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Inge Handriani
Universitas Mercu Buana
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Application of Random Contrast and Brightness Range Methods on Phytomedicine Leaf Image Dataset Purba, Mariana; Ayumi, Vina; Rahayu, Sarwati; Salamah, Umniy; Handriani, Inge; Farida, Ida
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8766

Abstract

This study aimed to enhance the performance of deep learning models in detecting and classifying medicinal plant leaf images by applying two data augmentation techniques, namely Random Contrast Augmentation (RCA) and Brightness Range Augmentation (BRA). The RCA technique randomly adjusted the contrast of images by calculating the pixel average and modifying each pixel value based on a contrast factor, thereby increasing the variation in image lighting. Meanwhile, BRA randomly altered the brightness of the images to simulate varying lighting conditions. The research process began with the collection of medicinal plant leaf image datasets, which were then divided into three parts: training data, validation data, and testing data. The dataset was then pre-processed to prepare the images before applying the augmentation. Augmentation techniques were employed to enrich the dataset by generating modified copies of images using RCA and BRA techniques. The application of both augmentation techniques resulted in a training dataset of 2,400 images, 300 validation images, and 300 testing images.
Business Process Maturity Model pada Penerapan Sistem Pemenuhan Layanan Teknologi Informasi dan Komunikasi Azizal, Destarian Yuski; Handriani, Inge
Jurnal Teknologi Informasi Vol 4, No 2 (2025): Oktober
Publisher : Universitas Teuku Umar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35308/jti.v4i2.12659

Abstract

Dalam menghadapi peningkatan permintaan layanan Teknologi Informasi dan Komunikasi (TIK) yang dinamis, organisasi perlu merespon dengan meningkatkan kecepatan dan kualitas sistem pemenuhan layanan TIK. Permasalahan yang terjadi di dalam penerapan sistem pemenuhan layanan TIK adalah kurangnya proses penilaian tingkat kematangan dan evaluasi yang dilakukan oleh suatu organisasi. Penelitian ini bertujuan untuk melakukan evaluasi penerapan sistem pemenuhan layanan TIK melalui penilaian tingkat kematangan menggunakan pendekatan Business Process Management (BPM). BPM menjadi pendekatan strategis untuk mengoptimalkan kinerja sistem pemenuhan layanan TIK melalui integrasi dengan pengelolaan proses bisnis. Metode yang digunakan adalah penilaian menggunakan Business Process Maturity Model (BPMM), dengan mengukur tingkat kematangan pada 7 faktor pada organisasi yakni strategic alignment, governance, methods, information technology, people, dan culture. Penelitian ini memiliki keterbaruan dalam hal penilaian tingkat kematangan penerapan sistem pemenuhan layanan TIK dengan pendekatan BPM yang hingga saat ini masih jarang dibahas pada penelitian dengan topik yang terkait, khususnya di Indonesia. Pada penelitian selanjutnya, dapat dilakukan kajian untuk mempertajam dalam hal pembuatan strategi, untuk mengingkatkan kualitas dari sistem pemenuhan layanan TIK.
Penerapan Metode Gamma Correction dan MobileNet Untuk Klasifikasi Citra Daun Purba, Mariana; Ayumi, Vina; Rahayu, Sarwati; Salamah, Umniy; Handriani, Inge; Ani, Nur
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 3 (2025): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i3.9459

Abstract

This study proposed an enhanced leaf image classification model by integrating gamma correction as a preprocessing technique with the MobileNet (MNET) architecture to improve visual feature extraction. The dataset consisted of 750 images representing five classes of medicinal plants, namely Psidium guajava, Syzygium polyanthum, Piper betle, Annona muricata, and Andrographis paniculata, obtained from personal documentation, online sources, and public datasets. Gamma correction was applied to adjust illumination and enhance leaf texture clarity, followed by resizing and normalization processes. Data augmentation was performed using rotation, contrast adjustment, horizontal and vertical flipping, brightness adjustment, and channel shifting to increase training data variation. The MobileNet architecture was expanded with additional layers, including global average pooling, flatten, Dense–ReLU, and Dense–softmax, enabling it to function as an efficient feature extractor and classifier. Experiments were conducted using a batch size of 32, 50 epochs, the Adam optimizer, and a learning rate of 0.0001. The combined MNET and gamma correction model achieved a training accuracy of 99.00%, a validation accuracy of 87.50%, and a testing accuracy of 84.16%.
Co-Authors A.A. Ketut Agung Cahyawan W Anita Ratnasari ARIS SETIAWAN Arishita Nurul Anastasia Ayumi, Vina Azizal, Destarian Yuski Bagus Priambodo Bagus Priambodo Bimo Fikri Wicaksono Danny Yudin Djahidin Deasty Kartika Sari Desyandri Desyandri Greenda Soundi Rabia Ida Farida Jumaryadi, Yuwan Misbahul Fajri Mukhlish Ibadurrahman Multazam, Akhmad Nia Rahma Kurnianda Nia Rahma Kurnianda, Nia Rahma Novianto Yosua Tampi Nur Ani Nur Ani Nur Fajriati Yuniar Purba, Mariana RAHMAN, SAEPUR Ricky Ariansyah Rinto Priambodo SAEPUR RAHMAN Salamah, Umniy Sari, Yunita Sartika Sarwati Rahayu Syahril Sidik, Akbar Noor