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All Journal dCartesian: Jurnal Matematika dan Aplikasi Jurnal MIPA Jurnal Teknik Informatika Jurnal TIMES JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 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 Journal of Agriculture (JoA) Jurnal Ilmiah Sains Bulletin of Social Informatics Theory and Application 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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Pattern Recognition of Puta Dino Fabric Using Web-Based Convolutional Neural Network Method Luther Alexander Latumakulita; Silviani Esther Rumagit; Hence Beedwel Lumentut; Frangky Jessy Paat; Jaidun Ramadhan Kaplale; Enny Itje Sela
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.
Identifying Disease Outbreak Messages on Social Media Using Graph Neural Network Variants with TF-IDF and Word Embedding Features Irwan Budiman; Mohammad Reza Faisal; Muliadi; Muhammad Rafi; Husnil Kamil; Luther Alexander Latumakulita; Luu Duc Ngo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 5 (2026): October 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i5.7837

Abstract

The swift dissemination of information regarding disease outbreaks via social media presents significant challenges in the automatic identification of pertinent outbreak-related messages, particularly within multilingual textual data. The objective of this research is to devise and assess classification models grounded in Graph Neural Networks for the detection of disease outbreak messages within Indonesian and English datasets. The methodology employed encompasses text normalisation, feature extraction utilizing TF-IDF and word embedding techniques, including Word2Vec, fastText, and GloVe, followed by classification employing Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT). The findings of this study indicate that the optimal performance on the Indonesian dataset was attained by GCN utilizing TF-IDF features amounting to 5000, resulting in an accuracy of 93%, whereas the superior GAT model attained 92% accuracy utilizing 4000 TF-IDF features. For the English dataset, the most effective GCN model reached an accuracy of 86.77% employing fastText, while the leading GAT model achieved 86.79% with the same fastText feature set. These results surpass those of previous investigations that employed CNN, LSTM, and BERT-based models for analogous tasks of disease outbreak message classification. This study concludes that GCN and GAT represent efficacious deep learning methodologies for the classification of multilingual disease outbreak messages. GCN demonstrates superior performance on Indonesian text utilizing TF-IDF-based features, while GAT exhibits marginally better performance on English text employing fastText features. In summary, graph-based models are capable of capturing relational information among textual features and delivering competitive classification performance when juxtaposed with earlier deep learning methodologies.
Co-Authors Aditya Lapu Kalua Aji Prasetya Wibawa Altien Rindengan Altien Rindengan Alwin Melkie Sambul Ambarita, Yolanda Margareta Anastasia, Lenshy Aprisilia Arista Mandagi Arthur G. Pinaria 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 T Salaki Deiby Tineke Salaki Djoni Hatidja Edi Priyanto Eliasta Ketaren, Eliasta Enny Itje Sela Fajar Purnama Felliks Tampinongkol, Felliks Frangky J. Paat Frangky J. Paat Frangky Jessy Paat Frangky Jessy Paat Gybert Saselah Hasanuddin Sirait Hence Beedwel Lumentut Husnil Kamil I Nyoman Gede Arya Astawa Irwan Budiman Irwan Karim Islam, Noorul Islam, Noourul Jabari, Nida Jaidun Ramadhan Kaplale Jantje Pongoh Jevenston Lalenoh John Socrates Kekenusa Julana Rarung Julana Rarung, Julana Jullia Titaley Karim, Irwan Koibur, Mayko Edison Kusuma, Samuel D. A. Lapihu, Dodisutarma Lindsay Mokosuli Liwu, Suzanne L. Lumintang, Sandy Laurentius Luu Duc Ngo 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 Mochamad Agung Wibowo Montolalu, Chriestie Ellyane Juliet Clara Muhammad Rafi Muhammad Reza Faisal, Muhammad Reza Muliadi 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 Rumambi, David P Salaky, Deiby Tineke Sambul, Alwin Melkie Sandra Pakasi Sandy Laurentius Lumintang Sanriomi Sintaro Saroyo Saroyo Saroyo Saroyo Selvie Tumbelaka Silviani Esther Rumagit 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 Winsy Weku Winsy Weku Yohanes Langi Yohanes Langi