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All Journal JUSIFOR : Jurnal Sistem Informasi dan Informatika Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Lukman Hakim
Universitas Bina Insan, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Library & Information Science

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Sistem Pendukung Keputusan Seleksi Penerimaan Karyawan Baru Di PT. Bank Rakyat Indonesia Cabang Lubuklinggau Mengunkan Metode Weighted Product Berbasis Web Dimas Ade Prasetyo; Joni Karman; Lukman Hakim
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 1 (2024): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i1.340

Abstract

One of the success factors for a company is its employees, therefore companies must have quality human resources, in order to build the company in a better direction. Negligence in selecting human resources will cause problems and can result in business failure. To obtain competent human resources, and in accordance with the required classification, proper selection is needed in recruitment. In order for the recruitment process to be objective, an appropriate method is needed for recruiting employees. The Weighted Product method can be used in decision making. In the recruitment process five criteria are used, namely recent education, suitability of major, age, work experience, work experience. In this case, prospective employees are compared with one another so as to provide an output of priority intensity values which results in a system that provides an assessment of each employee. This decision support system helps assess each employee, make changes to criteria, and change weight values. This is useful for facilitating decision making related to employee selection issues, so that employees who are most worthy of being accepted into the company will be obtained.
Penerapan Convolutional Neural Network Dalam Klasifikasi Daun Tanaman Obat Menggunakan Pendekatan Transfer Learning Herliya Yolanda; Lukman Hakim; Satriansyah Satriansyah; Tri Hasanah Bimastari Aviani
JUSIFOR : Jurnal Sistem Informasi dan Informatika Vol 4 No 2 (2025): JUSIFOR - Desember 2025
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/jusifor.v4i2.7056

Abstract

Medicinal plant leaves hold significant value in health and traditional medicine due to their bioactive compounds, which can be used to treat various diseases. However, identifying and classifying medicinal plant leaves remains challenging due to subtle visual differences that are difficult to recognize manually. Misidentification can hinder the development of herbal medicines and potentially pose risks to users. Therefore, an effective method is needed to accurately classify medicinal plant leaves. The dataset used in this study consists of 3,500 images, which are divided into training, validation, and test sets. The model training process is conducted using the VGG16 architecture, which is known for its effectiveness in feature extraction from images. The training results indicate that the model achieves an accuracy of 97%. Model evaluation is performed using a confusion matrix, which demonstrates that the model effectively distinguishes between 10 classes of medicinal plant leaves. The findings of this study are expected to contribute to the development of a more effective and efficient medicinal plant classification system, making it a potential tool to support decision-making in medicinal leaf classification tasks. This research not only focuses on model development but also highlights the importance of deep learning technology in healthcare, particularly in medicinal plant leaf classificationses.
Co-Authors Dimas Ade Prasetyo Herliya Yolanda Joni Karman Satriansyah Satriansyah Tri Hasanah Bimastari Aviani