83%, while for negative prediction is 0% and recall obtained from positive is 99%, neutral recall is 0.7% while negative is 0%. These results are the data obtained on Youtube comment
Indri Tri Julianto, Dede Kurniadi, Muhammad Rikza Nashrulloh, Asri Mulyani
Metaverse is a technology that allows us to buy virtual land. In the future life in the real world can be duplicated into the Metaverse to increase efficiency, effectiveness, and a world without being limited by space and time. To buy land in the Metaverse, one can be done by using SAND. SAND is a crypto asset from a game called The Sandbox which functions as a transaction tool where in that game we can buy land and build it for various purposes just like we can store our Non-Fungible Tokens there. Metaverse is a digital business that will promise in the future because it offers easy and fast transactions. This study aims to compare the exact algorithm for making predictions about the SAND cryptocurrency used to buy Metaverse land. 7 algorithms are being compared, namely Deep Learning, Linear Regression, Neural Networks, Support Vector Machines, Generalized Linear Models, Gaussian Process, and K-Nearest Neighbors. The research method used is Knowledge Discovery in Databases. The research results show that the Support Vector Machines Algorithm has the most optimal Root Means Square Error value, root_mean_squared_error: 0.022 +/- 0.062 (micro average: 0.062 +/- 0.000). Based on this comparison, the Support Vector Machines Algorithm is suitable for predicting SAND Metaverse prices.
Twitter merupakan sosial media yang sedang hype di era sekarang ini, sebuah aplikasi seperti twitter pasti memiliki banyak data seperti simbol, kata kata , angka , kalimat dan lain sebagainya. Namun tidak mudah mengumpulkan sebuah data dengan cara yang sederhana maka dibutuhkan sebuah data mining yang bertujuan untuk mengumpulkan dan mengolah data agar dapat dengan mudah mengekstrak informasi data tertentu. Proses Data mining ini menggunakan metode Naive Bayes Classifier untuk menghasilkan tingkat akurasi dari metode tersebut untuk data twitter tentang NFT(Non Fungible Token).
Gege Ardiyansyah, Ferdiansyah Ferdiansyah, Usman Ependi
Cryptocurrency is a digital asset designed by cryptography, such as Secure Hash Algorithm 2 (SHA-2) and Message Digest 5 (MD5). Cryptocurrency uses Blockchain technology to ensure security, transparency, ease of locating, and unchangeability. This makes cryptocurrency very popular in many sectors, especially in the financial industry. Although, the uncertainty and the dynamic change of cryptocurrency price make the risk for investment in this digital asset high. This is the reason why studies about cryptocurrency price prediction became popular globally. This study intended to predict cryptocurrency prices using hybrid GRU LSTM than setting up the epoch to get the most accurate prediction model. The researcher would make a web-based application that can be used by the public, especially those involved in cryptocurrency investment. The result was a web-based application that could predict the price of cryptocurrency for the next few days, which had been validated using data from the previous 7 days, 14 days, 30 days, 60 days, and 90 days.
This study analysis forecasting the bitcoin exchange rate against the USD. The dataset selected for this study starts from January 2015 to June 2022. This study's methodology uses autoregressive integrated moving average forecasting (ARIMA). The overall outcomes of this study were gathered from the statistical software Minitab 21.1. The Box Jenkins approaches are also used to predict the best model. To determine the ARIMA model parameter, this study did autocorrelation function (ACF) and partial autocorrelation function (PACF) analyses. According to the Box-Cox transformation method, log transformation was selected. The outcome demonstrates that the seasonal with the regular difference in the Bitcoin exchange rate against the USD is a stationary data series. The forecasting model used in this study is ARIMA (1,1,0) (2,1,1)12. This predicted model is identified through the Mean squared error by comparing the other guessing ARIMA models. After the prediction, 5 Month bitcoin exchange rate against the USD. Investors will be able to estimate the bitcoin exchange rate against the USD with the use of this information, but volatility must also be properly watched. This will aid investors in making better investment decisions and increase profits. In future studies, better consider another exchange rate of BTC and software experts will develop such type of software based on ARIMA models for prediction.