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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ASEAN Journal of Science and Engineering Education (AJSEE)
Hermawan Nugroho
University of Nottingham Malaysia
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Engineering Materials Science & Nanotechnology Other

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Constructive Alignment Approach for Capstone Project with Industry Involvement: Case Study in Malaysia University Hermawan Nugroho
ASEAN Journal of Science and Engineering Education Vol 2, No 1 (2022): AJSEE: March 2022
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (823.874 KB) | DOI: 10.17509/ajsee.v2i1.35271

Abstract

A capstone project is a project-based learning course designed to bring aspects of an undergraduate student’s experience.  To have an effective capstone project, it is imperative to properly design the curriculum with involvement from the industry.  To improve the performance of the capstone project course, we used the constructive alignment approach to design the curriculum.  In constructive alignment, we mapped the intended learning outcomes, the assessment tasks and the teaching/learning activities interchangeably with each other.  Experiments were conducted with students enrolled in the capstone project courses in our university from February 2016 to May 2017. The result shows that the new design was able to improve the attainment scores for both learning outcomes and program outcomes. This improvement was also in line with marks by industry experts indicating good potential.
Plant Disease Identification Using Image Processing: A Systematic Literature Review Minarni Minarni; Muhammad Ilhamdi Rusydi; Darwison Darwison; Hermawan Nugroho; Budi Sunaryo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7171

Abstract

This article is a literature review focusing on plant disease identification using image processing techniques. This review aims to provide a comprehensive analysis of dataset sources, preprocessing methodologies, segmentation techniques, feature extraction processes, and various classification methods, along with their associated accuracies. It also discusses challenges encountered and potential future research directions. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a literature search was conducted in the Scopus database to obtain primary studies. The search covered Scopus-indexed journals and proceedings published by IEEE, Elsevier, Springer, MDPI, and ACM between 2019 and 2025. The initial identification phase yielded 9,286 studies screened. Further screening was performed based on specific eligibility criteria, including relevance to the topic, year of publication, subject area, document type, and articles written in English, resulting in the selection of 82 studies for the review. The findings indicate that the most commonly used dataset is PlantVillage, followed by field data. The dominant preprocessing techniques include image enhancement and augmentation. For segmentation and feature extraction, the most frequently used methods were k-means and CNN, respectively. Sixty-one studies achieved an accuracy exceeding 90%. However, several key challenges remain: data limitations, methodological issues, and practical constraints. Future research should focus on developing more representative datasets, hybrid approaches that integrate classical and deep learning methods, and lightweight, adaptive decision support systems suitable for real-world agricultural applications. This review supports continued progress in this field by providing valuable insights for researchers developing image-based methods for identifying plant diseases.
Co-Authors . Darwison Budi Sunaryo Minarni Minarni Rusydi, Muhammad Ilhamdi