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All Journal Jurnal Sistem Komputer dan Informatika (JSON) INOVTEK Polbeng - Seri Informatika
Putu Mahendra, I Gusti Agung
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Computer Science & IT Control & Systems Engineering

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Peningkatan Akurasi Klasifikasi Ikan kepe-kepe (Famili Chaetodontidae) dengan EfficientNetV2 dan Bayesian Hyperparameter Tuning Putu Mahendra, I Gusti Agung; Muhammad Ikhsan Wibowo; Zuliar Efendi
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.9028

Abstract

Identifikasi cepat dan akurat spesies Chaetodontidae penting untuk monitoring keanekaragaman hayati laut, namun pendekatan manual tidak skala dan rentan kesalahan pada dataset besar. GAP riset yang kami tangani adalah: (i) ketiadaan kajian yang secara khusus mengombinasikan EfficientNetV2 dengan Bayesian hyperparameter tuning untuk klasifikasi Chaetodontidae, dan (ii) belum adanya evaluasi yang menekankan efisiensi penalaan adaptif beserta dampaknya terhadap performa. Kebaruan (novelty) studi ini ialah perancangan pipeline ringkas-efisien berbasis EfficientNetV2 dengan Bayesian Optimization   (10 percobaan) pada learning rate, dropout, dan unfreeze backbone, dipadukan augmentasi kuat (MixUp, CutMix) serta regularisasi (label smoothing, L2). Dataset mencakup 1.427 citra/13 spesies dengan praproses center-crop 80% dan resize 224×224. Konfigurasi terbaik (unfreeze=True, dropout=0,2, LR 3,73×10⁻⁴) mencapai val-accuracy 92,75% dan akurasi uji 97%, dengan precision–recall rata-rata >95%, menunjukkan generalisasi yang baik bahkan pada kelas bermorfologi mirip. Dibanding penalaan manual/grid, pendekatan ini lebih hemat eksperimen sekaligus meningkatkan akurasi. Temuan tersebut menegaskan bahwa integrasi EfficientNetV2 + Bayesian tuning efektif dan siap diadopsi untuk sistem identifikasi–monitoring ikan berbasis citra pada konteks konservasi laut Indonesia.
Implementation of AES-128 Encryption for Fingerprint Template Protection in ESP32-Based Biometric Ticketing System Subandri, Muhammad Asep; Tedyyana, Agus; Putu Mahendra, I Gusti Agung
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/mkks1830

Abstract

Biometric ticketing systems utilizing fingerprint recognition provide enhanced security and convenience for passenger identification in public transportation. However, the transmission of fingerprint templates over wireless networks without adequate cryptographic protection exposes the system to interception attacks and privacy breaches. This research implements AES-128 encryption in Cipher Block Chaining (CBC) mode to protect fingerprint templates transmitted within an ESP32-based biometric ticketing system. The implementation leverages the ESP32’s integrated mbedTLS library with hardware acceleration to achieve efficient cryptographic operations. Experimental evaluation using 10 fingerprint template samples demonstrates a 100% success rate for encryption-decryption operations. Performance measurements indicate an average encryption latency of 2.30 ms and decryption latency of 2.10 ms, with a data size overhead of 32 bytes (6.25%) due to Initialization Vector (IV) and PKCS7 padding. The results confirm that the proposed encryption scheme effectively secures biometric data transmission while maintaining system responsiveness suitable for real-time applications.
Co-Authors Agus Tedyyana, Agus Muhammad Asep Subandri Muhammad Ikhsan Wibowo Zuliar Efendi