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Alfirdausy, Roudlotul Jannah

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Author-ID : 7310337

Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Articles

Optimasi K-Nearest Neighbor Dengan Particle Swarm Optimization Untuk Klasifikasi Idiopathic Thrombocytopenic Purpura Alfirdausy, Roudlotul Jannah; Aliyyah, Izzatul; Fanani, Aris
Komputika : Jurnal Sistem Komputer Vol 13 No 1 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i1.10436

ABSTRAK – Immune Thrombocytopenic Purpura (ITP) merupakan suatu gangguan hematologi yang disebabkan oleh kerusakan pada trombosit akibat respons autoimun tubuh, yang mengakibatkan kemudahan terjadinya memar atau pendarahan yang berlebihan pada individu yang terkena. Pentingnya deteksi dini penyakit ITP tidak dapat diabaikan, karena kelainan ini dapat berdampak kronis atau jangka panjang. Oleh karena itu, penelitian ini bertujuan untuk mengklasifikasikan penyakit ITP dengan akurasi yang lebih baik guna menghindari kesalahan dalam diagnosis pasien, serta memungkinkan penanganan dan pengobatan yang tepat dan segera. Dalam penelitian ini, digunakan metode kombinasi PSO-KNN. Hasil yang diperoleh menunjukkan peningkatan yang signifikan dalam akurasi, sensitivitas, dan spesifisitas dibandingkan dengan metode KNN standar. Akurasi mencapai 91.8%, meningkat sebesar 4.9% dari KNN standar, sensitivitas mencapai 91.2%, meningkat sebesar 11.8% dari KNN standar, dan spesifisitas mencapai 92.6%, walaupun mengalami penurunan sebesar 3.7% dari KNN standar. Waktu pelatihan dan pengujian dengan metode PSO-KNN juga lebih efisien dibandingkan dengan KNN standar. Hal ini menunjukkan bahwa penggunaan algoritma PSO mampu mengoptimalkan hasil klasifikasi dari KNN, sehingga dapat menjadi alat yang lebih andal dalam diagnosis dini penyakit ITP.

Implementation of The Extreme Gradient Boosting Algorithm with Hyperparameter Tuning in Celiac Disease Classification Alfirdausy, Roudlotul Jannah; Ulinnuha, Nurissaidah; Utami, Wika Dianita
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 24 No 1 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4031

Celiac Disease (CeD) is an autoimmune disorder triggered by gluten consumption and involves the immune system and HLA in the intestine. The global incidence ranges from 0.5%-1%, with only 30% correctly diagnosed. Diagnosis remains challenging, requiring complex tests like blood tests, small bowel biopsy, and elimination of gluten from the diet. Therefore, a faster and more efficient alternative is needed. Extreme Gradient Boosting (XGBoost), an ensemble machine learning technique that utilizes decision trees to aid in the classification of Celiac disease, was used. The aim of this study was to classify patients into six classes, namely potential, atypical, silent, typical, latent and none disease, based on attributes such as blood test results, clinical symptoms and medical history. This research method employs 5-fold cross-validation to optimize parameters that are max depth, n estimator, gamma, and learning rate. Experiments were conducted 96 times to get the best combination of parameters. The results of this research are highlighted by an improvement of 0.45% above the accuracy value with the default XGBoost parameter of 98.19%. The best model was obtained in the trial with parameters max depth of 3, n estimator of 100, gamma of 0, and learning rate of 0.3 and 0.5 after modifying the parameters, yielding an accuracy rate of 98.64%, a sensitivity rate of 98.43%, and a specificity rate of 99.72%. This research shows that tuning the XGBoost parameters for Celiac

Implementation of The Extreme Gradient Boosting Algorithm with Hyperparameter Tuning in Celiac Disease Classification Alfirdausy, Roudlotul Jannah; Ulinnuha, Nurissaidah; Utami, Wika Dianita
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4031

Celiac Disease (CeD) is an autoimmune disorder triggered by gluten consumption and involves the immune system and HLA in the intestine. The global incidence ranges from 0.5%-1%, with only 30% correctly diagnosed. Diagnosis remains challenging, requiring complex tests like blood tests, small bowel biopsy, and elimination of gluten from the diet. Therefore, a faster and more efficient alternative is needed. Extreme Gradient Boosting (XGBoost), an ensemble machine learning technique that utilizes decision trees to aid in the classification of Celiac disease, was used. The aim of this study was to classify patients into six classes, namely potential, atypical, silent, typical, latent and none disease, based on attributes such as blood test results, clinical symptoms and medical history. This research method employs 5-fold cross-validation to optimize parameters that are max depth, n estimator, gamma, and learning rate. Experiments were conducted 96 times to get the best combination of parameters. The results of this research are highlighted by an improvement of 0.45% above the accuracy value with the default XGBoost parameter of 98.19%. The best model was obtained in the trial with parameters max depth of 3, n estimator of 100, gamma of 0, and learning rate of 0.3 and 0.5 after modifying the parameters, yielding an accuracy rate of 98.64%, a sensitivity rate of 98.43%, and a specificity rate of 99.72%. This research shows that tuning the XGBoost parameters for Celiac

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