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.

The Human Development Index (HDI) is a measure used to measure the success of human development in an area. There are several indicators used to compile the HDI value. Previously, regencies/cities were grouped based on the HDI indicator. The grouping is done using K-means cluster analysis with 4 categories, namely regencies/cities that have low, medium, high, and very high HDI indicator values. From the results of determining the category of the HDI indicator in an area, we need a function that can be used to classify an object into one of the HDI indicator value categories. The compilation of the classification function is carried out using discriminant analysis. The results obtained from the discriminant analysis are that there are 10 variables or indicators that fall into the discriminant function. The resulting discriminant function is quite good in classifying each group with a success rate of more than 85% and the discriminant function is supported by a fairly good validation success rate, namely with a classification accuracy of 93.20%.

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Multi-objective optimization based on ratio analysis plus full multiplicative form (MUL-TIMOORA) is an efficient decision-making method for solving multi-criteria group decision-making (MCGDM) processes. It uses three strategies to examine different alternatives and to determine their evaluation values. These strategies include ratio system approach (RSA), reference point approach (RPA), and full multiplicative form (FMF). However, this method presents some challenges in the examination model, such as the ability to aggregate and determine the final result according to these strategies without considering value differences, the complexity of calculating the aggregation and multi-time comparisons, and the probability of distinguishing circular reasoning rules in dominance theory. In addition, determining an appropriate instrument for handling uncertainty, inconsistency, and incompleteness of information and a suitable weight for each criterion and decision-maker for reducing human interventions are also considered and will be a complex MCGDM process. To overcome these weaknesses, we propose an extended MULTI-MOORA based on trapezoidal fuzzy neutrosophic numbers (TraFNNs) for MCGDM. We integrated it with ordinal priority approach (OPA) method to decide the initial weights for decision-makers and criteria without human subjective assessment. In addition, we used correlation coefficient and standard deviation (CCSD) technique to statistically compute the relationship between these strategies in resolving unique weights to obtain realistic results and eliminate the above issues. Finally, sensitivity and comparative analyses demonstrate the capability and effectivity of the extended method.INDEX TERMS Correlation coefficient and standard deviation method, multi-criteria group decisionmaking, MULTIMOORA method, trapezoidal fuzzy neutrosophic number.

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