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Sistem Penunjang Keputusan Pemilihan Vendor Dalam Supply Chain Menggunakan Metode Weighted Product Di PT. Nagasena Sinar Jaya Bagus Setiadi Gurmilang; Asep Juarna; Dharmayanti Dharmayanti
Cakrawala Repositori IMWI Vol. 6 No. 4 (2023): Cakrawala Repositori IMWI
Publisher : Institut Manajemen Wiyata Indonesia & Asosiasi Peneliti Manajemen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52851/cakrawala.v6i4.423

PT. Nagasena Sinar Jaya merupakan perusahaan yang bergerak dibidang pembuatan kertas carton box dan packaging membutuhkan aplikasi supply chain karena itu dilakukan satu aplikasi supply chain dari 7 vendor aplikasi supply chain. Standar dan kualitas produk mampu dipertahankan perusahaan tersebut untuk memenuhi kebutuhan konsumen. Namun dengan banyaknya usaha bisnis di bidang yang sama, PT. Nagasena Sinar Jaya harus memahami serta merencanakan strategi bisnis agar dapat bersaing dalam dunia bisnis. Pengguna Microsoft excel dalam mencatat semua transaksi di semua divisi PT. Nagasena Sinar Jaya tidak praktis dan tidak efektif untuk menghasilkan laporan. Diperlukan supply chain untuk mengatasi permasalah tersebut di atas agar semua divisi terintegrasi sehingga laporan akhir dapat didapat cepat dan tepat. Untuk menganalisis strategi bisnis PT. Nagasena Sinar Jaya menggunakan metode Weighted Product untuk mengembangkan strategi bisnis untuk masa sekarang maupun yang akan datang. Metode ini menggunakan beberapa bobot kriteria antara lain : pengalaman project, harga penawaran, waktu pengerjaan, garansi purna jual, kelengkapan dokumentasi. Weigted Product (WP) merupakan metode pengambilan keputusan dengan cara perkalian untuk menghubungkan rating atribut, dimana rating setiap atribut harus dipangkatkan dahulu dengan bobot atribut yang bersangkutan. Hasil dari ini adalah sebuah sistem pengambilan keputusan vendor dalam aplikasi supply chain, dimana sistem ini menunjukan bahwa Weighted Product (WP) dalam proses perangkingan pada pemilihan vendor dalam supply chain mempunyai nilai yang hampir sama, apabila metode dihitung secara manual.

Supplementary Factual Data An Encyclopaedia of Land Transportation Models in Jakarta Using Multimedia Technologies Dharmayanti, Dharmayanti; Permana, Dimas Dwi
Journal of Multimedia Trend and Technology Vol. 3 No. 2 (2024): Journal of Multimedia Trend and Technology
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/jmtt.v3i2.73

The development of multimedia technology in various learning applications shows an increasing trend. According to the source (Head of IT Division AR & Co Indonesia) "the future of Augmented Reality has many advantages compared to Virtual Reality because users can see and touch digital objects and can interact with digital elements. Using Augmented Reality technology means that it will give birth to a new type of interaction between humans and computers". Therefore, research was conducted to create an encyclopedia of Jakarta transportation using augmented reality technology. This encyclopedia book is in the form of a book containing seven models of Jakarta transportation with a marker in the form of a QR-code. Each marker on each page displays a three-dimensional model of transportation along with its animation. This encyclopedia book about Jakarta transportation was created using the ARToolkit library software. To create a three-dimensional organ using the Blender 2.64 application, while making the marker using Adobe Photoshop CS4. The output of this application is in the form of a three-dimensional vehicle model along with animation that will be seen and the webcam is highlighted on the marker.

Sistem Penunjang Keputusan Pemilihan Vendor Dalam Supply Chain Menggunakan Metode Weighted Product Di PT. Nagasena Sinar Jaya Bagus Setiadi Gurmilang; Asep Juarna; Dharmayanti Dharmayanti
Cakrawala Repositori IMWI Vol. 6 No. 4 (2023): Cakrawala Repositori IMWI
Publisher : Institut Manajemen Wiyata Indonesia & Asosiasi Peneliti Manajemen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52851/cakrawala.v6i4.423

PT. Nagasena Sinar Jaya merupakan perusahaan yang bergerak dibidang pembuatan kertas carton box dan packaging membutuhkan aplikasi supply chain karena itu dilakukan satu aplikasi supply chain dari 7 vendor aplikasi supply chain. Standar dan kualitas produk mampu dipertahankan perusahaan tersebut untuk memenuhi kebutuhan konsumen. Namun dengan banyaknya usaha bisnis di bidang yang sama, PT. Nagasena Sinar Jaya harus memahami serta merencanakan strategi bisnis agar dapat bersaing dalam dunia bisnis. Pengguna Microsoft excel dalam mencatat semua transaksi di semua divisi PT. Nagasena Sinar Jaya tidak praktis dan tidak efektif untuk menghasilkan laporan. Diperlukan supply chain untuk mengatasi permasalah tersebut di atas agar semua divisi terintegrasi sehingga laporan akhir dapat didapat cepat dan tepat. Untuk menganalisis strategi bisnis PT. Nagasena Sinar Jaya menggunakan metode Weighted Product untuk mengembangkan strategi bisnis untuk masa sekarang maupun yang akan datang. Metode ini menggunakan beberapa bobot kriteria antara lain : pengalaman project, harga penawaran, waktu pengerjaan, garansi purna jual, kelengkapan dokumentasi. Weigted Product (WP) merupakan metode pengambilan keputusan dengan cara perkalian untuk menghubungkan rating atribut, dimana rating setiap atribut harus dipangkatkan dahulu dengan bobot atribut yang bersangkutan. Hasil dari ini adalah sebuah sistem pengambilan keputusan vendor dalam aplikasi supply chain, dimana sistem ini menunjukan bahwa Weighted Product (WP) dalam proses perangkingan pada pemilihan vendor dalam supply chain mempunyai nilai yang hampir sama, apabila metode dihitung secara manual.

Generating Image Captions in Indonesian Using a Deep Learning Approach Based on Vision Transformer and IndoBERT Architectures Apandi, Ahmad; Mutiara, Achmad Benny; Dharmayanti, Dharmayanti
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.672

The primary objective of this research is to develop an image captioning system in Indonesian by leveraging deep learning architectures, specifically Vision Transformer (ViT) and IndoBERT. This study addresses the challenge of generating accurate and contextually relevant captions for images, which is a crucial task in the fields of computer vision and natural language processing. The main contribution of this research lies in integrating ViT for visual feature extraction and IndoBERT for linguistic representation to enhance the quality of image captions in Indonesian. This approach aims to overcome limitations in existing models by improving semantic understanding and contextual relevance in generated captions. The methodology involves data preprocessing, model training, and evaluation using the Flickr8k dataset, which was translated into Indonesian. The research employs various data augmentation techniques to enhance model performance. The model is trained on a combined architecture where ViT extracts visual features and IndoBERT processes textual information. The experimental procedures include training the model on the Indonesian-translated Flickr8k dataset and evaluating its performance using BLEU and METEOR scores. The training loss and validation loss graphs provide insights into the model’s learning process. The results indicate that the proposed model outperforms traditional CNN+LSTM and Transformer-based models in terms of BLEU and METEOR scores. A detailed analysis of these results highlights the advantages of using ViT and IndoBERT for this task. The findings of this research have significant implications for real-world applications, such as automatic image captioning for visually impaired users, content tagging for multimedia platforms, and improvements in machine translation. Future research can explore the integration of human evaluation metrics and the use of larger datasets to enhance generalizability.

Zero-Day Attack Detection Using Autoencoder and XGBoost Mujibbur Rohman; Dharmayanti
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3248

Advances in information and communication technology have significantly impacted progress in various sectors, but they have also given rise to increasingly complex network security threats. Cyberattacks such as Distributed Denial of Service (DDoS), ransomware, and software vulnerability exploits continue to increase year after year. Signature-based Intrusion Detection Systems are often ineffective in identifying novel cyberattacks since they rely solely on previously known attack patterns. To address this limitation, this study proposes a hybrid approach that integrates Autoencoders, including Dense and Memory-Augmented variants, with Extreme Gradient Boosting (XGBoost) to enhance zero-day attack detection using the UNSW-NB15 dataset. The research methodology encompasses data exploration, preprocessing with a split-before-transform strategy to prevent information leakage, Autoencoder training to model normal network behavior, reconstruction error computation for anomaly detection under both fixed and adaptive thresholding, and the utilization of these errors as input features for XGBoost classification. Experimental results demonstrate that adaptive thresholding improves F1 performance compared to fixed thresholds, while the hybrid Autoencoder–XGBoost integration achieves a significant performance boost. The proposed model consistently obtained F1 scores above 0.80 and PR-AUC values exceeding 0.81 with a balanced trade-off between precision and recall. These findings confirm that the hybrid approach is more effective, consistent, and adaptive in detecting intrusions, particularly zero-day attacks, and highlight its potential as a robust framework for advancing network security in dynamic threat environments.

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