EVALUASI PENENTUAN PERSONIL IT INFRASTRUKTUR PADA TNI AL MENGGUNAKAN KOMBINASI METODE AHP DAN VIKOR

In the current development of technology, information technology is not only used in the economic field, but is divided into various fields, including the military world of the Navy.  In order to promote the capability of human resources in the field of IT infrastructure in the Navy, it is necessary to select the right personnel in the maintenance of IT infrastructure assets, so as to obtain competent personnel output in answering future challenges. To facilitate the determination of the right IT infrastructure personnel, the author implements a system by combining the AHP and Vikor methods based on Python. The AHP method is used to determine the weight of criteria in the selection of IT personnel, and the Vikor method is used to rank the appropriate IT personnel. With this system, it is hoped that it can be a supporting application to help select IT personnel according to tactical, technical, strategic criteria.Dalam Perkembangan Teknologi Saat ini, Teknologi informasi tidak hanya digunakan dalam bidang ekonomi namun terbagi menjadi beberapa bidang termasuk dalam dunia militer TNI AL.  Untuk membina kemampuan SDM dalam bidang Infrastruktur IT pada TNI AL, dibutuhkan pemilihan personil yang tepat dalam menjaga asset infrastruktur IT, sehingga didapatkan keluaran SDM yang kompeten dalam menjawab tantangan ke depan. Untuk mempermudah penentuan personil IT Infrastruktur yang tepat penulis melakukan implementasi sistem dengan melakukan kombinasi metode AHP dan Vikor berbasis Python. Metode AHP akan digunakan sebagai penentuan bobot dari kriteria dalam pemilihan personil IT dan Metode Vikor untuk melakukan perangkingan terkait personil IT yang sesuai. Dengan adanya system ini diharapkan dapet menjadi aplikasi pendukung untuk membantu pemilihan personil IT yang sesuai dengan kriteria Taktis , Teknis, Strategis

Study and Analysis of End-to-End Encryption Message Security Using Diffie-Hellman Key Exchange Encryption

The development of the field of communication has progressed rapidly. One example is a message exchange application like Whatsapp. The advancement of technology and innovation in the field of communication has allowed us to connect with people around the world in an easier and faster way. However, with advances in communication technology, new challenges arise related to information security and privacy of messages that have been sent. One solution to overcome this problem is to apply Cryptographic Techniques. In cryptography, data sent over the network will be disguised in such a way with encryption techniques so that even if the data can be read, it cannot be understood by unauthorized parties. The data to be sent without being encoded is known as plaintext, and after being disguised in an encoding way, this plaintext will turn into ciphertext. The method chosen for this journal is the Diffie-Hellman Key Exchange. In this journal, an analysis will be carried out regarding the end-to-end process of securing encrypted messages using the Python programming language

Assessing Manual Dataset Creation For Xauusd Market Prediction : A Comparative Study Logistic Regression And Decision Tree Model

This study aims to develop a simplified dataset for more effective market prediction, focusing on the Forex trading of XAUUSD (Gold/USD). The dataset was gathered from the TradingView platform, covering the period from March 4, 2023, to December 21, 2023. The data collection method involved intensive observation of daily and weekly charts, utilizing Daily and Weekly Moving Average (MA) indicators and the concept of breakout. The analysis focused on measuring the distance between the Daily MA at the beginning and end of the period (start and stop), and utilizing this data for entry strategy in the following three time periods. The trading strategy adopted involves the simultaneous use of Buy and Sell orders, with a Stop Loss (SL) to Take Profit (TP) ratio of 1:2. TP was adjusted to accommodate aggressive price movements, while SL remained constant. The collected data was meticulously recorded and stored in Excel format for further analysis.With the prepared dataset, this research applies two AI models, Logistic Regression and Decision Tree, to predict the best trading decision – Buy or Sell. The study aims not only to create a useful dataset for market prediction but also to compare the effectiveness of two different AI methods in the context of Forex trading of XAUUSD. The results are expected to provide insights into which model is more accurate and efficient in analyzing and predicting market trends, with practical implications for traders and market analysts

Game Theorical Concept for Denial of Services (DoS) Attacks

Information security is a crucial aspect in today's digital era, where increased connectivity and data exchange involves a high risk of denial of services attacks. Increasing cybersecurity and privacy issues require more effective defense mechanisms to counter these threats. This research was conducted through design formulation using game theory for denial of service attacks. First scheme , use Bayes' theorem to determine the probability of a DDoS attack. The probability is equal.  The probability of attack and defense is 50-50.  Second scheme, one of players is dominan. A game theoretic framework as an approach to find out the possibility of denial of services between pairs of attacking/defending nodes using a Bayesian formulation. Game modeling can propose for developing better mitigation and detection approaches

Audio, Text, Image, and Video Digital Watermarking Techniques for Security of Media Digital

The proliferation of multimedia content as digital media assets, encompassing audio, text, images, and video, has led to increased risks of unauthorized usage and copyright infringement. Online piracy serves as a prominent example of such misuse. To address these challenges, watermarking techniques have been developed to protect the copyright of digital media while maintaining the integrity of the underlying content. Key characteristics evaluated in watermarking methods include capability, privacy, toughness, and invisibility, with robustness playing a crucial role. This paper presents a comparative analysis of digital watermarking methods, highlighting the superior security and effective watermark image recovery offered by singular value decomposition. The research community has shown significant interest in watermarking, resulting in the development of various methods in both the spatial and transform domains. Transform domain approaches such as Discrete Cosine Transform, Discrete Wavelet Transform, and Singular Value Decomposition, along with their interconnections, have been explored to enhance the effectiveness of digital watermarking methods

Improvement Of An Intrusion Detection System Based On Deep Belief Networks Models: A Review

Technology is rapidly evolving in a world powered by social networks, online transactions, cloud computing, and automated processes.  However, as technology develops, equal  cybercrime.  Cyber attacks are increasing rapidly, making cybersecurity a challenge in the digital era.  Intrusion detection systems (IDS) are an advancement that enhances  network security and protects an organization's data.  IDS helps  network administrators  detect  malicious activity within the network and alerts administrators to protect data  by taking  appropriate measures against these attacks.  Deep Belief Networks (DBN) are generative graphics models formed by stacking multiple Restricted Boltzmann Machines (RBMs).  High-dimensional representations can be identified and learned.Improving and evaluating Deep Belief Networks (DBN) for detecting cyber-attacks in a network of connected devices using the CICIDS2017 dataset. Several class balancing techniques were aplied and evaluated. The recomendation to improve IDS based on DBN is collect more data, increase the number of layers, tune the hyperparameters, regularize the network, and use more efficient training algorithms

Advanced Detection System Milkfish Formalin Android-Based Method Based on Image Eye Using Naive Bayes Classifier

In this paper researcher is trying to make an android-based application that can identify fish with formalin with previous version with adding some more data training. The method used in researcher methods naïve Bayes classifier as a detector (detector) with the object input in the form of fish eye image. The steps in the study include the training and testing process. In the training process used to build the model naïve classifier and estimation parameters. While testing process, implement the results of the model and parameter estimation have been built to detect fish formalin or not formalin. The trial results demonstrate the ability-based applications using the naïve Bayes 98.3% for object dimensions 10x10 imag

Modified Of Evaluating Shallow And Deep Neural Networks For Network Intrusion Detection Systems In Cyber Security

Abstract—Intrusion Detection Systems (IDS) have developed into a crucial layer in all contemporary Information and Communication Technology (ICT) systems as a result of a demand for cyber safety in real-world situations. IDS advises integrating Deep Neural Networks (DNN) because, among other things, it might be challenging to identify certain types of assaults and advanced cyberattacks are complex (DNNs). DNNs were employed in this study to anticipate Network Intrusion Detection System attacks (N-IDS). The network has been trained and benchmarked using the KDDCup-'99 dataset, and a DNN with a learning rate of 0.001 is used, running for 10 epochs for using the activation model experiment and 8 epochs for using the TensorFlow experiment. Keywords—Intrusion detection system, deep neural networks, machine learning, deep learnin