Perancangan Sistem Informasi Penjadwalan Appointment pada  Soraaya Studio

Anniwarni Nazara; Serniman Lahagu; Jesika Vinatalia Dachi; Wasis Haryono

doi:10.61132/neptunus.v3i3.909

Authors

Anniwarni Nazara Universitas Pamulang
Serniman Lahagu Universitas Pamulang
Jesika Vinatalia Dachi Universitas Pamulang
Wasis Haryono Universitas Pamulang

DOI:

https://doi.org/10.61132/neptunus.v3i3.909

Keywords:

Appointment, Information System, Nail Art, Scheduling, Web Application

Abstract

The rapid development of information and communication technology has opened up significant opportunities to enhance service efficiency in various fields, including the beauty industry. Soraaya Studio, which offers a range of beauty services, currently still manages appointment scheduling manually. This manual system leads to several challenges, such as difficulties in efficient schedule management, data duplication, recording errors, and disorganization in the service reservation process. Furthermore, communication with customers regarding schedule confirmation becomes less effective. With the increasing number of customers, this manual scheduling system is no longer able to meet the growing complexity of operational needs.To address these issues, a web-based appointment scheduling system has been designed, which is integrated and user-friendly. This system is built using the PHP programming language with a MySQL database and features a responsive interface, making it easily accessible from various devices, including desktops and mobiles. Key functionalities provided include real-time creation, modification, and cancellation of appointments, management of service availability, and automated notifications via email or text messages to customers and studio staff. The implementation of this system aims to improve the accuracy and speed of the scheduling process, reduce manual errors, and facilitate schedule monitoring by management and staff, thereby enabling Soraaya Studio to provide a more professional and satisfying service to its customers.

References

A’yun, D. Q., Tanzil, M. Y., & Julia, F. R. (2020). Pengaplikasian elemen desain pada motif batik mega mendung. Semin. Nas. Envisi Ind. Kreat., 118–127.

Alfikri, R. H., Utomo, M. S., Februariyanti, H., & Nurwahyudi, E. (2022). Pembangunan Aplikasi Penerjemah Bahasa Isyarat Dengan Metode Cnn Berbasis Android. J. Teknoinfo, 16(2), 183.

Herdianto, H., & Nasution, D. (2023). Implementasi Metode Cnn Untuk Klasifikasi Objek. METHOMIKA J. Manaj. Inform. dan Komputerisasi Akunt., 7(1), 54–60.

Khoiruddin, M., Junaidi, A., & Saputra, W. A. (2022). Klasifikasi Penyakit Daun Padi Menggunakan Convolutional Neural Network. J. Dinda Data Sci. Inf. Technol. Data Anal., 2(1), 37–45.

Malika, M., & Widodo, E. (2022). Implementasi Deep Learning Untuk Klasifikasi Gambar Menggunakan Convolutional Neural Network (Cnn) Pada Batik Sasambo. Pattimura Proceeding Conf. Sci. Technol., 335–340.

Perwati, I. G., Suarna, N., & Suprapti, T. (2024). Analisis Klasifikasi Gambar Bunga Lily Menggunakan Metode Convolutional Neural Network (Cnn) Dalam Pengolahan Citra. JATI (Jurnal Mhs. Tek. Inform., 8(3), 2908–2915.

Prabowo, R., Roudhoh, A., & Afifah. (2022). Klasifikasi Image Tumbuhan Obat Sirih dan Binahong Menggunakan Metode Convolutional Neural Network (CNN). J. Komputasi, 10(2), 48–58.

Pratiwi, A. O. C. (2023). Klasifikasi Jenis Anggur Berdasarkan Bentuk Daun Menggunakan Convolutional Neural Network Dan K-Nearest Neighbor. J. Ilm. Tek. Inform. dan Komun., 3(2), 201–224.

Rahmadhani, U. S., & Marpaung, N. L. (2023). Klasifikasi Jamur Berdasarkan Genus Dengan Menggunakan Metode CNN. J. Inform. J. Pengemb. IT, 8(2), 169–173.

Sinaga, M. H. V., Albirra, M., & Sidiq, M. F. (2024). Klasifikasi Gambar Pemandangan dengan Kecerdasan Buatan Berbasis CNN. J. JTIK (Jurnal Teknol. Inf. dan Komunikasi), 8(2), 412–417.

Soekarta, R., et al. (2023). IMPLEMENTASI DEEP LEARNING UNTUK DETEKSI JENIS OBAT proses kehidupan dan merupakan senyawa tubuh (Prabowo, 2021). Saat ini berbagai jenis dibedakan beberapa bentuk sediaan obat. Dalam lingkungan farmasi yang untuk automatisasi dalam pengelolaan obat s. 7(4), 455–464.

Soekarta, R., Nurdjan, N., & Syah, A. (2023). Klasifikasi Penyakit Tanaman Tomat Menggunakan Metode Convolutional Neural Network (CNN). Insect (Informatics Secur. J. Tek. Inform., 8(2), 143–151.

Syahputra, M. I., & Wibowo, A. T. (2020). Klasifikasi Genus Tanaman Anggrek berdasarkan Citra Kuntum Bunga Menggunakan Metode Convolutional Neural Network (CNN). e-Proceeding Eng., 7(2), 8015–8023.

Verdy, & Hartati, E. (2024). Klasifikasi Penyakit Mata Menggunakan Convolutional Neural Network Model Resnet-50. J. Rekayasa Sist. Inf. dan Teknol., 1(3), 199–206.

Zalvadila, A. (2023). Klasifikasi Penyakit Tanaman Bawang Merah Menggunakan Metode SVM dan CNN. J. Inform. J. Pengemb. IT, 8(3), 255–260.