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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Ilmiah FIFO Petir Kilat Jurnal Cendikia JSAI (Journal Scientific and Applied Informatics) Jurnal Abdi Masyarakat (JAM) SABDAMAS Journal of Social Responsibility Projects by Higher Education Forum Jurnal Pengabdian Masyarakat Nasional Journal Collabits
Sarwati Rahayu
Universitas Mercu Buana
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Public Health Social Sciences

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Journal : JSAI (Journal Scientific and Applied Informatics)

 1 
Automated Fruit Classification Menggunakan Model VGG16 dan MobileNetV2 Umniy Salamah; Anita Ratnasari; Sarwati Rahayu
JSAI (Journal Scientific and Applied Informatics) Vol 5 No 3 (2022): November 2022
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v5i3.3615

Abstract

Pengembangan robot atau mesin untuk membantu kegiatan pertanian memerlukan riset yang panjang. Teknologi tersebut harus dapat memiliki keahlian dalam melakukan berbagai macam aktivitas dan mampu mendeteksi objek yang menjadi sasaran pekerjaannya. Untuk memenuhi hal ini, riset untuk mendeteksi objek pertanian, misalnya buah, menjadi salah satu agenda riset yang perlu dilakukan dan dikembangkan. Tujuan penelitian ini adalah untuk mengetahui hasil perbandingan performa deep learning yaitu VGG16 dan MobileNetV2 untuk fruit classification. Penelitian ini menggunakan dataset dengan jumlah total 90.483 data dengan ukuran gambar 100x100 piksel dan jumlah kelas tanaman buah yang akan diklasifikasi adalah sebanyak 131 kelas. Pada proses testing menggunakan dataset yang ada, MobileNetV2 mendapatkan akurasi 98.4% dan ResNet50 mendapatkan akurasi 99,2%.
Model Sequential Resnet50 Untuk Pengenalan Tulisan Tangan Aksara Arab Marissa Utami; Sarwati Rahayu; Sulis Sandiwarno; Erwin Dwika Putra; Marissa Utami; Hadiguna Setiawan
JSAI (Journal Scientific and Applied Informatics) Vol 6 No 2 (2023): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v6i2.5379

Abstract

Research for Arabic handwriting recognition is still limited. The number of public datasets regarding Arabic script is still limited for this type of public dataset. Therefore, each study usually uses its dataset to conduct research. However, recently public datasets have become available and become research opportunities to compare methods with the same dataset. This study aimed to determine the implementation of the transfer learning model with the best accuracy for handwriting recognition in Arabic script. The results of the experiment using ResNet50 are as follows: training accuracy is 91.63%, validation accuracy is 91.82%, and the testing accuracy is 95.03%.
Komparasi Hasil Color Feature Extraction HSV, LAB dan YCrCb pda Algoritma SVM untuk Klasifikasi Spesies Burung Sarwati Rahayu; Andi Nugroho; Erwin Dwika Putra; Mariana Purba; Hadiguna Setiawan; Sulis Sandiwarno
JSAI (Journal Scientific and Applied Informatics) Vol 6 No 3 (2023): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v6i3.5920

Abstract

The classification of bird species is a problem often faced by ornithologists, and has been considered scientific research since antiquity. This study aims to evaluate the results of color feature extraction including HSV, LAB and YCrCb against the results of the SVM classification. In addition, the results of this study are useful to determine the performance of color feature extraction that is suitable for bird species classification. The dataset used was 22,617 bird species images. Based on experimental results, the effect of HSV on the SVM classification caused a decrease in accuracy by -0.33% while LAB and YCrCb on the SVM classification caused an increase in accuracy of 0.44% and 0.21%. However, the accuracy of the SVM classification does not yet have good performance so that further research will be carried out using other classifications, including convolutional neural networks and others.
Analisis Performa Metode Klasifikasi Dataset Multi-Class Kanker Kulit Menggunakan KNN dan HOG Rahayu, Sarwati; Sandiwarno, Sulis; Dwika Putra, Erwin; Utami, Marissa; Setiawan, Hadiguna
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6423

Abstract

Detection of skin cancer in its early phase is a challenge even for dermatologists. This study aims to analyze the performance of classification methods on multiclass skin cancer datasets using K-nearest neighbor (KNN) and histogram of oriented gradients (HOG). The dataset is taken publicly under the name Skin Cancer MNIST dataset: HAM10000 dataset totaling 10,015 data. The first experiment used the pixels per cell parameter of 8.8 and cells per block of 2.2 to get an accuracy of 60.58%. The second experiment used the pixels per cell parameter of 8.8 and cells per block of 2.2 to get an accuracy of 60.58%. The last experiment using the pixels per cell parameter of 8.8 and cells per block of 2.2 got the best accuracy of 61.43%.
Analisis Usabilitas Sistem Informasi Akademik Berdasarkan Usability Scale (Studi Kasus: Universitas Mercu Buana) Rahayu, Sarwati; Nugroho, Andi; Sandiwarno, Sulis; Salamah, Umniy; Dwika Putra, Erwin; Purba, Mariana; Setiawan, Hadiguna
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 3 (2024): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i3.7478

Abstract

The usability analysis on the website of Mercu Buana University (UMB) is an important research carried out to ensure that the site effectively supports the university's goals, especially in terms of the user's experience in completing academic and administrative goals with ethical and professional standards. This research was carried out during the period January 2024 to May 2024. The main purpose of this study is to measure the usability of the UMB website using a questionnaire method. The questionnaire used for the research adapted the System Usability Scale (SUS) which consisted of a total of 10 questions. Based on the calculation of each statement item having a minimum score of 0 and a maximum score of 2.5, the final score of each respondent ranged from 0 to l00. The average score obtained was 63,125. Based on the results of the score of 63,125, the UMB website has a score in the range of 50 to 70. This shows that the UMB website is in the "quite good" category but there is still a need for a little improvement. Some icons or layouts on the UMB website are not familiar to respondents. In addition, there needs to be guidelines developed to provide information on how to use the website for users who are using the UMB website for the first time.
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.
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 Achmad Kodar Andi Nugroho Andi Nugroho, Andi Anita Ratnasari ARDIANSYAH ARDIANSYAH Ardiansyah, Vicky Aris Prasetyo Ayumi, Vina Bagus Priambodo Bharodin, Oktia Putri Bima Kuswara Budiawan, Rezky Capah, Dwi Ade Handayani Dermawan Suprihatin Desi Ramayanti Eno Rizqi Shabrina Fanni Ramziani Faturahman, Firya Gumilang, Ahmad Juang Teguh Hadiguna Setiawan Hari Setiyawati Hilmy, Muhammad Hilmy Ida Farida Inge Handriani Inge Handriani, Inge Intan Della Rusvita Intan Sari Riyandini Jumaryadi, Yuwan Mariana Purba Marissa Utami Nia Rahma Kurnianda Nur Ani Nur Ani Nur Ismawati, Nur Purba, Mariana Putra, Erwin Dwika Ramadhan, Rafly Yogtavia Ratna Mutu Manikam Rico Lanjar Winata Rinto Priambodo Salamah, Umniy Saputra, Muhamad Bintang Septian Eka Ady Buananta Siti Maesaroh Sulis Sandiwarno Vera Yunita WIjaya, Hery Derajad