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The Palmprint Recognition Using Xception, VGG16, ResNet50, MobileNet, and EfficientNetB0 Architecture Aprilla, Diah Mitha; Bimantoro, Fitri; Suta Wijaya, I Gede Pasek
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7577

The palmprint is a part of the human body that has unique and detailed characteristics of the pattern of palm lines, such as the length and width of the palm (geometric features), principal lines, and wrinkle lines. It began to be developed as a tool for recognize a person. The palmprint dataset used comes from Kaggle, namely BMPD. The palmprint images in this dataset were taken in 2 sessions. In the first session, there was not much variation in rotation compared to the second session. This research uses Convolutional Neural Network (CNN) models with Xception, VGG16, ResNet50, MobileNet, and EfficientNetB0 architectures to see the best performance. The results of this research showed that the MobileNet model had the best performance with an accuracy of 96.6% and a loss of 14.3%. For Precision results of 94%, Recall 96%, and F1-Score 94%. Meanwhile, Xception obtained an accuracy of 88.3% and a loss of 52.9%, VGG16 70.8% and a loss of 109.8%, ResNet50 5.8% and a loss of 307.9%, and EfficientNetB0 3.3% and a loss of 340.1%.

PELATIHAN PENGOLAHAN DAN PEMASARAN PRODUK BERBASIS SINGKONG DI DESA GIRI TEMBESI Aprilla, Diah Mitha; Maulida, Sylmi; Putri, Indah Annisa; Fitriana, Nur Fadila; Julianti, Nova; Korniawan, Ojik Danang; Wahyudi, Reza Izam; Agustina, Rizki; Firdaus, Rizqi Rohmatul; Walid, Walid; Buhari, Nurliah
Jurnal Wicara Vol 2 No 1 (2024): Jurnal Wicara Desa
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/wicara.v2i1.4087

Giri Tembesi Village is a village located in Gerung District, West Lombok Regency, West Nusa Tenggara. This village consists of 7 hamlets spread across its territory. This village is also the southernmost tip of Gerung District. This village was originally part of Banyu Urip Village, Gerung District, West Lombok Regency, West Nusa Tenggara and was expanded in 2015. The population of Giri Tembesi Village based on 2022 data is 4431 people, with 1483 heads of families. Based on the results of observations and interviews conducted with the Village Government and the community regarding the Potential of Giri Tembesi Village, there is data that the community has a rice field area of around 183.2 Ha/m2 with agricultural land owners around 850 families. With Potential In this village, there are many people who work on agricultural products to sell and process them into products that have sales value such as mustofa cassava and potato donuts. However, limited public knowledge regarding the development of processed cassava-based products, and the lack of good processing techniques and a small market share hamper the development of the potential of the agricultural sector in the village. Therefore, efforts need to be made to improve the quality of cassava production and improve the processing process so that it can have a positive impact on the economic development and welfare of the people of Giri Tembesi Village. The main work program in implementing this KKN includes counseling on the creativity of processed cassava products, making processed foods, and marketing processed cassava products in Giri Tembesi Village.

PELATIHAN PENGOLAHAN DAN PEMASARAN PRODUK BERBASIS SINGKONG DI DESA GIRI TEMBESI Aprilla, Diah Mitha; Maulida, Sylmi; Putri, Indah Annisa; Fitriana, Nur Fadila; Julianti, Nova; Korniawan, Ojik Danang; Wahyudi, Reza Izam; Agustina, Rizki; Firdaus, Rizqi Rohmatul; Walid, Walid; Buhari, Nurliah
Jurnal Wicara Vol 2 No 1 (2024): Jurnal Wicara Desa
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/wicara.v2i1.4087

Giri Tembesi Village is a village located in Gerung District, West Lombok Regency, West Nusa Tenggara. This village consists of 7 hamlets spread across its territory. This village is also the southernmost tip of Gerung District. This village was originally part of Banyu Urip Village, Gerung District, West Lombok Regency, West Nusa Tenggara and was expanded in 2015. The population of Giri Tembesi Village based on 2022 data is 4431 people, with 1483 heads of families. Based on the results of observations and interviews conducted with the Village Government and the community regarding the Potential of Giri Tembesi Village, there is data that the community has a rice field area of around 183.2 Ha/m2 with agricultural land owners around 850 families. With Potential In this village, there are many people who work on agricultural products to sell and process them into products that have sales value such as mustofa cassava and potato donuts. However, limited public knowledge regarding the development of processed cassava-based products, and the lack of good processing techniques and a small market share hamper the development of the potential of the agricultural sector in the village. Therefore, efforts need to be made to improve the quality of cassava production and improve the processing process so that it can have a positive impact on the economic development and welfare of the people of Giri Tembesi Village. The main work program in implementing this KKN includes counseling on the creativity of processed cassava products, making processed foods, and marketing processed cassava products in Giri Tembesi Village.

Enhanced Identity Recognition Through the Development of a Convolutional Neural Network Using Indonesian Palmprints Aprilla, Diah Mitha; Husodo, Ario Yudo; Wijaya, I Gede Pasek Suta
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4169

The use of palmprint as an identification system has gained significant attention due to its potential in biometric authentication. However, existing models often face challenges related to computational complexity and the ability to scale with larger datasets. This research aims to develop an efficient Convolutional Neural Network (CNN) model for palmprint identity recognition, specifically tailored to address these challenges. A novel contribution of this study is the creation of an original palmprint dataset consisting of 700 images from 50 Indonesian college students, which serves as a foundation for future research in Southeast Asia. The dataset includes different scenarios with varying input sizes (32x32, 64x64, 96x96 pixels) and the number of classes (30, 40, 50) to assess the model's scalability and performance. Three CNN architectures were designed with varying layers, activation functions, and dropout strategies to capture the unique features of palmprints and improve model generalization. The results show that the best-performing model, Model 3, which incorporates dropout layers, achieved 95% accuracy, 96% precision, 95% recall, and 95% F1-score on 50 classes with 1.2 million parameters. Model 1 achieved 98% accuracy, 99% precision, 98% recall, and 98% F1-score on 40 classes with 1.7 million parameters. These findings demonstrate that the proposed CNN models not only achieve high accuracy but also maintain computational efficiency, offering promising solutions for real-time palmprint authentication systems. This research contributes to the advancement of biometric authentication systems, with significant implications for real- world applications in Southeast Asia.

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