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All Journal JOIV : International Journal on Informatics Visualization Faktor Exacta JTERA (Jurnal Teknologi Rekayasa) JURNAL PengaMAS Digital Zone: Jurnal Teknologi Informasi dan Komunikasi TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Jambura Journal of Electrical and Electronics Engineering JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Charity : Jurnal Pengabdian Masyarakat JURNAL POLI-TEKNOLOGI Journal of Computer Engineering, Electronics and Information Technology Jurnal Informatika: Jurnal Pengembangan IT
Wirmanto Suteddy
Universitas Pendidikan Indonesia
Aerospace Engineering Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences Transportation Other

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Offline Handwriting Writer Identification using Depth-wise Separable Convolution with Siamese Network Suteddy, Wirmanto; Agustini, Devi Aprianti Rimadhani; Atmanto, Dastin Aryo
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2148

Abstract

Offline handwriting writer identification has significant implications for forensic investigations and biometric authentication. Handwriting, as a distinctive biometric trait, provides insights into individual identity. Despite advancements in handcrafted algorithms and deep learning techniques, the persistent challenges related to intra-variability and inter-writer similarity continue to drive research efforts. In this study, we build on well-separated convolution architectures like the Xception architecture, which has proven to be robust in our previous research comparing various deep learning architectures such as MobileNet, EfficientNet, ResNet50, and VGG16, where Xception demonstrated minimal training-validation disparities for writer identification. Expanding on this, we use a model based on similarity or dissimilarity approaches to identify offline writers' handwriting, known as the Siamese Network, that incorporates the Xception architecture. Similarity or dissimilarity measurements are based on the Manhattan or L1 distance between representation vectors of each input pair. We train publicly available IAM and CVL datasets; our approach achieves accuracy rates of 99.81% for IAM and 99.88% for CVL. The model was evaluated using evaluation metrics, which revealed only two error predictions in the IAM dataset, resulting in 99.75% accuracy, and five error predictions for CVL, resulting in 99.57% accuracy. These findings modestly surpass existing achievements, highlighting the potential inherent in our methodology to enhance writer identification accuracy. This study underscores the effectiveness of integrating the Siamese Network with depth-wise separable convolution, emphasizing the practical implications for supporting writer identification in real-world applications.
Studi Komparasi Algoritma SVM Dan Random Forest Pada Analisis Sentimen Komentar Youtube BTS Syafia, Anisa Nur; Hidayattullah, Muhammad Fikri; Suteddy, Wirmanto
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5064

Abstract

Sentiment analysis of YouTube boy group BTS comments uses the NLP approach to detect emotional patterns based on two category labels, namely positive and negative. With NLP, positive or negative polarity in an entity can be allocated as well as predicted high and low performance from various classification sentiments. The machine learning algorithms used to measure the accuracy of sentiment analysis developed are the Support Vector Machine and Random Forest algorithms. The steps taken start from the data collection obtained from the BTS YouTube Comment dataset and then go through the data preprocessing stage. Then proceed to the feature extraction stage by converting text into digital vectors or Bag of Words (BOW) and classified using machine learning algorithms until the evaluation stage. From the results comparison of the evaluated algorithms, the accuracy value between the two algorithms is 96% for training data and 85% for data testing using the SVM algorithm, while for the Random Forest algorithm it is 82% for training data and 80% for data testing. This shows that the SVM algorithm produces a higher accuracy value than the Random Forest for sentiment analysis of YouTube boy group BTS comments.
PELATIHAN MULTIMEDIA BERBASIS GAME SEBAGAI ALTERNATIF MEDIA PEMBELAJARAN PADA GURU DI KABUPATEN PANGANDARAN Munawir, Munawir; Adiwilaga, Anugrah; Suteddy, Wirmanto; Agustini, Devi Aprianti Rimadhani; Pradeka, Deden; Putra, Muhammad Taufik Dwi; Septiana, Asyifa Imanda
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Publisher : LPPM UNIVERSITAS KHAIRUN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/pengamas.v6i3.7037

Abstract

Kegiatan Pengapdian ini bertujuan untuk menginvestigasi dan mengimplementasikan multimedia berbasis game sebagai alternatif media pembelajaran untuk guru di Kabupaten Pangandaran. Media interaktif ini dirancang menggunakan Microsoft PowerPoint yang dikemas dalam bentuk game, dengan harapan dapat meningkatkan efektivitas dan keterlibatan guru dalam proses pembelajaran. Metodologi penelitian melibatkan tahap desain, pengembangan, implementasi, dan evaluasi. Desain multimedia didasarkan pada prinsip-prinsip pembelajaran yang menarik dan berfokus pada penyampaian materi dengan pendekatan yang interaktif. Proses pengembangan melibatkan pemilihan konten yang relevan dengan kebutuhan guru di Kabupaten Pangandaran dan penyesuaian elemen-elemen game agar sesuai dengan kebutuhan pembelajaran. Implementasi media interaktif dilakukan melalui pelatihan kepada sejumlah guru sebagai kelompok uji coba. Evaluasi dilakukan dengan mengumpulkan data dari kuesioner pra dan pasca kegiatan. Analisis data dilakukan untuk mengukur tingkat keefektifan, keterlibatan, dan respons guru terhadap media pembelajaran berbasis game. Hasilnya rata-rata 75% setuju bahwa penggunaan power point sebagai media pembelajaran bermanfaat dalam meningkatkan semangat siswa, memudahkan penyampaian materi juga memudahkan siswa dalam memahami materi. Respon positif setalah kegiatan pelatihan dari peserta tercermin bahwa 90% dari mereka bersemangat untuk mencoba membuat media pembelajaran berbasis PowerPoint sesuai dengan yang diajarkan dalam pelatihan, serta menggunakannya dalam proses pembelajaran dan sebanyak 86% peserta menyatakan bahwa pelatihan berhasil meningkatkan kemampuan mereka, terutama dalam pembuatan media pembelajaran interaktif. Hasil penelitian diharapkan dapat memberikan pemahaman lebih dalam tentang potensi penggunaan multimedia berbasis game dalam konteks endidikan guru di Kabupaten Pangandaran. Implikasi praktis penelitian ini diharapkan dapat meningkatkan minat dan motivasi guru untuk memanfaatkan teknologi multimedia dalam proses pembelajaran, sehingga mendukung peningkatan kualitas endidikan di daerah tersebut
End-To-End Evaluation of Deep Learning Architectures for Off-Line Handwriting Writer Identification: A Comparative Study Suteddy, Wirmanto; Agustini, Devi Aprianti Rimadhani; Adiwilaga, Anugrah; Atmanto, Dastin Aryo
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1293

Abstract

Identifying writers using their handwriting is particularly challenging for a machine, given that a person’s writing can serve as their distinguishing characteristic. The process of identification using handcrafted features has shown promising results, but the intra-class variability between authors still needs further development. Almost all computer vision-related tasks use Deep learning (DL) nowadays, and as a result, researchers are developing many DL architectures with their respective methods. In addition, feature extraction, usually accomplished using handcrafted algorithms, can now be automatically conducted using convolutional neural networks. With the various developments of the DL method, it is necessary to evaluate the suitable DL for the problem we are aiming at, namely the classification of writer identification. This comparative study evaluated several DL architectures such as VGG16, ResNet50, MobileNet, Xception, and EfficientNet end-to-end to examine their advantages to offline handwriting for writer identification problems with IAM and CVL databases. Each architecture compared its respective process to the training and validation metrics accuracy, demonstrating that ResNet50 DL had the highest train accuracy of 98.86%. However, Xception DL performed slightly better due to the convergence gap for validation accuracy compared to all the other architectures, which were 21.79% and 15.12% for IAM and CVL. Also, the smallest gap of convergence between training and validation accuracy for the IAM and CVL datasets were 19.13% and 16.49%, respectively. The results of these findings serve as the basis for DL architecture selection and open up overfitting problems for future work.
Co-Authors Adiwilaga, Anugrah Adjhi, Dhimaz Purnama Afila Ansori Ana Rahma Yuniarti Atmanto, Dastin Aryo Aulia Putri Cendekia Bhima Arya Daniswara Dastin Aryo Atmanto Deden Pradeka Devi Aprianti Rimadhani Agustini Griffani Megiyanto Rahmatullah Hanafi, Mohamad Rizal Komarudin, Rizal Maulana Kuswanto Kuswanto Mochamad Iqbal Ardimansyah Muhammad Ayat Hidayat Muhammad Fikri Hidayattullah, Muhammad Fikri Munawir Munawir Munawir Munawir Nanditama, Rastra Wardana Naufal Nurdiansyah Putra, Muhammad Taufik Dwi Raditya Muhammad Rizki Nuriman Salman Abdul Jabbaar Wiharja Septiana, Asyifa Imanda Syafia, Anisa Nur Wiharja, Salman