Precious metals and cryptocurrencies are one of the most preferred investment instruments during the Covid-19 pandemic. There are three types of market efficiency, weak form efficiency, semi-strong, and strong form efficiency; this is disclosed in the efficient market hypothesis proposed by Fama in several of his works. Efficient or inefficient market can be seen from the returns obtained by the market participants, market participants will get a reasonable return if they are in an efficient market. This study aims to determine whether the precious metals and cryptocurrency markets are efficient in the weak form. Run test and Augmented Dickey-Fuller test are used to determine the randomness of price movements (random walk). The more random the price, the more efficient it will be in the weak form. The period used in this study is January 1, 2019 to June 30, 2021. The results of this study are the price of precious metals runs randomly during the Covid-19 pandemic, which means that the precious metal market is efficient in a weak form. Meanwhile, the results of the cryptocurrency return test show that the cryptocurrency market is inefficient in the weak form during the Covid-19 pandemic.
Fakultas Ekonomi dan Bisnis, Universitas Sumatera Utara, Medan, Christopher Lumbantobing, Isfenti Sadalia, Fakultas Ekonomi dan Bisnis, Universitas Sumatera Utara, Medan
Penelitian ini bertujuan untuk mengetahui dan menganalisis perbadingan kinerja cryptocurrency bitcoin, saham, dan emas. Jenis penelitian ini merupakan penelitian kuantitatif menggunakan metode komparatif. Populasi yang dalam penelitian ini yaitu harga penutupan bulanan bitcoin, saham LQ45, dan emas mulai dari tahun 2017 hingga 2021, yaitu sebanyak 180 data. Teknik pengambilan sampel yang digunakan dalam penelitian ini yaitu pengambilan sampel jenuh. Penelitian ini merupakan penelitian yang menggunakan data time series dan Jenis data yang digunakan adalah data sekunder. Data dikalkulasikan menggunakan program Microsoft Excel verdasarkan formula dari masing-masing variabel penelitian. Data diolah menggunkan aplikasi SPSS yaitu Uji ANOVA. Hasil Penelitian ini menunjukan bahwa terdapat perbedaan yang nyata antara bitcoin, saham dan emas bila dilihat dari return dan risk. Kemudian, terdapat perbedaan yang nyata antara kinerja bitcoin, saham dan emas yang diukur dengan metode Sharpe, Treynor, dan Jensen.
Abstract The recycling paper industry has a good potential market. The industry used recycled paper such as Old Corrugated Containers (OCC), Old Newspaper (ONP), mixed waste paper, and Sorted White Ledger (SWL) as raw material. In Indonesia, commonly, the industry got the raw material of about 50% by importing. The government provides regulation to ensure the sustainability of the industrial activity, that is the process of importing recycled paper and internal regulation to increase local raw materials. The objectives of the research were to study the chosen raw material for the recycling paper industry and to analyze the positioning strategy of consumption raw material for the recycling paper industry in Indonesia. This research used a qualitative description method by the Analytical Hierarchy Process (AHP) and Strength Weakness Opportunities Threat (SWOT) analysis. The data were obtained from a questionnaire distributed to the paper industry, paper researcher, and relevant agencies. Based on AHP, the strength criteria to choose raw material is the regulation and availability of raw material with values 0.323 and 0.243; and the alternative chosen is recycled paper raw material import. The result of SWOT analysis, the recycling paper industry in Indonesia, has an Internal Factor Analysis Strategy (IFAS) 0.05, and an External Factor Analysis Strategy (EFAS) - 0.03. That showed from SWOT quadrant matrix, the position coordinate point of IFAS and EFAS in quadrant II. That means the positioning strategy is diversification. The government, recycling paper industry, and relevant agencies to corporate to increase the collecting rate of recycled paper.
Procurement is the most important part of the pharmaceutical logistic cycle. It is the process of acquiring supplies after a properly selected list of products. The procurement system or model depends on the type of organization weather it is governmental or private, centralized or decentralized, autonomous or semiautonomous. The objectives of procurement system is to make available the right drug in an appropriate quantities of adequate quality from a reliable supplier at the right time with the lowest possible prices through an ethical and legal procedures. Prequalification of suppliers is the successful quality assurance activity. Needs and funds can be reconciled and a rational cut can be done by using ABC- VAN matrix technique. Purchasing should be by transparent competition through open tender, restrictive tender, restricted competition or in certain cases by direct negotiation by transparent committee leading to transparent contract. One of the most important procurement practice for the system to succeed is the reliable payment and efficient financial management and monitoring the supplier performance. The system should have an efficient quality assurance program with annual auditing and regular reports.
Akhmad Ridho Ashariansyah, Nur Iriawan, Adatul Mukarromah
Perdagangan merupakan sebuah kegiatan tukar menukar barang atau jasa yang dilakukan manusia untuk memenuhi kebutuhan hidup. Perkembangan sistem pembayaran yang dilakukan umat manusia dimulai dari sistem pertukaran barang atau barter, logam mulia seperti emas dan perak, koin, uang kartal, uang giral, dan uang elektronik (e-money). Selain itu, muncul cryptocurrency yaitu mata uang digital dengan sistem kriptografi dalam setiap proses transaksi datanya tanpa melalui pihak ketiga. Namun cryptocurrency memiliki kelemahan perubahan harga yang sangat besar dalam waktu yang sangat cepat. Pergerakan harga yang berfluktuasi sangat tinggi tersebut menyebabkan kekhawatiran pemilik aset kripto mengalami kerugian, maka pemodelan harga cryptocurrency sangat penting untuk dilakukan agar meminimalisir risiko kerugi-an. Berdasarkan pola pergerakan harga yang berfluktuasi sangat tinggi yang berbeda tiap periodenya maka dilakukanlah pemodelan harga cryptocurrency mengguna-kan Markov Switching Autoregressive (MSAR) dengan algoritma Expectation Maximization. Selain meminimkan risiko kerugian, penelitian ini juga ingin mengetahui model MSAR mana yang mampu mengklasifikasikan state dengan baik. Data yang digunakan yaitu harga harian cryptocurrency dengan nilai kapitalisasi pasar terbesar dari September 2015 hingga Januari 2020. Hasil penelitian menunjukkan bahwa bitcoin dan ripple menggunakan model MS(8)AR(1), sedangkan ethereum menggunakan model MS(9)AR(1). Selain itu model MS(8)AR(1) pada data ripple menjadi model dengan nilai akurasi tertinggi dibandingkan model lainnya dalam hal klasifikasi state.
Abstrak Bitcoin adalah salah satu cryptocurrency yang diminati untuk menjadi media investasi dalam mencapai keuntungan finansial. Meskipun investasi menggunakan Bitcoin sangat populer, investasi jenis ini memiliki risiko yang perlu dipertimbangkan. Untuk mengantisipasi risiko dalam berinvestasi menggunakan Bitcoin, sistem perdagangan diperlukan untuk berdagang secara otomatis. Sistem dibangun menggunakan dua metode komputasi, yaitu Recurrent Neural Network dan metode Reinforcement Learning yang kemudian disebut Recurrent Reinforcement Learning. Metode ini memerlukan nilai parameter yang tepat untuk memaksimalkan nilai sharpe ratio. Nilai sharpe ratio digunakan untuk mengukur kelebihan pengembalian, atau premi risiko, per unit deviasi dalam aset investasi atau strategi perdagangan. Dalam penelitian tugas akhir ini, dilakukan analisis terhadap parameter yang mempengaruhi kinerja sistem. Hasil yang diperoleh dari analisis yang telah dilakukan menyatakan bahwa sistem mendapatkan nilai sharpe ratio 0,10963. Nilai sharpe ratio yang didapatkan masih relatif tinggi karena suatu investasi dinilai beresiko rendah jika nilai sharpe ratio nya satu keatas. Kata Kunci:Bitcoin, Trading, Recurrent Reinforcement Learning,RRL Abstract Bitcoin is one of the cryptocurrency in demand to be an investment medium in achieving financial returns. Although investing using Bitcoin is very popular, this type of investment has risks that need to be considered. To anticipate risks in investing using Bitcoin, a trading system is needed to trade automatically. The system is built using two computational methods, namely Recurrent Neural Network and Reinforcement Learning method which is then called Recurrent Reinforcement Learning. This method requires the right parameter values to maximize the sharpe ratio value. Sharpe ratio values are used to measure excess returns, or risk premiums, per unit deviation in investment assets or trading strategies. In this final project research, an analysis of parameters that affect system performance is carried out. The results obtained from the analysis that has been done states that the system gets a sharpe ratio value of 0,10963. The value of the sharpe ratio obtained is still relatively high because an investment is considered low risk if the value of the sharpe ratio is one and above. Keywords: Bitcoin, Trading, Recurrent Reinforcement Learning, RRL
Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Yogyakarta Indonesia, Ezra Putranda Setiawan
Cryptocurrency or virtual currency is a kind of investment that is common since 2010. Nowadays, there are more than 2.000 cryptocurrencies around the world. Research about cryptocurrency in Indonesia still focused on the legal or religious status of its investment. This descriptive-quantitative research aim is to describe the return and the risk of cryptocurrency investment. Therefore, we use the descriptive analysis and the GARCH (1,1) model to the return data of 15 cryptocurrencies with the largest market value. Most of these cryptocurrencies can yield a higher return when compared with the return of the foreign exchange and stock market return. Despite this benefit, they exhibit higher risks and volatility clustering or heteroscedasticity. Further research should be done to reveal more properties of their return and performance in portfolio
The purpose of this study was to determine the evaluation of internal control and payroll accounting systems in hospital. In this research, the authors used a descriptive method of analyzing and collecting internal controls in the payroll accounting system in using primary data such as interviews with parties company and secondary like organizational structure. Data collection techniques were carried out using observation, interviews, and literature techniques. From the results of the study it can be concluded that the honorarium employee payroll system already running well can be seen from the flowchart which explains the separation of functions from the staffing department that makes payroll, the finance department checks the payroll and then gets to the Treasurer who directly distributes employee costs and then does the payment process and the accounting department verifies the cash proof transaction out and take notes in journals and ledgers in order to be able to account for any payroll transactions made for honorary employees.
The Industrial Revolution 4.0 has changed the human perspective in interacting and doing business fundamentally. Business systems that are transformed into online systems have an impact on the way humans conduct financial transactions. Principles contained in Industry 4.0. provide an illustration that interconnection with the internet and good data security will result in the provision of fast information to produce decisions. The study uses a literature study approach that examines data and information from journals, textbooks, and he internet to conduct a deeper study of the application of industry principles 4.0 to financial transactions and accounting. based on the results of the study concluded that the financing transaction media had developed into the Fintech industry and the transaction system in the form of a payment gateway. The large number of financial transactions with the payment gateway system requires companies to do IT Spending intangible asset in the form of Big Data and Cloud Computing security systems. The technology provides benefits for the company in terms of data analysis and provision of information to speed up the decision making process and secure storage systems and affordable costs. Through the information technology media, it will facilitate management to make decentralized decision making.
Abstrak Bitcoin dapat disebut juga sebagai emas digital. Hal ini karena Bitcoin dan Emas dianggap memiliki persamaan karakteristik. Persamaan karakteristik antara Emas dan Bitcoin adalah, nilainya yang berfluktuasi dan cenderung naik, jumlahnya yang sama-sama terbatas, membutuhkan biaya lebih dalam penambangannya, serta sama-sama tidak di kontrol oleh pemerintah menjadikan alasan mengapa Bitcoin disamakan dengan Emas, yang membedakan hanyalah Bitcoin bebentuk emas secara virtual sedangkan Emas berbentuk asset nyata (real). Persamaan antara Bitcoin dan Emas memungkinkan dua asset tersebut memiliki pengaruh atau efek menular (spillover) antara satu sama lain karena persamaan karakteristiknya. Penelitian ini dilakukan untuk mengetahui volatilitas spillover antara Bitcoin dan Emas, serta arah pergerakan volatilitasnya. Hasil analisis menggunakan GARCH menunjukan bahwa tidak terjadi volatility spillover antara Emas dan Bitcoin, begitu juga dengan uji Granger Causality menunjukkan tidak ada hubungan sebab akibat dari Emas ke Bitcoin. Sehingga, investor bisa mempertimbangkan untuk melakukan diversifikasi investasi pada dua instrumen tersebut baik Emas dan Bitcoin. Kata Kunci: volatility spillover, Bitcoin, Emas, GARCH, Granger Causality. Abstract Bitcoin can also be called digital gold. This is because Bitcoin and Gold are considered to have similar characteristics. The characteristic equation between Gold and Bitcoin is, its value fluctuates and tends to rise, the amount of which is equally limited, requires more costs in mining and is equally not controlled by the government making the reason why Bitcoin is equated with Gold, the only difference is Bitcoin forms gold is virtual while Gold is in the form of real assets. The equation between Bitcoin and Gold allows the two assets to have a spillover effect between each other because of their similarity in characteristics. This study was conducted to determine the spillover volatility between Bitcoin and Gold, and the direction of volatility. The results of the analysis using GARCH show that there is no volatility spillover between Gold and Bitcoin, as well as the Granger Causality test showing that there is no causal relationship from Gold to Bitcoin. Thus, investors can consider diversifying investments in these two instruments, both Gold and Bitcoin. Keywords: volatility spillover, Bitcoin, Gold, GARCH, Granger Causality.
Penelitian ini menganalisis pengaruh Jakarta Stock Composite Index (JKSE), saham LQ45 (LQ45), Dow Jones Industrial Average (DJIA), Nikkei 225 (JP225),Indeks Dollar (USDI), dan Gold Futures (GC terhadap harga Bitcoin (BTC). Data pengamatan penelitian adalah data bulanan dimulai dari Juli 2013 sampai Agustus 2018.Sumber data berasal dari laporan index dan harga Bitcoin Investing.com.Teknik menggunakan Vector Error Correction Model. harga Bitcoin(BTC) sebagai variabel dependen, dan Jakarta Stock Composite Index (JKSE), saham LQ45 (LQ45), Dow Jones Industrial Average (DJIA), Nikkei 225(JP225), Indeks dollar (USDI), dan Gold Futures (GC) sebagai variabel independen.Hasil penelitian memperlihatkan bahwa Jakarta Stock Composite Index(JKSE), saham LQ45 (LQ45), Dow Jones Industrial Average (DJIA), Nikkei 225 (JP225), Indeks Dollar (USDI), dan Gold Futures (GC) memberikan dampak signfikan terhadap harga Bitcoin (BTC). Efek dari keseluruhan faktor bersifat moderat dan mengarah pada keseimbangan jangka panjang.
Nany Salwa, Nidya Tatsara, Ridha Amalia, Aja Fatimah Zohra
ABSTRAK. Bitcoin merupakan mata uang virtual yang saat ini banyak diminati sebagai alternatif investasi. Metode ARIMA adalah salah satu metode yang digunakan untuk peramalan data deret waktu. Tujuan dari penelitian ini adalah untuk membuat model dan meramalkan harga bitcoin. Data yang digunakan adalah data sekunder yaitu berupa data harga bitcoin selama 60 periode mulai dari tanggal 10 Januari 2018 sampai dengan 10 Maret 2018 untuk memprediksikan harga bitcoinselama 30 periode kedepan mulai tanggal 11 Maret 2018 sampai dengan 09 April 2018. Dari hasil penelitian menunjukkan bahwa data harga bitcoin selama 60 periode tidak memenuhi asumsi stasioneritas terhadap rata-rata untuk itu dilakukan proses differencing tingkat 2 agar data menjadi stasioner. Model ARIMA yang dihasilkan adalah ARIMA(0,2,1) yaitu Zt = μ - 0,9647Zt-1 + at dan model tersebut cocok digunakan untuk peramalan data harga bitcoin. Hasil peramalan dengan menggunakan model ARIMA(0,2,1) menunjukkan bahwa harga bitcoin untuk 30 periode kedepannya mengalami penurunan secara perlahan dan hasil peramalan mendekati data sebenarnya. ABSTRACT. Bitcoin is a virtual currency that is currently much interested as an alternative investment. ARIMA method is one of the methods used for forecasting time series data. The purpose of this research is to create a model and predicted the price of the bitcoin. The data used are secondary data that is in the form of price bitcoin during 60 periods starting from January 10, 2018 up to 10 March 2018 to predict price bitcoin for 30 the next periods began March 11 and ended on 9 April 2018 2018. Based on the results of the study showed that the price of bitcoin during 60 periods did not fullfiled the assumptions of stasioneritas towards the mean. Therefore using the differencing level 2 process, so the data becomes stationary. The result of ARIMA model is ARIMA(0, 2, 1) Zt = μ - 0,9647Zt-1 + at and the model fits the data used for forecasting price bitcoin. The results of the forecasting model using ARIMA (0, 2, 1) shows that the price of the bitcoin for 30 periods has decreased gradually and forecasting results close to the actual data.
Uang elektronik menjadi pilihan yang mulai ramai digunakan oleh banyak orang, terutama para pengusaha, pebisnis dan investor, karena menganggap bahwa uang elektronik akan menggantikan uang fisik dimasa depan. Cryptocurrency muncul sebagai jawaban atas kendala uang eletronik yang sangat bergantung kepada pihak ketiga. Salah satu jenis Cryptocurrency yaitu Bitcoin. Analogi keuangan Bitcoin sama dengan analogi pasar saham, yakni fluktuasi harga tidak tentu setiap detik. Tujuan dari penelitian yang dilakukan yaitu melakukan prediksi harga Cryptocurrency dengan menggunakan metode KNN (K-Nearest Neighbours). Hasil dari penelitian ini diketahui bahwa model KNN yang paling baik dalam memprediksi harga Cryptocurrency adalah KNN dengan parameter nilai K=3 dan Nearest Neighbour Search Algorithm : Linear NN Search. Dengan nilai Mean Absolute Error (MAE) sebesar 0.0018 dan Root Mean Squared Error (RMSE) sebesar 0.0089.
Continuous improvement in quality has been made possible by the use of a complete quality program which fully utilizes the knowledge and capabilities of an entire workforce. The program includes formal quality policy deployment, training, a seven-step quality improvement process, and the use of poka-yoke or mistake-proofing devices. It is concluded that employees empowered to create continuous quality improvement have demonstrated that zero defects is an obtainable goal. True empowerment is only achieved by establishing quality policy based on employee involvement, establishing valid indicators, providing training to all employees, and encouraging all employees to use the latest quality improvement and mistake proofing techniques every day.>