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All Journal JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Tika KLIK: Kajian Ilmiah Informatika dan Komputer Instal : Jurnal Komputer Journal of Innovative and Creativity
Muslem R, Imam
Universitas Almuslim Bireuen
Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Journal : KLIK: Kajian Ilmiah Informatika dan Komputer

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Klasifikasi Citra Ikan Menggunakan Algoritma Convolutional Neural Network dengan Arsitektur VGG-16 Imam Muslem R; Teuku Muhammad Johan; Luthfi
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 2 (2023): Oktober 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i2.1209

Abstract

The development of technology in the field of image processing has inspired this research which aims to overcome challenges in classifying fish images using a Convolutional Neural Network (CNN) based approach. In this research, we utilize the VGG-16 architecture, a CNN model that has been proven capable of retrieving important features from images with significant depth. The dataset consists of 1088 fish images divided into four classes: Bangus, Glass Perchlet, Gold Fish, and Gourami. The initial process involves feature extraction via image embedding using the VGG-16 architecture. Next, a classification model is built using the Orange Data Mining tool. The experimental results show that this approach is able to provide good classification performance with significant accuracy in recognizing different fish species. The use of VGG-16 enables powerful and complex feature extraction, and experimental results show that this approach achieves a training accuracy of 96.2%. Furthermore, when the classification process uses data testing, this method produces an accuracy of 99.5%. This finding shows the great potential of the Convolutional Neural Network in overcoming the challenge of classifying fish images with very satisfactory results, which can be applied in various fields including marine science and remote sensing.
Image Classification pada Kasus American Sign Language Menggunakan Support Vector Machine Zulkifli; Imam Muslem R
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 2 (2023): Oktober 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i2.1242

Abstract

This study aims to develop and test a hand drawing classification model using the Support Vector Machine (SVM) algorithm to identify digits in American Sign Language (ASL). This method utilizes image processing techniques for extracting relevant features from hand images and SVM's ability to separate complex patterns. The training and test data consists of hand images representing the digits 0 through 9 in ASL. Tests are performed using test data that the model has never seen during training, to measure the performance and validity of the model in real-world situations. The results showed that the developed classification model was able to recognize digits in ASL with satisfactory accuracy, where the accuracy of the developed model was 99.8% with a loss of 0.018. Error analysis provides insight into situations that confuse the model and the potential for further improvement. The use of SVM in this ASL classification opens up new opportunities in strengthening communication accessibility for the hand sign language community. In conclusion, this model has the potential to make a positive contribution in facilitating communication and inclusion for deaf communities.
Klasifikasi Kualitas Buah Pisang Berdasarkan Citra Buah Menggunakan Stochastic Gradient Descent Dedy Armiady; Imam Muslem R
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 2 (2023): Oktober 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i2.1243

Abstract

Banana fruit quality is an important factor in meeting consumer demand and maintaining product quality in the supply chain. The development of automatic methods for classifying the quality of bananas is becoming increasingly important as the worldwide consumption of bananas grows. In this study, we propose a classification method for banana fruit quality using the Stochastic Gradient Descent (SGD) algorithm. This study aims to evaluate the performance of SGD in classifying the quality of bananas and to analyze the effect of selecting hyperparameters on the classification results. The dataset collected is a dataset containing pictures of bananas with various levels of ripeness and conditions. This dataset is used to train and test a classification model using SGD. During the experiment, hyperparameter tuning processes such as learning rate, momentum, and batch size were carried out to understand how these parameters affect the performance of SGD in classification. We report the results of evaluating the classification based on accuracy and analyze changes in performance with variations in hyperparameters. The results of this study indicate that SGD has the potential to classify the quality of bananas, where the optimal SGD model obtained a classification accuracy of 99.9%, compared to the standard SGD model which only obtained a classification accuracy of 94.7%.
Co-Authors Amelia, Nazwa Dedy Armiady Dewi, Rosna Erifki Rizki Putra Fina Meilinar Fitriani Fitriani Johan Johan Johan, T. M. Johan, T. Muhammad Khairuna, Ulfa Luthfi Ratna Dewi Rizal Rizal Rizki Putra, Erifki Sriwinar, Sriwinar Syafawi, Qamar Teuku Muhammad Johan Winar, Sri Zahira, Gaitsa Zulkifli