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All Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Nasional Teknologi Informasi dan Aplikasinya
Pradyto, Kadek Dwitya Adhi
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Computer Science & IT Education Engineering

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Implementasi Random Forest Dengan LASSO Dalam Klasifikasi Penyakit Yang Ditularkan Melalui Nyamuk Pradyto, Kadek Dwitya Adhi; Raharja, Made Agung
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 4 (2023): JNATIA Vol. 1, No. 4, Agustus 2023
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

Several diseases that can attack human health can be transmitted through disease vectors. One of the insects belonging to the disease vector is the mosquito. Diseases that can attack humans due to transmission through mosquitoes include malaria, dengue fever, chikungunya, yellow fever, rift valley fever, and many more. With so many types of diseases that are transmitted by mosquitoes and the symptoms that look quite similar, a classification process is carried out to distinguish the types of diseases. In this study, the classification was carried out using the Random Forest algorithm withthe LASSO algorithm for feature selection. It was found that the average accuracy values of the Random Forest before and after carrying out feature selection using LASSO were 88% and 76%, respectively. From the values obtained, it can be concluded that the Random Forest has better performance without feature selection using the LASSO method. Keywords: Classification, Random Forest, LASSO, Mosquito-Borne Diseases
Vector-Borne Disease Detection Using Random Forest and BPSO Raharja, Made Agung; Pradyto, Kadek Dwitya Adhi; Wibawa, I Gede Arta; Astawa, I Gede Santi
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.96722

Abstract

Vector-borne diseases such as malaria, dengue fever and yellow fever still pose a serious threat to public health. To distinguish between these diseases, an accurate classification process is required. In this study, Random Forest algorithm is used as a classification method due to its ability to overcome overfitting and provide good accuracy results. However, the large number of features in the data can cause redundancy and decrease the accuracy of the model. Therefore, the Binary Particle Swarm Optimization (BPSO) method is used as a feature selection technique to optimize the performance of Random Forest. The optimization process is also complemented by finding the best parameters using Random Search and Grid Search. Evaluation was conducted on a vector-borne disease dataset with 64 features and 11 disease classes. The results showed that the accuracy of the model increased from 90.48% to 100% after feature selection by BPSO which selected 37 best features, and Random Search proved to be more efficient in computation time than Grid Search. This research shows that the combination of Random Forest and BPSO can improve classification accuracy and efficiency in detecting vector-borne diseases.
Co-Authors I Gede Arta Wibawa I Gede Santi Astawa Raharja, Made Agung