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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Inspiration Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SemanTIK : Teknik Informasi Jurnal Informatika JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknoinfo Jiko (Jurnal Informatika dan komputer) JURNAL PengaMAS Jurnal Nasional Komputasi dan Teknologi Informasi Edumaspul: Jurnal Pendidikan Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar JURNAL IT: Media Infromasi STMIK Handayani Makassar Progresif: Jurnal Ilmiah Komputer INTI Nusa Mandiri Journal of System and Computer Engineering Jurnal Abdimas Bina Bangsa Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen JUSTIN (Jurnal Sistem dan Teknologi Informasi) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Mosfet Innovative: Journal Of Social Science Research Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Abdul Latief Arda
Universitas Handayani Makassar
Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Neuroscience Public Health Social Sciences Transportation Other

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

 1 
Perbandingan Metode KNN Dan LBPH Pada Klasifikasi Daun Herbal Isman; Andani Ahmad; Abdul Latief
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 3 (2021): Juni 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (636.347 KB) | DOI: 10.29207/resti.v5i3.3006

Abstract

Herbal plants are plants that can be used as alternatives in natural healing of diseases, parts of plants that can be used such as roots, stems, tubers and leaves, in Southeast Sulawesi there are currently 1000 herbal plants and 10 sub-ethnicities that have been inventoried, according to research conducted by the Ministry of Health (Kemenkes). Indonesia has 6,000 - 7,000 medicinal plants, Southeast Sulawesi Province has a variety of herbal plants that are not found in other areas, such as Komba - Komba or Balakacida (Chromolaena Odorata). However, in the present era, the number of herbal plants is not accompanied by the knowledge of the community about the herbal plants themselves. The purpose of this study is to classify herbal plants and to compare the performance results of the K-Nearest Neighbor Method and Local Binary Pattern Histogram. From the test results of five types of herbal leaves in Southeast Sulawesi with a total of 100 data sets, the accuracy value for the K-Nearest Neighbor (KNN) method is obtained total accuracy value is 97,5%, while for the Local Binary Pattern Histogram (LBPH) method the total value is 94% of total accuracy value.
Image Preprocessing Approaches Toward Better Learning Performance with CNN Tribuana, Dhimas; Hazriani; Arda, Abdul Latief
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5417

Abstract

Convolutional neural networks (CNNs) are at the forefront of computer vision, relying heavily on the quality of input data determined by the preprocessing method. An undue preprocessing approach will result in poor learning performance. This study critically examines the impact of advanced image pre-processing techniques on computational neural networks (CNNs) in facial recognition. Emphasizing the importance of data quality, we explore various pre-processing approaches, including noise reduction, histogram equalization, and image hashing. Our methodology involves feature visualization to improve facial feature discernment, training convergence analysis, and real-time model testing. The results demonstrate significant improvements in model performance with the preprocessed dataset: average accuracy, recall, precision, and F1 score enhancements of 4.17%, 3.45%, 3.45%, and 3.81%, respectively. Additionally, real-time testing shows a 21% performance increase and a 1.41% reduction in computing time. This study not only underscores the effectiveness of preprocessing in boosting CNN capabilities, but also opens avenues for future research in applying these methods to diverse image types and exploring various CNN architectures for comprehensive understanding.
Classification of Toraja Wood Carving Motif Images Using Convolutional Neural Network (CNN) Nurilmiyanti Wardhani; Asrul, Billy Eden William; Antonius Riman Tampang; Sitti Zuhriyah; Abdul Latief Arda
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5897

Abstract

Wood carving is a cultural heritage with deep meaning and significance for the Toraja ethnic group's culture. By understanding the meaning of each Toraja carving, both tourists and the local community can gain knowledge about Toraja culture, thereby preserving and maintaining the culture amidst modern developments. Image processing approaches, particularly the development of Convolutional Neural Networks (CNN), offer a solution for extracting information from the diverse and intricate patterns of Toraja wood carvings. This study is highly significant as it implements a deep learning model using the CNN algorithm optimized with the ResNet50 architecture. The methodology in this study involves adjusting the batch size during the model training phase and applying weak-to-strong pixel transformation during the double threshold hysteresis phase in the Canny Feature Extraction process on the edges of Toraja carving images, resulting in ResNet50 architecture accurately recognizing the patterns of Toraja wood carvings. The results demonstrate significant improvements in the performance of the ResNet50 architecture with the preprocessed dataset. average precision, recall, precision, and F1-Score improvements in each Toraja carving class. For the Pa' Lulun Pao class, it was found that the precision and recall values were 0.94, and the F1-Score was 0.94. The Pa’ Somba class also showed good results, with a precision value of 0.9697, a recall of 0.96, and an F1-Score of 0.9648. The Pa’ Tangke Lumu class showed even better results, with a precision value of 0.9898, a recall of 0.97, and an F1-Score of 0.9798. The Pa’ Tumuru class also demonstrated good performance, with a precision value of 0.9327, a recall of 0.97, and an F1-Score of 0.9500. This study not only underscores the effectiveness of processing in enhancing CNN capabilities but also opens opportunities for further research in applying these methods to various image types and exploring different CNN architectures.
Co-Authors Abdul jalil Abdul Rahman Agus Salim Akbar Iskandar Akmal Hidayah A Aksa, Andi Nurul Andani Achmad Andani Achmad Andani Ahmad Andi Taufiqurrahman Akbar Andi Zulkifli Nusri Andryanto A Ansar Ansar Antonius Riman Tampang Arthanugraha, Wahyu As'ad, Miftah Fadhli ASNIMAR ASNIMAR, ASNIMAR Dhimas Tribuana Dra Najira Umar Erwin, Ibnu Taimiyah Fajar Husain Asy'ari Fakhirah, Andi Lulu Hakis, Andi Wahyunita Hasanuddin, Zulfajri B Hasruddin Hazriani Hazriani, Hazriani Hendra Hendra Hermawati Mappiwali Hilyatul Auliyah Erwin Holyness Nurdin Singadimedja Imam Akbar, Imam Imran Taufik Imran Taufiq Isman Ita Sarmita Samad, Ita Sarmita J Sadly, Adnan Jusman Jusman Kherani, Riska M. Risal Muh Al Hijr Asqalani Muhammad Erwin Rosyadi. S Munawirah, Munawirah Nasrullah Nasrullah Nur Iman Nurul Humaera Baharudin Rahmaniar Rahmaniar, Rahmaniar Resky, Andi Muhammad Rian Sultan Asizan Rijal Rijal Saidi, Muliyana Sitti Sahara Syamel Sumarlina Sumarlina Supriadi Sahibu Surdin, Iskandar Syafruddin Syarif Taufik, Imran Tengku Riza Zarzani N Umar, Dra Najira Wardhani, Nurilmiyanti Wardi Wardi Wardi, Wardi Weya, Sermi Yuyun, Yuyun Zahir Zainuddin Zahir Zainuddin Zanudin Husain Zuhriyah, Sitti