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All Journal Jurnal Pilar Nusa Mandiri JTIM : Jurnal Teknologi Informasi dan Multimedia Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Jurnal Kecerdasan Buatan dan Teknologi Informasi Jurnal Computer and Technology
Erniwati, Surni
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Humanities Computer Science & IT Education Environmental Science Social Sciences

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Journal : Jurnal Pilar Nusa Mandiri

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DESIGNING GEOGRAPHIC INFORMATION SYSTEM CULINARY TOUR LOCATION IN THE WEST LOMBOK REGION MOBILE-BASED APPLICATION Erniwati, Surni; Subki, Ahmad
Jurnal Pilar Nusa Mandiri Vol 16 No 2 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v16i2.1663

Abstract

Lombok Island is an island in West Nusa Tenggara which is separated by the Lombok and Bali straits to the west and Alas Strait to the east of Sumbawa. The tourism potential in the West Lombok region is currently in great demand by local and foreign tourists because the tourist objects offered in the West Lombok region are very diverse, such as natural, religious, cultural, and culinary tours. Many restaurants offer culinary but often when indicating the location of a culinary, the information obtained is sometimes limited to street names and location characteristics. Meanwhile, the clarity of where the culinary location is not mapped in detail. So far, culinary connoisseurs use manual methods to find culinary locations such as Instagram, Facebook, and Blogspot. For tourists, this manual method is less effective because it consumes a lot of time and address information for getting to culinary locations is inadequate. One solution that can be used to obtain information is a geographic information system (GIS). The goal is to make it easier for culinary lovers to find culinary tourism locations. The research method used in this research is the Research and Development research method with preliminary stages, data and information collection, interviews and observations, system design with modeling, design validation, design revision, development, limited trial, limited trial revision, trial field, revision of field trials, dissemination, and implementation.
FINE-TUNING RESNET50V2 WITH ADAMW AND ADAPTIVE TRANSFER LEARNING FOR SONGKET CLASSIFICATION IN LOMBOK Wahyudi, Erfan; Imran, Bahtiar; Zaeniah; Erniwati, Surni; Karim, Muh Nasirudin; Muahidin, Zumratul
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.6485

Abstract

This study aims to develop a classification system for traditional Lombok songket fabric patterns using the ResNet50V2 architecture, optimized through fine-tuning and the AdamW optimizer. The data were collected directly from songket artisans in Lombok and categorized into three groups based on the origin of the patterns: Sade, Sukarara, and Pringgasela. The model was trained with data augmentation techniques, including rotation, shifting, and zooming, to increase data diversity. During the training process, fine-tuning was applied to the last layer of ResNet50V2, and optimization was performed using AdamW with a learning rate of 0.0001. The model was evaluated using a confusion matrix, classification report, and analysis of accuracy and loss. The experimental results showed that the model achieved 100% accuracy at the 15th epoch. Furthermore, experiments with different parameters (epochs, batch size, and learning rate) demonstrated that the 15th epoch provided the best results with 100% accuracy, while using higher epochs (30 and 40) did not necessarily yield better outcomes. This model is effective in identifying songket fabric patterns with good classification results for each class. Although the results are excellent, increasing the dataset size and exploring more complex model architectures could further enhance performance. Overall, this study demonstrates the significant potential of deep learning technology in classifying songket patterns with reliable accuracy in real-world applications.
Co-Authors Alfian Hidayat Apsih, Denda Hambali Hambali Imran, Bahtiar indriana, pathul Karim, Muh Nasirudin Lalu Isnaeni Rahman Maspaeni Maspaeni Muahidin, Zumratul Mutaqin, Zaenul Satriawan, Andre St Tuhpatussania Subki, Ahmad Tuhpatussania, Siti wahyuni, wenti ayu Zaeniah Zaeniah Zaeniah