Article Per Year (5 Year)
p-Index From 2021 - 2026
1.376
P-Index
This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Ta´dib JURNAL ISTEK SMATIKA JOIN (Jurnal Online Informatika) ISTEK Khazanah Pendidikan Islam
Undang Syaripudin, Undang
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Electrical & Electronics Engineering Other

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Implementation of Convolutional Neural Network CNN Algorithm to Detect Coffe Fruit Maturity Gerhana, Yana Aditia; Heryanto, Rafi Rai; Syaripudin, Undang; Suparman, Deden
ISTEK Vol. 13 No. 2 (2024): Desember 2024
Publisher : Fakultas Sains dan Teknologi UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/istek.v13i2.1247

Abstract

Fruit ripeness detection is important in the agriculture and food processing industries to ensure optimal product quality. Proper fruit ripeness can affect flavour, texture and nutrition, making it a key focus in production process monitoring and control. The fruit ripeness detection process still needs to be done manually, which can be inefficient and inaccurate. This research aims to address these challenges by implementing the CNN algorithm with VGG-19 architecture to detect coffee fruit ripeness automatically. The process involves collecting datasets of fruit images with various ripeness levels, image pre-processing including cropping and resizing, training the CNN VGG-19 model with feature learning and hyperparameter optimisation and evaluating model performance using a confusion matrix. This experiment aims to evaluate the model's performance in detecting fruit ripeness and measure the speed and efficiency of the CNN-based detection system with VGG-19 architecture. The results of this research are expected to help develop a better system for identifying fruit ripeness.
PENGEMBANGAN MODEL PENERIMAAN MAHASISWA BARU DI PERGURUAN TINGGI KEAGAMAAN ISLAM NEGERI Mohamad Erihadiana; Asep Muhyiddin; Tata Sukayat; Undang Syaripudin; Fildzah Arifah Yoda
Khazanah Pendidikan Islam Vol. 3 No. 2 (2021): Khazanah Pendidikan Islam
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kp.v3i2.11787

Abstract

Penelitian ini membahas Pengembangan Model Sistem Penerimaan Mahasiswa Baru (PMB) pada lembaga Perguruan Tinggi Keagamaan Islam Negeri (PTKIN). Dengan melewati pengembangan model aplikasi PMB di PTKIN ini dapat diperoleh input mahasiswa yang mampu berprestasi akademik tinggi. Untuk mengembangkan model tersebut,  maka dideskrispsikan terlebih dahulu tentang kebijakan Penerimaan Mahasiswa Baru Perguruan Tinggi Keagamaan Islam Negeri (PTKIN) di Indonesia dan melakukan studi tentang penerimaan mahasiswa baru di dua PTKIN yaitu UIN Sunan Gunung Djati Bandung dan UIN Sultan Syarif Kasim Riau. Di samping melakukan studi sistem penerimaan mahasiswa baru di perguruan tinggi terkemuka di Asia Tenggara yaitu Asia e-University Malaysia.  Berdasarkan hasil riset dan analisis teori, maka dibuatlah rancangan aplikasi penerimaan mahasiswa baru Perguruan Tinggi Agama Islam Negeri berbasis web atau teknologi informasi. Pendekatan jenis kualitatif, riset dan pengembangan (R & D) merupakan pendekatan yang digunakan dalam penelitian ini. Guna untuk menghasilkan hasil yang berbentuk PMB di PTKIN. Model tersebut adalah model berbasis online atau web yang dikembangkan untuk kepentingan mutu akademik. Aplikasi penerimaan PMB menggunakan pendekatan yang bertujuan objek pada metode Unified Modeling Language (UML) untuk mendapatkan hasil pada penelitian ini. Perancangannya memuat system dengan menggunakan model dan dapat dinyatakan bagian dari fungsi yang tersedia dengan menggunakan Use Case diagram. Kemudian system yang digunakan actor dengan aturan aktifitas merupakan analisis Activity diagram. Adapun yang menggunakan tahap perencanaan kelas dari tahap analisis kelas yaitu Activity class diagram. Kemudian sequence diagram, arsitekstur jaringan, dan arsitekstur sistem
Convolutional Neural Networks for Measuring Service Satisfaction of Hajj Pilgrims through Facial Expression Analysis Syaripudin, Undang; Jumadi, Jumadi; Ramdania, Diena Rauda; Lestari, Indah Sri; Nurfiani, Indri; Setyawan, Alfin Yogi; Harika, Maisevli; Mintarsih, Mimin
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1677

Abstract

Facial expressions serve as important non-verbal indicators of human emotions and can be leveraged to assess satisfaction levels in service environments. In the context of Hajj and Umrah, where verbal feedback may be limited due to language barriers or cultural factors, facial expression recognition offers a non-intrusive method to evaluate service quality. This study proposes a Convolutional Neural Network (CNN)-based model to detect emotional states such as happiness and dissatisfaction through facial expressions of pilgrims. A quantitative approach was adopted, employing preprocessing techniques including normalization, augmentation, and image resizing. The CNN architecture comprised multiple convolutional, pooling, and fully connected layers. The model was evaluated using accuracy, precision, recall, and F1-score metrics. Experiments with varying batch sizes (32, 64, 128, 256) across 50 epochs revealed that the optimal performance was achieved with a batch size of 64, resulting in an accuracy of 63%, precision of 66%, recall of 60%, and F1-score of 62%. During deployment, the model correctly classified 12 out of 16 real-world images, achieving a real-time accuracy of 78%. Therefore, the deployment results should be considered preliminary. Future studies will involve larger deployment samples, n-fold stratified cross-validation to obtain statistically reliable model performance, and subgroup analyses (e.g., lighting, facial pose, age, and gender) to better understand model behavior under diverse real-world conditions. All deployment images were collected with participant consent and processed without storing biometric data. These findings suggest that CNN-based emotion recognition can support real-time service evaluation and enhance the quality of pilgrim services during the Hajj and Umrah.
Co-Authors ALAWIYAH, RIZKA Anwar, Muhammad Afian Arief Fatchul Huda Asep Muhyiddin, Asep Ayu Puji Rahayu Cecep Nurul Alam Cecep Nurul Alam, Cecep Nurul Cepy Slamet Dede K Deden Suparman, Deden Devi Novitasari Diena Rauda Ramdania Erwan Yani Erwin Setiawan, Erwin Fildzah Arifah Yoda Heryanto, Rafi Rai Ijang Badruzaman Indah Sri Lestari, Indah Sri Jumadi Jumadi M. Ramdhani Maisevli Harika Mintarsih, Mimin Mohamad Erihadiana, Mohamad Mohamad Irfan Mohamad Irfan, Mohamad Muhammad Hamdani Muhammad Hamdani, Muhammad Nur Lukman Nurfiani, Indri Setyawan, Alfin Yogi Suhendar, Hilman Tata Sukayat Yana Aditia Gerhana, Yana Aditia Yogi Saputra