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All Journal Al-Isyraq: Jurnal Bimbingan, Penyuluhan, dan Konseling Islam Journal Medical Informatics Technology
Yanto, Y
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Predictive Modeling of Osteoporosis Risk Factors using XGBoost and Bagging Ensemble Technique Irmawati, I; Herdit Juningsih, Eka; Yanto, Y
Journal Medical Informatics Technology Volume 2 No. 1, March 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i1.27

Abstract

This study presents a predictive modeling framework for osteoporosis risk assessment using ensemble techniques, specifically XGBoost and Bagging. Leveraging a dataset comprising comprehensive health factors influencing osteoporosis development, including demographic details, lifestyle choices, medical history, and bone health indicators, the aim is to facilitate accurate identification of individuals at risk. The dataset consists of 1958 samples, evenly distributed between osteoporosis-positive and osteoporosis-negative cases. The methodology involves the separation of features and labels, followed by data splitting into training and testing sets. XGBoost, a powerful gradient boosting algorithm, is employed as the base estimator within a Bagging ensemble, enhancing predictive accuracy and generalization. The model is trained on the training set and evaluated using cross-validation techniques to ensure robustness and mitigate overfitting. The results of the classification report demonstrate promising performance metrics, with an overall accuracy of 88% on the test set. Precision and recall scores indicate strong predictive capabilities, particularly in correctly identifying osteoporosis-positive cases. The novel integration of XGBoost within a Bagging ensemble provides an innovative approach to osteoporosis risk prediction, harnessing the strengths of both algorithms to improve model performance. This research contributes to the advancement of osteoporosis management and prevention strategies by providing a reliable tool for early risk assessment. The combination of machine learning techniques with comprehensive health data offers a valuable approach to personalized healthcare, enabling targeted interventions and optimized resource allocation. Ultimately, this study aims to enhance patient outcomes and reduce the burden of osteoporosis-related morbidity and mortality.
EFEKTIVITAS LAYANAN BIMBINGAN KELOMPOK TEKNIK ROLE PLAYING UNTUK MENINGKATKAN KEPERCAYAAN DIRI SISWA KELAS XI DI SMA NEGERI 8 KOTA JAMBI Riyanto, Dedi; Yulianto, Y; Yanto, Y
Al-Isyraq: Jurnal Bimbingan, Penyuluhan, dan Konseling Islam Vol 7, No 2 (2024): Juli
Publisher : PABKI (Perkumpulan Ahli Bimbingan dan Konseling Islam) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59027/alisyraq.v7i2.571

Abstract

This research aims to increase the self-confidence of students in class XI of SMA Negeri 8 Jambi City. The approach to this research uses an experimental approach with a correlation method. The sampling technique used purposive sampling with a sample size of 10 people and 8 students who had low self-confidence were taken. Based on the research results, a significance value of 0.000 kecil dari 0.05 was obtained and the calculated t value was kecil t table or -0.675 kecil dari 0.2172. So Ho was rejected, which means there was an increase in students' self-confidence after undergoing treatment 3 times through the role playing technique group guidance service.
Co-Authors Eka Herdit Juningsih Irmawati, I Riyanto, Dedi Yulianto, Y