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p-Index From 2021 - 2026
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All Journal Ambidextrous : Journal of Innovation, Efficiency and Technology in Organization
Ronny Makhfuddin Akbar
Prodi Informatika, Universitas Islam Majapahit
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering

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Implementation of Arcface in Employee Attendance System Face Recognition Based Rifky Andika Fajrul Falah; Ronny Makhfuddin Akbar; Ahmad Syaifuddin
Ambidextrous Journal of Innovation Efficiency and Technology in Organization Vol. 5 No. 02 (2026): Ambidextrous: Journal of Innovation, Efficiency and Technology in Organization
Publisher : Takaza Innovatix Labs Ltd.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61536/ambidextrous.v5i02.646

Abstract

The employee attendance process is still largely done manually, potentially leading to recording errors, attendance misuse, and low efficiency in attendance data management. This study aims to implement a facial recognition-based employee attendance system using the ArcFace method through the InsightFace framework combined with location validation using geofencing. The system was developed web-based using FastAPI as the backend, Bootstrap 5 and JavaScript as the frontend, MySQL as the database, and SCRFD for face detection. System testing was conducted using the Black Box Testing method to test the system's main functions, while user evaluation was conducted using a questionnaire with a Likert scale. The test results showed that all system functions run according to the established functional requirements. In addition, the results of the user evaluation showed that all respondents gave a positive assessment of the system, indicating that the system is easy to use and well-received by users. Thus, the developed system is able to support the employee attendance process more effectively through the implementation of facial recognition, location validation, and web-based attendance data management.
Website-Based Honey Sales and Production Management Information System Rifki Hidayat; Ronny Makhfuddin Akbar; Ahmad Syaifuddin
Ambidextrous Journal of Innovation Efficiency and Technology in Organization Vol. 5 No. 02 (2026): Ambidextrous: Journal of Innovation, Efficiency and Technology in Organization
Publisher : Takaza Innovatix Labs Ltd.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61536/ambidextrous.v5i02.651

Abstract

The development of information technology provides opportunities for MSMEs to improve business management, including honey production and sales. MSME Madu Sukeri still records production, stock, customers, and transactions manually, which is ineffective and risky for errors. This study aims to develop a website-based honey sales and production management application that integrates production, stock, and sales management and facilitates online ordering for customers. The Waterfall method used includes needs analysis, design, implementation, and testing. The system was developed using PHP, MySQL, and Tailwind CSS with features for managing products, production, stock, customers, orders, payments, and sales reports. Customers can register, place orders, upload proof of payment, and view order status and history. Testing using Black Box Testing showed that all system functions run as required. The results show that the application is able to improve the effectiveness of production and sales management and facilitate the online ordering process.
Implementation of the Yolov8 Model for Component Detection and Image-Based Free Nutritional Food Nutrition Ovan Pramana Putra; Ronny Makhfuddin Akbar; Ahmad Syaifuddin
Ambidextrous Journal of Innovation Efficiency and Technology in Organization Vol. 5 No. 03 (2026): Ambidextrous: Journal of Innovation, Efficiency and Technology in Organization
Publisher : Takaza Innovatix Labs Ltd.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61536/ambidextrous.v5i03.698

Abstract

The Free Nutritional Food Program (MBG) is a government effort to improve the nutritional status of students by providing balanced nutritious meals. However, the process of verifying food components and evaluating nutritional content is still done manually, which is time-consuming, subjective, and inefficient. Previous studies have applied the YOLO algorithm for food detection and calorie estimation, but generally focused on individual consumption or only produced energy estimates. This study proposes the implementation of the YOLOv8 model integrated with a nutrition database to detect food components and estimate energy, protein, fat, and carbohydrates in the image-based Free Nutritional Food package. The dataset was obtained by extracting videos from the MBG package into 400 images consisting of 19 food classes. The dataset was annotated using bounding boxes, preprocessed, augmented, and divided into training, validation, and testing data. The model was trained using Roboflow with GPU support, then the detection results were integrated with the nutrition API and implemented in a web application to automatically present nutritional information. System performance was evaluated using precision, recall, and mean Average Precision (mAP) metrics. The contribution of this research is the development of an integrated system based on YOLOv8 to support monitoring of MBG nutritional quality objectively, efficiently, accurately, adaptively, sustainably, and support artificial intelligence-based decision making.
Co-Authors Ahmad Syaifuddin Ovan Pramana Putra Rifki Hidayat Rifky Andika Fajrul Falah