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Arjuna Subject : Ilmu Ekonomi, Ekonometrika dan Keuangan (semua kategori) - Ilmu Ekonomi, Ekonometrika dan Keuangan (Lain-Lain)
Articles 13 Documents
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Search results for , issue "Join Proceeding "Basic and Applied Science Conference (BASC) 2021 " : 13 Documents clear
Modeling of Rice Production in Indonesia Using Robust Regression with The Method of Moments (MM) Estimation Nugrahani , Isnaini Dyah; Susanti, Yuliana; Qona’ah, Niswatul
Nusantara Science and Technology Proceedings Join Proceeding "Basic and Applied Science Conference (BASC) 2021 & 1st Education Research and Appli
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2021.1111

Abstract

Indonesia is an agricultural country with the majority of the people making rice which is then processed product of rice as a staple food. However, in the last few years, rice production in Indonesia has decreased. Rice production data with influencing factors, namely, rice harvest area, land area affected by plant pests (OPT), rainfall, the population in Indonesia have outliers and have residuals that are not normally distributed so that regression analysis with the least-squares method cannot be used to estimate the amount of rice production. A robust regression model with Method of Moments (MM) estimation is used to solve outlier problems and violations of normality assumptions. This study aims to determine the robust MM estimation regression model to estimate rice production in Indonesia and determine the factors that significantly influence. The robust regression model of MM estimation on rice production in Indonesia shows that the increase in the amount of harvested area , the land area is exposed to plant pests (OPT) and the population will increase the amount of rice production, while the rainfall will reduce the amount of rice production with a high level of confidence. The variable harvested land area and the population has a significant effect on the amount of rice production. Based on the results obtained, it is hoped that there will be policies that consider factors that influence rice production to increase the amount of rice production in Indonesia.
Data Mining Using K-Means Clustering Algorithm for Grouping Countries of Origin of Foreign Tourist Hasanah, Herliyani; Sudibyo, Nugroho Arif; Galih, Rhezka Mahendra
Nusantara Science and Technology Proceedings Join Proceeding "Basic and Applied Science Conference (BASC) 2021 & 1st Education Research and Appli
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2021.1112

Abstract

Indonesia has enormous potential to develop the tourism sector. The role of the tourism sector in Indonesia's economic development is increasingly important. The contribution has been made by the tourism sector through foreign exchange earnings, regional income, regional development, investment, and employment increment as well as business development across various areas in Indonesia. One of the government's targets in the tourism sector is to increase foreign tourist visits. Grouping or clustering the countries of origin of the tourists need to be done to help the government in determining strategies. This study uses the K-means clustering algorithm to classify the data on the country of origin of tourists and evaluate the clusters using silhouette score for determining the appropriate number of clusters. The result of the silhouette score shows that K = 2 has a value of 0.8, which is the best cluster that can be used to classify data on the country of origin of tourists. Based on the test results of the clusters, both of the clusters were then identified as cluster 1 for the category of low visitors with 206 members and cluster 2 for the category of high visitors with 6 members, namely Malaysia, Singapore, China, Other Asia, Timor Leste, and Australia. The results of the clustering process are expected to be input data for further performance, namely mapping the right marketing strategy for the countries visiting Indonesia so as to increase foreign tourist visits to Indonesia.
Web-Based Learning Implementation to Improve Students' Learning Outcomes on Understand Evaluating the Public Policies’ Impact Material during the Covid-19 Pandemic Susilawati, Evi; Khaira, Imamul
Nusantara Science and Technology Proceedings Join Proceeding "Basic and Applied Science Conference (BASC) 2021 & 1st Education Research and Appli
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2021.1113

Abstract

The Covid-19 Pandemic (CoronaVirus Disease-19) caused the Government of the Republic of Indonesia to implement various policies to tackle the spread of Covid-19 so that those policies’ impacts need to be evaluated. This activity of learning to topic evaluate the impact of public policies during the Covid-19 pandemic was carried out in the Public Policy course. This research activity uses a web-based learning strategy. Based on these web-based learning activities, this study aims: (1) to see web-based learning applications on the evaluation material for the impact of public policies during Covid-19, and (2) to see the increase in students' abilities in understanding the material for evaluating the impact of public policies during the Covid-19 pandemic. This research is Classroom Action Research (CAR). The research subjects subject to action were students of semester VII of the Pancasila and Civic Education study program, Faculty of Teacher Training and Education of the Islamic University of North Sumatra in the 2020/2021 academic year. The data analysis in this study used qualitative and quantitative descriptive techniques. The validity of the data in this study was carried out using observation sheets and student learning achievements tests on the evaluation material for the impact of Public Policy during the Covid-19 pandemic. The results of this study indicate that there is an increase in the learning outcomes of students to understand the material for evaluating the impact of public policies during the Covid-19 pandemic using web-based learning.

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