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All Journal Jurnal Komputer Terapan Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Pengabdian Masyarakat Indonesia
Syefrida Yulina, Syefrida
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Public Health Social Sciences

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Performance Analysis of Classification Models in Multiclass Facial Expression Recognition Based on Eigenface Features Yulina, Syefrida; Rachmawati, Heni
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1742

Abstract

Facial Expression Recognition (FER) is currently widely explored by researchers in the field of Computer Vision. The application of Machine Learning and Deep Learning methods is useful in developing an intelligent system that is accurate in recognizing facial expressions such as emotions. This is inseparable from the type of dataset and classification method used which certainly affects the desired results. To choose the right method, it is necessary to compare the performance of these methods. This study focuses on comparing the performance results of four classification methods namely, Convolutional Neural Network (CNN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes Classifier (NBC) on a multiclass dataset for seven classes of facial emotion labels based on Eigenface feature selection uses the Personal Component Analysis (PCA) algorithm. The test parameters used to perform method comparisons are accuracy, recall, precision, f1-score, as well as the Receiving Operating Characteristic (ROC) and Area Under Curve (AUC) curves. The results of the analysis state that the SVM method has the highest accuracy value, while other methods show varying performance based on recall, precision, f1-score, and ROC and AUC analysis. This research was conducted on the FER 2013 dataset which showed that the classification method tested had quite good performance according to the test parameters.
Implementasi Kurikulum Merdeka Menuju Pemulihan Pembelajaran Pada Mim 02 Pekanbaru Nengsih, Warnia; Widyasari, Cyntia; Yulina, Syefrida; Guslinda, Guslinda
Jurnal Pengabdian Masyarakat Indonesia Vol 4 No 4 (2024): JPMI - Agustus 2024
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpmi.2168

Abstract

Kurikulum Merdeka merupakan sebuah inovasi dalam sistem pendidikan Indonesia yang bertujuan untuk mencetak siswa yang lebih kreatif, mandiri, dan mampu beradaptasi dengan cepat terhadap perubahan lingkungan. Adapun tujuan dari Kurikulum Merdeka adalah mengembalikan otoritas sekolah dan pemerintah daerah untuk mengelola sendiri pendidikan yang sesuai dengan kondisi di daerahnya. Mempercepat pencapaian tujuan pendidikan nasional serta menyiapkan tantangan global era revolusi 4.0. implementasi Kurikulum Merdeka pada MIM 02 tidaklah mudah. Proses transisi dan implementasi kurikulum ini sangat kompleksitas serta membutuhkan beberapa perubahan dan penyesuaian dalam proses transformasinya.  Diperlukan pendampingan yang tepat dan dukungan dari berbagai pihak agar dapat terlaksana dengan baik. Hal ini juga perlu ditunjang dengan keberadaan IPTEKS sehingga mendukung pelaksanaan program dan kegiatan tersebut. Adapun tujuan dari kegiatan ini adalah untuk meningkatkan kualitas pendidikan di MIM 2 Pekanbaru dan memberikan dukungan yang diperlukan bagi para praktisi pendidikan agar dapat terus berkembang secara profesional. Sehingga diharapkan terjadinya peningkatan kualitas dan kompetensi Guru dan tenaga kependidikan di MIM 02 Pekanbaru menuju perbaikan kualitas Pendidikan Indonesia.
Performance Analysis of Classification Models in Multiclass Facial Expression Recognition Based on Eigenface Features Yulina, Syefrida; Rachmawati, Heni
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1742

Abstract

Facial Expression Recognition (FER) is currently widely explored by researchers in the field of Computer Vision. The application of Machine Learning and Deep Learning methods is useful in developing an intelligent system that is accurate in recognizing facial expressions such as emotions. This is inseparable from the type of dataset and classification method used which certainly affects the desired results. To choose the right method, it is necessary to compare the performance of these methods. This study focuses on comparing the performance results of four classification methods namely, Convolutional Neural Network (CNN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes Classifier (NBC) on a multiclass dataset for seven classes of facial emotion labels based on Eigenface feature selection uses the Personal Component Analysis (PCA) algorithm. The test parameters used to perform method comparisons are accuracy, recall, precision, f1-score, as well as the Receiving Operating Characteristic (ROC) and Area Under Curve (AUC) curves. The results of the analysis state that the SVM method has the highest accuracy value, while other methods show varying performance based on recall, precision, f1-score, and ROC and AUC analysis. This research was conducted on the FER 2013 dataset which showed that the classification method tested had quite good performance according to the test parameters.
Optimasi Model CNN untuk Identifikasi Jenis Bunga Berdasarkan spektrum Warna nengsih, warnia; Yulina, Syefrida
Jurnal Komputer Terapan Vol 10 No 1 (2024): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jkt.v10i1.6274

Abstract

This research takes the form of Flower Species Recognition using Convolutional Neural Network (CNN) to optimize the identification of flower types based on color spectrum. The color spectrum of flowers can vary significantly between species and even within a single species. This can pose a challenge in developing a model capable of identifying flower types with high accuracy amidst a wide spectrum of color variations. Selecting an appropriate CNN architecture and optimizing model hyperparameters to achieve optimal performance is a complex process. Careful exploration of various architectures and optimization techniques is necessary to improve the accuracy of flower type identification. The dataset used is collected from various repository sources, comprising images of flowers captured under different lighting conditions, representing diverse color spectra. In this study, data preprocessing stages include color spectrum normalization, feature extraction, and data augmentation to enhance dataset diversity. The CNN model in this research is optimized through network architecture optimization. Model evaluation is performed using standard performance evaluation metrics such as accuracy, precision, and recall. It is expected that this research will yield a CNN model capable of identifying flower types with good accuracy levels, despite facing a wide range of color spectrum variations. This will facilitate the identification and grouping of flower types based on their visual characteristics..
Co-Authors Heni Rachmawati HENI RACHMAWATI Nurwijayanti Rahmatya, Disha Warnia Nengsih Widyasari, Cyntia Yohana Dewi Lulu Widyasari