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All Journal JITU : Journal Informatic Technology And Communication Spektrum Industri
Pramitaningrum, Erlita
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology

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Klasifikasikan Jenis Cacat Kulit Menggunakan SMOTE-GoogLeNet Prananda, Alifia Revan; Frannita, Eka Legya; Pramitaningrum, Erlita; Hidayat, Anwar; Setiawan , Wawan Budi; Purwaningsih , Nunik
JITU Vol 8 No 1 (2024)
Publisher : Universitas Boyolali

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Abstract

Deep learning has been proven to be able to provide significant contributions to several fields, including industry. It has also been proven that it has resulted in an outstanding performance for classification, detection, and even segmentation processes. In the leather industry, it also successfully gave valuable results, especially for the leather defect inspection process. However, despite its outstanding performance, it remained a drawback because it produced insignificant results if employed in a small or imbalanced dataset. This research work focuses on the analysis of the implementation of the data balancing method for improving the performance of the deep learning method for classifying the types of leather defects. This research work was done by employing three processes. In the first step, we utilized the data balancing method to balance the data proportion. In the next step, we employed GoogLeNet as a deep learning architecture for training and testing processes. Our experiment was conducted in two scenarios. The first scenario was done by using the original dataset. Whereas the second scenario was accomplished by utilizing the data balancing method before training and testing. According to the experiment results, implementing the data balancing method successfully increased the performance of the deep learning method by more than 15%. It can be inferred that the proportion or the number of data strongly affected the performance of deep learning models.
Market and Industrialization Opportunities of Rumah Unggul Sistem Panel Instan (RUSPIN) Technology Using Business Model Canvas Pramitaningrum, Erlita; Nugraha, Dimas Hastama
Spektrum Industri Vol. 21 No. 1 (2023): Spektrum Industri - April 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/si.v21i1.58

Abstract

Rumah Unggul Sistem Panel Instan (RUSPIN) technology is the development of Rumah Instan Sederhana Sehat (RISHA) with several improvements. Besides resistance to earthquakes, this technology has advantages such as ease and speed of installation compared to conventional houses. As a new technological innovation that has been proven, to develop RUSPIN technology to be an industrialization model in the future, therefore a study of technology business plan is required. Research objectives eager to see the market and industrialization opportunities of RUSPIN technology. The study using Business Model Canvas (BMC) concept which strived to combine 9 business aspects such as customer segments, value proposition, channels, customer relationships, revenue streams, key resources, key activities, key partners, and cost structure into one complete concept map. As the result of this study, the market opportunity for RUSPIN technology is very large with the market segmentation is families that do not own a home and the target market for Low-Income Families (MBR). RUSPIN technology also has an opportunity to be industrialized by looking at the demand side (market opportunities), while from the supply side, efforts are needed to increase the number of certified RUSPIN applicators and developers.
Co-Authors Alifia Revan Prananda Dimas Hastama Nugraha, Dimas Hastama Eka Legya Frannita Hidayat, Anwar Purwaningsih , Nunik Setiawan , Wawan Budi