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            "given": "Dika Adi",
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          "id": "NIM2213030017"
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      "contact_email": "digilib@unpkdr.ac.id",
      "department": "KODEPRODI57201#Sistem Informasi",
      "date": "2026-06-26",
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      "institution": "Universitas Nusantara PGRI Kediri",
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      "abstract": "Perkembangan pembelajaran daring dan hibrid saat ini menuntut metode pengukuran tingkat fokus peserta didik yang objektif, namun pemantauan yang ada masih sering dilakukan secara manual dan subjektif. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi tingkat fokus berbasis citra wajah menggunakan arsitektur MobileNetV3 agar dapat berjalan secara real-time pada perangkat dengan keterbatasan sumber daya. \r\n\t\tMetode penelitian yang digunakan adalah (CRISP-DM) dengan memanfaatkan  AFLW2000-3D yang memiliki variasi orientasi kepala berupa parameter yaw dan pitch dalam kondisi nyata (in-the-wild). Tahapan penelitian meliputi preprocessing citra, pelabelan otomatis berdasarkan ambang batas (threshold) sudut kepala, serta penerapan transfer learning menggunakan bobot pre-trained dari ImageNet. Selain itu, penelitian ini juga membandingkan performa MobileNetV3 dengan model YOLOv8 pada implementasi deteksi fokus secara real-time.\r\n\t\tHasil penelitian menunjukkan bahwa model MobileNetV3 mampu mencapai tingkat akurasi sebesar 93%, dengan nilai precision 0,84, recall 0,87, dan F1-score 0,85. Dalam pengujian real-time menggunakan Streamlit, model ini menunjukkan performa kecepatan yang lebih baik dibandingkan YOLOv8 dengan capaian rata-rata FPS sebesar 2,5–4,7 pada perangkat CPU. Hasil pengujian juga menunjukkan bahwa MobileNetV3 memiliki stabilitas prediksi yang lebih baik pada berbagai kondisi arah pandangan wajah.\r\n\t\tKesimpulan dari penelitian ini adalah arsitektur MobileNetV3 efektif dan stabil digunakan sebagai solusi pemantauan fokus secara otomatis karena memiliki keseimbangan antara efisiensi komputasi dan akurasi prediksi. Limitasi penelitian ini terletak pada ketergantungan terhadap kondisi pencahayaan dan sudut kamera tertentu sehingga pengembangan selanjutnya disarankan untuk menambah variasi , integrasi deteksi ekspresi wajah, serta implementasi multi-face detection agar sistem dapat digunakan pada lingkungan pembelajaran yang lebih luas",
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      "title": "KLASIFIKASI TINGKAT FOKUS PESERTA DIDIK BERDASARKAN HEAD POSE ESTIMATION MENGGUNAKAN CNN MOBILENETV3",
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      "referencetext": "A Nugroho, & SYLLA AYU KUSUMAHATI. (2022). PENERAPAN CONVOLUTIONAL NEURAL NETWORK DENGAN TRANSFER LEARNING MOBILENETV2 PADA KLASIFIKASI PENYAKIT KULIT WAJAH.\r\nAlamsyah, D., & Pratama, D. (2020). IMPLEMENTASI CONVOLUTIONAL NEURAL NETWORKS (CNN) UNTUK KLASIFIKASI EKSPRESI CITRA WAJAH PADA FER-2013. Jurnal Teknologi Informas, 4(2).\r\nAlgoritma, P., Bayes, N., Aulia, H., Syifa, N., Nugroho, A., & Firliana, R. (2023). Habibi Aulia Nur Syifa Perbandingan Algoritma Naïve Bayes Classifier Dan K-Nearest Neighbors Untuk Analisis Sentimen Covid-19 Di Twitter.\r\nAndresangsya, A., Indriati, R., & Muzaki, M. N. (2025). Sistem Deteksi Moulting Lobster Air Tawar Menggunakan Teknologi IoT. JSITIK: Jurnal Sistem Informasi Dan Teknologi Informasi Komputer, 3(2), 93–100. https:\/\/doi.org\/10.53624\/jsitik.v3i2.708\r\nAngga Cahyo Pradikdo, & Aidina Ristyawan. (2018). MODEL KLASIFIKASI ABSTRAK SKRIPSI MENGGUNAKAN TEXT MINING UNTUK PENGKATEGORIAN SKRIPSI SESUAI BIDANG KAJIAN. 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MODEL ATENSI CITRA WAJAH AUDIENS BERBASIS EDGE MACHINE LEARNING.\r\nHoward, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., Le, Q. V, Adam, H., Ai, G., & Brain, G. (n.d.). Searching for MobileNetV3.\r\nImelda, Y., Karmila Daulay, L., Paramitha Purba, D., & Syahputra, H. (n.d.). JETBUS Journal of Education Transportation and Business Deteksi Gerakan Kepala Secara Real-Time Menggunakan OpenCV dan Python. 2(1), 426.\r\nIqbal, A. M., Jumadi, J., & Nurlatifah, E. (2025). Implementasi YOLOv8 Sebagai Pendeteksi Nominal Uang Rupiah Kertas Berbasis Android. SMATIKA JURNAL, 15(02), 268–277. https:\/\/doi.org\/10.32664\/smatika.v15i02.1545\r\nMarkoulidakis, I., Rallis, I., Georgoulas, I., Kopsiaftis, G., Doulamis, A., & oulamis, (2021). Multiclass Confusion Matrix Reduction Method and Its Application on Net Promoter Score Classification Problem. 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Pendidikan Matematika, 01.\r\nRedmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. http:\/\/arxiv.org\/abs\/1506.02640\r\nRianti, A., Wachid, N., Majid, A., Fauzi, A., Sistem, P., Informasi, T., Telekomunikasi, S., Upi Di Purwakarta, K., & Edu, A. (n.d.). CRISP-DM: Metodologi Proyek Data Science.\r\nRohmah, A., & Pradikto, S. (n.d.). Jurnal Cakrawala Pendidikan dan Biologi Volume. 2, Nomor. 4, Tahun. 2025, 42–53. https:\/\/doi.org\/10.61132\/jucapenbi.v2i1.140\r\nSchröer, C., Kruse, F., & Gómez, J. M. (2021). A Systematic Literature Review on Applying CRISP-DM Process Model. Procedia Computer Science, 181, 526–534. https:\/\/doi.org\/10.1016\/j.procs.2021.01.199\r\nSimangunsong, J., Simanjuntak, N. D., & Matondang, A. A. (2025). Penerapan Transfer Learning untuk Klasifikasi Citra Bunga Berbasis Convolutional Neural Network. 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Information (Switzerland), 16(11). https:\/\/doi.org\/10.3390\/info16110949\r\nWardhana, S. F. D., & Nugroho, A. (2025). Perbandingan Arsitektur MobileNetV2 dan MobileNetV3 Dalam Klasifikasi Jenis Jeruk. Jurnal Ilmu Komputer Dan Bisnis, 16(1), 25–34. https:\/\/doi.org\/10.47927\/jikb.v16i1.916\r\nZhou, N., Liang, R., & Shi, W. (2021). A Lightweight Convolutional Neural Network for Real-Time Facial Expression Detection. IEEE Access, 9, 5573–5584. https:\/\/doi.org\/10.1109\/ACCESS.2020.3046715\r\nZhu, X., Lei, Z., Liu, X., Shi, H., & Li, S. Z. (n.d.). Face Alignment Across Large Poses: A 3D Solution. Retrieved www.cbsr.ia.ac.cn\/users\/xiangyuzhu\/."
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