Emotions, being transient and variable, differ across locations, times, and individuals. Automatic emotion identification holds significant importance across various domains, such as education and business. In education, emotional analysis contributes to intelligent electronic learning environments, while in business, it aids in assessing customer satisfaction with products. This study advocates the application of Principal Component Analysis (PCA) to enhance the performance of text emotion classification using the Artificial Neural Network (ANN) method. PCA, a pattern identification method, reduces text dimensions, improving the classification process by determining word similarities. PCA offers the advantage of dimension reduction without compromising information integrity. The classification approach involves two stages: one after PCA dimension reduction and the other without PCA post TF-IDF stage. The study's conclusive findings, incorporating PCA in ANN classification, demonstrated a notable increase in recall for the happy class, reaching 0.92 compared to the pre-PCA score of 0.91. Furthermore, precision in the sadness class improved to 0.90, surpassing the pre-PCA precision of 0.80. This affirms the efficacy of integrating PCA in enhancing the accuracy and performance of emotion classification in text analysis.

Kerang Batik (Paphia undulata) memiliki pola cangkang yang mirip batik, dengan warna dasar cangkang yang bervariasi dari kuning cerah hingga gelap. Sebagai komoditas ekspor Indonesia yang permintaannya terus meningkat, penting untuk menjaga standar kualitas tinggi agar kerang siap ekspor. Penelitian ini menyelidiki metode kontrol kualitas yang efektif untuk kerang batik yang layak ekspor dengan mengambil sampel dari perusahaan terkait. Setelah proses pra-pemrosesan citra, dilakukan ekstraksi fitur, termasuk fitur bentuk (eccentricity, metric) dan fitur tekstur (GLCM). Fitur-fitur ini digunakan dalam algoritma SVM (Support Vector Machine) dengan kernel RBF, yang dipilih karena kemampuannya menangani data non-linear, untuk mencapai akurasi optimal. Metode optimasi PSO (Particle Swarm Optimization) juga diterapkan untuk meningkatkan akurasi lebih lanjut. Penelitian menunjukkan bahwa SVM dengan kernel RBF mencapai akurasi tertinggi sebesar 96,43% pada sudut 45° dan 90°. Setelah dioptimalkan dengan PSO, akurasi meningkat menjadi 97,86% pada sudut 90°. Dengan demikian, penerapan PSO pada algoritma SVM secara signifikan meningkatkan akurasi klasifikasi.
 
Kata kunci: SVM, PSO, Kerang Batik, Kernel RBF, GLCM

This research focuses on car classification and the use of the ResNet-50 neural network architecture to improve the accuracy and reliability of car detection systems. Indonesia, as one of the countries with high daily mobility, has a majority of the population using cars as the main mode of transportation. Along with the increasing use of cars in Indonesia, many automotive industries have built factories in this country, so the cars used are either local or imported. The importance of car classification in traffic management is a major concern, and vehicle make and model recognition plays an important role in traffic monitoring. This study uses the Vehicle images dataset which contains high-resolution images of cars taken from the highway with varying viewing angles and frame rates. This data is used to analyze the best- selling car brands and build car classifications based on output or categories that consumers are interested in. Digital image processing methods, machine learning, and artificial neural networks are used in the development of automatic and real-time car detection systems.The ResNet-50 architecture was chosen because of its ability to overcome performance degradation problems and study complex and abstract features from car images. Residual blocks in the ResNet architecture allow a direct flow of information from the input layer to the output layer, overcoming the performance degradation problem common in neural networks. In this paper, we explain the basic concepts of ResNet-50 in car detection and popular techniques such as optimization, augmentation, and learning rate to improve performance and accuracy. in this study, it is proved that ResNet has a fairly high accuracy of 95%, 92% precision, 93% recall, and 92% F1-Score.