PERAMALAN INFLASI DI INDONESIA MENGGUNAKAN METODE AUTOREGRESSIVE MOVING AVERAGE (ARMA)

A good inflation rate for a country is an inflation rate that has a low and stable value so that able to realize fast and controlled economic growth. Forecasting can be one of the steps that can provide an overview of the value of inflation in Indonesia for the government or related agencies to formulate and maintain inflation stability in Indonesia. In this study, a forecasting analysis was carried out to determine the prediction of inflation in Indonesia in 2021 using the Autoregressive Moving Average (ARMA) method. From the results of the research that has been done, the best model to predict this case is using the ARMA model (3,0,0) because it produces the smallest AIC value of 0.2373 and the smallest RMSE of 7.81. From this model, the results of forecasting inflation rates for the months of May to December 2021 are also obtained with a range of 0.1% to 0.3%. The graphic pattern of the predicted results follows the actual data line pattern, which means that this model is good to use. Abstrak Tingkat inflasi yang baik bagi suatu negara adalah tingkat inflasi yang memiliki nilai yang rendah dan stabil, sehinga mampu mewujudkan pertumbuhan ekonomi yang cepat dan terkendali. Peramalan dapat menjadi salah satu langkah yang dapat memberikan gambaran nilai inflasi di Indonesia bagi pemerintah atau badan yang terkait untuk menyusun dan mempertahankan kestabilan inflasi di Indonesia. Dalam penelitian ini, dilakukan analisis peramalan untuk mengetahui prediksi angka inflasi di Indonesia tahun 2021 menggunakan metode Autoregresif Moving Average (ARMA). Dari hasil penelitian yang telah dilakukan, model terbaik untuk meramalkan kasus ini yaitu menggunakan model ARMA (3,0,0) karena menghasilkan nilai AIC paling kecil yaitu 0.2373 dan RMSE terkecil sebesar 7.81. Dari model tersebut juga didapatkan hasil peramalan angka inflasi untuk bulan Mei hingga Desember 2021 dengan kisaran 0.1% hingga 0.3%. Pola grafik dari hasil prediksi mengikuti pola garis data aktual yang berarti bahwa model ini baik untuk digunakan.

CHARACTERIZATION OF THE RESISTIVE AND INDUCTIVE LOADS OF AN ENERGY DISTRIBUTION SYSTEM WITH JULIA FRACTAL SETS

The present paper characterizes the resistive and inductive loads of an electric distribution system by Julia fractal sets, in order to discover other observations enabling the elevation of new theoretical approaches. The result shows that indeed the electrical load reflects a clear graphic pattern in the fractal space of the Julia sets. This result, then, is a new contribution that extends the universal knowledge about fractal geometry.

Auto clustering of the variety of pulse signals based on their symbolic description

In a number of applied studies of geophysics, medicine, cosmophysics, atomic physics and other fields of knowledge, useful information is often hidden in the character of the behavior of a stream of frequency modulated pulses, which are represented by a large variety of forms, significantly different from each other up to several orders value of magnitude amplitude and durations. Noise is often present in the signal. Under these conditions, the problem arises of identifying both individual pulses and groups of pulses to assess the connection between their dynamic characteristics and the state of system. To solve the problem by a method is proposed that includes signal cleaning from interference, the operation of extracting and converting pulses into a code representing a sequence of invariant amplitude and time transformations of similar pulses combined by a single graphic pattern called “symbol”. All symbols extracted from the signal make up the alphabet. A procedure for narrowing the dimension of the alphabet is shown, which allows you to automatically divide it into clusters according to the degree of coincidence of the code. The results of the practical application of the developed method for the selection of base classes of the geoacoustic emission (GAS) signals related to the objective data of the state of the signal-generating medium are presented. The study used data from the archives of observations IKIR FEB RAS.

A graphic pattern feature-mapping-based data-driven condition monitoring method for diesel engine malfunction identification and classification

Effective condition monitoring of diesel engine can ensure the reliability of large-power machines and prevent catastrophic consequences. Cylinder pressure is capable of reflecting the whole combustion process of diesel engine, and hence can help to identify the malfunctions of the diesel engine during operation. In this paper, a graphic pattern feature-mapping method is proposed for graphic pattern feature recognition in data-driven condition monitoring. The graphic feature extraction and recognition are linked by labeled feature-mapping. It is used for identifying the running condition of the diesel engine via analyzing the cylinder pressure signal of the diesel engine. The different types of the malfunctions which are caused by different parts of the diesel engine such as induction system, valve actuating mechanism, fuel system, fuel injection system, etc. can be identified just by cylinder pressure signal. The bench experiment of a large-power diesel engine is performed to validate this graphic pattern recognition method. The results show that it has good accuracy on multi-malfunction identification and classification when the engine operates at one speed and one load.

Empirical models in the description of prickly pear shoot (Nopal) drying kinetics

ABSTRACT The objective of this study was to describe the technological process involved in the drying kinetics of fresh-cut prickly pear shoots through numerical and analytical solutions. Shoots of two different prickly pear species were used, ‘Gigante’ and ‘Miúda’. Drying was performed at different temperatures (50, 60, 70 and 80 °C) and weighing procedures were made continuously. The experimental data were expressed as moisture ratio. The Page model showed the best fit to the drying kinetics of minimally processed ‘Gigante’ and ‘Miúda’ prickly pear shoots, with the best coefficients of determination and Chi-square. Peleg and Wang & Singh models can not be used to simulate the drying of ‘Gigante’ and ‘Miúda’ prickly pear shoots within the evaluated range of temperatures, showing an incoherent graphic pattern.

Differential Geometry, the Informational Surface and Oceanic Art: The Role of Pattern in Knowledge Economies

Graphic pattern (e.g. geometric design) and number-based code (e.g. digital sequencing) can store and transmit complex information more efficiently than referential modes of representation. The analysis of the two genres and their relation to one another has not advanced significantly beyond a general classification based on motion-centred geometries of symmetry. This article examines an intriguing example of patchwork coverlets from the maritime societies of Oceania, where information referencing a complex genealogical system is lodged in geometric designs. By drawing attention to the interplay of graphic pattern and number-based code and its role in the knowledge economies of maritime societies, the article offers new insight into possible ways of designing a digital informational surface that captures the behaviour of an operational system, allowing both for differentiation and integration.

A Utilization Method of Big Sensor Data to Detect Tool Anomaly in Machining Process

The essential features and scale of sensor data was discussed to monitor the tool anomaly in the machining process from the pattern variation of large scale sensor data such as vibration and effective power. The cycle data, the time series sensor data collected with an acceleration or power sensor in one periodical machining of the given groove shape, had been measured periodically. In this study, the graphic pattern formed by overwriting the time series cycle data on a specific coordinate system was treated as the “big sensor data”. The big data from the effective power sensor can stably respond to the cutting power changes and showed a strong possibility as a detecting device for tool anomaly such as abrasive wear and chipping. While the big data from the acceleration sensor only responded to a big event like the chattering vibration. The number of cycle data needed to generate the big sensor data also affected on the detection sensitivity for tool anomaly. It had been required a family of time series sensor data enough to represent the cutting power change as a visual graphic pattern.