Skripsi

KNOWLEDGE DISCOVERY DALAM MEMPREDIKSI SKOR SUSTAINABLE DEVELOPMENT GOALS (SDGS) SUATU NEGARA

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SDGs score prediction is important in assisting the planning and evaluation of policies to achieve these global targets. This study aims to compare the performance of several regression algorithms in predicting the SDGs score: Polynomial Regression, Support Vector Regression, Random Forest Regression, and Gradient Boosting Regressor. The dataset used consists of data on country names, years, SDGs score, and scores for each goal by applying the Knowledge Discovery in Databases. The evaluation results show that the polynomial regression algorithm provides the most optimal results, as indicated by the lowest MAE, RMSE, and MSE values among the different models, at 0.141, 0.294, and 0.086. In addition, the R2 value produced was also the highest among the other models, at 0.999. The polynomial regression model was then used to predict Indonesia’s SDGs score for 2025 and produced a value of 70.38 with a 95% Confidence Interval of 70.31 to 70.45. The findings of this study indicate that a simple but accurate regression model in capturing non-linear patterns can produce a more optimal evaluation compared to complex models. This study confirms that the suitability of the algorithm to the characteristics of the data is crucial and shows that the KDD approach is capable of producing valuable knowledge findings in the context of SDG achievement analysis and prediction.

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Detail Information

Series Title

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Call Number

T1895962025

Publisher

Indralaya : Prodi Sistem Informasi, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xiv, 83 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

006.350 7

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Prodi Sistem Informasi
Algoritma Skor Sustanable--Analisis Data

Specific Detail Info

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Statement of Responsibility

MI

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