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All Journal dCartesian: Jurnal Matematika dan Aplikasi Jurnal MIPA Jurnal Teknik Informatika Jurnal TIMES JOIV : International Journal on Informatics Visualization Knowledge Engineering and Data Science BAREKENG: Jurnal Ilmu Matematika dan Terapan ILKOM Jurnal Ilmiah Jurnal Ilmiah Behongang Journal of Information Systems and Informatics Journal of Applied Data Sciences Operations Research: International Conference Series JURNAL REKAYASA INFORMASI SWADHARMA (JRIS) Journal of Agriculture (JoA) Jurnal Ilmiah Sains Bulletin of Social Informatics Theory and Application Journal of Artificial Intelligence and Technology Information d'Cartesian: Jurnal Matematika dan Aplikasi Jurnal Informatika dan Rekayasa Perangkat Lunak
Luther Latumakulita
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Journal : Journal of Applied Data Sciences

 1 
Comparison of MobileNet and VGG16 CNN Architectures for Web-based Starfish Species Identification System Latumakulita, Luther Alexander; Paat, Frangky J.; Saroyo, Saroyo; Karim, Irwan; Astawa, I Nyoman Gede Arya; Sirait, Hasanuddin
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.456

Abstract

Bunaken Marine Park (BMP) is famous for its rich marine biodiversity. BMP is an asset for the marine tourism industry of the Manado city government, and the North Sulawesi Province of Indonesia needs to be strengthened. This research aims to build a web-based intelligent system using a convolutional neural network (CNN) to identify starfish species to initiate developing a media center marine biota identification system of BMP. Two CNN architectures, namely MobileNet and VGG16, were conducted to produce identification models. The first stage carried out a training process on 1800 starfish image data and then evaluated using the 5-fold cross-validation technique. Validation results show that MobileNet is superior to the VGG16 architecture by achieving validation accuracy of 100% for each fold while VGG16 produces validation accuracy in the range of 94% to 100%. On the other hand, in the second stage of model testing, it was found that VGG16 worked better than MobileNet in identifying 200 new data. The Best Model produced by VGG16 achieved testing accuracy of 100% while MobileNet produced 99.5%. However, stability analysis of the identification models produced by both architectures shows that MobileNet has relatively small loss values ranging from 0.00069325 to 0.00214802 as well as smaller standard deviation values of 0.27 compared to 0.61 produced by VGG16. These findings indicate MobileNet is more stable in carrying out identification work compared to VGG16 of, thus the best model provided by MobileNet is taken to deploy in the web platform which is created using the Python flask framework. The proposed system can be used to strengthen the marine tourism industry as a media center of educational marine biota using deep learning approaches.
Pattern Recognition of Puta Dino Fabric Using Web-Based Convolutional Neural Network Method Latumakulita, Luther Alexander; Rumagit, Silviani Esther; Lumentut, Hence Beedwel; Paat, Frangky Jessy; Kaplale, Jaidun Ramadhan; Sela, Enny Itje
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1103

Abstract

This study aims to develop an intelligent system capable of recognizing traditional woven motifs of Puta Dino, a culturally significant textile from Tidore Island. These motifs are visually complex, poorly documented, and hard for the public to distinguish, highlighting the need for a digital tool to support cultural preservation and accurate identification. This research is the first to build a structured Puta Dino motif database and provide an integrated model designed for real-world use. The approach captured primary images of eight validated motifs and applied systematic preprocessing, including normalization and data augmentation, to enhance variability and strengthen the dataset. A lightweight deep learning model predicated on a convolutional neural network was designed to achieve a compromise between accuracy and computational efficiency. The system was evaluated through cross-validation and independent test data, as well as multiple real-world trials utilizing a web interface. These trials involved different image capture scenarios, including from a distance, moderate distance, close and angled views, and when the fabric surface was folded. The model architecture and system interface with the system are illustrated in the relevant figures, and the tables provide performance data on the system’s training, accuracy in motif classification, and achieved results in real-world conditions. The system demonstrated excellent classification accuracy in controlled test conditions. It showed real-world competency, accurately classifying most motifs in various conditions. The data also point to specific issues with motif recognition in extreme distortion cases, which reflect the typical issues of laboratory-to-field model deployment. The outcomes clearly demonstrate both the possibilities and the limitations of the currently available recognition of culturally significant textiles. The study concludes by exploring the possibilities of expanding the dataset and increasing the depth of learning through more sophisticated techniques, as well as enhancing accessibility to promote sustained community and cultural engagement.
Co-Authors Aji Prasetya Wibawa Altien Rindengan Altien Rindengan Alwin Melkie Sambul Ambarita, Yolanda Margareta Anastasia, Lenshy Aprisilia Arista Mandagi Arthur G. Pinaria Arundaa, Rillya Assa, Jan Rudolf Benny Pinontoan Bernard Bernard, Bernard Bobby Polii Budiman, Glenn Chriestie E. J. C. Montolalu Chriestie E. J. C. Montolalu Dedie Tooy Deiby Tineke Salaki Djoni Hatidja Edi Priyanto Eliasta Ketaren, Eliasta Enny Itje Sela Fajar Purnama Felliks Tampinongkol, Felliks Frangky J. Paat Frangky Jessy Paat Gybert Saselah Hence Beedwel Lumentut, Hence Beedwel I Nyoman Gede Arya Astawa Islam, Noorul Islam, Noourul Jabari, Nida Jantje Pongoh Jevenston Lalenoh John Socrates Kekenusa Julana Rarung Julana Rarung, Julana Jullia Titaley Kaplale, Jaidun Ramadhan Karim, Irwan Koibur, Mayko Edison Kusuma, Samuel D. A. Lapihu, Dodisutarma Lindsay Mokosuli Lisapaly, Carmen Emanuela Dwiva Liwu, Suzanne L. Mairi, Vitrail Gloria Mamuaja, Christine F Manarisip, Endrue Jehezkiel Mandagi, Franklin Mans Mananohas Mans Mananohas, Mans Marni Sumarno Marni Sumarno, Marni Max R Kumaseh Miske Silangen Montolalu, Chriestie Ellyane Juliet Clara NELSON NAINGGOLAN NELSON NAINGGOLAN Ngangi, Stefano C.W. Ngangi, Stephano Caesar Wenston Noorul Islam Noviania, Reski Oessoe, Yoakhim Y.E. Paat, Frangky J Paat, Franky Pagewang, Yalon Bu'tu Pinatik, Herry F Pinilas, Andar Alwein Pioh, Diane Raintung, Stephanie Marceline Rindengan, Altien J. Rinny Mamarimbing Rumagit, Silviani Esther Rumambi, David P Salaky, Deiby Tineke Sandra Pakasi Sandy Laurentius Lumintang Sanriomi Sintaro Saroyo Saroyo Selvie Tumbelaka Sirait, Hasanuddin Sofia Wantasen Steven Ray Sentinuwo Sulu, Brian Sumual, Gery Josua Surahman, Ade Takaendengan, Mahardika Inra Tangkeallo, Sindy C. T. Teltje Koapaha Tenda, Edwin Tineke M. Langi Vederico Pitsalitz Sabandar Wibowo, Mochamad Agung Winsy Weku Winsy Weku Yohanes Langi Yohanes Langi