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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Zona
Goldameir, Noor Ell
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Computer Science & IT Earth & Planetary Sciences Environmental Science

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Classification of the Human Development Index in Indonesia Using the Bootstrap Aggregating Method Goldameir, Noor Ell; Yolanda, Anne Mudya; Adnan, Arisman; Febrianti, Lusi
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2B (2021): Article Research October 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v6i1.11173

Abstract

Successful development of the quality of human life in a region is determined by the Human Development Index (HDI). Human development performance based on the HDI can be measured: long and healthy life, knowledge, and a decent standard of living. The HDI is usually grouped into several categories to facilitate the classification of the HDI level of each region. This study aimed to determine the ability of the bootstrap aggregating (bagging) method to classify the HDI by district/city. Bagging is a stochastic machine learning approach that can eliminate the variance of the classifier by producing a bootstrap ensemble to obtain better accuracy results. The dependent variable in this study was the HDI by district/city in 2020. In contrast, life expectancy at birth, expected years of schooling, mean years of schooling, and real expenditure per capita are adjusted as independent variables. Bagging was applied to the high and low categories of HDI data. The bagging method demonstrated good classification performance due to only eight classification errors, namely the HDI data which should be in the high category but classified into the low category by the bagging method. Based on the results of calculations with 25 replications, it can be concluded that the bagging method has a very good performance, with an accuracy value of 92.3%, the sensitivity of 100%, and specificity of 83.33%. The bagging method is considered very good for the classifying the HDI by district/city in Indonesia in 2020 because it has a balanced accuracy of 91.67%.
The Comparison of Accuracy on Classification Climate Change Data with Logistic Regression Adnan, Arisman; Yolanda, Anne Mudya; Erda, Gustriza; Goldameir, Noor Ell; Indra, Zul
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.11914

Abstract

Machine learning methods can be used to generate climate change models. The goal of this study is to use logistic regression machine learning algorithms to classify data on greenhouse gas emissions. The data used is climate change data of several countries obtained from The World Bank, with total greenhouse gas emissions as the response variable and 61 other attributes as explanatory variables. This data is preprocessed using min-max normalization to handle unbalanced ranges, and then the data is split into 70% training data and 30% testing data. Based on the logistic regression modeling, it was discovered that the data from the min-max transformation resulted in better modeling than the data modeling without the transformation process. The accuracy, precision, sensitivity, and specificity of the transformation are 87.60%, 87.76%, 87.04%, and 88.14%, respectively
Peramalan Exponential Smoothing Holt-winters Pada Data Jumlah Produksi Kelapa Sawit di Perkebunan Besar Swasta Provinsi Riau Farhan Hardinata; Noor Ell Goldameir
Jurnal Zona Vol 8, No 2 (2024)
Publisher : Pelantar Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52364/zona.v8i2.117

Abstract

Palm oil is one of the largest plantation commodities in Indonesia, especially in the Riau province. The expansion of administrative coverage in palm oil companies is one of the factors affecting palm oil production. The data used in this study consists of palm oil production figures from large private estates in Riau province from January 2006 to December 2022. The methods used are the multiplicative and additive Holt-Winters Exponential Smoothing. This study aims to forecast using the multiplicative and additive Holt-Winters Exponential Smoothing models. Based on the results, the multiplicative Holt-Winters Exponential Smoothing model has a MAPE value of 13.99%, while the additive Holt-Winters Exponential Smoothing model has a MAPE value of 22.33%.
Co-Authors Anne Mudya Yolanda Arisman Adnan Farhan Hardinata Febrianti, Lusi Gustriza Erda Indra, Zul