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DETEKSI EXPLOIT REVERSE HTTPS DENGAN METODE LOGISTIC REGRESSION
Reverse HTTPS attacks conceal malware communication within encrypted traffic. This research detects these threats using the Logistic Regression method on raw data from the Mobile-Trojan Metasploit Traffic. The data flow feature extraction results obtained through the CICFlowMeter tool are crucial for preserving the dataset's overall quality. Modeling was conducted without resampling techniques …
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- x, 111 hlm.; ilus.; 29 cm
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- T1949222026
EKSTRAKSI INDICATOR OF COMPROMISE MENGGUNAKAN BILSTM-CRF DENGAN EMBEDDING FAS…
Indicator of Compromise (IoC) merupakan artefak digital penting dalam Cyber Threat Intelligence (CTI), seperti IP address, domain, URL, hash file, dan nama malware, yang digunakan untuk membantu identifikasi aktivitas berbahaya. Ekstraksi IoC dari teks CTI masih menjadi tantangan karena data umumnya bersifat semi-terstruktur serta mengandung variasi istilah teknis yang kompleks. Penelitian ini …
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- xiv, 85 hlm.; ilus.; 29 cm
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- T1956512026
ANALISIS KINERJA CDN CLOUDFLARE TERHADAP PARAMETER QUALITY OF SERVICE DAN QUA…
3D video streaming services require a network infrastructure capable of supporting large-scale data transmission in a stable and efficient manner. A Content Delivery Network (CDN) plays an important role in improving content distribution performance by bringing servers closer to end users. This study aims to analyze the performance of the Cloudflare CDN in terms of Quality of Service (QoS) and …
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- xvii, 141 hlm.; ilus.; tab.; 29 cm.
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- T2014432026
IMPLEMENTATION AND ANALYSIS OF THE STATE-ACTION-REWARD-STATE-ACTION (SARSA) A…
Flooding is one Malware is one of the major threats to cybersecurity and continues to evolve, making it increasingly difficult to detect using conventional methods. This study aims to implement and analyze the State-Action-Reward-State-Action (SARSA) and Deep Q-Network (DQN) algorithms for binary malware classification based on static features extracted from the dataset. Data preprocessing, fea…
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- xvi, 189 hlm.; ilus.; tab.; 29 cm.
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- T2011382026
SEGMENTASI JANTUNG ANAK MENGGUNAKAN VISION TRANSFORMER DAN SEGMENT ANYTHING M…
Congenital heart disease in children requires accurate early detection through ultrasonography (USG) imaging. This study aims to develop a deep learning–based system for view classification and hole segmentation in pediatric cardiac images. A Vision Transformer (ViT) model was employed to classify five cardiac views (4CH, 5CH, LA, SA, and SUB), while segmentation was performed using YOLO11-se…
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- xvii, 66 hlm.; ilus.; 29 cm
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- T1951032026
SELEKSI FITUR KLASIFIKASI PENYAKIT INFARK MIOKARD MENGGUNAKAN GENETIC ALGORIT…
In this study, the feature selection method employed is the Genetic Algorithm (GA), while the classification method used is the Support Vector Machine (SVM), with the aim of improving the performance of myocardial infarction disease classification. The study utilizes the Myocardial Infarction Complications dataset, which consists of 123 features. The research process includes several preprocess…
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- xiv, 81 hlm.; ilus.; tab.; 29 cm.
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- T2012862026
OPTIMASI ALGORITMA K-NEAREST NEIGHBOR (KNN) DENGAN NORMALISASI DAN SELEKSI FI…
Myocardial infarction complications require early, accurate prediction for clinical intervention. This study optimizes the K-nearest neighbor (KNN) algorithm to classify these complications using Z-Score normalization and three feature selection methods: Information Gain, Gain Ratio, and Symmetrical Uncertainty.Using a dataset from the UCI Machine Learning Repository, preprocessing included mea…
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- xiii, 92 hlm.; ilus.; tab.; 29 cm.
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- T2012632026
PENERAPAN METODE RANDOM FOREST DALAM SISTEM PERINGATAN BANJIR BERBASIS INTERN…
Flooding is one of the most common natural disasters and can cause significant losses. This study aims to design and develop an Internet of Things (IoT)-based flood warning system using the Random Forest algorithm. The system utilizes BMP280 sensors to measure water level and an optocoupler sensor to measure water flow velocity. Sensor data are processed by an ESP32 microcontroller and sent to …
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- xvi, 97 hlm.; ilus.; tab.; 29 cm.
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- T2012592026
PERBANDINGAN IOB1 DAN IOE TAGGING SCHEME PADA NAMED ENTITY RECOGNITION UNTUK …
Advanced Persistent Threat (APT) reports in Cyber Threat Intelligence (CTI) are generally presented as unstructured text, making them difficult to analyze manually and requiring automated methods to extract important entities quickly and accurately. This study aims to analyze the performance of Named Entity Recognition (NER) using a Bidirectional Long Short-Term Memory (BiLSTM) model with two s…
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- xviii, 56 hlm.; ilus.; 29 cm
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- T1956712026
EKSTRAKSI INDICATOR OF COMPROMISE PADA CYBER THREAT INTELLIGENCE MENGGUNAKAN …
Penelitian ini bertujuan untuk mengembangkan sistem ekstraksi Indicator of Compromise (IoC) dari teks Cyber Threat Intelligence (CTI) menggunakan pendekatan Named Entity Recognition (NER). Permasalahan utama dalam CTI adalah data yang bersifat semi-terstruktur sehingga menyulitkan identifikasi informasi penting secara manual. Metode yang digunakan adalah model BiLSTM-CRF dengan skema BIO untuk …
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- 93 hlm.; ilus.; 29 cm
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- T1956412026
IMAGE CAPTIONING BERBASIS DEEP LEARNING PADA CITRA REUMATOLOGI
The advancement of medical imaging technology has produced increasingly complex data that require fast and accurate analysis. However, the interpretation of medical images still requires considerable time, relies heavily on medical professionals, and is prone to interpretation errors. Therefore, this study aims to develop an Image Captioning system capable of automatically generating descriptio…
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- xvii, 106 hlm.; ilus.; tab.; 29 cm.
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- T2009632026
IMAGE CAPTIONING BERBASIS DEEP LEARNING PADA CITRA JANTUNG ANAK
Diagnosis of congenital heart disease in children, namely Atrial Septal Defect (ASD), Atrioventricular Septal Defect (AVSD), and Ventricular Septal Defect (VSD), is hindered by the limited availability of cardiologists in interpreting echocardiography images, necessitating an automated system based on artificial intelligence. This study implements and evaluates a deep learning-based image capti…
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- xvi, 112 hlm.; ilus.; tab.; 29 cm.
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- T1999602026
GENERATIVE AI BERBASIS DEEP LEARNING PADA ANALISIS CITRA ULTRASONOGRAFI
Ultrasound (USG) image analysis for detecting Congenital Heart Disease (CHD), such as Atrial Septal Defect (ASD), Ventricular Septal Defect (VSD), and Atrioventricular Septal Defect (AVSD), is still limited by the scarcity of clinical datasets. This study evaluates three Generative Adversarial Network (GAN) architectures, namely Deep Convolutional GAN (DCGAN), Wasserstein GAN with Gradient Pena…
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- xiv, 103 hlm.; ilus.; tab.; 29 cm.
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- T2009922026
PERANCANGAN PROTOTIPE CLINICAL DECISION SUPPORT SYSTEM UNTUK PREDIKSI RISIKO …
Type 2 diabetes is a chronic disease that requires early detection to reduce the risk of complications. This study aims to design a web-based prototype Clinical Decision Support System (CDSS) to predict diabetes risk using the XGBoost algorithm and improve interpretability through the SHAP method. The dataset used was obtained from Kaggle and consisted of approximately 100,000 records with eigh…
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- xv, 161 hlm.; ilus.; tab.; 29 cm.
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- T2007582026
SISTEM PREDIKSI BANJIR MENGGUNAKAN SENSOR TEKANAN DAN WATERFLOW SENSOR DENGAN…
Most current flood prediction studies rely on secondary data with limited temporal resolution and Deep Learning models with high computational demands. This research addresses this gap by designing an early warning system that is computationally efficient, utilizing direct physical sensor data within a controlled simulation. The objectives are to measure water flow velocity (m/s) using an optoc…
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- xv, 100 hlm.; ilus.; tab.; 29 cm.
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- T2005362026
ANALISIS KOMPARATIF MACHINE LEARNING DAN EXPLAINABLE ARTIFICIAL INTELLIGENCE …
Traffic accidents are a road safety issue that can result in fatalities. This study aims to compare the performance of machine learning models namely, Random Forest, XGBoost and LightGBM and to explain the prediction results of the best model using the Explainable Artificial Intelligence (XAI) approach, with SHapley Additive exPlanations (SHAP) employed as the interpretation method. The data us…
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- xiv, 98 hlm.; ilus.; tab.; 29 cm.
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- T2005642026
EVALUASI METODE UNSUPERVISED LEARNING DALAM PENGELOMPOKAN DATA KECELAKAAN LAL…
Traffic accidents are one of the transportation issues that require distribution analysis to identify areas with different accident characteristics. This study aims to compare the K-Means, DBSCAN, and Hierarchical Clustering methods in clustering traffic accident data based on geographical location and accident severity. The dataset used is derived from traffic accident data in the United Kingd…
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- xvi, 109 hlm.; ilus.; tab.; 29 cm.
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- T2005732026
PENINGKATAN KINERJA KLASIFIKASI GANGGUAN JANTUNG PADA SINYAL ELEKTROKARDIOGRA…
Electrocardiogram (ECG) signals represent the electrical activity of the heart and are used to record disorders such as arrhythmia and heart failure. Due to their non-stationary nature, ECG signals require a time-frequency domain approach to capture their dynamic characteristics more accurately. This study aims to develop and evaluate machine learning-based heart disorder classification models …
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- xxvi, 229 hlm.; ilus.; tab.; 29 cm.
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- T2002302026
MULTI OBJECT TRACKING PADA CITRA JANTUNG ANAK MENGGUNAKAN DEEP LEARNING
Congenital heart disease (CHD) in children, such as atrial septal defects (ASD), ventricular septal defects (VSD), and atrioventricular septal defects (AVSD), requires accurate diagnosis through dynamic analysis. However, existing methods for analyzing echocardiographic video are often limited to frame-by-frame analysis and are not yet capable of consistently tracking temporal changes. This stu…
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- xv, 104 hlm.; ilus.; tab.; 29 cm.
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- T2002432026
IMAGE CAPTIONING PADA CITRA SERVIKS UNTUK LAPORAN MEDIS BERBASIS MODEL DEEP L…
Image captioning is a task in the fields of computer vision (CV) and natural language processing (NLP) that aims to generate textual descriptions from an image. In this study, various combinations of encoder–decoder architectures were designed and evaluated to improve captioning performance on cervical medical images from the International Agency for Research on Cancer (IARC). The encoders us…
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- xiv, 99 hlm.; ilus.; tab.; 29 cm.
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- T2001052026
PENGEMBANGAN MODEL SEGMENTASI LESI CITRA PRA-KANKER SERVIKS MENGGUNAKAN YOLO …
Cervical cancer is one of the leading causes of morbidity and mortality among women, making early detection of precancerous lesions essential. However, lesion segmentation in cervical images still faces several challenges, including unclear object boundaries, illumination variations, imaging artifacts, and class imbalance between lesion and background, which reduce the performance of deep learn…
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- xviii, 181 hlm.; ilus.; tab.; 29 cm.
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- T2000472026
IMPLEMENTASI OBJECT DETECTION BERBASIS YOU ONLY LOOK ONCE UNTUK IDENTIFIKASI …
This study aims to develop a deep learning-based object detection system using the YOLOv11n algorithm to identify foreign objects on coal conveyor belt systems. The study is motivated by the limitations of manual inspection methods in maintaining detection consistency and accuracy within mining environments characterized by high visual complexity, such as dust, uneven illumination, motion blur,…
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- xiv, 81 hlm.; ilus.; tab.; 29 cm.
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- T2004232026
PENINGKATAN KINERJA SEGMENTASI CITRA PRA-KANKER SERVIKS MELALUI AUGMENTASI DA…
Cervical cancer is a leading cause of death among women. The subjectivity of Visual Inspection with Acetic Acid (VIA) screening encourages the use of Artificial Intelligence (AI) for medical image segmentation automation. However, limited datasets frequently cause model overfitting. This research aims to improve the segmentation performance of cervical precancerous images on the YOLOv11-seg mod…
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- xiii, 98 hlm.; ilus.; tab.; 29 cm.
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- T2000512026
ANALISIS HYBRID FEATURE ENGINEERING UNTUK PENENTUAN TINGKAT KEPARAHAN LESI PR…
Visual diagnosis via colposcopy is prone to observer subjectivity, making a more objective computational system necessary. This study explores two approaches: hybrid feature engineering (color, texture, contour) using machine learning (ML) via a rule-based system that adapts the Sweden score method, and end-to-end architectures based on YOLO (v8, v11, v12, v26). The dataset is sourced from the …
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- xvi, 125 hlm.; ilus.; tab.; 29 cm.
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- T2000382026
KLASIFIKASI CITRA PUNCTA LACRIMAL NORMAL DAN ABNORMAL MENGGUNAKAN DEEP LEARNING
This study aims to classify normal and abnormal puncta lacrimal images using deep learning methods and to analyze the impact of data augmentation strategies on model performance. The dataset consisted of 61 images, including 30 normal and 31 abnormal images, which underwent a preprocessing stage by resizing all images to 256 × 256 pixels. Nine deep learning architectures were evaluated, includ…
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- xv, 91 hlm.; ilus.; tab.; 29 cm.
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- T2004162026
KLASIFIKASI SERANGAN SPYWARE ANDROID MENGGUNAKAN METODE CONVOLUTIONAL NEURAL …
The increasing use of Android devices has led to a rise in security threats, particularly spyware attacks that threaten user privacy. Conventional signature-based detection methods have limitations in detecting new spyware variants. This study aims to classify Android spyware attacks using the Convolutional Neural Network (CNN) method. The dataset used is CIC-MalMem2022, consisting of memory du…
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- vx, 82 hlm.; ilus.; 29 cm
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- T1922872025
DETEKSI SERANGAN SSL PINNING BYPASS DAN DDOS PADA JARINGAN SMARTHOME MENGGUNA…
The rapid development of the Internet of Things (IoT) has accelerated the implementation of smart home systems connected to the internet. However, this advancement also increases the risk of cyberattacks, particularly SSL Pinning Bypass, which threatens communication security, and Distributed Denial of Service (DDoS), which disrupts service availability. This study aims to detect both types of …
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- xiii, 66 hlm.; ilus.; tab.; 29 cm
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- T1933952026
PENDEKATAN NAMED ENTITY RECOGNITION TERHADAP IOB1 DAN IOB2 TAGGING SCHEME UNT…
Cyber Threat Intelligence (CTI) is essential to support cyber threat detection and mitigation, particularly for Advanced Persistent Threat (APT) activities that are commonly reported in unstructured text. This condition makes critical information difficult to utilize automatically without an entity extraction process. This study aims to analyze the performance of Named Entity Recognition (NER) …
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- xiv, 48 hlm.; ilus.; 29 cm
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- T1933302026
DETEKSI ADVANCED PERSISTENT THREAT (APT) PADA CYBER THREAT INTELLIGENCE (CTI)…
The development of information technology has increased cyber attack threats, especially Advanced Persistent Threat (APT), so appropriate methods are needed to detect attacks based on Cyber Threat Intelligence (CTI) data. The main problems in this study are data imbalance and the difficulty in determining the most important features to improve detection results. To address these problems, this …
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- xiv, 48 hlm.; ilus.; 29 cm
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- T1932792026
PENGEMBANGAN SISTEM KLASIFIKASI PENYAKIT ALZHEIMER PADA CITRA MAGNETIC RESONA…
Alzheimer's disease is a slowly progressing neurodegenerative disorder characterized by memory decline, visual-spatial impairment, executive function impairment, and personality and behavioral changes. Early detection of this disease is crucial for proper treatment. This study used MRI images to detect Alzheimer's disease, as MRI can provide a more detailed picture of brain structure and networ…
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- xvii, 98 hlm.; ilus.; 29 cm
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- T1932752026
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