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Prediksi Risiko Penyakit Jantung Menggunakan Xgboost dengan Optimasi Hyperparameter Berbasis GridsearchCV Syawaluddin Khadafi Parinduri; Antoni Pribadi; Nurkholis
Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK ) Vol. 4 No. 1 (2026): Maret: Jurnal Elektronika dan Teknik Informatika Terapan (JENTIK)
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jentik.v4i1.1517

Abstract

Penyakit jantung tetap menjadi penyebab utama morbiditas dan mortalitas global, mendorong kebutuhan akan metode prediksi risiko yang akurat dan efisien. Penelitian ini bertujuan untuk mengembangkan model prediksi penyakit jantung yang robust menggunakan algoritma XGBoost, dengan penekanan pada peningkatan kinerja melalui optimasi hyperparameter berbasis GridSearchCV. Data yang digunakan berasal dari UCI Machine Learning Repository, mencakup 302 sampel dengan 14 atribut klinis dan demografis pasien. Metode penelitian melibatkan prapemrosesan data, pembagian data secara stratified menjadi set pelatihan (training set, 75%) dan set pengujian (testing set, 25%), serta implementasi model XGBoost. GridSearchCV dengan validasi silang 5-fold diterapkan secara sistematis untuk mencari kombinasi hyperparameter optimal. Hasil optimasi menghasilkan konfigurasi terbaik dengan learning_rate sebesar 0,05, max_depth 3, n_estimators 200, min_child_weight 5, gamma 0,3, subsample 0,8, dan colsample_bytree 1,0, dengan rata-rata akurasi validasi silang (cross-validation) sebesar 84,52%. Evaluasi pada set pengujian menunjukkan akurasi sebesar 77,63%, presisi 78,57%, recall 80,49%, F1-score 79,52%, dan ROC-AUC 0,88. Recall yang tinggi menunjukkan kemampuan model dalam mendeteksi lebih dari 80% kasus positif penyakit jantung, yang kritis dalam konteks diagnosis medis. Kesimpulan dari studi ini adalah bahwa kombinasi XGBoost dengan strategi optimasi hyperparameter menggunakan GridSearchCV merupakan pendekatan yang efektif untuk prediksi risiko penyakit jantung, berpotensi menjadi alat bantu diagnostik yang berharga dalam praktik klinis
Prediksi Jumlah Penduduk Malaysia Berdasarkan Faktor Demografi dan Etnis Menggunakan Metode Random Forest Nurkholis Nurkholis; Andri Nofiar. Am; Hayatul Khairul Rahmat; Rahmad Akbar
Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK ) Vol. 3 No. 4 (2025): Desember: Jurnal Elektronika dan Teknik Informatika Terapan (JENTIK)
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jentik.v3i4.1550

Abstract

Accurate population prediction is essential for development planning, particularly in multiethnic countries such as Malaysia, yet conventional statistical approaches generally do not explicitly incorporate ethnic composition. This study aims to develop a population prediction model for Malaysia using a Random Forest Regressor that accounts for demographic factors and the composition of six ethnic groups. Historical data from 16 Malaysian states spanning 1981–2025 (673 state-year observations) were processed through feature engineering, including population lag variables, growth rate, and ethnic proportions, yielding 21 independent variables. The model was trained using a time-aware train-test split (80% training, 20% testing) and validated with 5-fold time series cross-validation. Results on the test set (2020–2025) showed strong performance, with R² = 0.9769, RMSE = 259 thousand people, MAE = 97 thousand people, and sMAPE = 3.17%. Feature importance analysis revealed that the previous year’s population (pop_lag1) and gender composition were the most dominant predictors. Projections indicate Malaysia’s population will reach approximately 38.5 million by 2030. These findings demonstrate that Random Forest with demographic-ethnic features can produce more accurate population predictions, providing a data-driven policy support tool for the Malaysian government.
Co-Authors Agung Pramono Agung Pramono Am, Andri Nofiar Andri Nofiar Andri Nofiar Am Andri Nofiar. Am Antoni Pribadi Belia Afifah Belia Afifah Belia Afifah Dzulhijjah Yetti Dzulhijjahyetti Febrianton, Adi Fenty Kurnia Oktorina Fenty Oktorina Fina Nasari Fitri Fitri habibie, indra Harmi Yelmi Hayatul Khairul Rahmat Indra Habibie Indra Habibie Lutfia Bamatraf Marhadi Sastra Mohammad Alkadri Perdana Muhammad Firjatullah Nur Akbar Muhammad Ridwan Muhammad Rukhshah Nina Veronika Nofiar, Andri Nurbit Nurbit Perdana, M. Alkadari Rahmad Akbar Rahman, Zumardi Ratu Natalia Marjani Ufayrah Rukhshah, Muhammad Siti Masrohatin Syawaluddin Khadafi Parinduri Tri Kurniaty Wahyuni Wahab Yedi Sispurwanto Zulfikar