Enthusiastic : International Journal of Applied Statistics and Data Science

Real-Time Wi-Fi Signal Monitoring from a User Perspective in a Wireless Environment Using the Internet of Things

Mohd Zaki Ibrahim, Megat Muhammad Ridzuan, Arbaiah Inn, Rosilah Hassan — Sat, 15 Jul 2023 00:00:00 +0000

Mobility issues, such as handover, should be considered in a wireless environment. Real-time Wi-Fi monitoring from a user perspective is important because it is used to keep track of the Wi-Fi performance and status. Thus, improving network efficiency allows users to work more efficiently. The monitoring currently being held on the Wi-Fi is not in a real-time perspective. The monitoring is only focused on the connection between the controller and the Access Point (AP) and the AP to the user devices. We proposed a way to monitor the Wi-Fi from a real-time user perspective in a wireless environment. This project will use the Raspberry Pi as a device (RP). This is because RP has an operating system that can replace personal computers in terms of monitoring the access point from the user’s point of view. This device will make monitoring tasks more efficient and faster for the user to identify the problems occurring at the Wi-Fi network. This research will also enable the usage of the existing Internet of Things (IoT) to develop new things. To conclude, monitoring using an IoT device can project the view of the Wi-Fi performance from a user perspective.

Modeling and Forecasting Volatility in USD/GBP Exchange Rate

Niswatul Qona’ah — Tue, 17 Oct 2023 00:00:00 +0000

Rate changes can occur hourly, daily, or in large incremental shifts. These changes may impact firms by changing the cost of commodities imported from other countries and the demand for their goods among foreign consumers. Therefore, it is essential to forecast exchange rates to manage this business effect. This study aims to determine the best model for predicting volatility in the exchange rate between USD and GBP. In particular, we analyze exchange rates using the Autoregressive Integrated Moving Average (ARIMA) model and the volatility or variance model by Generalized Autoregressive Conditional Heteroscedasticity (GARCH). To determine the best model, the performance of each model is evaluated with several criteria, namely Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results show that EGARCH(1,1) has the best forecasting performance in the out-sample section because it can better capture out-sample data patterns with minimum RMSE, MAE, and MAPE.

Analyzing Potential Rice Harvest Area in Mojokerto Regency in 2021 Using Area Sample Framework (ASF)

Moh. Alfian Nugroho, Fera Yuliana, Arief Krisnah Sholikhan, Yesy Diah Rosita — Tue, 24 Oct 2023 00:00:00 +0000

The agricultural sector is crucial for achieving SDG 2, addressing hunger, ensuring food security, and promoting sustainable agriculture. This study applies the Area Sample Framework (ASF) to estimate rice harvest yields in Mojokerto Regency, emphasizing the importance of accurate agricultural data for effective policy formulation and SDG support. ASF utilizes square segment-based sampling units to provide potential rice harvest area data. However, research on the accuracy of ASF-derived data, especially for predicting the next year’s rice harvest, is limited. This study evaluates ASF data accuracy for 2019, 2020, and 2021 using three key metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Results show varying accuracy each year. In 2019, MAPE was 91%, with MAE and RMSE around 2,714.75 ha and 15,463,954.79 ha, indicating high accuracy. Conversely, in 2021, MAPE rose to 107%, with MAE and RMSE near 2,680.09 ha and 14,677,241.22 ha, revealing lower prediction accuracy. This study underscores the importance of continuous monitoring and enhancing data accuracy to support sustainable agriculture and food security, especially in regions like Mojokerto Regency. Further research should investigate factors affecting harvested area efficiency and ways to improve prediction accuracy for effective SDG implementation.

MRI-Based Brain Tumor Classification Using Inception Resnet V2

Tue, 24 Oct 2023 00:00:00 +0000

Brain tumors are one of the most fatal disorders owing to the uncontrolled proliferation of abnormal cells inside the brain. Digital images are obtained using Magnetic Resonance Imaging (MRI), which is a medical instrument that can assist doctors and other medical personnel in assessing and diagnosing the presence and type of brain tumors. However, manual and subjective classification is time-consuming and error prone. Hence, an objective, automatic, and more reliable method is needed to classify MRI images of brain tumors. Artificial intelligence is considered appropriate to determine the type of brain tumor via MRI images to overcome the constraints of conventional testing methods. One method for performing automatic classification is the Convolutional Neural Network (CNN). This work demonstrates how the Inception Resnet v2 architecture in CNN is utilized to classify MRI brain tumors into four categories via transfer learning, namely glioma tumors, meningioma tumors, no tumors, and pituitary tumors. The accuracy value of the generated model reached 93.4% after running for 20 epochs. It infers that artificial intelligence is beneficial in identifying a brain tumor objectively to help doctors and radiologists in the medical field.

The Implementation of the Generalized Space-Time Autoregressive (GSTAR) Model for Inflation Prediction

Feby Hestuningtias, Muhammad Hasan Sidiq Kurniawan — Mon, 30 Oct 2023 00:00:00 +0000

The macroeconomic indicator used to measure a country’s economic balance is inflation. The increase in the price of goods and services causes an increase in inflation, which impacts the decrease in the value of money so that people’s purchasing power for goods and services will decrease and result in slow economic growth. One way to determine future inflation is by forecasting. The Generalized Space-Time Autoregressive (GSTAR) model is a time series model involving time and location. This study aims to predict future inflation using the GSTAR model, which uses differencing without uniform location weights, inverse distance, and normalized cross-correlation. The results showed that the models obtained were the GSTAR (2,1) and GSTAR (5,1)I(1) models. The best model to predict inflation is the GSTAR (5,1)I(1) model with the normalized cross-correlation weight, which had Root Mean Square Error (RMSE) value of 0.5743, which was smaller than the GSTAR (2,1) model.

Application of Geographically Weighted Regression Method on the Human Development Index of Central Java Province

Mon, 30 Oct 2023 00:00:00 +0000

Spatial data are data containing information on the location or geography of a region on the representation of objects on earth. Geographically Weighted Regression (GWR) is a development of the Ordinary Least Square (OLS) theory into a weighted regression model that considers spatial effects, resulting in a parameter estimation that can only be used to predict each location where the data are observed. The Human Development Index (HDI) is an essential indicator for measuring success in efforts to build human quality of life. HDI data regencies/cities in Central Java are interconnected, so it is said to be spatial data and there are spatial effects in it. Therefore, the GWR method was applied to obtain faculties affecting HDI in Central Java Province. The data used were secondary data in 2020. The determination coefficients of the GWR model ranged between 76.09% and 87.16%. If the variable values of population density and Gross Regional Domestic Product (GRDP) increase by one unit in each district/city in Central Java Province, the HDI variable value increases. These results were visualized on a dashboard providing information about the characteristics of HDI and independent variables, GWR parameter estimates, and the significance of independent variables in each regency/city.

Analysis of the Fastest Cost and Route Using the Graph Theory and Network Analysis

Dewi Rahmawati, Marsela Putri, Rosmawati, Farhanul Hakim — Mon, 30 Oct 2023 00:00:00 +0000

Samarang is one of the subdistricts in the Garut Regency with the most tourism potential. According to 2021 data from Garut Statistics Indonesia, the tourism potential in the Samarang included nature tourism, cultural tourism, special interest tourism, and artificial tourism. Nature tourism had the highest proportion of approximately 49.15% compared to other tourism. Still, there is limited access to these tourist sites due to considerable distance, damaged road conditions, and remoteness from settlements. This research aims to determine the optimal route, cost, and travel time to access the tourist location. Based on this, the methods used were graph theory and network analysis methods. The distance determination was done using Google Maps and quantitative analysis. The results showed that the shortest distance to access tourist sites in the Samarang from the center of Garut city was about 15.55 km, required the cost of IDR132,000, and travel duration of 52 minutes. At the same time, the results of the network analysis method revealed that the normal route was around 21.35 km, which cost IDR158,000 and required a travel time of 66 minutes.

A Mathematical Model for Determining the Facility Layout Plan of a Plastic Injection Factory

Harun Kaklıkkaya, Fatma Çetin, Şule Uyumaz, Tuğba Saraç — Mon, 30 Oct 2023 00:00:00 +0000

In this study, a mathematical model has been developed to make the facility layout planning of the annex building, which is being built right next to the old building of a factory that produces plastic parts. Some or all parts of the departments, such as injection machines, semi-finished products, product, raw material warehouses, molding room, and paint shop in the old building of the factory can be moved to the new building. Nevertheless, some cannot be moved. Since injection machines use cranes for electricity, plumbing, and mold transport, relocation is very costly. Different layouts are proposed with the developed mathematical model for seven scenarios that allow and do not allow partitions of the departments. The proposed layout plans were compared in terms of the total carrying costs of the products and the carrying costs of the sections. In this way, the factory has been presented with a significant cost reduction opportunity.