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All Journal JURNAL TEKNOLOGI DAN OPEN SOURCE International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET)
Veti Apriana
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education

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APPLICATION OF THE SMART METHOD FOR PROVIDING SCHOLARSHIPS IN HIGH SCHOOLS Veti Apriana; Sifa Fauziah; Wati Erawati
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 2 No. 12 (2023): NOVEMBER
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/ijset.v2i12.302

Abstract

Providing scholarships is an important step to support the nation's best sons and daughters in pursuing education up to the tertiary level. Many agencies and companies provide scholarships as a form of assistance to ensure the sustainability of the nation's next generation in the future. Nevertheless, the implementation of scholarships at the senior high school level still raises questions regarding conformity with the targets and criteria that have been set. This study aims to apply the Simple Multi Attribute Rating Technique (SMART) method in determining scholarship recipients for high school students. By using the SMART method, it is hoped that the selection process for scholarship recipients can be more effective and fair with the criteria used such as class ranking scores, parents' income, number of dependents on parents, and non-academic achievements. The end result of applying the SMART method is in the form of student rankings indicating their chances of getting a scholarship. The higher the ranking obtained, the greater the opportunity for students to receive scholarships, the highest ranking was achieved by student number 19 with an acquisition value of 97, indicating that this student is entitled to a scholarship. This study shows that the SMART method can be implemented in a decision support system to determine scholarship recipients for high school students.
Implementation of YOLOv8 and DETR for Multi-Level Tomato Ripeness Detection with Real-Time Bounding Boxes Muhammad Rizky Heriadi Putra; Deni Setiawan; Ahnaf putra hafezi; Rachmat Adi Purnama; Veti Apriana; Rame Santoso
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.5083

Abstract

Tomato ripeness detection is an essential component in the development of automated agricultural systems, enabling improvements in harvesting accuracy, sorting consistency, and supply chain standardization. Conventional grading processes rely heavily on manual observation, which is subjective, labor-intensive, and unsuitable for large-scale operations. Recent advancements in deep learning enable automated recognition of visual maturity indicators through object detection frameworks, offering a more reliable and scalable solution. This study examines the implementation of two modern detection models, YOLO and DETR, for multi-level tomato ripeness classification involving four distinct maturity stages. The research workflow includes dataset collection, annotation, preprocessing, model training, threshold calibration, and systematic evaluation to assess detection stability and classification behavior under real-world variability.Analysis of model outputs demonstrates that both architectures are capable of identifying multiple ripeness stages with useful levels of consistency, although each model exhibits strengths under different operational conditions. YOLO provides advantages in scenarios requiring real-time responsiveness and deployment on resource-limited hardware, making it suitable for mobile automation and field-based harvesting systems. DETR shows improved interpretive behavior in visually complex environments, particularly when samples exhibit subtle maturity differences or appear in overlapping cluster formations. The findings indicate that no single model is universally optimal and that deployment choice should be based on application requirements, environmental constraints, and operational objectives. This research contributes practical insight to the integration of artificial intelligence in agriculture and provides a foundation for future work exploring model fusion, advanced feature learning, or multispectral input integration to further enhance maturity classification performance.
Co-Authors Ahnaf putra hafezi DENI SETIAWAN Indradewa, Rhian Muhammad Rizky Heriadi Putra Rachmat Adi Purnama Rame Santoso Wati Erawati