Article Per Year (5 Year)
p-Index From 2021 - 2026
1.993
P-Index
This Author published in this journals
All Journal Compiler Jurnal Manajemen Informatika bit-Tech Scientific Journal of Informatics KLIK: Kajian Ilmiah Informatika dan Komputer
Agus Suhendar
Universitas Teknologi Yogyakarta
Computer Science & IT

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : bit-Tech

 1 
Classification of Hand Gestures Using Random Forest and MediaPipe in an Educational Mathematics Game M. Ridha Ansari Adriansyah; Agus Suhendar
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3172

Abstract

Conventional mathematics learning at the elementary level often lacks interactivity, leading to low student motivation. This issue hinders the development of foundational analytical skills, despite significant time allocation in the curriculum. This research addresses this pedagogical problem by developing an educational game that replaces traditional input methods with kinesthetic interaction, aiming to directly enhance student engagement. The proposed method is a real-time hand gesture detection system built on a desktop platform. The system utilizes the MediaPipe framework to accurately extract 21 key hand landmarks from a live video feed, which serve as robust features for analysis. These features are then classified using a Random Forest algorithm, chosen for its efficiency and high performance in handling complex data, with an undersampling technique applied to ensure a balanced dataset. The performance evaluation showed that the developed classification model achieved a high accuracy of up to 98% on the test data. The resulting functional prototype allows users to answer addition and subtraction problems intuitively through hand gestures, featuring direct visual feedback and a score-tracking mechanism. This study successfully demonstrates that digital image processing can be effectively leveraged to create an engaging and adaptive mathematics learning experience. This approach not only addresses motivation in mathematics but also demonstrates the potential of gesture-based kinesthetic learning for designing a new class of engaging educational tools across various subjects, highlighting its broader impact on future educational game design.
Application of Support Vector Machine Algorithm and Image Processing in Coffee Bean Quality Classification Febi Wulan Dini; Agus Suhendar
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3283

Abstract

This research was conducted to address the problem of the coffee bean sorting process, which is still performed manually in Empat Lawang Regency. The process is time-consuming, requires a large amount of human labor, and often results in inconsistent quality assessment. To overcome this, the study developed an automated classification system based on Support Vector Machine (SVM) utilizing Image Processing. The dataset was obtained directly from local collectors and consists of 740 coffee bean images, encompassing 286 good beans, 240 moldy beans, and 214 damaged beans. Feature extraction was performed based on three main characteristics color, size, and texture. Color features were calculated using the mean of RGB and HSV, while size features were obtained from the calculation of area, perimeter, and roundness. Texture features were extracted using the GLCM method. The SVM model was built using the RBF kernel and optimized with parameters C = 2 and gamma = 0.1. The evaluation results showed an accuracy of 94.37%, precision of 94.41%, recall of 94.37%, and an F1-score of 94.35%. The novelty of this research lies in the integration of color size texture features for the three-class classification of coffee beans using a lightweight model that is easily implementable at the MSME scale. However, the model is still limited to single-object images. Therefore, further research is suggested to include multi-bean datasets and consider deep learning methods that are more adaptive to variations in the number and position of coffee beans, such as CNN with YOLO or R-CNN.
Implementation of the KNN Algorithm for Food Recommendation System Based on Users' Nutritional Needs Silvia Mairiani Rosdilillah; Agus Suhendar
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3449

Abstract

This study develops a web-based food recommendation system using the K-Nearest Neighbors (KNN) algorithm to provide personalized food recommendations based on users' nutritional needs and preferences. Many individuals struggle to create balanced diets due to insufficient knowledge or time, which can lead to malnutrition or obesity. To address this, the system calculates users' nutritional needs using Basal Metabolic Rate (BMR) and Total Daily Energy Expenditure (TDEE), incorporating preference filtering provided by users. The KNN algorithm then analyzes a food database to identify items that best match the users' nutritional profiles. The system features two primary interfaces: a user interface for inputting nutritional data and displaying recommendations, and an administrative interface for managing food data, user information, and recommendation history. The system was evaluated through Black Box Testing, which confirmed that all main features function as intended. The KNN algorithm demonstrated effectiveness by providing relevant food recommendations that align with users' individual nutritional requirements. Key evaluation metrics, such as recommendation accuracy and user satisfaction, validate the system's performance. This approach highlights the system’s potential in offering personalized nutrition advice, with a focus on real-time decision-making. Future work will aim to incorporate additional dietary factors and expand the food database to enhance the system’s adaptability and precision.
Co-Authors Alvina Putri Damayani Elisabeth Kurnia Andini Febi Wulan Dini Fitria Ningsih Gabriel Nicolas Sumampow Ikhsan Akbar Lintang Ardi Avdillah M. Ridha Ansari Adriansyah Mawar Pratama sari Murti Retnowo Silvia Mairiani Rosdilillah Visco Adam Bramasta