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Design of GIS-Based Attendance Application at SMA Santika East Jakarta Saputro, Mohammad Ikhsan; Pertiwi, Santhi; Suryatno, Agung; Setiadi, Dedi; Sopian, Abu; Rifqi, Agven Muharis; Agustino, Rano
Jurnal Teknologi Informatika dan Komputer Vol. 11 No. 1 (2025): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v11i1.2569

In the digital era and industry 4.0 like today, human resource management (HR) is important for schools. One important aspect in human resource management (HR) in a school is employee attendance. Currently, for employee attendance at SMA Santika East Jakarta, the fingerprint attendance system is still used, which has shortcomings, such as the possibility of misuse of number codes that can lead to falsification of attendance. This study aims to develop an attendance application based on the Geographic Information System (GIS) at SMA Santika East Jakarta to improve the accuracy and reliability of the employee attendance system. In the context of the digital era and industry 4.0, proper employee attendance management is crucial, especially when employees work outside the office. To overcome this problem, this study designs a GIS-based attendance application that is able to track employee locations in real-time when taking attendance, both in the school environment and outside the school. The research methodology involves observation, interviews, and literature studies to obtain relevant data. The development model used is a prototype, which includes needs analysis, design, code development, testing, and system support. The application is developed using the React Native framework and TypeScript programming language, and is integrated with the Odoo system via REST API. With this application, it is hoped that SMA Santika East Jakarta can reduce the risk of attendance falsification and improve human resource management, as well as increase operational efficiency and accuracy of employee attendance data.

Computer Vision: Deteksi Masker Wajah Prediksi Usia Jenis Kelamin dengan Teknik Deep Learning Menggunakan Algoritma Convolutional Neural Network (CNN) Sopian, Abu; Setiadi, Dedi; Suryatno, Agung; Agustino, Rano
Jurnal Teknologi Informatika dan Komputer Vol. 10 No. 2 (2024): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v10i2.2395

Sejak pandemi COVID-19, penggunaan masker wajah menjadi langkah penting untuk mencegah penyebaran virus, memerlukan sistem otomatis untuk mendeteksi kepatuhan penggunaan masker. Teknologi computer vision muncul sebagai solusi potensial untuk mempermudah deteksi penggunaan masker dalam skala besar. Selain itu, teknologi pengenalan wajah telah berkembang pesat, memungkinkan identifikasi atribut lain seperti jenis kelamin dan usia dari gambar wajah. Penelitian ini mengembangkan model deep learning berbasis Convolutional Neural Network (CNN), khususnya MobileNet, untuk mendeteksi masker wajah dan memprediksi atribut wajah seperti jenis kelamin dan usia, meskipun sebagian wajah tertutupi masker. Model ini bertujuan meningkatkan efisiensi deteksi masker serta memberikan informasi demografis yang berguna dalam berbagai bidang, seperti kesehatan, retail, dan keamanan publik. Penelitian ini menggunakan pendekatan eksperimen dengan dataset gambar wajah yang mencakup individu dengan dan tanpa masker, serta data tambahan untuk prediksi jenis kelamin dan usia. Model dilatih dengan teknik transfer learning, dan dilakukan evaluasi menggunakan metrik precision, recall, F1-score, serta mean absolute error (MAE) untuk prediksi usia. Hasil eksperimen menunjukkan akurasi deteksi masker mencapai 99%, sedangkan prediksi jenis kelamin dan usia memiliki akurasi 98,75%, dengan sensitivity 98,5% dan specificity 99%. Implementasi model dalam aplikasi real-time menggunakan OpenCV dan Tkinter menunjukkan latensi deteksi rendah dan responsivitas yang baik. Penelitian ini memberikan kontribusi signifikan dalam pengembangan sistem otomatis berbasis teknologi computer vision untuk aplikasi praktis di berbagai sektor, sekaligus meningkatkan keselamatan publik melalui deteksi masker yang akurat dan cepat.

Design of GIS-Based Attendance Application at SMA Santika East Jakarta Saputro, Mohammad Ikhsan; Pertiwi, Santhi; Suryatno, Agung; Setiadi, Dedi; Sopian, Abu; Rifqi, Agven Muharis; Agustino, Rano
Jurnal Teknologi Informatika dan Komputer Vol. 11 No. 1 (2025): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v11i1.2569

In the digital era and industry 4.0 like today, human resource management (HR) is important for schools. One important aspect in human resource management (HR) in a school is employee attendance. Currently, for employee attendance at SMA Santika East Jakarta, the fingerprint attendance system is still used, which has shortcomings, such as the possibility of misuse of number codes that can lead to falsification of attendance. This study aims to develop an attendance application based on the Geographic Information System (GIS) at SMA Santika East Jakarta to improve the accuracy and reliability of the employee attendance system. In the context of the digital era and industry 4.0, proper employee attendance management is crucial, especially when employees work outside the office. To overcome this problem, this study designs a GIS-based attendance application that is able to track employee locations in real-time when taking attendance, both in the school environment and outside the school. The research methodology involves observation, interviews, and literature studies to obtain relevant data. The development model used is a prototype, which includes needs analysis, design, code development, testing, and system support. The application is developed using the React Native framework and TypeScript programming language, and is integrated with the Odoo system via REST API. With this application, it is hoped that SMA Santika East Jakarta can reduce the risk of attendance falsification and improve human resource management, as well as increase operational efficiency and accuracy of employee attendance data.

Acceptance and Success Model for AI Use in Higher Education: Development, Instrument Decomposition, and Its Triangulation Testing Subiyakto, Aang; Huda, Muhammad Q; Hakiem, Nashrul; Suseno, Hendra B; Arifin, Viva; Azmi, Agus N; Sani, Asrul; Yuniarto, Dwi; Hartawan, Muhammad S; Suryatno, Agung; Muji, Muji; Kurniawan, Fachrul; Kusumawati, Ririen; Balogun, Naeem A; Ahlan, Abd. Rahman
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.619

Prior social computing studies described that the performance of technology products is about how the product use benefits the users, including Artificial Intelligence (AI). To have an impact, ensuring how AI is used is a prerequisite after the development. Furthermore, its use is also influenced by how users accept AI. This study aimed to develop an acceptance and success model of AI use in the higher education world from the user perspective, to decompose the model into its instrument level, and to test the validity and reliability of the research instrument. The researchers developed the model by adopting and combining the Technology Acceptance Model (TAM) and the Information System Success Model (ISSM) and adapting the proposed model in the context of AI use in higher education learning. The measurement items were derived from definitions of the variables and indicators of the model. The instrument was tested sequentially using triangulation methods. The quantitative testing was online survey with about 51 respondents and the qualitative one was interview involving five experts. This study may contribute methodologically as one of the guidance for novice scholars in similar works. It may relate to the clarity of the research procedure and the implementation of the mixed testing methods. Of course, the assumptions, samples, and data used in the study cannot be generalized for the other studies. Referring to the model development, the proposed model may not cover the other factors related to the ethical, cultural, and organizational barriers for adopting AI. These barriers may also affect its acceptance and success. Thus, the adoption of the factors related the barriers may also be interesting to study further.

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