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Politeknik Negeri Lhokseumawe
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Klasifikasi Citra Songket Aceh Menggunakan Metode Probabilistic Neural Network Ismi Amalia; Indra Mawardi; Indrawati Indrawati; Muhammad Arhami; Muhammad Muhammad; Guntur Syahputra
Jurnal Serambi Engineering Vol 8, No 3 (2023): Juli 2023
Publisher : Fakultas Teknik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jse.v8i3.6132

Abstract

Tujuan penelitian ini untuk mengklasifikasikan citra Songket Aceh. Data penelitian menggunakan sepuluh motif Songket Aceh dan data diperoleh dari tempat usaha tenun Songket Nyak Mu. Tahapan penelitian ini adalah akuisisi citra, pra-proses, ekstraksi fitur, klasifikasi dan evaluasi. Ekstraksi fitur tekstur citra Songket Aceh menggunakan metode Gray Level Co-occurrence Matrix (GLCM). Fitur-fitur yang digunakan pada penelitian ini adalah entropy, energy, sum of squares: variance, difference entropy dan autocorrelation. Metode Probabilistic Neural Network (PNN) diaplikasikan untuk klasifikasi citra Songket Aceh. Metode Leave-One-Out Cross Validation (LOOCV) digunakan untuk pembagian data latih dan data uji. Hasil klasifikasi citra Songket Aceh dengan metode PNN adalah sebesar 93%.
Perbandingan Akurasi Algoritma Principal Component Analysis Dengan Algoritma Convolutional Neural Network Dalam Pengenalan Wajah indrawati; ismi amelia; Guntur Syahputra3; alfa nerfsiha; Amir D
Jurnal Serambi Engineering Vol. 9 No. 2 (2024): April 2024
Publisher : Faculty of Engineering, Universitas Serambi Mekkah

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Abstract

Facial recognition technology is used in various fields such as criminal identification, security purposes, finding missing people, diagnosing diseases, forensic investigations, identifying people on social media platforms, opening mobile phones, access control of meeting rooms, bank vaults. This paper presents a performance comparison between PCA and CNN algorithms. The aim of this research is to test the classification and compare the accuracy of PCA and CNN algorithms in face recognition. The method is to test the classification of Euclidean distance weights and compare performance tests which include; precision, recall and accuracy. The results showed that the accuracy of the PCA algorithm predicted TP by 100%, while the CNN algorithm predicted TP by 82%, while FN predicted 0. The recall performance on the PCA algorithm predicted TP by 100%, while on the CNN algorithm the recall performance predicted TP by 82%. Accuracy in the PCA algorithm is known that the TP prediction result is 100%, where TN, FN and FP are 0, while the accuracy performance in the CNN algorithm, TP is 82%, FN is 18%, TN and FP are 0.
Penerapan Metode Advanced Encryption Standard pada Sistem Penyimpanan Data Menggunakan Cloud Computing Sebagai Software-as-a-Service Novira Dwina; Nisha Khairani; Muhammad Nasir; Indrawati Indrawati
Journal of Artificial Intelligence and Software Engineering Vol 3, No 1 (2023)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v3i1.4183

Abstract

Cloud computing adalah model komputasi yang memungkinkan akses yang mudah dan fleksibel terhadap sumber daya komputasi seperti server, jaringan, penyimpanan, basis data, perangkat lunak, dan layanan lainnya melalui internet. Istilah "cloud" mengacu pada internet itu sendiri, dan cloud computing memungkinkan pengguna untuk menggunakan sumber daya ini tanpa perlu memiliki infrastruktur fisik atau perangkat keras secara langsung. Ada beberapa hal yang dapat diterapkan dalam cloud computing salah satunya layanan  penyimpanan  data.  Penelitian ini berfokus pada sistem penyimpanan data dengan menggunakan teknologi cloud computing untuk dapat mengupload file dari client menuju ke server ownCloud. OwnCloud merupakan aplikasi platform open-source yang memungkinkan untuk membuat layanan penyimpanan data dan berbagi berkas sendiri di lingkungan cloud. Diperlukan sistem keamanan yang baik dari ownCloud sendiri agar dapat melindungi file yang di upload dari pihak-pihak yang tidak bertanggung jawab (seperti halnya penyadapan) sebelum file tersebut dikirim menuju server. Penelitian ini menggunakan metode Advanced Encryption Standard (AES) yang merupakan standar enkripsi yang digunakan secara luas untuk melindungi data sensitif sehingga data yang dikirimkan tidak dapat diubah ataupun dirusak oleh pihak lain. Pengujian enkripsi dan dekripsi dengan algoritma AES kemudian dilakukan pada file txt dan mkv. Hasil pengujian terhadap file yang diuji dengan metode AES dapat terenkripsi dengan benar dan terhindar dari serangan bruteiforceiattack.Kata kunci— Cloud computing, ownCloud, Advanced Encryption StandardAbstractCloud computing is a computing model that enables easy and flexible access to computing resources such as servers, networks, storage, databases, software, and other services via the internet. The term "cloud" refers to the internet itself, and cloud computing allows users to use these resources without the need to own the physical infrastructure or hardware directly. There are several things that can be applied in cloud computing, one of which is data storage services.  This research focuses on data storage systems using cloud computing technology to be able to upload files from clients to the ownCloud server. OwnCloud is an open-source platform application that allows you to create your own data storage and file sharing services in a cloud environment. A good security system is needed from ownCloud itself so that it can protect uploaded files from irresponsible parties (such as wiretapping) before the file is sent to the server. This research uses the Advanced Encryption Standard (AES) method which is an encryption standard that is widely used to protect sensitive data so that the data sent cannot be changed or damaged by other parties. Encryption and decryption tests with the AES algorithm were then performed on txt and mkv files. The results of testing the files tested with the AES method can be encrypted correctly and protected from brute force attacks. Keywords— Cloud computing, ownCloud, Advanced Encryption Standard
Co-Authors alfa nerfsiha Amalia, Ismi Amir D Amri Amri Arhami, Muhammad Ariefin Ariefin Atthariq, Atthariq Cut Ita Erliana Dahlan Abdullah Guntur Syahputra Guntur Syahputra3 Hafizh Hidayat Hari Toha Hidayat Indra Mawardi ismi amelia Itsna Fachrina Jamaluddin Kania Putri Alaska Miswar Miswar Muhammad Ihsan Dacholfany Muhammad Muhammad Muhammad Nasir Nabila Maghfirah Naziruddin Naziruddin Nisha Khairani Novira Dwina Nurul Husna Taufiqurrahman Taufiqurrahman