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Waskita , Arya Adyhaksa
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Penerapan Model T5-Small untuk Abstractive Text Summarization pada Berita Olahraga Prasetyo Ramadhan, Dandy; Waskita , Arya Adyhaksa
Jurnal Ilmu Komputer Vol 4 No 1 (2026): Jurnal Ilmu Komputer (Edisi Januari 2026)
Publisher : Universitas Pamulang

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Abstract

Sports news is characterized by its length and dense information, so readers often have difficulty quickly obtaining the main information. Manual summary creation is inefficient, while research on automatic summary systems in Indonesian, especially in the sports domain, is still very limited. This study develops an abstractive text summarization model based on the Transformer architecture (T5-Small) to generate summaries of Indonesian sports news. The dataset was obtained from Kaggle and then went through a pre-processing stage including data cleaning, text normalization, tokenization using T5Tokenizer, and the application of padding and truncation to match the model's input format. The model was trained using a data split of 80% for training, 10% for validation, and 10% for testing. Performance evaluation was conducted using the ROUGE-1, ROUGE-2, and ROUGE-L metrics by comparing the model summary against the reference summary (gold standard). The evaluation results using the ROUGE metric indicate that the model has quite good performance in producing relevant summaries. The ROUGE-1 value of 0.6011 indicates that more than half of the unigrams in the model summary match the reference summary. The ROUGE-2 value of 0.3940 indicates the model's ability to capture relationships between words, or bigrams, with a near 40% agreement rate. Meanwhile, the ROUGE-L value of 0.5411 confirms that the model's sentence sequence structure aligns with the original summary. Overall, these three values ​​confirm the model's ability to produce informative and consistent summaries.
Analisis Klasifikasi Tumor Otak dengan Algoritma Berbasis Convolutional Neural Network dan Transformers Zhafar Usamah; Waskita , Arya Adyhaksa; Taswanda Taryo
Jurnal Ilmu Komputer Vol 4 No 1 (2026): Jurnal Ilmu Komputer (Edisi Januari 2026)
Publisher : Universitas Pamulang

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This study focuses on comparing the performance of Convolutional Neural Networks (CNN) and Vision Transformers (ViT) in classifying brain tumors using Magnetic Resonance Imaging (MRI) data. The MRI images are grouped into four categories: normal, glioma, meningioma, and pituitary, which represent healthy brain conditions and several common types of brain tumors. Before the classification process, data preprocessing was carried out to improve image quality and consistency. This included resizing images and normalizing intensity values. The dataset was then divided into training and testing sets using three different ratios: 70:30, 80:20, and 90:10, allowing the models to be evaluated under varying data conditions.The CNN and ViT models were designed to extract important features from medical images using different approaches. CNN uses convolutional and pooling layers to capture local spatial features, making it well suited for identifying texture and structural patterns in MRI images. In contrast, ViT applies a self-attention mechanism that enables the model to learn global relationships across the entire image. To make the system more user-friendly, a graphical user interface (GUI) based on Tkinter was developed. This interface allows users to select datasets, train the models, and view evaluation results such as graphs and confusion matrices interactively. Model performance was assessed using accuracy, precision, recall, and F1-score metrics. The results show that CNN consistently achieved more stable and higher performance across all data splits. At the 90:10 ratio, CNN reached an accuracy of 95%, while ViT achieved 88%. Similar trends were observed at the 80:20 and 70:30 ratios
Komparasi Kinerja Sistem Rekomendasi Destinasi Wisata Menggunakan Content Based Filtering Dan Retrieval Augmented Generation (RAG) Awaludin, Rachmat Aziz; Waskita , Arya Adyhaksa; Mardiyanto
Jurnal Ilmu Komputer Vol 4 No 1 (2026): Jurnal Ilmu Komputer (Edisi Januari 2026)
Publisher : Universitas Pamulang

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Advances in artificial intelligence have driven the development of recommendation systems in the tourism sector, which is characterized by diverse destinations. This condition often makes it difficult for tourists to select destinations that match their preferences. Based on literature studies, Content-Based Filtering (CBF) is widely used due to its efficiency; however, it has limitations in understanding contextual information. In contrast, the Retrieval Augmented Generation (RAG) approach has been developed to improve recommendation quality through semantic understanding. This study aims to compare the performance of CBF and RAG in tourism destination recommendation systems. CBF employs TF-IDF and cosine similarity to measure content similarity, while RAG integrates retrieval and generation processes using the LLaMA 3.2 model and the FAISS vector database. The research methodology includes data collection, text preprocessing, system implementation, and evaluation using context recall, faithfulness, answer relevancy, and similarity metrics. The results indicate that CBF achieved a context recall of 0.317, faithfulness of 1.000, answer relevancy of 0.190, and similarity of 0.293, demonstrating high accuracy with respect to source data but limited contextual understanding. Meanwhile, RAG achieved a context recall of 1.000, faithfulness of 0.783, answer relevancy of 0.617, and similarity of 0.715, indicating superior performance in generating relevant recommendations. In conclusion, RAG outperforms CBF in contextual and semantic aspects, while CBF remains more efficient in processing explicit data. This study is expected to serve as a reference for developing more adaptive and personalized tourism recommendation systems
Implementasi Yolov5 Deteksi Mata Lelah Berbasis Android Suri, Ajeng Permata; Waskita , Arya Adyhaksa; Mardiyanto
Jurnal Ilmu Komputer Vol 4 No 1 (2026): Jurnal Ilmu Komputer (Edisi Januari 2026)
Publisher : Universitas Pamulang

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Abstract

Excessive screen exposure can trigger digital eye strain, reducing visual comfort, attention, and overall productivity. Prior studies in computer vision indicate that deep learning–based object detection, particularly the YOLO family, can recognize facial and eye-related visual patterns efficiently, making it suitable for early-warning systems on mobile devices. This study aims to implement YOLOv5 to detect signs of eye fatigue in real time using the front camera of an Android smartphone. The novelty of this work lies in deploying a lightweight object-detection model on-device through TensorFlow Lite and integrating an automatic notification mechanism as a preventive intervention. The proposed methodology includes collecting and labeling an eye-image dataset into two classes (awake and drowsy), training a YOLOv5 model in Google Colab, optimizing and converting the trained model to TensorFlow Lite, and integrating it into an Android application for live-camera inference. System performance is evaluated using accuracy, precision, recall, and inference speed (FPS). Experimental results show that the system achieves 95.6% accuracy, 94.3% precision, 96.1% recall, and an Average speed of 22 FPS, enabling responsive detection and timely notifications. In conclusion, the Android-based YOLOv5 implementation is feasible as a preventive solution to help users monitor eye-fatigue symptoms and encourage healthier screen-use habits.
Co-Authors Awaludin, Rachmat Aziz Mardiyanto Prasetyo Ramadhan, Dandy Suri, Ajeng Permata Taswanda Taryo Zhafar Usamah