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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Transmisi: Jurnal Ilmiah Teknik Elektro Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Jurnal Teknologi dan Manajemen Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Majalah Ilmiah MOMENTUM Jurnal Informatika Upgris Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of New Media Technology ILKOM Jurnal Ilmiah Jurnal Teknologi Sistem Informasi dan Aplikasi Systemic: Information System and Informatics Journal Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Building of Informatics, Technology and Science Infotekmesin Jurnal Teknologi Dan Sistem Informasi Bisnis Journal of Robotics and Control (JRC) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Teknologi Informasi Cyberku Moneter : Jurnal Keuangan dan Perbankan
Ricardus Anggi Pramunendar
Faculty Of Computer Science, Universitas Dian Nuswantoro
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Journal : International Journal of New Media Technology

 1 
Evaluating the Impact of Particle Swarm Optimization Based Feature Selection on Support Vector Machine Performance in Coral Reef Health Classification Bastiaans, Jessica Carmelita; Hartojo, James; Pramunendar, Ricardus Anggi; Andono, Pulung Nurtantio
IJNMT (International Journal of New Media Technology) Vol 11 No 2 (2024): Vol 11 No 2 (2024): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v11i2.3761

Abstract

This research explores improving coral reef image classification accuracy by combining Histogram of Oriented Gradients (HOG) feature extraction, image classification with Support Vector Machine (SVM), and feature selection with Particle Swarm Optimization (PSO). Given the ecological importance of coral reefs and the threats they face, accurate classification of coral reef health is essential for conservation efforts. This study used healthy, whitish, and dead coral reef datasets divided into training, validation, and test data. The proposed approach successfully improved the classification accuracy significantly, reaching 85.44% with the SVM model optimized by PSO, compared to 79.11% in the original SVM model. PSO not only improves accuracy but also reduces running time, demonstrating its effectiveness and computational efficiency. The results of this study highlight the potential of PSO in optimizing machine learning models, especially in complex image classification tasks. While the results obtained are promising, the study acknowledges several limitations, including the need for further validation with larger and more diverse datasets to ensure model robustness and generalizability. This research contributes to the field of marine ecology by providing a more accurate and efficient coral reef classification method, which can be applied to other image classifications.
Enhancing Support Vector Machine Classification of Nutrient Deficiency in Rice Plants Through Particle Swarm Optimization-Based Feature Selection Hartojo, James; Bastiaans, Jessica Carmelita; Pramunendar, Ricardus Anggi; Andono, Pulung Nurtantio
IJNMT (International Journal of New Media Technology) Vol 11 No 2 (2024): Vol 11 No 2 (2024): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v11i2.3762

Abstract

The research focuses on the classification of nutrient deficiencies in rice plant leaves using a combination of Support Vector Machine (SVM) and Particle Swarm Optimization (PSO) methods for feature selection. Image features are extracted using Histogram of Oriented Gradients (HOG), which is then optimized with PSO to select the most relevant features in the classification process. Indonesia is one of the largest rice producers in the world, with food security as a major issue that requires sustainable solutions, especially in the agricultural sector. The growth and yield of rice plants are highly dependent on the availability of nutrients such as Nitrogen (N), Phosphorus (P), and Potassium (K). However, traditional observation methods to detect nutrient deficiencies in plants become inefficient as the scale of production increases. The dataset used includes images of rice leaves showing nitrogen (N), phosphorus (P), and potassium (K) deficiencies. Experiments show that the SVM model optimized with PSO provides a classification accuracy of 83.19% and a runtime of 129.63 seconds with 1150 best feature combinations out of 2303 extracted features, which is higher accuracy and faster runtime than the model that does not use PSO. These results show that the integration of PSO in the feature selection process not only improves the accuracy of the model, but also reduces the required computation time. This research makes an important contribution to the development of an automated system for the classification of nutrient deficiencies in crops, which can be implemented in large farms or other agricultural fields.
Evaluating the Impact of Particle Swarm Optimization Based Feature Selection on Support Vector Machine Performance in Coral Reef Health Classification Bastiaans, Jessica Carmelita; Hartojo, James; Pramunendar, Ricardus Anggi; Andono, Pulung Nurtantio
IJNMT (International Journal of New Media Technology) Vol 11 No 2 (2024): Vol 11 No 2 (2024): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v11i2.3761

Abstract

This research explores improving coral reef image classification accuracy by combining Histogram of Oriented Gradients (HOG) feature extraction, image classification with Support Vector Machine (SVM), and feature selection with Particle Swarm Optimization (PSO). Given the ecological importance of coral reefs and the threats they face, accurate classification of coral reef health is essential for conservation efforts. This study used healthy, whitish, and dead coral reef datasets divided into training, validation, and test data. The proposed approach successfully improved the classification accuracy significantly, reaching 85.44% with the SVM model optimized by PSO, compared to 79.11% in the original SVM model. PSO not only improves accuracy but also reduces running time, demonstrating its effectiveness and computational efficiency. The results of this study highlight the potential of PSO in optimizing machine learning models, especially in complex image classification tasks. While the results obtained are promising, the study acknowledges several limitations, including the need for further validation with larger and more diverse datasets to ensure model robustness and generalizability. This research contributes to the field of marine ecology by providing a more accurate and efficient coral reef classification method, which can be applied to other image classifications.
Enhancing Support Vector Machine Classification of Nutrient Deficiency in Rice Plants Through Particle Swarm Optimization-Based Feature Selection Hartojo, James; Bastiaans, Jessica Carmelita; Pramunendar, Ricardus Anggi; Andono, Pulung Nurtantio
IJNMT (International Journal of New Media Technology) Vol 11 No 2 (2024): Vol 11 No 2 (2024): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v11i2.3762

Abstract

The research focuses on the classification of nutrient deficiencies in rice plant leaves using a combination of Support Vector Machine (SVM) and Particle Swarm Optimization (PSO) methods for feature selection. Image features are extracted using Histogram of Oriented Gradients (HOG), which is then optimized with PSO to select the most relevant features in the classification process. Indonesia is one of the largest rice producers in the world, with food security as a major issue that requires sustainable solutions, especially in the agricultural sector. The growth and yield of rice plants are highly dependent on the availability of nutrients such as Nitrogen (N), Phosphorus (P), and Potassium (K). However, traditional observation methods to detect nutrient deficiencies in plants become inefficient as the scale of production increases. The dataset used includes images of rice leaves showing nitrogen (N), phosphorus (P), and potassium (K) deficiencies. Experiments show that the SVM model optimized with PSO provides a classification accuracy of 83.19% and a runtime of 129.63 seconds with 1150 best feature combinations out of 2303 extracted features, which is higher accuracy and faster runtime than the model that does not use PSO. These results show that the integration of PSO in the feature selection process not only improves the accuracy of the model, but also reduces the required computation time. This research makes an important contribution to the development of an automated system for the classification of nutrient deficiencies in crops, which can be implemented in large farms or other agricultural fields.
Co-Authors Abdul Syukur Abu Salam Ade Yusupa Affandy Affandy Agus Winarno, Agus Agustina, Feri Ahmad Akrom Ahmad Akrom Akrom, Ahmad Al-Azies, Harun ALI MUQODDAS Alvin, Fris Alzami, Farrikh Andi Kamaruddin Apriyanto Alhamad Arie Nugroho, Arie Arifin, Zaenal Arya Rezagama Sudrajat Azzahra, Tarissa Aura Baroroh, Nurul Bastiaans, Jessica Carmelita Catur Supriyanto Catur Supriyanto Catur Supriyanto Catur Supriyanto D, Ishak Bintang Darmawan, Aditya Aqil De Rosal Ignatius Moses Setiadi Dewi Nurdiyah Diana Aqmala Dibyo Adi Wibowo Dwi Puji Prabowo Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Dzuha Hening Yanuarsari, Dzuha Hening Edi Noersasongko Enrico Irawan Erlin Dolphina Etika Kartikadarma Evanita Evanita, Evanita F. Alzami Fafaza, Safira Alya Fajrian Nur Adnan Fakhrurrozi Fakhrurrozi, Fakhrurrozi Farikh Al Zami Fathorazi Nur Fajri Fatkhuroji Fatkhuroji Fauzi Adi Rafrastara Fikri Diva Sambasri Fikri Diva Sambasri Firmansyah, Muhammad Ilham Go, Agnestia Agustine Djoenaidi Guruh Fajar Shidik Hamid, Maulana As’an Hartojo, James Harun Al Azies Hasan Asari Haydar, Muhammad Rifqi Fajrul Hendri Ramdan Henry Bastian, Henry I Ketut Eddy Purnama Ifan Rizqa Ika Novita Dewi Imran, Bahtiar Irham Ferdiansyah Katili Iswahyudi Iswahyudi Karim, Muh Nasirudin Karis W. Kartika, Gita khoiriya latifah Khoirunnisa, Emila Khoirur Rizky, Muhammad Ivan Kristhina Evandari Kurnia Prayoga Wicaksono Kurniawan Aji Saputra Kurniawan, Defri Kusumawati, Yupie Lalang Erawan Lesmarna, Salsabila Putri M. Arif Soeleman M. Arif Soleman Maulana, Isa Iant Megantara, Rama Aria Mira Nabila Moch Arief Soeleman Moch. Sjamsul Hidajat Mochamad Arief Soeleman Mochamad Hariadi Moh Yusuf, Moh Moh. Arief Soeleman Moh. Yusuf Mohammad Arif Mohammad Syaifur Rohman Muhammad Naufal, Muhammad Muljono, - Muslih Muslih Muslih Muslih Noor Wahyudi Nuanza Purinsyira Nugroho, Muhammad Bayu Nur Azise Nurhindarto, Aris Nurhindarto, Aris Pergiwati, Dewi Prabowo, D.P. Pulung Nurtantio Andono Pulung Nurtantyo Andono Puri Sulistiyawati Puri Sulistiyawati Puri Sulistiyawati Purwanto Purwanto Purwanto Purwanto Purwanto Purwanto Putu Samuel Prihatmajaya R.A. Megantara Rama Aria Megantara Rama Aria Megantara Ramadhan Rakhmat Sani Ramadhani, Irfan Wahyu Ratmana, Danny Oka Riadi, Muhammad Fatah Abiyyu Rifqi Mulya Kiswanto Ritzkal, Ritzkal Rohman, Muhammad Syaifur Rony Wijanarko Rozada, Akfi Ruri Suko Basuki Santoso, Siane Saputra, Filmada Ocky Saputra, Resha Mahardhika Saraswati, Galuh Wilujeng Sasono Wibowo Sinaga, Daurat Soeleman, M. Arief Sri Winarno Stefanus Santosa Sulistyowati, Tinuk Sutini Dharma Oetomo Tamamy, Aries Jehan Teguh Tamrin Ullumudin, D.I.I Usman Sudibyo Vincent Suhartono Vincent Suhartono Vincent Suhartono Wibowo, Gentur Wahyu Nyipto Wildanil Ghozi Winarsih, Nurul Anisa Sri Yudha Tirto Pramonoaji Yuliman Purwanto Yuslena Sari, Yuslena Yuventius Tyas Catur Pramudi Zainal Arifin Hasibuan