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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Cut Agusniar
Malikussaleh University
Computer Science & IT Engineering

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DETEKSI DAUN HERBAL DAN BERACUN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI TANAMAN HERBAL DAN BERACUN: HERBAL AND POISONOUS LEAF DETECTION USING CONVOLUTIONAL NEURAL NETWORK FOR HERBAL AND POISONOUS PLANT CLASSIFICATION Della Adelia; Zahratul Fitri; Cut Agusniar
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6025

Abstract

Indonesia is one of the tropical countries with the greatest biodiversity in the world, including around 50,000 plant species and around 7,500 used by the community as raw materials for traditional medicine. However, the large number of plant species poses a challenge in distinguishing between edible plants and those that contain toxins, because herbal and toxic plants often have similar morphological characteristics of their leaves. Therefore, this study was conducted to design a classification system to distinguish between herbal and toxic plant leaves, which is expected to be utilized by the community as a preventive measure to reduce the risk of poisoning due to incorrect plant identification. The system was built using a Convolutional Neural Network (CNN) architecture implemented through the Flask framework and equipped with a rule-based system to determine plant categories based on the model's prediction results. The dataset consists of 960 images with 8 classes of local Indonesian plant leaves, where four categories include herbal plants (moringa, mint, betel, and basil) and four categories of poisonous plants (saga rambat, bandotan, gympie-gympie, and jelatang). This study conducted experiments on several CNN architectures, including custom and pretrained models (EfficientNetB0, MobileNetV2, and ResNet50V2). The best results were obtained from the EfficientNetB0 model trained using images with an input shape of 224×224 pixels, a batch size of 24, and the Adam optimizer, achieving a training accuracy of 99.51% and validation accuracy of 98.96%. This model demonstrated superior accuracy compared to other models, such as the custom model (93.88%), MobileNetV2 (99.50%), and ResNet50V2 (99.38%). The evaluation results show that the EfficientNetB0 model has excellent performance, with an overall classification accuracy of 99.00%, precision of 99.00%, recall of 99.00%, and an F1-score of 99.00%.
OPTIMALISASI PENERIMAAN SISWA DI SMAN 2 SERUWAY MENGGUNAKAN METODE DATA ENVELOPMENT ANALYSIS Evi Ariani; Dahlan Abdullah; Cut Agusniar
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6530

Abstract

This study aims to analyze the efficiency level of the student admission process at SMAN 2 Seruway, Aceh Tamiang Regency, using the Data Envelopment Analysis (DEA) method. DEA was chosen because it is able to evaluate the relative efficiency of decision-making units that have many inputs and outputs without assuming the form of a production function. This method is considered more flexible than other methods such as MOORA, which are deterministic. The inputs used include parents' occupations, school of origin, and student majors, while the outputs are the Final School Exam (UAS) scores for science and social studies subjects. Data were analyzed for three Decision-Making Units (DMUs): 2022, 2023, and 2024. The results showed that 2022 was inefficient (efficiency 0.93), while 2023 and 2024 experienced high efficiency (1.14 and 1.06). Values above 1 indicate super-efficiency, indicating superior resource utilization. The final science exam score consistently dominated efficiency, while social studies scores showed no contribution. Similarly, from an input perspective, student majors were the most influential factor, while parental occupation and school of origin tended to be less influential. This study is limited by its three-year data period and its failure to consider non-academic variables. Practical implications of this research include the need to strengthen science subjects and map appropriate majors within student admissions strategies. Furthermore, DEA results can be used as a policy basis for allocating school resources more efficiently
Co-Authors Dahlan Abdullah Della Adelia Evi Ariani Zahratul Fitri