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All Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Telematika Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD
Margolang, Khairul Fadhli
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Computer Science & IT Electrical & Electronics Engineering

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Pengenalan Masker Wajah Menggunakan VGG-16 dan Multilayer Perceptron Margolang, Khairul Fadhli; Riyadi, Sugeng; Rosnelly, Rika; Wanayumini, Wanayumini
Jurnal Telematika Vol. 17 No. 2 (2022)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v17i2.519

Abstract

Penggunaan masker wajah pada masa pandemi Covid-19 dapat diidentifikasi berdasarkan citra yang diambil dari wajah seseorang kemudian diklasifikasi berdasarkan hasil ekstraksi fiturnya. VGG 16 merupakan sebuah pre-trained CNN model yang dapat mengekstrak 4.096 fitur dari sebuah citra dan melakukan transfer learning kepada algoritme multilayer perceptron dalam mengklasifikasikan seseorang menggunakan masker wajah atau tidak. Hasil dari penelitian ini menunjukkan bahwa kombinasi aktivasi ReLu dengan optimasi adaptive moment (Adam) dan stochastic gradient descent (SGD), kombinasi ReLu dan Adam, menghasilkan performa klasifikasi terbaik dengan nilai accuracy, precision, dan recall sebesar 98,1%.
Analisis Keamanan Data Rekam Medis Digital Menggunakan Algoritma Kriptografi AES Hakim, Arief Rahman; Margolang, Khairul Fadhli
Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Vol. 8 No. 2 (2025): J-SISKO TECH EDISI JULI
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jsk.v8i2.11461

Abstract

Keamanan data rekam medis digital merupakan aspek krusial dalam sistem informasi kesehatan elektronik (EHR). Kriptografi berperan penting dalam menjaga kerahasiaan dan integritas informasi pasien. Algoritma Advanced Encryption Standard (AES) dikenal luas sebagai metode kriptografi simetris yang kuat dan efisien. Penelitian ini bertujuan untuk menganalisis keamanan dan efisiensi algoritma AES dalam melindungi data rekam medis digital. Penelitian dilakukan melalui simulasi enkripsi dan dekripsi terhadap data medis dalam berbagai ukuran file menggunakan Python. Hasil menunjukkan bahwa AES mampu menjaga integritas dan kerahasiaan data, serta memiliki waktu proses yang relatif cepat. Penelitian ini memberikan kontribusi terhadap pengembangan sistem keamanan data medis berbasis enkripsi. Kata Kunci : Keamanan Data, Rekam Medis Digital, AES, Kriptografi, EHR
Early Detection of Diabetes Using a Machine Learning Model Based on Laboratory Data Hakim, Arief Rahman; Br Tarigan, Yuni Franciska; Margolang, Khairul Fadhli
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 10, No 1 (2025): Vol 10, No 1 (2025) : InfoTekJar September
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v10i1.12810

Abstract

Diabetes mellitus is a chronic disease whose prevalence continues to increase worldwide, with a projected number of sufferers reaching 643 million by 2030. Early detection of diabetes is crucial to prevent serious complications such as cardiovascular disease, kidney failure, and nerve damage. This study aims to compare the performance of four machine learning algorithms (Random Forest, Support Vector Machine, Logistic Regression, and K-Nearest Neighbors) in detecting diabetes based on clinical parameters, and to identify the most significant predictor variables. The study uses the Pima Indians Diabetes dataset consisting of 768 samples with 8 predictor variables (number of pregnancies, glucose, blood pressure, skin thickness, insulin, BMI, diabetes pedigree function, and age). Data is divided into a training set (70%) and a testing set (30%) using stratified sampling. Data preprocessing includes handling missing values, feature scaling using StandardScaler, and handling imbalanced data using the SMOTE technique. Performance evaluation uses accuracy, precision, recall, F1-score, and Area Under Curve (AUC-ROC) metrics. Results show that the Random Forest model achieves the best performance with an accuracy of 81.8%, precision of 79.2%, recall of 78.5%, F1-score of 78.8%, and AUC of 0.88. Support Vector Machine achieves an accuracy of 78.0%, Logistic Regression 76.0%, and K-Nearest Neighbors 74.5%. Feature importance analysis identifies glucose (28.5%), BMI (19.8%), and age (16.5%) as the most significant predictors in diabetes detection. The Random Forest model produces 17 false negatives and 12 false positives from 231 testing samples. The study concludes that Random Forest is the most effective algorithm for early diabetes detection with good accuracy and superior interpretability through feature importance.
Co-Authors Arief Rahman Hakim Br Tarigan, Yuni Franciska Rika Rosnelly, Rika Sugeng Riyadi Wanayumini