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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
ANALISIS PERBANDINGAN ALGORITMA ANN, ANN-PSO, DAN ANN-GA UNTUK KLASIFIKASI KE…
Flight departure delays affect operational efficiency and the quality of air transportation services. This study compares the performance of the Artificial Neural Network (ANN), ANN optimized using Particle Swarm Optimization (ANN-PSO), and ANN optimized using Genetic Algorithm (ANN-GA) for flight delay classification using a two-class dataset, namely on-time and delayed flights, based on opera…
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- xiv, 79 hlm.; ilus.; tab.; 29 cm.
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- T1931722026
ANALISIS PERBANDINGAN ALGORITMA MACHINE LEARNING UNTUK PREDIKSI KETERLAMBATAN…
This research was conducted because departure delays on the Light Rail Transit can reduce passenger comfort and satisfaction. Therefore, a predictive model that can estimate delays accurately is needed. The purpose of this study is to implement, compare, and determine the best machine learning algorithm for predicting LRT departure delays in Canberra. The dataset used consists of static and rea…
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- xiv, 81hlm.; ilus.; tab.; 29 cm.
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- T1931772026
ANALISIS PERBANDINGAN ALGORITMA MACHINE LEARNING UNTUK KLASIFIKASI TINGKAT KE…
Traffic accidents are a serious public safety issue that requires data-driven analytical approaches. This study aims to classify traffic accident severity into three classes, namely fatal, serious, and slight, using machine learning algorithms. Four algorithms are evaluated: Random Forest, Light Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). M…
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- xv, 73 hlm.; ilus.; tab.; 29 cm.
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- T1931602026
INTRUSION RESPONSE SYSTEM SERANGAN SIBER MAN-IN-THE-MIDDLE DENGAN METODE RAND…
The rapid adoption of Cyber-Physical Systems (CPS) has improved operational efficiency across critical sectors but has simultaneously increased exposure to cyber threats, particularly Man-in-the-Middle (MITM) attacks that covertly intercept and manipulate communication. In CPS environments, such attacks pose serious risks to system reliability and operational safety, thereby requiring security …
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- xiv, 91 hlm.; ilus.; tab.; 29 cm.
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- T1931252026
ANALISA LOAD BALANCING PADA CLOUD COMPUTING MENGGUNAKAN METODE LEAST CONNECTION
Cloud computing provides dynamic computing resources over a network. However, an increasing number of user requests can lead to higher server workloads and decreased service performance. This study aims to analyze the performance of load balancing using the Least Connection method with HAProxy as the load balancer. The system was implemented in a VMware-based virtual machine environment consist…
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- xv, 69 hlm.; ilus.; tab.; 29 cm
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- T1936322026
IMPLEMENTASI METODE FASTER REGION CONVOLUTIONAL NEURAL NETWORK UNTUK DETEKSI …
Accurate and efficient ship detection has become an urgent necessity amid increasing maritime activities, including security monitoring, law enforcement, and maritime traffic management. This study aims to implement the Faster R-CNN (Region-based Convolutional Neural Network) method for ship detection to improve efficiency and accuracy compared to conventional methods. The data used in this stu…
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- iii, 56 hlm.; ilus.; 29 cm
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- T1627402024
ANALISIS POLA KECELAKAAN LALU LINTAS KENDARAAN BERMOTOR DI KOTA PALEMBANG TAH…
Penelitian ini bertujuan untuk menganalisis pola kecelakaan lalu lintas di Kota Palembang menggunakan pendekatan machine learning berbasis clustering. Data sekunder diperoleh dari catatan resmi Kepolisian Kota Palembang mencakup periode 2021–2024 dengan total 2.658 data dan 35 variabel awal. Setelah melalui proses prapemrosesan, dilakukan pembersihan data, penghapusan variabel yang tidak rele…
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- xiii, 93 hlm.; ilus.; tab.; 29 cm
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- T1937202026
ANALISIS LOKASI RAWAN KRIMINALITAS PENCURIAN MOTOR PADA TAHUN 2021-2024 DI KA…
Penelitian ini bertujuan mengidentifikasi pola tingkat kerawanan kriminalitas pencurian kendaraan bermotor (curanmor) di Kabupaten Musi Banyuasin menggunakan pendekatan unsupervised learning. Empat algoritma clustering ini, yaitu K-Means, DBSCAN, Hierarchical Clustering, dan Gaussian Mixture Model digunakan untuk mengelompokkan data kejadian kriminal berdasarkan variabel jenis kendaraan, jumlah…
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- x, 80 hlm.; ilus.; tab.; 29 cm
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- T1937392026
DETEKSI SERANGAN MALWARE BOTNET MENGGUNAKAN METODE LOGISTIC REGRESSION
Botnets are a serious cyberattack threat that infects computer networks controlled by botmasters to carry out malicious activities. Various types of botnets have emerged over the years, posing a significant threat to cybersecurity. These botnets' malicious activities vary from executing instruction-based attacks such as DDoS attacks, flooding, and spamming. This study used the CICIoT2023 datase…
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- xiii, 85 hlm.; ilus.; 29 cm
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- T1906802026
DETEKSI SERANGAN DOS PADA JARINGAN SMART HOME IPV6 MENGGUNAKAN METODE LOGISTI…
Denial of Service (DoS) attacks pose a serious threat to IPv6-based smart home networks, causing disruptions in device connectivity and significantly reducing system performance. This study aims to detect DoS attacks in IPv6 smart home networks using the Logistic Regression machine learning algorithm. The dataset was generated from network traffic captured using the THC-IPv6 tool, followed by f…
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- x, 52 hlm.; ilus.; 29 cm
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- T1907252025
SUPPLY CHAIN ASSET TRACKING MENGGUNAKAN ALGORITMA POS (PROOF OF STAKE) DENGAN…
Supply Chain Management involves several parties who play a role in the process of delivering goods or services so it requires transparency regarding transaction records for all parties involved with the aim of avoiding falsification of transaction data. To overcome this problem, this research aims to build a security system using the Proof-of-Stake (poS) method. A collection of blocks containi…
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- xiv, 88 hlm.; ilus.; 29 cm
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- T1628692024
DETEKSI ANCAMAN SSL PINNING BYPASS DAN DDOS MENGGUNAKAN NAIVE BAYES PADA KEAM…
The development of smart home technology provides convenience for users in managing household devices automatically and through internet connectivity; however, it also raises potential security threats, such as SSL Pinning Bypass, which allows attackers to intercept communications, and Distributed Denial of Service (DDoS) attacks, which can disrupt service availability. To address these issues,…
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- xv, 101 hlm.; ilus.; tab.; 29 cm.
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- T1903172025
KLASIFIKASI AKTIVITAS BOT MAXIMAL EXTRACTABLE VALUE (MEV) PADA TRANSAKSI BLOC…
Maximal Extractable Value (MEV) bot activity on blockchain networks poses a significant challenge, as MEV bots exploit transaction-processing mechanisms to gain profit in ways that may hinder fairness, increase gas fees, and disrupt network stability. This study employs the Extreme Gradient Boosting (XGBoost) model to classify MEV bot activity in Ethereum blockchain transactions using numerical…
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- xiv, 86 hlm.; lamp.; 29 cm.
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- T1903792025
DETEKSI SERANGAN DDOS DAN MITM PADA SISTEM SMART HOME MENGGUNAKAN METODE LIGH…
This study aims to detect and classify Distributed Denial of Service (DDoS) and Man-in-the-Middle (MiTM) attacks in smart home networks using the Light Gradient Boosting Machine (LightGBM) algorithm. With the rapid growth of Internet of Things (IoT) devices, cybersecurity challenges have become crucial due to vulnerabilities in smart home devices. This research utilizes the COMNETS SMARTHOME da…
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- xiii, 88 hlm.; ilus.; tab.; 29 cm.
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- T1901922025
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