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Lamasitudju, Chairunnisa
Teknik Informatika, Fakultas Teknik, Universitas Tadulako
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Automatic identification of herbal medicines using deep learning on leaf images Anita Ahmad Kasim; Lukman Nadjamudiin; Muhammad Bakri; Chairunnisa Ar Lamasitudju; Puguh Budi Prakoso; Anindita Septiarini; Bima Prihasto
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Indonesia has a high diversity of medicinal plants that are widely used in traditional healthcare practices. Identification of medicinal plants is commonly based on leaf morphology; however, similarities in leaf shape, texture, and color often cause misidentification, particularly among non-experts. This limitation highlights the need for an automated and reliable identification approach. The primary objective of this study is to develop and evaluate a deep learning–based system for the automatic identification of medicinal plants using leaf images, with a specific focus on comparing the performance and efficiency of MobileNetV2 and ResNet50V2 architectures. The research design adopts an experimental approach using an internally collected dataset of medicinal plant leaf images representing multiple plant classes. The dataset is divided into training and testing sets to evaluate model generalization. The methodology involves image preprocessing steps, including resizing, normalization, and data augmentation, followed by the application of transfer learning using MobileNetV2 and ResNet50V2 as feature extractors. Both models are trained under the same experimental settings and evaluated using standard classification metrics, including accuracy, precision, recall, F1-score, and confusion matrix analysis. The main outcomes and results indicate that both deep learning models achieve high classification performance. MobileNetV2 achieves an accuracy of 98.77%, precision of 98.84%, recall of 98.77%, and F1-score of 98.77%, while ResNet50V2 achieves an accuracy of 97.53%, precision of 97.87%, recall of 97.53%, and F1-score of 97.58%. The results demonstrate that MobileNetV2 provides slightly superior performance with lower computational complexity. In conclusion, lightweight deep learning architectures such as MobileNetV2 are effective and efficient for medicinal plant leaf identification and are suitable for implementation in mobile or resource-constrained environments.
Perancangan Aplikasi Manajemen Tugas Berbasis Web Menggunakan Algoritma Greedy untuk Meningkatkan Efisiensi Kerja Muhamad Fudhail; Chairunnisa Ar Lamasitudju
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3285

Abstract

This study aims to develop a web-based task management application with the application of the Greedy algorithm to automatically determine task priorities based on urgency and deadlines. The system was developed using a prototyping approach and tested in five work divisions with a total of 50 task data used during the testing process. System performance evaluation was conducted through functional testing, algorithm testing using pre-test and post-test schemes, and usability testing using the System Usability Scale (SUS) method. The test results showed that the application of the Greedy algorithm was able to improve the timeliness of task completion, which in the pre-test stage was in the range of 20%–60% and showed an increase in the post-test stage across all divisions. Usability testing involving 10 respondents resulted in a System Usability Scale (SUS) score of 81, which falls into the Excellent category. These results indicate that the system is not only effective in determining task priorities, but also easy to use and well received by users. This study contributes to the application of the Greedy algorithm in web-based task management systems as an efficient digital solution. However, testing was still conducted on a limited number of users, so further development and evaluation on a larger scale is needed to test the system's performance comprehensively.
Co-Authors Afifa, Afifa Ahmad, Tjoet Muty Amriana Amriana Amriana Andhyka, Andhyka Andi Hendra Anindita Septiarini, Anindita Anisa Yulandari Anita Ahmad Kasim Anita Candra Dewi Aqsha, Ismail Ashadi, Nini Rahayu Bagaskara, Febrian Chrissma Bima Prihasto Deny Wiria Nugraha Dewi, Shabrina Syntha Dival Maulana, Muhammad Dwi Shinta Angreni Dwiwijaya, Kadek Agus Fachrozi, Ahmad Fahmil, Fahmil Fizar Syafaat Hamzah Nur Harani, Makruf Hendra Jaya Ihalauw, Sahron Angelina Karim, Khairul Insan Karnita Sumbaluwu, Harlin Feby Krama, Tri Laala, Jonathan Zebina Laila, Rahma Landusa, Natalia Anastasya Lukman Nadjamudiin Makmur, Elfira Miftah Miftah Miftah Miftah Miftah Miftah, Miftah Mohammad Yazdi Pusadan Muhamad Fudhail Muhammad Asriadi MUHAMMAD BAKRI Muhammad Bakri Ngemba, Hajra Nini Rahayu Ashadi Nini Rahayu Ashadi Ninik Rahayu Ashadi Nouval Trezandy Lapatta Nuridayanti, Nuridayanti Nursalim, Moh. Agung Pramadinda, Alda Nur Pratama, Moh. Rezki Priska, Salsa Dilah Puguh Budi Prakoso Putri, Fadiah Suryani Rahmah Laila Reonaldi, Muh. Ilham Rifki, Moh Rinianty, Rinianty Rizka Ardiansyah Ryfial Azhar Saleh, Muhammad Taufik Septiano A Pratama Septiano Anggun Pratama Setialaksana, Wirawan - Setiawan, Dita Widayanti Shabrina Syntha Dewi Siti Fitriana Siti Rahmawati Sulaiman, Dwi Reski Anandari Sulaiman, Dwi Rezky Anandari Syahrullah Syahrullah Syaiful Hendra Widiani, Ni Nengah Widyati, Made Ayu Sri Wirdayanti Yulianti, Indira Yuri Yudhaswana Joefrie Yusuf Anshori