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All Journal Tadulako Science and Technology Journal
Iman Setiawan
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Agriculture, Biological Sciences & Forestry Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

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INTERPOLTION OF SULFUR DIOXIDE (SO2) AND NITROGEN DIOXIDE (NO2 ) IN YOGYAKARTA SPECIAL PROVINCE USING THE COKRIGING METHOD Andina Aulia Pramesti; Nur’eni; Iman Setiawan
Tadulako Science and Technology Journal Vol. 4 No. 1 (2023): TADULAKO SCIENCE AND TECHNOLOGY JOURNAL
Publisher : LPPM Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/sciencetech.v4i1.16392

Abstract

Air is a mixture of gases found in the layer that surrounds the earth. The cokriging method is the development of the kriging method to estimate a variable that minimizes estimation errors by utilizing cross-correlation between several variables. This research was conducted using data from 45 coordinate locations for air monitoring by the Department of Environment and Forestry of the Special Region of Yogyakarta in 2017. The results showed that the best model for estimating sulfur dioxide and nitrogen dioxide air pollution was the spherical model with the smallest Mean Square Error (MSE) value. which is 317,527. Interpolation of sulfur dioxide and nitrogen dioxide content values using cokringing resulted in 100,390 new points. The value of air pollution is in the range of 0-40, which according to ISPU means that at that point, good air quality has no impact on humans but causes a certain smell and injury to some plant species.
Grouping of Provinces in Indonesia Based on Infrastructure Development Indicators Using the Ward Method with a Multiscale Bootstrap Approach Nurfatra; Junaidi; Iman Setiawan
Tadulako Science and Technology Journal Vol. 4 No. 1 (2023): TADULAKO SCIENCE AND TECHNOLOGY JOURNAL
Publisher : LPPM Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/sciencetech.v4i1.16393

Abstract

Infrastructure plays an important role in improving the quality of life and human welfare. Infrastructure is a facility that is needed by every country, including Indonesia, to support various community activities in general in everyday life. However, the problem of inequality in infrastructure development in Indonesia is still a challenge for the government. This study aims to classify provinces in Indonesia based on indicators of infrastructure development. The method used in this grouping is the ward method with the multiscale bootstrap approach to determine the validity of the formed cluster. The results of the grouping show that we obtained 7 clusters where clusters with poor infrastructure development status are cluster 7, clusters with fairly good infrastructure development status, are cluster 6, clusters with good infrastructure development status, are cluster 1, cluster 2, cluster 3 and cluster 4, while cluster with a very good infrastructure development status, are cluster 5. From the 7 clusters formed, we obtained 4 clusters with an approximately unbiased (AU) value greater than and equal to 95, defined as valid clusters and 3 clusters with an AU value of less than and equal to 95, defined as invalid clusters
Sales Prediction of Palu Arshop Clothing Using the High Order Chen Fuzzy Time Series Method Marni Sagap; Nur'eni; Iman Setiawan
Tadulako Science and Technology Journal Vol. 3 No. 2 (2023): Tadulako Science and Technology Journal
Publisher : LPPM Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/sciencetech.v3i2.17313

Abstract

Introduction: Arshop is one of the clothing stores in Palu City that is in great demand by the community. As one of the many clothing stores in Palu City Arshop to find a strategy to increase sales. One way that can be used is to make predictions to determine strategies to increase sales. Method: Higher-order Chen fuzzy time series method to predict the time series data of Arshop Palu clothing sales. Chen's high-order fuzzy time series is a time series analysis that can capture varied data patterns, one of which is seasonal patterns, and is formed based on two or more data in the past. Results and Discussion: The results of this study indicate that the high-order Chen fuzzy time series method has an accuracy rate of MAPE 15.59%, which is categorized as good the prediction results of the comparison between various orders show that the fourth-order Chen fuzzy time series is the best for predicting clothing sales of Arshop Palu. Conclusion: The prediction of clothing sales at Arshop Palu using the higher-order Chen fuzzy time series method resulted in a MAPE of 15.59%, which shows good accuracy because it is less than 20%. Based on the comparison of the accuracy values of the four orders, the fourth-order FTS proved to be the most effective for predicting the clothing sales of Arshop Palu.
Co-Authors Andina Aulia Pramesti Junaidi Junaidi Marni Sagap Nur'Eni Nurfatra Nur’eni