(untitled) (Journal of Muhammadiyah’s Application Technology, 2026)
ABSTRAK Perkembangan teknologi informasi meningkatkan penggunaan email institusi sebagai sarana komunikasi akademik, namun juga menimbulkan risiko keamanan seperti spam, phishing, dan akses tidak sah. Penelitian ini bertujuan untuk mengimplementasikan model Autoencoder dalam mendeteksi anomali pada log email mahasiswa Universitas Muhammadiyah Makassar. Metode yang digunakan adalah pendekatan kuantitatif berbasis unsupervised learning dengan memanfaatkan data log email yang telah melalui tahap preprocessing dan feature engineering. Model Autoencoder dirancang menggunakan arsitektur encoder-decoder dengan lima hidden layer untuk mempelajari pola aktivitas email normal. Proses deteksi anomali dilakukan menggunakan nilai reconstruction error dengan threshold pada persentil ke-99. Hasil penelitian menunjukkan bahwa model mampu mendeteksi aktivitas anomali dengan baik, di mana sekitar 1% data teridentifikasi sebagai anomali dari keseluruhan dataset. Temuan ini menunjukkan bahwa metode Autoencoder efektif digunakan untuk mendeteksi aktivitas mencurigakan pada sistem email institusi dan berpotensi mendukung peningkatan keamanan sistem informasi di lingkungan perguruan tinggi. Kata Kunci: Autoencoder, deteksi anomali, log email, unsupervised learning, keamanan sistem informasi. ABSTRACT The development of information technology has increased the use of institutional email as a medium for academic communication, but it has also introduced security risks such as spam, phishing, and unauthorized access. This study aims to implement an Autoencoder model for anomaly detection in student email logs at Universitas Muhammadiyah Makassar. The research employed a quantitative approach based on unsupervised learning using email log data that had undergone preprocessing and feature engineering. The Autoencoder model was designed using an encoder-decoder architecture with five hidden layers to learn normal email activity patterns. Anomaly detection was performed using reconstruction error values with a threshold set at the 99th percentile. The results showed that the model was able to detect anomalous activities effectively, where approximately 1% of the data were identified as anomalies from the entire dataset. These findings indicate that the Autoencoder method is effective for detecting suspicious activities in institutional email systems and has the potential to enhance information system security in higher education environments. Keywords: Autoencoder, anomaly detection, email logs, unsupervised learning, information system security.