\n\t\n\t\t\n\t\t\t\n\t\t\tWith the rapid development of technology and the large amount of digitization in various fields of human life, it is necessary to pay attention to the security and certainty of privacy so that there is no leakage of confidential data, both in the private and governmental domains, especially in Indonesia. In this case cyber-attacks will grow and become more numerous; therefore we need a security principle that can prevent these cyber-attacks, especially in sending something that is sensitive which can be called cryptography. One of the applications that can be implemented regarding this cryptography is the Whatsapp application. WhatsApp claims that the application is safe from data theft and messages being intercepted. However, this is doubtful with the presence of Whatsapp Mod which offers more features than the official application. The security of the modified Whatsapp is questionable, so in this study a test was carried out using the MobSF Framework to find out whether there were security holes that could endanger its users. The results of this research are in the form of a report issued by MobSF regarding the level of danger of the Whatsapp Mod Application. With this research, it is hoped that it will be able to make Whatsapp Mod users aware of the dangers of modified applications and Whatsapp can provide advice and strict action against the Whatsapp Mod developers.\n\t\t\t\n\t\t\n\t\n
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.
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.
The International Journal of Computer Engineering in Research Trends (IJCERT) is a peer-reviewed, open access journal that publishes high-quality research papers, reviews, short communications, and notes in the field of computer science engineering and its research trends. The journal covers a wide range of topics in computer science and engineering, including: Welcome to the International Journal of Computer Engineering in Research Trends (IJCERT), is a peer-reviewed, open access journal dedicated to publishing innovative research papers, reviews, short communications, and notes in the field of computer science engineering and related disciplines. IJCERT encourages conceptual, state-of-the-art, research, standard, implementation, experimental, application, and industrial case study discussions in various areas, including: computer architecture, computer networks, software engineering, information security, artificial intelligence, machine learning, data science, robotics, cyber-physical systems, the internet of things, and other areas of computer science engineering and Its Applications.
The Internet has revolutionized education and learning, presenting both opportunities and challenges with the continuous evolution of web-based technologies. The earlier version of the web, known as Web 1.0, was primarily a readonly medium, while Web 2.0 allowed for greater interactivity with read/write capabilities. Now, the emerging version of the web, Web 3.0, is considered to be a technologically advanced medium that not only facilitates read/write capabilities but also enables a machines to carry out some of the thinking that was previously expected only of humans. In a relatively short period of time, Web 2.0 and Web 3.0 have introduced new tools and technologies that have greatly facilitated web-based education and learning. This paper will explore the definition, evolution, and characteristics of Web 3.0, as well as discuss potential future technologies, trends, tools, and services that can support online learning, personalization, and knowledge construction powered by the Semantic Web.
Cryptocurrency is in great demand as an investment medium to gain financial benefits. A common problem that is often faced is how to predict the movement of the value of electronic money in the future. Investors/traders usually only see price movements and buy/sell Cryptocurrency assets intuitively, so mistakes often occur in making transactions. To anticipate and minimize this, you can use an algorithm that can help predict Cryptocurrency price movements. Extreme Learning Machine (ELM) is a development method of a simple feedforward neural network using one hidden layer or commonly known as Single Hidden Layer Feedforward Neural NetworksTesting is done by doing several trials for each percentage value, namely 60%, 65%, 70%, 75%, 80%. Tests were carried out using the binary sigmoid activation function, the number of hidden neurons was 20 and the weight range was [-1,1]. The best prediction results using MAPE are generated on Bitcoin data with the smallest error value of 2.8590% Keywords: Cryptocurrency; Investation; Extreme Learning Machine ; Prediction  Abstrak Cryptocurrency banyak diminati untuk menjadi media investasi dalam meraih keuntungan finansial. Masalah umum yang sering dihadapi adalah bagaimana meramalkan pergerakan nilai dari uang elektronik pada masa mendatan. Investor/ trader biasanya hanya melihat pergerakan harga dan melakukan jual/beli aset Cryptocurrency secara intuitif, sehingga sering terjadi salah dalam melakukan transaksi. Untuk mengantisipasi dan meminimalisir hal tersebut maka dapat menggunakan sebuah algoritme yang dapat membantu dalam meramalkan pergerakan harga Cryptocurrency . Extreme Learning Machine (ELM) merupakan metode pengembangan dari jaringan syaraf tiruan feedforward sederhana dengan menggunakan satu hidden layer atau biasa dikenal dengan Single Hidden Layer Feedforward Neural Networks . Pengujian dilakukan dengan melakukan beberapa kali percobaan untuk setiap nilai persentase yaitu 60%, 65%, 70%, 75%, 80%. Pengujian dilakukan menggunakan fungsi aktivasi sigmoid biner, jumlah hidden neuron 20 serta rentang bobot [-1,1]. Hasil prediksi terbaik menggunakan MAPE dihasilkan pada data Bitcoin dengan nilai kesalahan terkecil yaitu 2.8590% Kata Kunci: Cryptocurrency; Investasi; Extreme Learning Machine ; Prediksi
Perubahan teknologi semakin lama semakin pesat diberbagai bidang, termasuk teknologi digital yang merupakan revolusi dari teknologi analog dan elektronik. Dekade ini semua serba bermetamorfosis menjadi digital, termasuk akhirnya muncul uang digital, yaitu cryptocurrency. Cryptocurrency sebagai bentuk digital cash beroperasi dengan bantuan teknik yang disebut kriptografi. Kriptografi sendiri adalah proses yang menerjemahkan semua informasi yang dapat dibaca menjasi kode yang tidak dapat dipecah sama sekali. Cryptocurrency menggunakan blockchain sebagai buku utama, yang semua sistemnya dikelola oleh yang disebut penambang. Mata uang crypto memiliki sistem yang sedikit rumit yang tidak dengan mudah dapat dipahami, jadi pengetahuan tentang cryptocurrency mau tidak mau harus dipelajari, dipahami agar dalam implementasi tidak mengalami dampak yang merugikan. Jenis jenis cryptocurrency, serta kekurangan dan kelebihannya akan dikupas sekilas dalam artikel ini.
Blockchain has shown great potential in various fields due to its technical advantages such as peer-to-peer, timestamp, consensus algorithm and encryption. In blockchain, the protection method of transaction data or copyright is crucial and cryptographic digital signature technology has been applied as one of the copyright protection methods. The multi-signature scheme provides higher security than single-signature schemes, enhances the transparency of transactions and contracts, and is widely used in distributed systems utilizing distributed ledger technology in blockchain. Multisignature requires multiple parties to cooperate in order to produce a valid signature, reducing the risk of exposing the entire system to a single point of failure when compared to single-signature schemes. This is an important role in transactions or contracts that require consensus among multiple parties, where each party can sign to implement the agreement, increasing transparency and preventing disputes. However, the cryptographic digital signature is resource consuming and inefficient because the verification of the signature consumes lots of computational resources and excessive number of communications. Therefore, we have proposed an efficient multi-signature scheme based on Schnorr for copyright protection on Ethereum.
Among the new way of exchanging money, using crypto currency has been very popular. Its also an investment to get good returns over the period of time. Cryptocurrency has grown to more than 120 million investors around the world as per a survey of 2021.Its growing at the 15 to 20% ratio around the world every year. This fact leads to a serious consideration of security and its vulnerabilities in block chain. Apart from market risks, high volatility, lack of rules and regulations, cyber risks are one of the most required types which needs proper attention and technical understanding. Because the crypto currencies are fully decentralized the risk of attacks is exposed and in most of the cases defenseless. Proof of stake and proof of work are two major algorithms followed by almost all crypto currencies to allot stocks to the holders. In this paper, different types of risks and attacks with POS and POW are explained with its mitigation. The problems and outcomes are examined, reviewed and conferred in case of Ethereum and Bitcoin crypto currencies. These currencies decentralized frameworks and anonymity attracts unlawful activities. Recognizing and preventing them needs understanding of the mechanism of attacks which are discussed in easiest possible ways for even a new-bee or an outsider person.
The growing popularity of cryptocurrencies has caused the market demand for graphics cards to reach unusual heights for their efficient cryptomining capabilities. Graphics cards are not only used for crypto mining but also video editing, video streaming, and video games, this causes an unavailability of graphics card supply due to high demand, especially for cryptomining needs and leads to unusual prices increases which makes it difficult for graphics card consumers and miners to buy graphics cards at normal price. Therefore, it is necessary to predict the price of NVIDIA graphics cards based on the influence of cryptocurrency prices. The methodology used is KDD, and the algorithm used to make predictions is SVR because its ability to overcome the overfitting problem so it can produce more accurate predictions, besides that in this study the grid search algorithm is applied to determine optimal parameters. In this study, 6 graphics cards and 2 cryptocurrencies were used which produced the 6 best prediction models which were chosen based on the RMSE value. GTX 1050 has RMSE value of 0.2028, GTX 1050 Ti has RMSE value of 0.14564, GTX 1060 has an RMSE value of 0.07629, while in the RTX 30 series, RTX 3070 has an RMSE value of 0.03178, RTX 3080 has RMSE value of 0.0388, and RTX 3090 has RMSE of 0.06259. From these results, it can be stated that RTX 30 series has better accuracy than GTX 10 series in making predictions. RBF is better than linear which only excels on the GTX 1060.
Kemajuan teknologi yang semakin pesat membuat banyak bidang mengalami perubahan termasuk didalamnya bidang investasi asset digital terutama crypto. Ada banyak cara yang dilakukan oleh para trader maupun investor dalam melakukan perdagangan Bitcoin yang merupakan salah satu asset digital di dunia crypto. Indodax merupakan salah satu platform buatan local Indonesia yang melayani transaksi perdagangan asset digital. Analalisis teknikal dan fundamental dilakukan untuk memprediksi pergerakan harga bitcoin, namun volatilitas yang tinggi menyebabkan pergerakan bitcoin sulit untuk diprediksi. Penggunaan Reccurrent Neural Network yang merupakan sub bidang ilmu dari Machine Learning merupakan salah satu cara untuk dapat melakukan prediksi terhadap bitcoin.Kata Kunci : RNN, LSTM, Bitcoin, Indodax, Training, Testing
Abstract: The Abstract contains a brief description of the purpose of writing, the method used, and the results of the study (if the results of the research). Abstract contains 200-300 words. Abstract written in Indonesian and English. Abstract typing is done single-spaced with narrower margins than the right and left margins of the main text. Keywords need to be included to describe the area of the problem being studied and the main terms that underlie the implementation of the research. Key words can be single words or combinations of words. Number of keywords 3-5 words. These keywords are required for computerization. Searching for research titles and abstracts is made easier with these key words. Abstrak: Abstrak memuat uraian singkat mengenai tujuan penulisan, metode yang digunakan, dan hasil penelitian (bila hasil dari penelitian). Abstrak berisi 200-300 kata. Abstrak ditulis dalam Bahasa Indonesia dan Bahasa Inggris. Pengetikan abstrak dilakukan dengan spasi tunggal dengan margin yang lebih sempit dari margin kanan dan kiri teks utama. Kata kunci perlu dicantumkan untuk menggambarkan ranah masalah yang diteliti dan istilah-istilah pokok yang mendasari pelaksanaan penelitian. Kata-kata kunci dapat berupa kata tunggal atau gabungan kata. Jumlah kata-kata kunci 3-5 kata. Kata-kata kunci ini diperlukan untuk komputerisasi. Pencarian judul penelitian dan abstraknya dipermudah dengan kata-kata kunci tersebut.
Saat ini perkembangan teknologi terus mengalami peningkatan diberbagai bidang, termasuk bidang keuangan. Pada bidang keuangan inovasi terus dilakukan, salah satunya adalah adanya mata uang digital (cryptocurrency). Bitcoin merupakan salah satu cryptocurrency dengan kapitalisasi pasar terbesar di dunia. Selain sebagai mata uang, Bitcoin juga dapat digunakan untuk investasi. Namun berinvestasi dengan Bitcoin mempunyai risiko tinggi. Hal ini dikarenakan harganya yang fluktuatif. fluktuatif ini erat kaitannya dengan kebijakan ekonomi dunia. Ketika jumlah permintaan Bitcoin meningkat harga yang dimiliki juga meningkat begitupun sebaliknya. Unsur ketidakpastian ini mempengaruhi harga Bitcoin sehingga menyebabkan harga Bitcoin bersifat kabur (fuzzy). Penelitian ini bertujuan meramalkan harga Bitcoin menggunakan metode FTS model Lee, yang merupakan pengembangan dari beberapa model FTS sebelumnya, yaitu Song dan Chissom, Cheng dan Chen. Menurut sebagian besar penelitian sebelumnya, model Lee dinyatakan mampu menyampaikan hasil peramalan yang lebih tepat daripada model klasik dari Fuzzy Time Series. Penelitian ini menggunakan orde satu, orde dua dan orde tiga, dimana peneliti memperoleh hasil nilai error dari orde satu sebesar 5.419%, orde dua sebesar 4.042% dan orde tiga sebesar 2.819%. Namun dari ketiga orde tersebut yang dapat digunakan untuk meramalkan harga Bitcoin periode selanjutnya adalah orde satu, yakni sebesar 53584.8 USD. Hal ini dikarenakan relasi yang dihasilkan pada periode selanjutnya tidak didapati pada grup-grup yang telah ditentukan sebelumnya dalam FLRG orde dua dan orde tiga. Sehingga meskipun orde dua dan orde tiga memiliki tingkat akurasi lebih kecil dibandingkan orde satu pada data harga Bitcoin, namun metode FTS Lee orde dua dan orde tiga tidak dapat diramalkan untuk periode 2 Januari 2022.
Pandemi COVID-19 yang belum berhenti menyebabkan kondisi ekonomi Indonesia kian memburuk. Masyarakat yang terkena dampak pemotongan upah akibat pandemi harus mencari cara untuk mendapatkan pendapatan pasif. Salah satu cara untuk mendapatkan hal tersebut adalah berinvestasi. Cryptocurrency adalah salah satu instrumen investasi berbasis aplikasi yang memiliki return tinggi. Aplikasi Pintu adalah aplikasi pertama yang menyediakan fasilitas mobile apps pada penggunanya. Aplikasi yang dirilis pada tahun 2020 ini sudah memiliki banyak ulasan yang diberikan oleh penggunanya. Ulasan ini dibutuhkan untuk mengetahui apakah ulasan yang diberikan bersifat positif atau negatif. Analisis sentimen pada aplikasi Pintu dipilih untuk melihat sentimen pengguna yang akan dibagi menjadi dua kelas sentimen yaitu positif dan negatif. Klasifikasi dilakukan dengan algoritma Maximum Entropy dengan perbandingan metode pembobotan kata Term Frequency (TF), Term Frequency-Inverse Document Frequency (TF-IDF) dan Binary. Model klasifikasi terbaik dilihat berdasarkan nilai akurasi yang dievaluasi dengan 5-Fold Cross Validation. Hasil klasifikasi model Maximum Entropy dengan Binary memiliki tingkat akurasi sebesar 83,21% sedangkan hasil klasifikasi model Maximum Entropy dengan Term Frequency hanya sebesar 83,01% dan model Maximum Entropy dengan Term Frequency-Inverse Document Frequency hanya sebesar 83,20%. Hal ini menunjukkan bahwa tidak terdapat perbedaan yang signifikan pada model algoritma Maximum Entropy dengan metode pembobotan kata Term Frequency (TF), Term Frequency-Inverse Document Frequency (TF-IDF) dan Binary. Keywords: Cryptocurrency, Binary, Term Frequency, Term Frequency-Inverse Document Frequency, Maximum Entropy
Bitcoin merupakan mata uang digital yang menggunakan sistem kriptografi pertama di dunia. Tujuan utama diciptakannya Bitcoin adalah memungkinkan kedua belah pihak untuk melakukan transaksi secara langsung tanpa campur tangan pihak ketiga. Meskipun Bitcoin merupakan sebuah mata uang, banyak orang yang menggunakan Bitcoin sebagai alat untuk berinvestasi karena harganya cenderung naik cepat dalam waktu singkat. Namun, bukan berarti tidak memiliki risiko. Berinvestasi ke Bitcoin memiliki risiko yang tinggi karena volatilitas harganya sangat tinggi. Penelitian ini bertujuan untuk membandingkan algoritma yang digunakan untuk memprediksi harga Bitcoin. Dalam penelitian ini akan dilakukan prediksi terhadap harga Bitcoin dengan membandingkan empat model algoritma yaitu Linear Regression , Neural Network , Deep Learning , dan k-Nearest Neighbor (k-NN) . Tingkat akurasi dari tiap model algoritma akan diuji dengan metode validasi K-Fold Cross Validation dan dievaluasi menggunakan Root Mean Square Error (RMSE). Hasil dengan uji T-Test dalam penelitian ini menyimpulkan bahwa model terbaik untuk memprediksi harga Bitcoin adalah model algoritma Linear Regression dan Neural Network , yaitu dengan hasil RMSE 296.227 +/- 60.125 ( micro average : 301.655 +/- 0.000) dan 338.988 +/- 47.837 ( micro average : 342.000 +/- 0.000). Kata kunci: Perbandingan algoritma, Bitcoin, prediksi
Blockchain mining is a power &resource consuming task, which requires multiple-levels of optimization, both at resource &task level. Over the years, a wide variety of mining optimization models are proposed by researchers, but most of them are applicable only to a subset of mining types. For instance, mining models used for Proof-of-Work (PoW) consensus-based mining, are not applicable for Delegated Proof-of-Stake (DPoS), and other consensus types. This limits the scalability of these models, which reduces their adoptability for dynamic blockchain systems (DBSes). These DBSes utilize different consensus models as per context of data storage, and are widely used by blockchain designers to deploy high-efficiency, and low delay storage solutions. A standard mining optimization solution is not available for such scenarios, due to which researchers & system designers opt for deployment-specific optimizations, which need to be redesigned for each blockchain system. To remove this drawback, a standard blockchain mining optimization model is proposed in this text. This model uses a combination of Genetic Algorithm (GA) & Particle Swarm Optimization (PSO) for solving two different issues. The GA model is used to optimize miner set selection, which will be used for consensus, while the PSO model optimizes the responses from these miner sets depending upon their temporal mining performance. Due to optimum miner set selection, only higher efficiency miner nodes are used for mining the blockchain. While due to performance optimization of these miner nodes, their internal mining efficiency is improved.This efficiency is evaluated in terms of delay & power needed for single block mining w.r.t. blockchain length. It was observed that a combination of these models is capable of enhancing mining speed, with reduced power consumption, and higher mining throughput. Due to this improvement the proposed HBSBA model outperforms most of the recently proposed blockchain mining models. The model was evaluated on DPoS, Proof-of-Authority (PoA), Proof-of-Stake (PoS), and PoW based consensus models, and a delay reduction of 14.5%, throughput improvement of 8.3%, and reduction in energy consumption by 4.6% when compared with various state-of-the-art models. Due to this improvement, the proposed model is applicable for a wide variety of medium to large scaled blockchain mining applications.
One of the potentials that can be done by smart contracts is the application of buying and selling business in e- commerce. In a study conducted by the British research institute Merchant Machine as reported in databoks.katadata.co.id states that, out of the 10 list of countries that have the fastest e-commerce growth, Indonesia leads the ranks of these countries with growth reaching 78% in the year 2018. As you know, blockchain not only develops cryptocurrency but also in financial services and smart contract payments. Legal arrangements that apply to smart contracts governed by contract and sale and purchase laws in Indonesia have not yet been found, and need to be explored in order to provide certainty regarding legal protection that can guarantee parties who use smart contracts to guarantee legal certainty and fairness in their application. The research that will be conducted in answering these questions uses a systematic review method, the researcher traces the legislation regarding consumer protection, as well as the principles of electronic contract law that can be applied. The results of the study concluded that with the regulations regarding electronic contracts, smart contracts are legal contracts that can be applied in Indonesia.
Irfan Fadil, Muhammad Agreindra Helmiawan, Yanyan Sofiyan
Bitcoin is a cryptocurrency known to have high price fluctuation. Investment depends on price fluctuations which have a high level of risk. Bitcoin investment has these principles. To avoid losses and gain profits, there needs a method that may be used to make forecasts of the price of bitcoin accurately. In this research, Bitcoin price predictions were deployed based on bitcoin price data obtained in the past (Time Series Forecasting) using the method Support Vector Regression. The data retrieved is weekly Bitcoin price data from January 2018 to March 2020. Bitcoin price data is nonlinear, so a kernel is used Radial Basis Function. Meanwhile, the variables of the Support Vector Regression method are optimized using Grid Search Method. The purpose of the research is to determine the accuracy of the Support Vector Regression method by looking at the result of the Mean Absolute Percentage Error value. The research showed that the Mean Absolute Percentage Error value obtained was equal to 10,74 % with parameter value$\mathbf{C=5,} \boldsymbol{\varepsilon=0.004}$, and$\boldsymbol{\gamma=0.07}$. The Mean Absolute Percentage Error value indicates that the prediction results are categorized as a good prediction.
Bitcoin has received a lot of attention from investors, researchers, regulators, and the media. It is a known fact that the Bitcoin price usually fluctuates greatly. However, not enough scientific research has been done on these fluctuations. In this study, long short-term memory (LSTM) modeling from Recurrent Neural Networks, which is one of the deep learning methods, was applied on Bitcoin values. As a result of this application, anomaly detection was carried out in the values from the data set. With the LSTM network, a time-dependent representation of Bitcoin price can be captured, and anomalies can be selected. The factors that play a role in the formation of the model to be applied in the detection of anomalies with the experimental results were evaluated.
Emre Ertürk, Murat Doğan, Umit Kadiroglu, Enis Karaarslan
Cultural heritage assets are in danger of extinction or damage due to lack of publicity and financial problems. Technological advances can play a role in their preservation and promotion. This study aims to create a blockchain-based cultural property protection system which we named the Heirloom. The proposed system uses blockchain and IPFS. This system will allow foundations to receive funding to protect cultural assets without using an intermediary. The cultural assets are transformed into unique digital items using the NFT (Non-Fungible Token) technology. The metadata of the created NFTs is stored in the distributed file system IPFS (InterPlanetary File System). An autonomous working system is provided with smart contracts. The supporters give donations to earn their share of protection and maintenance rights. The proof of concept implementation is promising. A case study on protecting old olive trees in Milas has also started with a local foundation. Possible outcomes will be the ease of getting funds for preserving cultural heritage and increasing awareness. Future studies will include working on different methods for decreasing the costs of the system and integrating augmented and virtual reality technologies.
Many traders believe in and use Twitter tweets to guide their daily cryptocurrency trading. In this project, we investigated the feasibility of automated sentiment analysis for cryptocurrencies. For the study, we targeted one cryptocurrency (NEO) altcoin and collected related data. The data collection and cleaning were essential components of the study. First, the last five years of daily tweets with NEO hashtags were obtained from Twitter. The collected tweets were then filtered to contain or mention only NEO. We manually tagged a subset of the tweets with positive, negative, and neutral sentiment labels. We trained and tested a Random Forest classifier on the labeled data where the test set accuracy reached 77%. In the second phase of the study, we investigated whether the daily sentiment of the tweets was correlated with the NEO price. We found positive correlations between the number of tweets and the daily prices, and between the prices of different crypto coins. We share the data publicly.
Social media news are most important in today's worlds, it puts positive or negative influence on social views. There is a wide propagation of fake news on social media so it will be difficult to believe on the news. Fake news has negative impacts on individuals as well as on society. Information spreads rapidly over the social media and so there is a need of mechanism which detects and stops the spreading of fake news. Therefore, detection of fake news is the need of time and also a challenging problem. The goal of this proposed research work is to detect fake news and minimize spreading of the fake news. In the proposed research a machine learning approach is used for detection of fake news with blockchain framework. In first section a supervised machine learning techniques is design to identify the trustiness of specific news while blockchain framework revoke the malicious activity of spreading fake news. A blockchain environment is created with mining, smart contract as well as Proof of Work (PoW) of consensus. The current systematic review broadly focuses on the various methods to detect fake news in social media. After partial implementation of system, performance evaluation has done with traditional blockchain framework. It is found that 10% less time for transaction verification by consensus in P2P environment over the existing systems.
Silvana Secinaro, Francesca Dal Mas, Valerio Brescia, Davide Calandra
Purpose This study aims to offer a bibliometric and coding analysis of blockchain articles published in the accounting, auditing and accountability fields. Design/methodology/approach The data were collected using the Scopus database and a bibliometric and qualitative coding analysis with the keywords “blockchain” and “accounting” or “auditing” or “accountability.” Of the 514 initial sources, 93 peer-reviewed papers, book chapters and conference proceedings in the areas of business, management and accounting were finally selected. Nonscientific sources such as nonpeer-reviewed books and white papers were excluded. Findings This study reveals a promising and multidisciplinary field of research dominated by scholars and less by practitioners. Qualitative research, especially discourse analysis, is the most used method among authors. This study gives some useful insights about blockchain's definition and characteristics, business models, processes involved, connection with other technologies and relationships with accounting theories. Among the most interesting insights, the results confirm that technology as an external force can create an intersection among several research areas: accounting, auditing, accountability, business, management, computer science and engineering fields. Finally, in terms of research themes, although blockchain has a clear effect on auditing accounting, the links with the area of accountability are less clear and validated. Originality/value This study highlights the current state of the field, combining methodological approaches and providing valuable future research insights. Additionally, it is also a starting point for professionals to fully understand blockchain's characteristics and potential with a constructive and systemic approach.
Similar to decentralized communication systems, a new technology called Blockchain (BC) has the potential to store and manage data in a decentralized manner. By removing the role of third party, all member in the chain has equal access to data. The concept BCT (Blockchain Technology) is not just limited to the cryptocurrencies, but it has been implemented in other areas like e-health, voting, finance, education, smart contract and even in Databases. This paper discusses various Blockchain applications and platforms and then compares these platforms on basis of different parameters. Despite of the advantages, Blockchain faces a significant issue of privacy. This paper examines various security related issues and challenges and presents an account of known possible attacks.