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All Journal JSAI (Journal Scientific and Applied Informatics) Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Jurnal Teknologi Informasi
Inge Handriani, Inge
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Journal : JSAI (Journal Scientific and Applied Informatics)

 1 
SISTEM PENCATATAN DAN PENDATAAN MANAJEMEN SUMBER DAYA MANUSIA DENGAN MODEL SCRUM (STUDI KASUS: PT BINTANG TRANS KHATULISTIWA) RAHMAN, SAEPUR; SETIAWAN, ARIS; HANDRIANI, INGE
JSAI (Journal Scientific and Applied Informatics) Vol 2, No 1 (2019): Applied of Informatics
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v2i1.138

Abstract

Manajemen sumber daya manusia atau biasa disebut Human Resource Development (HRD) merupakan salah satu bagian terpenting dalam suatu perusahan atau organisasi. Karena, fokus utama manajemen sumber daya manusia adalah memberikan kontribusi bagi sukses atau tidaknya suatu organisai atau perusahaan.  Tugas dan kegiatan HRD dalam suatu perusahaan adalah menangani permasalahan-permasalahan yang berkenaan dengan pengelolaan sumber daya manusia pada sebuah organisasi (perusahaan). Pengelolaan data sumber daya manusia pada PT. Bintang Trans Khatulistiwa (BTK) masih bersifat manual, sehingga perusahaan kesulitan dalam memperoleh informasi yang berhubungan dengan sumber daya manusia secara cepat dan akurat. Untuk menangani permasalahan tersebut, maka kami mengusulkan pembuatan suatu sistem informasi manajemen Sumber Daya Manusia yaitu Aplikasi Sistem Pencatatan dan Pendataan Manajemen Sumber daya Manusia yang nantinya  dapat membantu bagian HRD untuk menjalankan tugasnya, mulai dari proses pendataan karyawan, pendataan absensi, pengajuan cuti, proses payroll atau  penggajian dan penilaian karyawan hingga pembuatan laporan, serta dapat menghasilkan informasi yang dapat digunakan untuk pengambilan keputusan bagi seorang manajer. Dalam pengembangan sistem ini menggunakan pendekatan metode Scrum dan menggunakan metode cause effect diagram (fishbone) sebagai dasar untuk melakukan analisa permasalahan, sehingga nantinya akan didapatkan solusi-solusi untuk pembuatan sistem pada penelitian ini.Keywords— Sistem informasi, sumber daya manusia, Scrum, Fishbone
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 A.A. Ketut Agung Cahyawan W Ayumi, Vina Azizal, Destarian Yuski Bagus Priambodo Desyandri Desyandri Djahidin, Danny Yudin Ida Farida Jumaryadi, Yuwan Misbahul Fajri Multazam, Akhmad Nia Rahma Kurnianda, Nia Rahma Nur Ani Purba, Mariana RAHMAN, SAEPUR Salamah, Umniy Sari, Yunita Sartika Sarwati Rahayu Syahril Sidik, Akbar Noor