Dhiyaussalam

EDUCATION

Master of Informatics - UIN Sunan Kalijaga Yogyakarta

Scopus Publications

Optimization of Random Forest Hyperparameters with Genetic Algorithm in Classification of Lung Cancer
Dhiyaussalam and Shofwatul Uyun
IEEE
Lung cancer is a type of cancer with the highest death rate compared to other cancers. Cancer can be classified using histopathological methods which are obtained using biopsies. Manual classification of cancer on histopathological images is work intensive and highly susceptible to human error. Cancer classification from histopathological images can be done using computer assistance using computer vision and machine learning. This research proposes the following stages: data collection, feature extraction from images, feature selection, building a Random Forest model, optimizing hyperparameters using a Genetic Algorithm, and evaluating the performance of the model. The histopathological images that have been collected will have their color and texture features extracted. The extraction process produces 9 RGB features and 9 HSV features for color features. Meanwhile, texture features produce 6 types of features, namely dissimilarity, correlation, homogeneity, contrast, ASM, and energy, which are then searched for values from four different angles to produce 24 texture features. A total of 42 features were produced. All these features are then selected using the correlation coefficient and the remaining 24 features will be used to build a classification model using Random Forest. The classification model that has been built is then optimized by setting hyperparameters automatically so that the resulting model is reliable and better than general models. The hyperparameters that are optimized are $n$ estimators, max depth, max features, and criterion. By using a Genetic Algorithm, all hyperparameters are adjusted automatically to get hyperparameters with the best model performance. The Random Forest model with hyperparameters with default values succeeded in getting an accuracy of 98.82 % and a 10-fold cross-validation value of 99.39%. Meanwhile, the model that has been optimized using the Genetic Algorithm with the best hyperparameters $n$ estimators = 300, max depth = 100, max features = log2, and criterion = entropy produces an accuracy of 98.83% and a 10-fold cross-validation value of 99.50%. The Random Forest model with hyperparameters optimized using the Genetic Algorithm succeeded in outperforming the Random Forest model with default hyperparameters. It is proven that optimizing hyperparameters using a Genetic Algorithm can improve the performance of the Random Forest model.


Classification of Headache Disorder Using Random Forest Algorithm
Dhiyaussalam, Adi Wibowo, Fajar Agung Nugroho, Eko Adi Sarwoko, and I Made Agus Setiawan
IEEE
Headache disorder is one of the most often illness. At least 50% of the world’s population has experienced a headache. Primary headaches have several types; migraine, tension, cluster, and medication overuse. Computer aid for diagnosis could help people locate the headache type without the need to meet the doctor. The Random Forest algorithm was used in this study to produce a reliable model for classifying the headaches types and generate feature importance. In this study, the Migbase dataset was used, and several parameters of the algorithm were tuned to produce the best model. Based on the experiment results, the best accuracy reaching 99,56% with the Random Forest parameters are 100 for n_estimators, 33 for max_features, and 5 for max_depth.

RECENT SCHOLAR PUBLICATIONS

Optimization of Random Forest Hyperparameters with Genetic Algorithm in Classification of Lung Cancer
S Uyun
2023 6th International Seminar on Research of Information Technology and 2023



OPTIMALISASI HYPERPARAMETER RANDOM FOREST MENGGUNAKAN ALGORITMA GENETIKA PADA KLASIFIKASI KANKER PARU-PARU
NIM Dhiyaussalam
UIN SUNAN KALIJAGA YOGYAKARTA 2023



Merancang Strategi Pemasaran di Era Digital pada UMKM Rumah Makan Padang Pergaulan Yogyakarta
DGA Candra, HA Ariesta, A Fatwanto
Jurnal Bakti Saintek: Jurnal Pengabdian Masyarakat Bidang Sains dan 2022



Classification of headache disorder using random Forest algorithm
A Wibowo, FA Nugroho, EA Sarwoko, IMA Setiawan
2020 4th International Conference on Informatics and Computational Sciences 2020

MOST CITED SCHOLAR PUBLICATIONS

Classification of headache disorder using random Forest algorithm
A Wibowo, FA Nugroho, EA Sarwoko, IMA Setiawan
2020 4th International Conference on Informatics and Computational Sciences 2020
Citations: 11



Merancang Strategi Pemasaran di Era Digital pada UMKM Rumah Makan Padang Pergaulan Yogyakarta
DGA Candra, HA Ariesta, A Fatwanto
Jurnal Bakti Saintek: Jurnal Pengabdian Masyarakat Bidang Sains dan 2022
Citations: 3