cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
muhammad Muhajir
Contact Email
mmuhajir@uii.ac.id
Phone
+6289637608885
Journal Mail Official
enthusiastic@uii.ac.id
Editorial Address
Jl. Teknika, Krawitan, Umbulmartani, Kec. Ngemplak, Kabupaten Sleman, Daerah Istimewa Yogyakarta 55584
Location
Kab. sleman,
Daerah istimewa yogyakarta
INDONESIA
Enthusiastic : International Journal of Applied Statistics and Data Science
Published by Universitas Islam Indonesia
ISSN : 2798253X     EISSN : 27983153     DOI : 10.20885
Core Subject : Economy, Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics
ENTHUSIASTIC is an international journal published by the Statistics Department, Faculty of Mathematics and Natural Sciences, Universitas Islam Indonesia. ENTHUSIASTIC publishes original research articles or review articles on all aspects of the statistics and data science field which should be written in English. ENTHUSIASTIC has the vision to become a reputable journal and publish good quality papers. We aim to provide lecturers, researchers both academic and industry, and students worldwide with unlimited access to be published in our journal. Specifically, these scopes of the ENTHUSIASTIC journal are: 1. Statistical Disaster Management 2. Actuarial Science 3. Data Science 4. Statistics of Social and Business 5. Statistics of Industry
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 73 Documents
 2   3   4   5   6 
Predict Farmer Exchange Rate in the Food Crop Sector Using Principal Component Regression Effendi, Melody; Ardhyatirta, Ricardo; Angelina, Sheila Gracia; Ohyver, Margaretha
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 3 Issue 1, April 2023
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol3.iss1.art7

Abstract

Farmer Exchange Rate (FER) in Indonesia is very concerning. According to BPS data, there are various regions that experience increases and decreases every year. The goal of this paper is to predict Farmer Exchange Rate in the food crop sector using Principal Component Regression (PCR) since there is multicollinearity in the data. Therefore, with the PCR method using data based on 33 different provinces in Indonesia can determine the Farmer Exchange Rate with supporting factors. The model used can help farmers to be able to improve the welfare and economic growth of Indonesia as it depends on farmers. Further analysis found that the Harvest Area, production, and Human Development Index had an effect on farmer exchange rate. By using this model, it is expected that farmers in Indonesia have an increasing level of welfare and solve multicollinearity problem.
Marketability Study of Mathematical Sciences Students in Universiti Kebangsaan Malaysia (UKM) Lim Chui Ting; Ong Wen Xuan; Muhammad Aris Fadzilah, Nurulain Nabilah Binti; Nora Binti Muda
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 3 Issue 1, April 2023
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol3.iss1.art9

Abstract

Higher Education Institute (HEI) has a vital role in developing human capital of a country. Measuring the quality of teaching and learning system in HEI and also industry’s satisfaction level is important to ensure the marketability of HEI graduates. This study examined Universiti  Kebangsaan Malaysia (UKM) Mathematical Sciences students’ marketability by determining industry’s satisfaction level on students’ skills and abilities during industrial training and identifies factors that affect students’ marketability. There were 22 student attributes that were categorized into four factors. Mean scores and Relative Importance Analysis determined the satisfaction and importance level of each attribute studied respectively. Besides, Penalty-Reward Contrast Analysis (PRCA) showed that affective factor was categorized as a basic factor where its existence did not increase but its absence decreased the industry’s satisfaction level. For Importance Performance Analysis (IPA), cognitive, affective, and cognitive & psychomotor factors were observed in the first quadrant which had high importance level but low performance level. Lastly, all four factors were found in the loyal customer zone and at an excellent level through Customer Satisfaction Index (CSI) analysis. In conclusion, UKM Mathematical Sciences students have high marketability in general, but preservation and improvement should be implemented on important attributes to enhance their marketability.
Feasibility Analysis of Smart Wheelchairs Based on Voice Recognition for People with Disability Fathanah Muntasir, Nurul; Muhammad Risafli Raif; Rahmat Hermawan; Muh. Anshar
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 3 Issue 1, April 2023
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol3.iss1.art8

Abstract

Technological developments have accelerated the advancement of assistive technology, hence increasing human life feasibility. One of which is smart wheelchairs with a voice recognition to facilitate people with disability. However, from various smart wheelchair developments, there have been no detailed test results related to the efficiency analysis, the feasibility of the voice recognition feature on the smart wheelchair, and the satisfaction of users in using it. In this study, observations were conducted using a simple regression method, and test user satisfaction using the USE questionnaire. Based on calculation results, the learnability score was 78.81%, indicating that the wheelchair was easy to understand. The efficiency score was 85%, meaning that users found it easy to carry out their daily activities. The memorability score was 85%, indicating that it was easy to remember. The error score was 77.38%, meaning that smart wheelchairs were easy to use. The satisfaction score was 88.57%, meaning that the users felt very comfortable. The conclusion is users are satisfied with smart wheelchairs using voice recognition, meaning that it provides feasible use for a variety of people with disability. The results can be used as a foundation in continuing the development of technological features in smart wheelchairs.
Exploring Daily Activity Pattern Using Spatio-Temporal Statistics with R for Predicting Trip Production Willdan, Muhamad; Ramadhan, Raihan Iqbal; Kresnanto, Nindyo Cahyo; Putri, Wika Harisa
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 3 Issue 1, April 2023
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol3.iss1.art6

Abstract

Spatio-temporal data modelling is one of the methods in data analysis that uses space (spatial) and time (temporal) approaches. This study used Spatio-temporal statistical modelling to observe the daily activity patterns of people. Spatio-temporal modelling selected for support activity-based transportation demand. This research identifies community mobility patterns that will provide trip production data for transportation demand prediction. Using Spatio-temporal statistical modelling benefit this study because statistical this model can make model components in a physical system appearing to be random. Even if they are not, the models are helpful as they have accurate and precise predictions. In this study, descriptive analysis was used. Incorporating statistical distributions into the model is a natural way to solve the problem. This research collects daily activity data from 400 respondents recorded every 15 minutes. From this data, a pattern of respondents’ daily activities was formed, which was analyzed using R. Software R also visualizes data on daily activities of the community in Spatio-temporal modelling. This research aims to depict the daily activity patterns to predict trip production. This research found three clusters of trip production patterns with specific groups member and specific patterns between workdays and holidays.
MSME Sales Clustering Based on Business Aid Distribution Priority Using K-Affinity Propagation Tarisya Qurrota A'yuni; Baiq Nina Febriati; Lazuardy Ilham Effendie; Muhammad Muhajir; Yotenka , Rahmadi
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 3 Issue 1, April 2023
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol3.iss1.art10

Abstract

In rural areas of Indonesia, micro, small, and medium enterprises (MSMEs) are often isolated; however, they have been proven to play an important role as the economic backbone of millions of communities. In fact, the sluggish development of MSMEs in Indonesia become a severe problem for the community welfare. The government continues to strive for the welfare of the local communities, one of which is by supporting the existing MSMEs. However, the provision of government assistance may not be optimal for the incorrect target of the MSMEs. This study informs the government and other related parties regarding subdistrict groups whose MSMEs are considered to be their target. The k-affinity propagation method was used to find a set of representative examples, called exemplars, that best summarize the data. The result shows that sub-districts clusters based on general welfare in five commodities. K-affinity propagation algorithm clusters vary by commodity. Data fluctuation from each commodity’s three factors causes this. From this research, it can be determined which subdistricts have the most or least prosperous MSMEs in each of the five commodities analyzed.
Real-Time Wi-Fi Signal Monitoring from a User Perspective in a Wireless Environment Using the Internet of Things Ibrahim, Mohd Zaki; Megat Muhammad Ridzuan; Inn, Arbaiah; Hassan, Rosilah
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 3 Issue 2, October 2023
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol3.iss2.art1

Abstract

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.
Application of Geographically Weighted Regression Method on the Human Development Index of Central Java Province Hasibuan, Devi Octaviani; Pau Teku, Heribertin; Drostela Putri, Maria Fatima; Setyawan, Yudi; Dwi Bekti, Rokhana
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 3 Issue 2, October 2023
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol3.iss2.art6

Abstract

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.
Modeling and Forecasting Volatility in USD/GBP Exchange Rate Qona’ah, Niswatul
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 3 Issue 2, October 2023
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol3.iss2.art2

Abstract

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.  
MRI-Based Brain Tumor Classification Using Inception Resnet V2 Azzahra, Thalita Safa; Jessica Jesslyn Cerelia; Farid Azhar Lutfi Nugraha; Anindya Apriliyanti Pravitasari
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 3 Issue 2, October 2023
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol3.iss2.art4

Abstract

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.
Analyzing Potential Rice Harvest Area in Mojokerto Regency in 2021 Using Area Sample Framework (ASF) Nugroho, Moh. Alfian; Yuliana, Fera; Sholikhan, Arief Krisnah; Rosita, Yesy Diah
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 3 Issue 2, October 2023
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol3.iss2.art3

Abstract

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.  

Page 4 of 8 | Total Record : 73

 2   3   4   5   6 

Filter by Year

2021 2025


Reset
Filter By Issues
All Issue Volume 5 Issue 2, October 2025 Volume 5 Issue 1, April 2025 Volume 4 Issue 2, October 2024 Volume 4 Issue 1, April 2024 Volume 3 Issue 2, October 2023 Volume 3 Issue 1, April 2023 Volume 2 Issue 2, October 2022 Volume 2 Issue 1, April 2022 Volume 1 Issue 2, October 2021 Volume 1 Issue 1, April 2021