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All Journal Klik - Jurnal Ilmu Komputer Journal of Information Systems and Technology Research
Utari, Aspirani
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Sistem Absensi Karyawan Menggunakan Self Potrait dan Geolocation Pada PT Sucofindo Palembang Saputra, Tommy; Utari, Aspirani; Teisnajaya, Usep; Twenty Agustine, Grace
Klik - Jurnal Ilmu Komputer Vol. 4 No. 2 (2023): Klik - Jurnal Ilmu Komputer
Publisher : Fakultas Ilmu Komputer Universitas Sumatera Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56869/klik.v4i2.553

Abstract

Penelitian yang telah dilakukan pada PT Sucofindo Palembang diperoleh masalah bahwa sistem absensi yang diterapkan saat ini masih menggunakan sistem manual, dimana proses absensi dilakukan dengan mencatat pada lembar kertas sehingga membutuhkan waktu yang lama dan boros penggunaan kertas. Rentan juga adanya manipulasi data atau terjadi data yang ganda, sehingga tidak mendapatkan hasil yang akurat. Tentunya hal tersebut membuat proses absensi menjadi kurang efektif. Salah satu dari solusi untuk mengatasi permasalahan di atas yaitu dengan membangun sebuah sistem yang terkomputerisasi untuk menggantikan metode manual dan membantu karyawan dalam proses absensi kehadiran di PT Sucofindo Palembang. Metode yang digunakan pada penelitian ini adalah metode waterfall secara terurut dimulai dari analisis, desain, pengkodean, pengujian, dan pemeliharaan. Proses desain rancangan sistem menggunakan UML (Unified Modeling Language) yang terdiri dari use case diagram, activity diagram, sequence diagram, class diagram, dan rancangan database. Hasil dari penelitian ini yaitu sebuah sistem absensi karyawan menggunakan self portrait dan geolocation berbasis web. Sistem ini dibuat agar karyawan bisa melakukan absensi dengan mudah menggunakan handphone dengan self portrait dan Geolocation, yang mana input absensi ini hanya bisa dilakukan di area PT Sucofindo Palembang saja sehingga mengurangi tingkat kecurangan dalam input absensi, karena dapat diakses menggunakan ponsel yang karyawan miliki, dan membantu Admin dalam merekap data absensi di PT Sucofindo Palembang
Learning Rate and Epoch Analysis for Medicinal Plant Identification Using GLCM and BPNN Nurqolbiah, fatihani; Absharina, Eriene Dheanda; Utari, Aspirani; Febriady, Mukhlis; Saputra, Tommy
Journal of Information Systems and Technology Research Vol. 5 No. 1 (2026): January 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i1.1413

Abstract

Accurate identification of medicinal plants is essential for pharmacology and biodiversity conservation. However, traditional methods rely heavily on subjective visual inspection, which is prone to misclassification due to subtle differences in leaf textures. A primary challenge that remains unaddressed is the understanding of hyperparameter sensitivity within limited datasets, particularly when the subjects exhibit extremely high visual similarity. This study proposes an automated identification approach using Gray-Level Co-occurrence Matrix (GLCM) and Backpropagation Neural Network (BPNN) to classify three Indonesian medicinal species: white ginger, mango ginger, and yellow turmeric. The distinctive focus of this research lies in its attempt to differentiate these specific plants, which possess leaf texture characteristics so similar that they are often indistinguishable to the human eye. This approach involves a systematic analysis of learning rate and epoch parameters to optimize convergence for these nearly identical texture features. A dataset of 63 images was transformed into five GLCM statistical features to serve as the primary inputs for the BPNN. Experimental results demonstrate that classification performance is highly sensitive to parameter tuning. The system achieved its peak accuracy of 65.03% using a learning rate of 0.1 and 100 epochs. The findings reveal that smaller learning rates and limited training iterations facilitate more stable convergence when processing data with high feature similarity. While the accuracy indicates potential for further development, this study provides a significant contribution to creating objective identification methods for visually similar plants and offers empirical insights into optimal parameter selection for texture-based neural network architectures.
Co-Authors Absharina, Eriene Dheanda Febriady, Mukhlis Nurqolbiah, Fatihani Saputra, Tommy Teisnajaya, Usep Twenty Agustine, Grace