Imam Nurhadi Purwanto
Brawijaya University

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MODEL EOQ (ECONOMIC ORDER QUANTITY) PADA PERMINTAAN LINEAR, KERUSAKAN PRODUK, DAN IJIN PENUNDAAN DALAM PEMBAYARAN Wahyu T.M., Mega; Marsudi, Marsudi; Purwanto, Imam Nurhadi
Jurnal Mahasiswa Matematika Vol 1, No 4 (2013)
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METODE QUASI-NEWTON MENGGUNAKAN FORMULA POWELL-SYMMETRIC-BROYDEN DAN SYMMETRIC-RANK-ONE Agustin, Winda; Purwanto, Imam Nurhadi
Jurnal Mahasiswa Matematika Vol 1, No 4 (2013)
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PENENTUAN PEMENANG TENDER SECARA ELEKTRONIK HOSTING INTERNET 10 MBPS DENGAN MENGGUNAKAN METODE WEIGHTED PRODUCT (WP) DAN METODE ANALYTIC HIERARCHY PROCESS (AHP) Rahmasari, Fauzhia; Purwanto, Imam Nurhadi
Jurnal Mahasiswa Matematika Vol 2, No 1 (2014)
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PENERAPAN MODEL ECONOMIC PRODUCTION QUANTITY (EPQ) BACKORDER PADA PRODUKSI SELANG SPIRAL MULTI DIAMETER Jayanti, Melisa Rizki; Purwanto, Imam Nurhadi
Jurnal Mahasiswa Matematika Vol 2, No 1 (2014)
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PENERAPAN PROGRAM LINEAR FUZZY GOAL PROGRAMMING DALAM PENDISTRIBUSIAN TAS PADA CV. GOENO Latunussa, Welhelmina P.; Purwanto, Imam Nurhadi
Jurnal Mahasiswa Matematika Vol 2, No 2 (2014)
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OPTIMALISASI JADWAL PETUGAS FRONT OFFICE HOTEL MENGGUNAKAN MODEL 0-1 GOAL PROGRAMMING Mentari, Yolanda Putri; Purwanto, Imam Nurhadi
Jurnal Mahasiswa Matematika Vol 2, No 2 (2014)
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PENGAMBILAN KEPUTUSAN TARGET USIA MENIKAH MENGGUNAKAN FUZZY AHP-TOPSIS (Studi Kasus di Kecamatan Klojen Kota Malang) Mudhaniva, Inne Byas; Purwanto, Imam Nurhadi
Jurnal Mahasiswa Matematika Vol 3, No 3 (2015)
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PENERAPAN METODE GOAL PROGRAMMING PADA OPTIMASI PERENCANAAN PRODUKSI JAKET DAN KAOS (Studi Kasus : UKM “Tomswork”, Malang) Ranggasasmita, Ivan Takjud; Purwanto, Imam Nurhadi
Jurnal Mahasiswa Matematika Vol 3, No 3 (2015)
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PENGAMBILAN KEPUTUSAN TARGET USIA MENIKAH MENGGUNAKAN FUZZY AHP-TOPSIS (Studi Kasus di Kecamatan Klojen Kota Malang) Mudhaniva, Inne Byas; Purwanto, Imam Nurhadi
Jurnal Mahasiswa Matematika Vol 3, No 5 (2015)
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Health Risk Classification Using XGBoost with Bayesian Hyperparameter Optimization Anam, Syaiful; Purwanto, Imam Nurhadi; Mahanani, Dwi Mifta; Yusuf, Feby Indriana; Rasikhun, Hady
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6307

Abstract

Health risk classification is important. However, health risk classification is challenging to address using conventional analytical techniques. The XGBoost algorithm offers many advantages over the traditional methods for risk classification. Hyperparameter Optimization (HO) of XGBoost is critical for maximizing the performance of the XGBoost algorithm. The manual selection of hyperparameters requires a large amount of time and computational resources. Automatic HO is needed to avoid this problem. Several studies have shown that Bayesian Optimization (BO) works better than Grid Search (GS) or Random Search (RS). Based on these problems, this study proposes health risk classification using XGBoost with Bayesian Hyperparameters Optimization. The goal of this study is to reduce the time required to select the best XGBoost hyperparameters and improve the accuracy and generalization of XGBoost performance in health risk classification. The variables used were patient demographics and medical information, including age, blood pressure, cholesterol, and lifestyle variables. The experimental results show that the proposed approach outperforms other well-known ML techniques and the XGBoost method without HO. The average accuracy, precision, recall and f1-score produced by the proposed method are 0.926, 0.920, 0.928, and 0.923, respectively. However, improvements are needed to obtain a faster and more accurate method in the future.