Utilization of Smartphone Devices and Use of Social Media in North Maluku

Basic guidelines. This document is itself an example of the desired layout (inclusive of this abstract) and can be used as a template. It contains information regarding desktop publishing format, type sizes, and typefaces. Style rules are provided that explains how to handle equations, units, figures, tables, abbreviations, and acronyms. Sections are also devoted to the preparation of acknowledgments, references, and authors' biographies. The abstract is limited to 150–200 words and cannot contain equations, figures, tables, or references. It should concisely state what was done, how it was done, principal results, and their significance

Simple Method for Measuring Small Retardance

Small retardances are encountered in many experimental works. Internal stresses, weakly birefringent materials, optical windows and formation of contaminating surface layers are sources of small retardances. Most known methods for retardance measurements fail to determine accurately their values which are sometimes essential in the evaluation of experimental results. In this work, we present a method for accurate measurement of a small retardance. Our study aims to find the retardance error in a birefringent full-wave plate which, if perfect, is considered as of zero retardance. Our treatment will make use of a previously presented model for simultaneous calibration of two phase plates

Sentiment Analysis Using Random Forest Algorithm-Online Social Media Based

Every day billions of data in the form of text flood the internet be it sourced from forums, blogs, social media, or review sites. With the help of sentiment analysis, previously unstructured data can be transformed into more structured data and make this data important information. The data can describe opinions / sentiments from the public, about products, brands, community services, services, politics, or other topics. Sentiment analysis is one of the fields of Natural Language Processing (NLP) that builds systems for recognizing and extracting opinions in text form. At the most basic level, the goal is to get emotions or 'feelings' from a collection of texts or sentences. The field of sentiment analysis, or also called 'opinion mining', always involves some form of data mining process to get the text that will later be carried out the learning process in the mechine learning that will be built. this study conducts a sentimental analysis with data sources from Twitter using the Random Forest algorithm approach, we will measure the evaluation results of the algorithm we use in this study. The accuracy of measurements in this study, around 75%. the model is good enough. but we suggest trying other algorithms in further research

Time series analysis of remotely sensed water quality parameters in arid environments, Saudi Arabia

The monitoring of inland water resources in arid environments is an essential element due to their fragility. Reliable prediction of the water quality parameters helps to control and manage the water resources in arid regions. Water quality parameters were estimated using remote sensing data acquired from the beginning of 2017 until the end of 2018. The prediction of the water quality parameters was comprehended by using an adjusted autoregressive integrated moving average (ARIMA) and its extension seasonal ARIMA (S-ARIMA). Maximum Chlorophyll Index (MCI), Green Normalized Difference Vegetation Index (GNDVI) and Normalized Difference Turbidity Index (NDTI) were the tested water quality parameters using Sentinel-2 sensor on temporal resolution basis of the sensor. Results indicated that the implementation of the ARIMA model failed to sustain a reliable prediction longer than one-month time while S-ARIMA succeeded to maintain a robust prediction for the first 3 months with confidence level of 96%. MCI has its ARIMA at (1,2,2) and S-ARIMA at (1,2,2) (2,1,1)6, GNDVI has its ARIMA at (2,1,2) and S-ARIMA at (2,1,2) (2,2,2)6, and finally, NDTI has its ARIMA at (2,2,2) and S-ARIMA at (2,2,2) (1,1,2)6. The accuracy of S-ARIMA predictions reached 82% at 6-month prediction period. Meanwhile, there was no solid prediction model that lasted till 12 months. Each of the forecasted water quality parameters is unique in its prediction settings. S-ARIMA model is a more reliable model because the seasonality feature is inherited within the forecasted water quality parameters. © 2020, Springer Nature B.V