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Rano Agustino
Universitas Mohammad Husni Thamrin
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology Nursing Public Health Social Sciences Other

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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

Abstract

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

Abstract

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.
YOLOv12 for Human Object Detection in Real-time Video Surveillance Systems Widodo, Yohanes Bowo; Sibuea, Sondang; Agustino, Rano
Jurnal Teknologi Informatika dan Komputer Vol. 11 No. 2 (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.v11i2.2789

Abstract

This research discusses the application of the YOLO (You Only Look Once) model to detect human objects in real-time video surveillance systems. This model was developed in response to the increasing need for efficiency and accuracy in video surveillance analysis, particularly in identifying abnormal or malicious activities. The application of deep learning technology, especially the YOLO model, has been shown to provide better performance in object recognition compared to traditional methods, such as SVM and Haar-Cascade, which often experience limitations in terms of speed and accuracy. One significant contribution of the use of YOLO lies in its ability to detect objects simultaneously in high-speed video, which is crucial in surveillance contexts that require rapid response to incidents. The implementation of YOLO also promises better collaboration between edge and cloud computing, allowing video processing to be carried out closer to the data source, reducing latency and improving data security. With this approach, the system can generate relevant information for rapid decision-making, such as monitoring human behavior in public settings and detecting suspicious activity. The analysis of this study highlights the significant potential of YOLO in improving real-time video surveillance systems and demonstrates that more accurate object detection capabilities can improve overall public safety. Through this model, we hope to revolutionize surveillance practices, adapt to modern needs, and provide a solid foundation for further development in the field of video surveillance.
Membangun Sistem Model Learning Style Inventory Untuk Pencapaian Prestasi Belajar Mahasiswa Program E-Learning Agustino, Rano; Pertiwi, Santhi
Jurnal Inovasi Pendidikan MH Thamrin Vol. 4 No. 2 (2020): Jurnal Inovasi Pendidikan MH Thamrin
Publisher : LPPM Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jipmht.v4i2.418

Abstract

In improving the quality of education by teaching, it is the thing that has the greatest effectiveness. Because of a good and appropriate teaching method, it is likely to increase the absorption of knowledge for students, so that the quality of knowledge from students increases. There are several ways to improve the quality of teaching, one of which is to know the learning styles of these students. Methods to determine learning styles such as Visual Auditorial Kinesthetic (VAK), Learning Style Inventory, Myers-Briggs Type Indicator (MBTI) and others, but in this study using the Learning Style Inventory (LSI) introduced by David Kolb. To see the Learning Style of each student, an online questionnaire was held. Respondents in this study were online students of the Mohammad Husni Thamrin University Informatics Engineering Study Program with 50 students as respondents. By using the Linear Regression model, it shows a significance value of 0.02 which is less than 5%, so the analysis can be continued. On the other hand, the results are based on the value of R Square, which is 0.099 or 0.1, where it is the Coefficient of Determination or it can be interpreted that the LSI Value Variable has the ability to influence 10% of the Student Value Variable.
YOLO in Suspicious Human Activity Recognition for Intelligent Environmental Security Systems: A Review Widodo, Yohanes Bowo; Sibuea, Sondang; Agustino, Rano
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3243

Abstract

The rapid growth of intelligent environmental security systems has intensified the need for accurate and real-time suspicious human activity recognition. Computer vision techniques, particularly deep learning–based object detection models, have emerged as key enablers in addressing these challenges. Among them, You Only Look Once (YOLO) has gained significant attention due to its high detection speed, end-to-end architecture, and suitability for real-time surveillance applications. This review paper presents a comprehensive analysis of the application of YOLO-based models in suspicious human activity recognition for intelligent environmental security systems. It examines the evolution of YOLO architectures, their adaptations for activity and behavior analysis, and their integration with surveillance frameworks. The review further discusses commonly used datasets, performance evaluation metrics, and comparative results reported in existing studies. In addition, key challenges such as occlusion, varying illumination, complex backgrounds, privacy concerns, and computational constraints are highlighted. Finally, the paper outlines future research directions, including hybrid models, multi-modal data fusion, edge-based deployment, and explainable AI, to enhance the robustness and reliability of YOLO-driven security systems. This review aims to provide researchers and practitioners with a structured understanding of current advancements and open issues in YOLO-based suspicious human activity recognition.
Co-Authors Adhes Gamayel Agus Wiyatno Amin Sakaria, Muhammad Asniati Djaali, Nur Dedi Setiadi Dedi Setiadi Effendi, Muhammad Ridwan Febrianti Widyahastuti Febrianto Febrianto Febrianto Febrianto, Febrianto Fitriansyah, Ahmad Gustiawan, Handa Handa Gustiawan M Amin Sakaria Mauluddin, Syahrul Moh Ikhsan Saputro Mohammad Ikhsan Saputro Mohammad Ikhsan Saputro, Mohammad Ikhsan Muhammad Amin Sakaria Muhammad Ridwan Effendi Narji, Mohammad Nur Asniati Djaali Pertiwi, Santhi Prima Nanda Fauziah Restu Ayuningtias, Mutia Rifqi, Agven Muharis Sakaria, M Amin Santhi Pertiwi Santhi Pertiwi Saputro, Moh Ikhsan Sinta Restuasih Sondang Sibuea Sopian, Abu Suryatno, Agung Widyahastuti, Febrianti Yohanes Bowo Widodo