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EVALUASI KEGUNAAN APLIKASI VIRTUAL REALITY UNTUK PELATIHAN PEKERJA GERAI MINU…
Indonesia’s rapidly expanding beverage industry demands efficient training methods for workers who must prepare drinks accurately under pressure. Traditional training often faces high costs, limited resources, and reliance on instructors, making it less accessible. This study introduces a Virtual Reality (VR) simulation developed with Unity to provide an immersive, cost-effective training alt…
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- xix, 123 hlm.; ilus.; tab, 29 cm
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- T1968892026
SEGMENTASI CITRA PARU-PARU MENGGUNAKAN ARSITEKTUR CUSTOM U-NET
Manual analysis of COVID-19 chest CT scans is often time-consuming and prone to subjectivity, thereby hindering diagnostic efficiency. Therefore, this study aims to develop an automatic segmentation system using a Custom U-Net architecture with a pre-trained DenseNet-169 encoder to accurately map infection areas, including Ground Glass Opacity (GGO), Consolidation, and Pleural Effusion classes.…
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- xx, V-20 hlm.; ilus.; 29 cm
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- T1961082026
KLASIFIKASI JENIS KASUS DI KEJAKSAAN NEGERI CILEGON MENGGUNAKAN METODE SUPPOR…
The development of text processing technology allows for automated data classification using machine learning methods. In the case classification process, manual text grouping is often time-consuming and error-prone. This study aims to build a text classification system capable of automatically grouping case descriptions into specific categories. The methods used in this study are Support Vecto…
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- xv, 96 hlm.; ilus.; tab.; 29 cm.
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- T2012912026
OPTIMASI PRECISION PADA LESI HISTOPATOLOGI HATI MENGGUNAKAN YOLOV11
Rapid and reliable pathological evaluation is made possible in large part by the automatic detection of liver histological abnormalities. Using YOLOv11 on the Roboflow Liver Disease public dataset, this work aims to maximize precision in the detection of four lesions: ballooning, fibrosis, inflammation, and steatosis. After pre-processing the data with a 640×640 resize, the model was trained u…
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- xx, 96 hlm.; ilus.; 29 cm
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- T1962742026
APLIKASI GENERASI OTOMATIS SPRITE SHEET KARAKTER 2D BERBASIS ZERO123++ UNTUK …
erkembangan pesat kecerdasan buatan, khususnya dalam model generatif, telah membuka peluang besar bagi otomatisasi dalam industri pengembangan game. Namun, pengembang indie dan studio kecil masih menghadapi kendala signifikan dalam pembuatan aset game 2D, terutama sprite sheet karakter, yang secara tradisional memerlukan waktu lama, biaya tinggi, dan upaya manual untuk menghasilkan berbagai sud…
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- xviii, 145 hlm.; ilus.; 29 cm
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- T1960572026
OPTIMASI KERNEL CUDA BERBASIS MEMORY-AWARE UNTUK SELF-ATTENTION PADA INFERENS…
The self-attention mechanism in Large Language Models (LLMs) is computationally intensive and memory-bound, posing significant challenges for inference on consumer-grade hardware. This research proposes a memory-aware optimization of CUDA kernels for the self-attention mechanism within the GPT-2 architecture, integrating Shared Memory Tiling, Streaming Softmax, and Kernel Fusion to minimize glo…
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- xviii, 68 hlm.; ilus.; 29 cm
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- T1955422026
PERBANDINGAN FUZZY TIME SERIES CHEN DAN LEE PADA PREDIKSI JUMLAH KUNJUNGAN PA…
The number of outpatient visits in hospitals often fluctuates, making it difficult for hospital management to plan administrative services, particularly in managing patient queues, as well as the allocation of resources, especially administrative staff, effectively. Therefore, a prediction method is needed to estimate the number of patient visits in the future. This study aims to apply and comp…
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- xix, 169 hlm.; ilus.; 29 cm
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- T1962402026
KLASIFIKASI PNEUMONIA DAN NORMAL BERDASARKAN CITRA X-RAY PARU-PARU MENGGUNAKA…
This study aims to develop a pneumonia classification model on chest X-ray images using a Convolutional Neural Network with the DenseNet-121 architecture for early detection of lung diseases. The dataset consists of 5,856 images divided into training, validation, and testing sets. The research stages include preprocessing, image augmentation, model training using a progressive fine-tuning strat…
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- xxiv, V-7 hlm.; ilus.; 29 cm
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- T1955372026
ANALISIS SENTIMEN BERBASIS TRANSFORMER (INDOBERT) PADA ULASAN PENGGUNA APLIKA…
This study implements and compares the performance of the IndoBERT deep learning model in analyzing the sentiment of Gojek application reviews using 3-class and 5-class schemes. Data was extracted via web scraping, yielding 66,709 raw reviews, which were then preprocessed and balanced into 23,115 (3-class) and 32,899 (5-class) datasets. Training was executed with an 80:10:10 ratio across six sc…
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- xix, V-9 hlm.; ilus.; 29 cm
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- T1955952026
KLASIFIKASI KOMENTAR JUDI ONLINE MENGGUNAKAN INDOBERT
The rapid growth of social media has enabled the misuse of comment sections as a medium for promoting online gambling. The high volume of comments makes manual moderation ineffective, thus requiring an automated classification system. This study aims to develop a classification system for online gambling comments using the IndoBERT model and to evaluate its performance based on accuracy, precis…
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- xxiii, VI-2 hlm.; ilus.; 29 cm
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- T1955942026
ANALISIS SENTIMEN TERHADAP ULASAN APLIKASI DANA MENGGUNAKAN WORD2VEC DAN LONG…
DANA has become one of the most popular e-wallet applications in Indonesia. This development has elicited various reactions and responses from users in the form of reviews. These reviews can provide important insights for improving the quality of the application's services. This study aims to conduct sentiment analysis on user reviews by utilizing Long Short-Term Memory (LSTM) as the model arch…
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- xvii, VI-2 hlm.; ilus.; 29 cm
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- T1955622026
PERBANDINGAN MACHINE LEARNING XGBOOST DAN DEEP LEARNING CNN 1D UNTUK MENDETEK…
Phishing is a form of cyberattack that exploits fraudulent websites to obtain users’ sensitive information. This study aims to compare the performance of the XGBoost machine learning algorithm and CNN 1D deep learning in detecting phishing URLs using tabular URL data. XGBoost was chosen because it is effective in handling tabular data and capable of modeling nonlinear relationships among feat…
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- xvi, 61 hlm.; ilus.; 29 cm
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- T1961092026
PERBAIKAN KUALITAS CITRA DENGAN NONLINEAR ACTIVATION FREE NETWORK UNTUK KLASI…
Alzheimer’s disease is a progressive neurodegenerative disorder that is the leading cause of dementia. The use of Magnetic Resonance Imaging (MRI) in ResNet18-based classification can be hindered by the presence of noise, particularly Rician noise, which obscures important structural details and reduces model performance. This study employs the Nonlinear Activation Free Network (NAFNet) to im…
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- xviii, 56 hlm.; ilus.; 29 cm
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- T1962072026
PERBANDINGAN FUZZY TIME SERIES CHENG DAN LEE PADA PREDIKSI PENGUNJUNG WISATA …
Fluctutations in daily visitor numbers at Kampung Radja Jambi present challanges for optimal operation planning. This study aims to compare the performance of the Fuzzy Time Series (FTS) Cheng and FTS Lee methods in forecasting daily visitor number using secondary data from Januari 1, 2024 to December 31, 2025. After preprocessing, 648 observations were analyzed using FTS approach, and forecast…
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- xii, 65 hlm.; ilus.; 29 cm
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- T1961472026
PENGEMBANGAN MODEL PENERJEMAHAN BAHASA INGGRIS KE BAHASA JAWA MENGGUNAKAN LST…
One of the main challenges in English to Javanese machine translation is the model's difficulty in capturing contextual relationships between words, especially in complex sentences. Without an Attention Mechanism, the model tends to be unable to focus on the relevant parts of the source sentence during the translation process. This study aims to develop a Neural Machine Translation (NMT) model …
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- xviii, 283 hlm.; ilus.; 29 cm
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- T1960512026
ANALISIS DAN PERBANDINGAN ARSITEKTUR PIPELINE DATA DAN SISTEM PEMANTAUAN PENE…
Penelitian ini bertujuan menganalisis hubungan kausalitas antara indeks kebahagiaan, pengeluaran pemerintah, dan pertumbuhan ekonomi di 12 negara emerging market selama periode 2013–2024. Data yang digunakan merupakan data sekunder yang diperoleh dari World Happiness Report dan World Bank. Metode analisis yang digunakan adalah Panel Vector Autoregression (PVAR) dengan uji kausalitas Granger u…
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- xvii, VI-3 hlm.; ilus.; 29 cm
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- T1955632026
PREDIKSI HASIL SPMB JALUR TKA DENGAN PENDEKATAN ENSEMBLE LEARNING STACKING
The Seleksi Penerimaan Murid Baru (SPMB) through the Tes Kemampuan Akademik (TKA) pathway for senior high schools in Palembang is a competitive process that creates uncertainty in determining students’ admission chances. This study aims to develop a prediction model for student admission using a machine learning approach based on ensemble learning stacking. The dataset consists of 4,952 appli…
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- xiii, 24 hlm.; ilus.; 29 cm
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- T1959792026
REKOMENDASI PEMILIHAN SEKOLAH MENENGAH ATAS BERBASIS DOMISILI DENGAN MENGGUNA…
School selection at the senior high school level is an important decision amid intense competition in the student admission process and multiple influencing factors such as academic performance, domicile, and school capacity. This study aims to develop a recommendation system based on stacking ensemble learning that combines Random Forest and XGBoost as base learners, with Logistic Regression a…
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- xv, 140 hlm.; ilus.; 29 cm
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- T1959772026
ANALISIS SENTIMEN TERHADAP CRYPTOCURRENCY MENGGUNAKAN MODEL BERT
The rapid development of digital technology has driven various innovations in the financial sector, one of which is cryptocurrency, which is increasingly discussed on social media and various digital platforms. On the other hand, price volatility, security issues, and evolving regulations have led to diverse public opinions regarding cryptocurrency. Therefore, this study aims to analyze public …
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- xvi, 164 hlm.; ilus.; 29 cm
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- T1960122026
SEGMENTASI MULTI-CLASS BERBASIS TRANSFORMER PADA CITRA MEDIS MRI OTAK
Brain tumor segmentation from Magnetic Resonance Imaging (MRI) is crucial for accurate diagnosis and therapy planning. CNN architectures like U-Net are considered the gold standard, but they have intrinsic limitations in processing long-range spatial context globally. This study proposes the use of a Vision Transformer architecture, specifically UNETR, and compares its performance against a bas…
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- xviii, 53 hlm.; ilus.; 29 cm
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- T1959242026
ANALISIS SENTIMEN ULASAN PENGGUNA GOPAY MENGGUNAKAN INDOBERT
The rapid growth of digital wallets in Indonesia, particularly GoPay, has generated tens of thousands of user reviews containing valuable information about service satisfaction. This study aims to classify GoPay application user satisfaction into positive, neutral, and negative categories using the Fine-Tuning IndoBERT (Indonesian BERT) method. As a Transformer-based model trained specifically …
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- xv, 121 hlm.; ilus.; 29 cm
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- T1959122026
ANALISIS SENTIMEN PUBLIK TERHADAP CHATGPT DI PLATFORM X MENGGUNAKAN ROBERTA
ChatGPT is an artificial intelligence technology that triggers diverse public opinions on social media, particularly on the X platform. The high volume of data and unstructured text characteristics make manual mapping of public perception difficult. Therefore, this study aims to develop an automated sentiment analysis system to classify public opinion toward ChatGPT into Negative, Neutral, and …
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- xiv, 134 hlm.; ilus.; 29 cm
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- T1959332026
ANALISIS SENTIMEN TERHADAP CONTENT CREATOR PAPI ABE MENGGUNAKAN METODE LONG S…
The phenomenon of Papi Abe as a family content creator who has gone viral on TikTok has generated a wide range of public responses, both positive and negative, particularly regarding father-child interactions in parenting content and the potential for child exploitation. Therefore, it is important to understand public opinions toward Papi Abe as a basis for evaluating and improving content qual…
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- xvi, V-23 hlm.; ilus.; 29 cm
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- T1955152026
ANALISIS SENTIMEN MASYARAKAT TERHADAP AWAL KEPEMIMPINAN PRESIDEN PRABOWO SUBI…
This research aims to analyze public sentiment toward the early leadership of President Prabowo Subianto using the IndoBERT language model. Public opinions were collected from various social media platforms, including X, YouTube and TikTok, resulting in a dataset of 78,851 texts labeled into positive and negative sentiments. The data underwent preprocessing stages such as cleaning, case folding…
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- xi, VI-2 hlm.; ilus.; 29 cm
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- T1950812026
DETEKSI PENYAKIT PADA TANAMAN MANGGA BERDASARKAN CITRA DAUN DENGAN ALGORITMA …
Healthy and diseased mango plants are difficult to distinguish at early stages due to insignificant differences in leaf color, texture, and spot patterns, which often leads to delayed identification and results in decreased mango production and fruit quality. This study develops an Android-based application to detect diseases in mango plants based on leaf images using the YOLO11 algorithm. The …
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- xiv, V-14 hlm.; ilus.; 29 cm
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- T1950952026
PREDIKSI HASIL TANGKAPAN IKAN YANG DISETORKAN KE PPS NIZAM ZACHMAN JAKARTA DE…
Prediction is the process of estimating future conditions using historical data as the basis for decision-making. This research develops a web-based fish catch prediction system using XGBoost algorithm based on time series with nonlinear regression method to predict fish production results submitted to the port. The data used is aggregate fish catch data from 12 fishing points during 2020– 20…
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- xiv, V-7 hlm.; ilus.; 29 cm
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- T1951092026
IMPLEMENTASI ALGORITMA LIGHT GRADIENT BOOSTING MACHINE UNTUK PREDIKSI WAKTU P…
Formula 1 qualifying sessions play a crucial role in determining race outcomes, as grid position strongly influences competitive advantage, particularly on circuits with limited overtaking opportunities. This study aims to implement the Light Gradient Boosting Machine (LightGBM) algorithm to predict drivers’ qualifying lap times in Formula 1 using historical time-series data from the 2018–2…
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- xxi, IV-16 hlm.; ilus.; 29 cm
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- T1951182026
PENGGUNAAN CONDITIONAL GENERATIVE ADVERSARIAL NETWORK (CGAN) UNTUK PENINGKATA…
This study implements a Conditional Generative Adversarial Network (CGAN) to enhance the contrast of fetal abdominal ultrasound images. The novelty lies in increasing the Reconstruction Loss weight (λL1) to 1000 to ensure structural stability in medical data. Using U-Net and PatchGAN architectures, the model was trained against CLAHE targets. The results demonstrate significant enhancement wit…
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- xxiv, 103 hlm.; ilus.; 29 cm
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- T1958272026
DETEKSI DAN PENGENALAN TEKS PADA TABEL NILAI GIZI MENGGUNAKAN PADDLEOCR DAN T…
Nutrition facts tables on food packaging are a crucial source of information for consumers, but manual data extraction is often inefficient and prone to errors due to varied table structures and small text sizes. This research proposes an automated system for text detection and recognition in nutrition tables by integrating two advanced deep learning models: PaddleOCR and TrOCR. PaddleOCR is em…
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- xvii, 202 hlm.; ilus.; 29 cm
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- T1958192026
OPTIMASI PERILAKU KARAKTER BOSS PADA PERMAINAN MULTIPLAYER MENGGUNAKAN FUZZY …
Adaptive Artificial Intelligence (AI) plays a significant role in shaping player experience in digital games, particularly in action-based combat scenarios where behavioral dynamics influence perceived challenge, fairness, and engagement. This study investigates the implementation of a Fuzzy State Machine (FuSM) compared to a conventional Finite State Machine (FSM) for boss behavior in a 2D mul…
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- xvii, 142 hlm.; ilus.; 29 cm
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- T1958052026
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