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All Journal Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Kevin Novebrianto
Universitas Pamulang
Computer Science & IT Languange, Linguistic, Communication & Media Library & Information Science

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Penerapan Metode Simple Additive Weighting (SAW) pada Sistem Pendukung Keputusan Pemilihan Karyawan Terbaik Berbasis Web Nurhasanah Nurhasanah; Nurhalimah Nurhalimah; Yuliana Yuliana; Kevin Novebrianto
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 7 No. 3 (2026): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v7i3.2079

Abstract

Employees are vital assets for any institution, playing a crucial role in maintaining sustainability, driving development, and enhancing competitiveness. Optimal employee performance directly impacts organizational productivity, making periodic evaluations for selecting the best employee a necessity. However, this process often runs suboptimally due to the absence of a structured evaluation method. Human Resource Development (HRD) frequently faces difficulties caused by subjective assessment tendencies, which can trigger friction among employees. To address these issues, this study develops a web-based Decision Support System (DSS) using the Simple Additive Weighting (SAW) method. The evaluation criteria include responsibility, attendance, compliance with rules, teamwork, and work morale. The system testing was conducted using 12 employee records as simulated alternatives, where the calculation results demonstrated that this web-based system successfully produces a structured and precise evaluation, with the top rank achieved by the alternative securing a perfect preference score of 100.
Implementasi Algoritma Hybrid CNN-ViT (LeViT-128) dalam Klasifikasi Motif Batik Indonesia Kevin Novebrianto; Hadi Zakaria
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 7 No. 3 (2026): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v7i3.2087

Abstract

Batik is an Indonesian cultural heritage featuring a wide variety of motifs with high inter-class visual similarity. The visual and manual identification process is highly subjective and inefficient. To address this classification problem, this study implements a deep learning approach using a hybrid Convolutional Neural Network (CNN) and Vision Transformer (ViT) architecture, specifically the LeViT model. This integration aims to synergize CNN's ability to extract local features—such as lines and dots—with ViT's self-attention mechanism to capture global spatial context efficiently. The research methodology follows the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. The model was trained on a dataset of 5,292 images covering 39 batik motif classes. To address data imbalance, class weight adjustments and image augmentation techniques were applied. Based on the final evaluation using the One-vs-Rest approach on the test set, the model achieved an overall accuracy of 90.05% and a Weighted F1-Score of 89.84%, demonstrating the effectiveness and reliability of the hybrid CNN-ViT algorithm in classifying batik motifs with high visual complexity.
Co-Authors Nurhalimah Nurhalimah Nurhasanah Nurhasanah Yuliana Yuliana Zakaria, Hadi