Klasifikasi Absensi Face Geo-Location Menggunakan Metode CNN pada PT Indomarco Prismatama
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Jan 10, 2025
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The rapid development of information technology forces companies to continue to improve operational efficiency, including in managing employee attendance. An accurate and efficient attendance system is essential to monitor employee attendance and ensure operational effectiveness. However, traditional attendance using fingerprints still has weaknesses, such as the potential for data manipulation through NIK input without physical presence. This study aims to develop a facial recognition-based attendance system using the Convolutional Neural Network (CNN) method that can ensure employee attendance in real-time. This technology is expected to improve the accuracy and reliability of the attendance system, as well as reduce the potential for fraud. The implementation of this system at PT Indomarco Prismatama, known as the Indomaret retail network, aims to simplify the attendance process for field employees and make it easier for management to monitor employee attendance in various locations.
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Mulyana, D. I., & Aribatullah. (2025). Klasifikasi Absensi Face Geo-Location Menggunakan Metode CNN pada PT Indomarco Prismatama. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 6(1), 303-317. https://doi.org/10.35870/jimik.v6i1.1193
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Aldiani, D., Dwilestari, G., Susana, H., Hamonangan, R., & Pratama, D. (2024). Implementasi Algoritma CNN dalam Sistem Absensi Berbasis Pengenalan Wajah. Jurnal Informatika Polinema, 10(2), 197-202. https://doi.org/10.33795/jip.v10i2.4852.
Apriadi, P., & Sutrisna, E. (2023). Perancangan Aplikasi Absensi Karyawan Berbasis Mobile Menggunakan GPS (Studi Kasus PT. Trans Retail Indonesia). Journal Automation Computer Information System, 3(1), 1-9. https://doi.org/10.47134/jacis.v3i1.54.
Apriadi, P., & Sutrisna, E. (2023). Perancangan Aplikasi Absensi Karyawan Berbasis Mobile Menggunakan GPS (Studi Kasus PT. Trans Retail Indonesia). Journal Automation Computer Information System, 3(1), 1-9. https://doi.org/10.47134/jacis.v3i1.54.
Arjun, A. (2024). Klasifikasi Citra Pada Tingkat Kematangan Buah Pisang Menggunakan Algoritma Deep Learning. Jurnal Ekonomi Manajemen Sistem Informasi, 5(3), 203-208. https://doi.org/10.31933/jemsi.v5i3.1786.
Arugia, A. W., Junirianto, E., & Maria, E. Design and Build Web and API on “Absenplus” with Face Recognition using Deep Learning Method. Tepian, 3(2), 65-75.
Denata, I., Rismawan, T., & Ruslianto, I. (2021). Implementation of Deep Learning for Classification Type of Orange Using The Method Convolutional Neural Network. Telematika: Jurnal Informatika dan Teknologi Informasi, 18(3), 297-307. https://doi.org/10.31315/telematika.v18i3.5541.
Firmansyah, A., Itsnan, A. F., Apip, A., Mullia, R. T., & Rosyani, P. (2024). SISTEM ABSENSI MAHASISWA MENGGUNAKAN FACE RECOGNITION DENGAN ALGORITMA CNN. AI dan SPK: Jurnal Artificial Intelligent dan Sistem Penunjang Keputusan, 1(4).
Fitri, I., Asmar, M. R., & Rabhasy, A. (2020). DATA CLUSTER MAPPING OF GLOBAL COVID-19 PANDEMIC BASED ON GEO-LOCATION: DATA CLUSTER MAPPING OF GLOBAL COVID-19 PANDEMIC BASED ON GEO-LOCATION. Jurnal Mantik, 4(1), 511-520.
Irawan, N. A., & Kadafi, A. R. (2023). Perancangan Sistem Informasi Presensi Online Karyawan Berbasis Website dengan Face Record dan Geo Location. Bulletin of Computer Science Research, 3(6), 413-419. https://doi.org/10.47065/bulletincsr.v3i6.294.
Isputrawan, M. F., & Suriyanti, S. (2023). Pengembangan Aplikasi absensi berbasis web menggunakan face recognition. Jurnal Teknoinfo, 17(1), 55-65. https://doi.org/10.33365/jti.v17i1.2243.
Khadapi, M., Noviriandini, A., Andarsyah, R., Kurniawati, K., & Lestari, S. (2021). PENGEMBANGAN APLIKASI ABSENSI FACE RECOGNATION MENGGUNAKAN OPENCV LIBRARY DAN ALGORITMA SIFT. Jurnal Informatika Kaputama (JIK), 5(2), 348-355.
Kuswanto, H., Wijonarko, B., & Cahyo, H. D. (2021). Aplikasi Absensi Menggunakan Location Base Services Berbasis Mobile. CONTEN: Computer and Network Technology, 1(1), 31-40. https://orcid.org/0000-0003-0189-6059.
Nawawi, M., Irawan, R. H., & Mahdiyah, U. (2023). Sistem Absensi Berbasis Face Recognition di SMA Queen Al-Falah. Jurnal Nusantara Of Engineering, 6(02), 151-158.
Putra, K. B. V., Bayupati, I. P. A., & Arsa, D. M. S. (2021). Klasifikasi Citra Daging Menggunakan Deep Learning dengan Optimisasi Hard Voting. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(4), 656-662.
Radikto, R., Mulyana, D. I., Rofik, M. A., & Zakaria, M. O. Z. (2022). Klasifikasi Kendaraan pada Jalan Raya menggunakan Algoritma Convolutional Neural Network (CNN). Jurnal Pendidikan Tambusai, 6(1), 1668-1679. https://doi.org/10.31004/jptam.v6i1.3179.
Rahayu, R. E. G., & Pujaeri, Z. K. (2020). Rancang Bangun Sistem Informasi Absensi Fingerprint, Agenda, Mading Digital di SMK Wikrama 1 Garut Berbasis Web. Jurnal Algoritma, 17(2), 561-568.
Rosalina, R., & Wijaya, A. (2020). Pendeteksian Penyakit pada Daun Cabai dengan Menggunakan Metode Deep Learning. Jurnal Teknik Informatika dan Sistem Informasi, 6(3). https://doi.org/10.28932/jutisi.v6i3.2857.
Sari, K. (2022). Perancangan Sistem Absensi Facial Recognition Menggunakan CNN dan Liveness Detector pada BPR Central Dana Mandiri. Jurnal Informatika Dan Rekayasa Komputer (JAKAKOM), 2(1), 70-80. https://doi.org/10.33998/jakakom.2022.2.1.63.
Siami, M. I., & Hamid, M. (2022). Penerapan Deteksi Penggunaan Masker pada Sistem Absensi Karyawan menggunakan Metode Deep Learning. JAMI: Jurnal Ahli Muda Indonesia, 3(2), 141-148. https://doi.org/10.46510/jami.v3i2.118.