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All Journal KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sains dan Informatika Elsains : Jurnal Elektro Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Logic : Jurnal Rancang Bangun dan Teknologi Jurnal Pengabdian Kepada Masyarakat (Mediteg) Aisyah Journal of Informatics and Electrical Engineering Scientific Journal of Informatics Jurnal Teknimedia: Teknologi Informasi dan Multimedia Journal of Applied Computer Science and Technology (JACOST) Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Brilliance: Research of Artificial Intelligence Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) As-Sakinah : Jurnal Hukum Keluarga Islam Informatika Sains Teknologi
Veri Julianto
Politeknik Negeri Tanah Laut
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Mathematics Social Sciences Other

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Modified Particle Swarm Optimization on Feature Selection for Palm Leaf Disease Classification Julianto, Veri; Ahmad Rusadi Arrahimi; Oky Rahmanto; Mohammad Sofwat Aldi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.6049

Abstract

Palm oil plantations in Indonesia face challenges in enhancing productivity and profitability, notably due to pest attacks that reduce production. Early identification and classification of plant conditions, particularly palm oil leaves, are crucial for mitigating losses. This study explores the application of artificial intelligence, specifically computer vision and machine learning, for disease detection. Various machine learning techniques, including Local Binary Pattern (LBP), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), have been used in different studies with varying accuracy. This research focuses on modifying Particle Swarm Optimization (PSO) for feature selection in identifying diseases in palm oil leaves. The PSO modification combined with logistic regression and Bayesian Information Criterion (BIC) significantly enhances KNN performance. Accuracy improved from 95.75% to 97.85%, while precision, recall, and F1-score reached approximately 98.80%. Additionally, the modified KNN+PSO achieved the shortest computation time of 0.0872 seconds, indicating high computational efficiency. These results demonstrate that the PSO modification not only improves accuracy but also computational efficiency, making it an effective method for enhancing KNN performance in detecting palm oil leaf diseases.
Development of TOGA Edu-Tourism with Augmented Reality Technology Meldayanoor, Meldayanoor; Julianto, Veri; Darmawan, Muhammad Indra; Mustofa, Jupri; Adelia, Firda
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 14 No. 4 (2025): August 2025
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtepl.v14i4.1268-1281

Abstract

The development of Edu-tourism based on family medicinal plants (TOGA) in Tirta Jaya Village, supported by Augmented Reality (AR) technology, can enhance education, environmental conservation, and local economic opportunities. This Edu-tourism initiative offers an innovative and sustainable tourism model that positively impacts the tourism sector and the economy.  This study aims to explore and analyze the potential for developing TOGA-based Edu-tourism with AR technology support as an innovative educational tourism model, providing benefits to the community and the environment, while adding value to sustainable local products. This study employs a qualitative descriptive approach with three main stages: a literature review and field data collection to analyze the potential of TOGA and AR technology; an evaluation of TOGA potential based on tourism attraction feasibility, flagship product prioritization, and value-added analysis using the Hayami method; and the development of AR components for interactive education. The results indicate that the combination of TOGA and AR technology can increase public awareness of health, medicinal plant conservation, and local economic empowerment through interactive educational tourism. The educational tourism attraction TOGA Tirta Jaya Village has a high feasibility index, with an average score above 79%, making it worthy of development.   Keywords: Added value; Augmented reality; Edu-tourism; Family medicinal plants (TOGA); TOGA products.
Evaluating Random Forest Algorithm: Detection of Palm Oil Leaf Disease Rahmanto, Oky; Julianto, Veri; Arrahimi, Ahmad Rusadi
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.4798

Abstract

This research investigates the application of machine learning techniques for detecting diseases in oil palm leaves, utilizing a dataset of 1,119 images sourced from plantations in the Tanah Laut district. The dataset comprises 488 diseased and 631 healthy leaf samples, which were carefully cropped to isolate leaf areas and labeled with the assistance of domain experts. For feature extraction, both Lab and RGB color spaces were considered, alongside Haralick texture features, resulting in a total of eleven features per pixel. To reduce dimensionality and select relevant features, Principal Component Analysis (PCA) and Random Forest methods were applied. Support Vector Machine (SVM) was subsequently employed for the classification of leaf health status, and model performance was evaluated using accuracy, precision, recall, and F1 score metrics, all derived from a confusion matrix. The study finds that PCA and Random Forest significantly enhance model performance, improving the ability to distinguish between healthy and diseased leaves. These findings provide valuable insights for the development of automated disease detection systems in oil palm plantations, with potential applications in precision agriculture. Additionally, the results suggest pathways for further research into plant disease diagnostics, highlighting the role of advanced machine learning techniques in enhancing crop management and supporting sustainable agricultural practices.
Co-Authors Ade Kurniawan Ade Kurniawan Adelia, Firda Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Arif Suprianto Arif Supriyanto Dewi Saadah Dhika Sinta Pratama Dwi Ratna Sari, Dwi Ratna Eka Wahyu Sholeha Fathurahmani Fathurahmani Fathurahmani, Fathurahmani Heldalina Hendrik Setyo Utomo herpendi, herpendi Jaka Permadi Jamil, Khafifah Karima Zam Zam Khairul Anwar Hafizd, Khairul Anwar Lastriani Lastriani Lastriani Lastriani Meldayanoor Meliana Mohammad Sofwat Aldi Muhammad Indra Darmawan Muhammad Muslih Amirudin Muhammad Noor Muhammad Rusyadi Arrahimi Mustofa, Jupri Norfifah Noviyanti Nur Maida Oktaviyani Oktaviyani Oktaviyani, Oktaviyani Oky Rahmanto Pratama, Dhika Sinta Rabini Sayyidati Rahman, Ramdani Rahmanto, Oky Ramdani Rahman Ramitha Ramitha Ramitha, Ramitha Rhomadona, Herfia Saadah, Dewi Sabella, Billy Wan Yuliyanti Wan Yuliyanti Wijayati, Titik Winda Aprianti Yundari, Yundari Yunita Prastyaningsih