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All Journal Statistika Jurnal Statistika Universitas Muhammadiyah Semarang Intizar Indonesian Journal of Artificial Intelligence and Data Mining Prosiding University Research Colloquium

Laelatul Khikmah

Institut Teknologi Statistika Dan Bisnis Muhammadiyah Semarang

Author-ID : 2977138

Religion Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology

Published : 9 Documents Claim Missing Document

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Frameworks Comparative Study of Classification Models Based on Variable Extraction Model for Status Classify of Contraception Method in Fertile Age Couples in Indonesia Laelatul Khikmah
Indonesian Journal of Artificial Intelligence and Data Mining Vol 2, No 1 (2019): March 2019
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

In terms of minimizing the risk of death in mothers the use of contraceptive methods really needs to be improved and the success of the use of contraceptive methods. This study aims to compare several popular classification models used to classify the status of the use of contraceptive methods in fertile age couples in Indonesia so that they can be used and the implementation of policies that are more impartial using the variable extraction integration method. The proposed model in this study is a comparative study of classification models include Logistic Regression (LR), k-Nearest Neighbor (k-NN), Naïve Bayes (NB), C4.5, and CART. For the purpose of testing the model, Accuracy, AUC, F-measure, Sensitivity (SN), Specificity (SP), Positive Predictive Value (PPV), and Negative Predictive Value (NPV) are used to test frameworks comparative study of classification models. Based on the experimental results, RL shows superior and stable performance compared to other methods. It can be concluded, the RL method is the right choice method to classify the status of use of contraceptive methods in couples of childbearing ages in Indonesia.

Implementation of C4.5 Algorithm for Critical Land Prediction in Agricultural Cultivation Areas in Pemali Jratun Watershed Deden Istiawan; Laelatul Khikmah
Indonesian Journal of Artificial Intelligence and Data Mining Vol 2, No 2 (2019): September 2019
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Watershed is a complex system that is built on physical systems, biological systems and human systems that are related to each other. Each component has a distinctive nature and its existence is related to other components so as to form a unified ecosystem. Land use that does not pay attention to the conservation requirements of land and water causes land degradation which ultimately results in critical land. The impact of critical land is not only the withdrawal of soil properties, but also results in a decrease in production functions. Prediction of the critical level of land is needed to reduce the level of damage to the watershed, so that it can be used for policy making by the relevant agencies. In this research C4.5 algorithm will be applied to predictions of critical land in agricultural cultivation areas using critical land parameters. Based on the results of the research on critical land classification of agricultural cultivation areas in the jratun pemali watershed it can be concluded that the C.45 algorithm can be implemented to predict critical land in agricultural cultivation areas with an accuracy rate of 92.47%.

Co-Authors Dina Novitasari Dini Gartina Faikul Fahmi Istiawan, Deden Santoso, Arif Gunawan Wulan Septya Ningrum

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Journal : Indonesian Journal of Artificial Intelligence and Data Mining