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All Journal JURNAL PENDIDIKAN TAMBUSAI Progresif: Jurnal Ilmiah Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) Afeksi: Jurnal Penelitian dan Evaluasi Pendidikan INTERNATIONAL JOURNAL OF EDUCATION, INFORMATION TECHNOLOGY, AND OTHERS Jurnal Pendidikan dan Pembelajaran Jurnal Sains dan Teknologi STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Jurnal Ilmiah Informatika dan Ilmu Komputer
Fadli, Muhammad
Politeknik Negeri Lampung
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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DIAGNOSIS CEREBROVASCULAR ACCIDENTS MENGGUNAKAN TEKNIK SMOTEEN DENGAN MEMBANDINGKAN METODE KLASIFIKASI DECISION TREE DAN XGBOOST Fadli, Muhammad; Purwanti, Dian Sri; Surono, Muhammad; Dewantoro, Mahendra; Suryono, Ryan Randy
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2025

Abstract

Cerebrovascular Accident (stroke) is a critical health issue in Indonesia, often leading to high mortality and long-term disability. Early detection through machine learning has emerged as a promising approach to improve diagnosis and treatment outcomes. This study aims to compare the performance of two classification algorithms, Decision Tree and Extreme Gradient Boosting (XGBoost), in diagnosing stroke using the SMOTEENN (Synthetic Minority Over-sampling Technique and Edited Nearest Neighbor) technique to address data imbalance. The dataset used contains 5110 samples with 11 independent variables and one dependent variable (stroke status), obtained from a public repository. After preprocessing and data balancing, both models were trained and evaluated based on accuracy, precision, recall, and F1-score. The results show that XGBoost outperforms Decision Tree in all evaluation metrics, achieving an accuracy of 96.48%, precision of 94.75%, recall of 99.03%, and F1-score of 96.85%, compared to Decision Tree’s accuracy of 91.55%, precision of 89.82%, recall of 95.32%, and F1-score of 92.49%. These findings confirm that the combination of XGBoost and SMOTEENN provides a more effective and reliable classification model for early stroke diagnosis. Future research is encouraged to explore deep learning techniques to further enhance diagnostic accuracy.
Modeling Personal Agency through Fiction: Insights from A Good Girl’s Guide to Murder Dewi Suryanti; Muhammad Fadli; Retno Anggraini; Refdi Akmal
DIAJAR: Jurnal Pendidikan dan Pembelajaran Vol. 4 No. 3 (2025): Juli 2025
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/diajar.v4i3.4651

Abstract

A Good Girl’s Guide to Murder, a young adult novel by Holly Jackson, presents a compelling narrative of mystery while offering extensive opportunities to explore social and psychological dynamics through its main character. This study investigates the potential of the novel as a pedagogical tool by bridging young adult fiction and learning theories; analyzing how the protagonist, Pippa Fitz-Amobi, embodies Albert Bandura’s social cognitive theory. The research proposes fresh insights into their integration. Through the examination of personal agency, this study reveals how Pip’s agency shapes her decision-making and resilience in navigating complex situations. The findings highlight the effectiveness of young adult literature in illustrating fundamental learning principles, offering a modeling personal agency for its young readers.
Co-Authors Budiman, Ega David Kurniawan Dewantoro, Mahendra Dewi Suryanti Dian Puspita Sari Doris Juarsa Fajri, Ratu Chaterine Fudholi, Muhammad Fahmi Indriani, Yulia Kevinda Sari Marbudi, Marbudi Maruly, Riyan Meidasari, Reski Prasetio, Mugi Purwanti, Dian Sri Refdi Akmal, Refdi Retno Anggraini Retno Anggraini Ryan Randy Suryono Sanjaya, Ival Setyani, Tria Suffah, Faila Surono, Muhammad Suryanti, Dewi Susanto, Erliyan Redy Susanto, Erliyan Redy Syaifuddin Muhammad Mirza Valdi Mughni Budiman Windriani, Dian