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All Journal Microbiology Indonesia SMATIKA Information System for Educators and Professionals : Journal of Information System Information Management For Educators And Professionals (IMBI) Syntax Literate: Jurnal Ilmiah Indonesia ALQALAM Amwaluna Jurnal Ekonomi dan Keuangan Syariah Syntax Idea JURSIMA (Jurnal Sistem Informasi dan Manajemen) Jurnal Dirosah Islamiyah JuTEkS (Jurnal Teknik Elektro dan Sains) Tadbir: Jurnal Manajemen Dakwah Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Sains Indonesia Formosa Journal of Multidisciplinary Research (FJMR) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) JAKA (Jurnal Akuntansi, Keuangan dan Auditing) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Edukasia: Jurnal Pendidikan dan Pembelajaran Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Widyaparwa Gunung Djati Conference Series AL ULYA: JURNAL PENDIDIKAN ISLAM JURSIMA Jurnal Manajemen Dan Bisnis Ekonomi DIROSAT: Journal of Education, Social Sciences & Humanities AMMA : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Ekonomi dan Manajemen Akuntansi: Jurnal Riset Ilmu Akuntansi AN-NIDA: Jurnal Pendidikan Islam THORIQOTUNA: Jurnal Pendidikan Islam
Saeful Anwar
Universitas Ibn Khaldun Bogor
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Social Sciences Other

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Perbandingan Kinerja VGG 16 dan ResNet untuk Pengenalan Ekspresi Wajah Mahasiswa Berbasis CNN pada Smart Learning Environment Dian Ade Kurnia; Fatihanursari Dikananda; Saeful Anwar; Dadang Sudrajat; Abdul Aziz
TEMATIK Vol. 12 No. 2 (2025): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2025
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v12i2.2590

Abstract

Perkembangan teknologi kecerdasan buatan (AI) dan visi komputer telah membuka peluang besar dalam penerapan pengenalan ekspresi wajah pada berbagai bidang. Dalam konteks pendidikan tinggi, keterlibatan mahasiswa selama proses belajar menjadi faktor penting yang masih sulit diukur secara objektif menggunakan metode konvensional. Namun pada kenyataannya, penelitian sebelumnya masih jarang menguji performa arsitektur CNN populer secara langsung di lingkungan pembelajaran nyata dengan kondisi pencahayaan dan pose yang beragam. Penelitian ini berkontribusi dengan membandingkan kinerja dua arsitektur deep learning, yaitu VGG-16 dan ResNet, dalam klasifikasi ekspresi wajah mahasiswa pada Smart Learning Environment. Penelitian dilakukan dengan pendekatan eksperimen kuantitatif melalui lima tahapan, yaitu pengumpulan data wajah mahasiswa di kelas, preprocessing berupa cropping, resizing, dan augmentasi, pengembangan model CNN, pelatihan menggunakan data split 80% training dan 20% validasi, serta evaluasi dengan metrik akurasi, presisi, recall, dan F1-score. Hasil eksperimen menunjukkan bahwa VGG-16 unggul dalam mengenali ekspresi suka dengan nilai F1-score tertinggi sebesar 85%, sedangkan ResNet relatif lebih baik pada ekspresi bosan dengan F1-score 73,2%. Sementara itu, keduanya sama-sama lemah dalam mengenali ekspresi tidak suka. Temuan ini mengimplikasikan bahwa VGG-16 lebih sesuai digunakan untuk mendukung analisis keterlibatan mahasiswa secara real-time dalam Smart Learning Environment berbasis AI.
Pengaruh Augmentasi Data dan Dropout terhadap Generalisasi Model Deteksi Kerusakan Panel Surya Irfan Ali; Rudi Kurniawan; Dadang Sudrajat; Saeful Anwar; Nining Rahaningsih
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Automatic defect detection in photovoltaic (PV) panels is a crucial challenge for maintaining energy efficiency and reliability in renewable power systems. However, the limited availability of labeled datasets and high environmental variability often lead deep learning models to overfit and lose generalization capability. This study investigates the combined effects of data augmentation and dropout regularization on improving the generalization performance of transfer learning-based models for multi-class PV defect classification. Two pretrained architectures, VGG16 and InceptionV3, were fine-tuned using the Faulty Solar Panel dataset comprising six defect categories. Experiments were conducted under three main configurations: (1) baseline without regularization, (2) augmentation only, and (3) combined augmentation–dropout with dropout rates of 0.2, 0.3, and 0.5. Performance evaluation employed accuracy, precision, recall, macro-F1, and confusion matrix analysis for each defect class. The results demonstrate that the combination of data augmentation and moderate dropout (0.3) significantly enhances generalization, achieving 92.10% accuracy and 0.90 macro-F1 with the InceptionV3 architecture. Higher dropout values (0.5) caused slower convergence and decreased accuracy. These findings confirm that balanced integration of augmentation and dropout effectively mitigates overfitting and strengthens model robustness under limited and imbalanced data conditions. The proposed approach provides practical implications for implementing reliable, lightweight, and deployable deep learning-based inspection systems in real-world PV monitoring using edge computing devices.
Integrasi Deep Learning Multimodal Untuk Peramalan Penjualan Toko Menggunakan Keras Functional API Khaerul Anam; Dadang Sudrajat; Saeful Anwar; Rudi Kurniawan
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Store sales forecasting based on historical data has been widely studied; however, most conventional approaches remain limited to single time series data and are less capable of capturing the complex influence of external factors. Existing knowledge suggests that deep learning can improve forecasting accuracy compared to traditional statistical methods, but what remains unclear is the extent to which multimodal integration—combining time series, economic, and categorical data—can enhance predictive performance in a dynamic retail context. This study aims to develop and evaluate a multimodal deep learning model using the Keras Functional API for store sales forecasting. The methodology involves collecting and processing daily transaction data, oil prices, holidays, and store information, followed by preprocessing, feature engineering, normalization, and time-window construction stages. Four architectures were tested—LSTM, 1D CNN, CNN+RNN, and Multiscale CNN—with performance evaluation conducted using Mean Absolute Error (MAE). The results indicate that multimodal integration yields a significant improvement compared to single-source data, with the 1D CNN model achieving the best performance at an MAE of 57,4318. The discussion highlights that integrating external variables such as oil prices and holidays enhances the robustness of predictions, while the main challenges remain in high computational requirements and limited model interpretability. This study concludes that the multimodal deep learning approach provides a scientific contribution by enriching the literature on sales forecasting while offering practical implications for the retail sector in inventory management, promotional planning, and data-driven decision-making.
Co-Authors Abas Mansur Tamam Abdul Azis Abdul Aziz Ade Irma Purnamasari Adhitya Wibisono Afif, M Nur Afifatu Fachrudin Agus Moh. Sholahuddin Ahmad Bahrum Maula Rahman Ahmad Munajim Akhmad Alim Ananda Rafly Anisa Aprinus Salam Arif Nur Hakim Arif Rinaldi Dikananda Ase Kurniawan Atang Sutandi Azkal Muhammad Azkiya Azzam Izzudin Bani Nurhakim Basuki Sumawinata Dadang Sudrajat Darmono Taniwiryono Dian Ade Kurnia Dicky Miftakhul Rizki Ester Angeline Farid Ali Ma'ruf Fatihanursari Dikananda Fithri Muliawati Ghaida Refiana Zahra Hadianto Nur Fadhli Happy Widiastuti Herni Syahara Heru B. Pulunggono Hoerudin, Cecep Wahyu Husni Mubarok Ibdalsyah Ibdalsyah Ibnu Hajar Ike Suryani Dewi Indah Ratna Ningsih Irfan Ali Isni Rinjani Iwan Setiawan Joko, Ganang Prihatmoko Kevin Salsabil Arlandy Khaerul Anam M Danang Mukti Darmawan Masyhurul Khamis Melawati Melawati Mia Lasmi Wardiyah, Mia Lasmi Muhammad Hilmi Mulyawan Mulyawan Nabil Makarim Nafhan Khairuddin Fathin Najla Kayla Nanda Octavia Naufal Ridho Setyo Laksono Nema Widiantini Nining R Nining Rahaningsih Nisa Dienwati Nuris Novayanti Magdalena Gultom Novia Wulandari Nur Afif nur syarief abdullah Nurhadi Kastamin Odi Nurdiawan Rahma Amelia Purnama Rahmah Mulanti Rahman, Riem Rahayu Resti Dwi Anjani Ridwan Siskandar Rifqi Nurfadillah Rudi Kurniawan Ruli Herdiana Rusbandi Rusbandi Ryan Hamonangan Saeful Amri Shofian Yunus Siswanto Siti Komariyah Sofyan Alhaq Suratun Suratun Syifa Rahmatul Awaliyah Tati Supra Ula Nur Azizah Windi Herlita Vidila Wiyoto Wiyoto Yundari, Yundari Zumrotul Fauziah