Peringkasan Ekstraktif Teks Bahasa Indonesia dengan Pendekatan Unsupervised Menggunakan Metode Clustering

Perkembangan teknologi informasi yang pesat membuat volume informasi yang dapat diakses oleh manusia melalui internet menjadi tidak terbendung. Hal ini menyebabkan pembaca harus dapat memilah informasi yang penting maupun merangkum informasi yang sangat masif. Pembaca perlu merangkum/meringkas banyaknya informasi menjadi informasi utama yang layak untuk ditelaah. Proses merangkum/meringkas informasi yang banyak dari berbagai sumber merupakan permasalahan yang tidak mudah. Peringkasan teks dapat dilakukan secara otomatis oleh komputer menggunakan teknologi kecerdasan buatan. Penelitian ini mengusulkan metode peringkasan teks berbasis clustering dengan K-means clustering. Metode yang diusulkan telah diuji dengan Rouge score menggunakan dataset benchmark. Berdasarkan hasil pengujian, sistem peringkasan teks yang dibangun cukup baik, yaitu memiliki nilai F1 score dari Rouge-1 = 49,37%, Rouge-2 = 38,18% dan Rouge-L = 46,87%. Hasil yang diperoleh adalah bahwa metode ini lebih baik dari 3 metode unsupervised yang telah digunakan sebelumnya yaitu SumBasic, LSA dan LexRank

Clustering based feature selection using Partitioning Around Medoids (PAM)

High-dimensional data contains a large number of features. With many features, high dimensional data requires immense computational resources, including space and time. Several studies indicate that not all features of high dimensional data are relevant to classification result. Dimensionality reduction is inevitable and is required due to classifier performance improvement. Several dimensionality reduction techniques were carried out, including feature selection techniques and feature extraction techniques. Sequential forward feature selection and backward feature selection are feature selection using the greedy approach. The heuristics approach is also applied in feature selection, using the Genetic Algorithm, PSO, and Forest Optimization Algorithm. PCA is the most well-known feature extraction method. Besides, other methods such as multidimensional scaling and linear discriminant analysis. In this work, a different approach is applied to perform feature selection. Cluster analysis based feature selection using Partitioning Around Medoids (PAM) clustering is carried out. Our experiment results showed that classification accuracy gained when using feature vectors' medoids to represent the original dataset is high, above 80%

Review HKI- Hak Cipta - RINGKAS,NET oleh Bu Lisna Zahrotun

Review HKI- Hak Cipta - RINGKAS,NET oleh Bu Lisna Zahrotu

Lembar Review HKI-Hak Cipta RINGKAS.NET Dewi Soyusiawaty

Lembar Review HKI-Hak Cipta RINGKAS.NET Dewi Soyusiawat

Transforming traffic surveillance: a YOLO-based approach to detecting helmetless riders through CCTV

CCTV systems, while ubiquitous for traffic surveillance in Indonesian roadways, remain underutilized in their potential. The integration of AI and Computer Vision technologies can transform CCTV into a valuable tool for law enforcement, specifically in monitoring and addressing helmet non-compliance among motorcycle riders. This study aims to develop an intelligent system for the accurate detection of helmetless motorcyclists using image analysis. The approach relies on deep learning, involving the creation of a dataset with 764 training images and 102 testing images. A deep convolutional neural network with 23 layers is configured, trained with a batch size of 10 over ten epochs, and employs the YOLO method to identify objects in images and subsequently detect helmetless riders. Accuracy assessment is carried out using the mean Average Precision (mAP) method, resulting in a notable 82.81% detection accuracy for riders without helmets and 75.78% for helmeted riders. The overall mAP score is 79.29%, emphasizing the system's potential to substantially improve road safety and law enforcement effort

Parallelization of Partitioning Around Medoids (PAM) in K-Medoids Clustering on GPU

K-medoids clustering is categorized as partitional clustering. K-medoids offers better result when dealing with outliers and arbitrary distance metric also in the situation when the mean or median does not exist within data. However, k-medoids suffers a high computational complexity. Partitioning Around Medoids (PAM) has been developed to improve k-medoids clustering, consists of build and swap steps and uses the entire dataset to find the best potential medoids. Thus, PAM produces better medoids than other algorithms. This research proposes the parallelization of PAM in k-medoids clustering on GPU to reduce computational time at the swap step of PAM. The parallelization scheme utilizes shared memory, reduction algorithm, and optimization of the thread block configuration to maximize the occupancy. Based on the experiment result, the proposed parallelized PAM k-medoids is faster than CPU and Matlab implementation and efficient for large dataset