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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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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Rancang Bangun Sistem Informasi Manajemen Bank Sampah Studi Kasus Pada Bank Sampah Panggung Berseri (BSPB) Veri Julianto; Hendrik Setyo Utomo; Herpendi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 3 (2019): Desember 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1090.218 KB) | DOI: 10.29207/resti.v3i3.1133

Abstract

Waste is the result of the dynamics of life that can cause problems if it is not properly managed. Many methods had been used to help overcome waste management. The Waste Bank is one of the solutions to help solve waste management. Bank Sampah Panggung Berseri is one of the communities that actively carries out waste management around the Pangung village. BSPB has problems related to solid waste management. The management in question is the management of waste data that is still conventional, archiving is not optimal, has not managed customer data savings properly. In this research also added a marketplace feature where people can make transactions from balances obtained from waste sold by buying basic necessities. The method in this research is to collect data at BSPB, analyze data and develop applications using the prototype method. The results of this study are all the features or functions of the system run well by testing the functionality used the Black Box testing method. In testing using the usability testing method has shown the level of satisfaction for the parameters of usability, ease of learning, ease of use and satisfaction gives an average value of 4.38 from the range of values ​​1-5. This shows that the system made can be said to satisfy BSPB users and customers.
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.
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