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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) International Journal of Supply Chain Management International Journal of Industrial Innovation and Mechanical Engineering
Irfan Wahyudin
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Astronomy Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science Industrial & Manufacturing Engineering Transportation

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OPTIMIZING THE CLASSIFICATION ASSISTANCE THROUGH SUPPLY CHAIN MANAGEMENT FOR TELEMATICS SMES IN INDONESIA USING DEEP LEARNING APPROACH Tosida, Eneng Tita; Wahyudin, Irfan; Andria, Fredi; Wihartiko, Fajar Delli; Hoerudin, Andi
International Journal of Supply Chain Management Vol 9, No 3 (2020): International Journal of Supply Chain Management (IJSCM)
Publisher : International Journal of Supply Chain Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (393.737 KB)

Abstract

This study aims to optimize the classification process of providing assistance to Indonesian Telematics Small and Medium Enterprises (SMEs) using a deep learning approach. The data used is the 2016 Economic Census data. The research was conducted comprehensively through the process of comparing performance through several approaches. Deep learning performance shows an optimal accuracy rate of 99.03%, higher than other approaches of the Adaboost and Adaboos-Bagging Ensemble (92.0%), LVQ (93.11%) and Backpropagation (89.1%). The deep learning approach still has shortcomings in terms of tracing attributes that affect the provision of assistance. Unlike the case with an ensemble approach that is able to display priority attributes, and these results are also validated with relevant research results. Research development opportunities can be done through the integration of EXPLAIN and IME models in the deep learning model, making it easier for stakeholders to prioritize attributes that affect the delivery of telematics SMEs. This is expected to encourage the improvement of SMEs competitiveness in facing the challenges of the Industrial Revolution 4.0.
Cluster Analysis for SME Risk Analysis Documents Based on Pillar K-Means Irfan Wahyudin; Taufik Djatna; Wisnu Ananta Kusuma
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 2: June 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i2.2385

Abstract

In Small Medium Enterprise’s (SME) financing risk analysis, the implementation of qualitative model by giving opinion regarding business risk is to overcome the subjectivity in quantitative model. However, there is another problem that the decision makers have difficulity to quantify the risk’s weight that delivered through those opinions. Thus, we focused on three objectives to overcome the problems that oftenly occur in qualitative model implementation. First, we modelled risk clusters using K-Means clustering, optimized by Pillar Algorithm to get the optimum number of clusters. Secondly, we performed risk measurement by calculating term-importance scores using TF-IDF combined with term-sentiment scores based on SentiWordNet 3.0 for Bahasa Indonesia. Eventually, we summarized the result by correlating the featured terms in each cluster with the 5Cs Credit Criteria. The result shows that the model is effective to group and measure the level of the risk and can be used as a basis for the decision makers in approving the loan proposal. 
Implementation of VGG6 for Image Processing of Aromatic Plants Using Python Adriana Sari Aryani; Irfan Wahyudin; Kotim Subandi
International Journal of Industrial Innovation and Mechanical Engineering Vol. 1 No. 3 (2024): August: International Journal of Industrial Innovation and Mechanical Engineeri
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijiime.v1i3.38

Abstract

Big Data Analytics has gained significant popularity in recent years, with many companies integrating it into their information technology roadmaps to enhance business performance. However, surveys indicate that Big Data Analytics demands substantial resources, including technology, costs, and talent, which often leads to failures in the initial stages of implementation. This study proposes a VGG6 architecture approach, intended to provide a framework for the initial implementation of Big Data Analytics. The study's outcomes include the implementation of the VGG6 architecture for processing images of aromatic plants using Python. Furthermore, this approach enabled the development of a Minimum Viable Product (MVP) solution that adheres to general Big Data principles, such as the 3Vs (Volume, Velocity, and Variety), and encompasses key technological components: 1) Data Storage and Analysis, 2) Knowledge Discovery and Computational Complexity, 3) Scalability and Data Visualization, and 4) Information Security.
Co-Authors Adriana Sari Aryani Eneng Tita Tosida Fajar Delli Wihartiko Fredi Andria Hoerudin, Andi Kotim Subandi Taufik Djatna Wisnu Ananta Kusuma