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All Journal Jurnal Tekinkom (Teknik Informasi dan Komputer) STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer
Purwanti, Dian Sri
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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PERANCANGAN PENERAPAN ALGORITMA KRIPTOGRAFI AES 256 UNTUK KEAMANAN DATABASE APLIKASI MANAJEMEN SISWA Purwanti, Dian Sri; Fadli, Muhammad; Surono, Muhammad; Susanto, Erliyan Redy
STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Vol. 4 No. 2 (2025): Mei
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/storage.v4i2.5237

Abstract

Mengelola sistem informasi berbasis web memerlukan keamanan data, terutama di institusi pendidikan di mana informasi pribadi siswa disimpan. Basis data aplikasi manajemen siswa di SMKN 4 Bandar Lampung dibuat lebih aman dalam penelitian ini dengan memanfaatkan teknik kriptografi AES 256. Urgensi penelitian ini ditekankan oleh meningkatnya risiko terhadap sistem informasi akademik yang dapat mengakibatkan pelanggaran data dan penyalahgunaan. Implementasi dan analisis kinerja AES 256 untuk menjaga integritas dan keamanan data siswa adalah topik utama dari penelitian ini. Mengelola sistem informasi berbasis web memerlukan keamanan data, terutama di lembaga pendidikan di mana informasi pribadi siswa disimpan. Basis data aplikasi manajemen siswa di SMKN 4 Bandar Lampung dibuat lebih aman dalam penelitian ini dengan memanfaatkan teknik kriptografi AES 256. Urgensi penelitian ini ditekankan oleh meningkatnya risiko terhadap sistem informasi akademik yang dapat mengakibatkan pelanggaran data dan penyalahgunaan. Implementasi dan analisis kinerja AES 256 untuk menjaga keamanan dan integritas data siswa adalah topik utama dari penelitian ini.
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.
Co-Authors Dewantoro, Mahendra Fadli, Muhammad Muhammad Fadli Ryan Randy Suryono Surono, Muhammad Susanto, Erliyan Redy