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All Journal Rekursif: Jurnal Informatika Jurnal Pseudocode Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Eksplora Informatika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Abdi Insani Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat (ABDIRA) TRIBUTE: JOURNAL OF COMMUNITY SERVICES Indonesian Journal of Computer Science and Engineering
Julia Purnama Sari
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Nursing Public Health Social Sciences Other

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Journal : Rekursif: Jurnal Informatika

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Sistem Pendukung Keputusan Pemilihan Tanaman Hortikultura Berdasarkan Karakteristik Lahan Menggunakan Metode Moora (Studi Kasus: Kabupaten Kepahiang) Coastera, Funny Farady; Sari, Julia Purnama; Pasaribu, Bryan
Rekursif: Jurnal Informatika Vol 12 No 1 (2024): Volume 12 Nomor 1 Maret 2024
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v12i1.32860

Abstract

Indonesia is an agricultural country where almost all of its population works in the agricultural sector. Horticulture is one of the subsectors in agriculture that studies the cultivation of crops such as vegetables, fruits, medicinal plants and ornamental plants. Areas in Bengkulu Province, especially Kepahiang Regency, have people who are mostly farmers. The suitability of land with crops to be planted has an important role in increasing crop productivity. Of course, this method will not get maximum results. This encourages researchers to build a system that can provide decision assistance for farmers and communities in determining suitable crops for planting on a land in the Kepahiang Regency area. This decision support system was created using the MOORA method which consists of 17 land criteria and 35 horticultural crops. This system produces output in the form of ranking horticultural crops that are suitable for planting. The functionality testing process of this expert system went well using black box testing and resulted in 100% functional success. Evaluation of the accuracy of the MOORA method for ranking horticultural plants based on land characteristics resulted in an accuracy rate of 75%. Keywords: Horticulture, Decision Support System, MOORA Method, Land Characteristics.
Implementasi Metode Multi Objective Optimization on The Basis of Ratio Analysis (MOORA) Pada Sistem Pendukung Keputusan Prioritas Penanganan Genangan Banjir Salsabila, Elvina; Utama, Ferzha Putra; Sari, Julia Purnama
Rekursif: Jurnal Informatika Vol 12 No 2 (2024): Volume 12 Nomor 2 November 2024
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v12i2.39532

Abstract

Banjir atau genangan merupakan peristiwa dimana air melimpah atau menggenangi daratan atau lahan yang semestinya kering yang menyebabkan kerugian ekonomi bagi penduduk. Salah satu tujuan penelitian ini yaitu mengetahui hasil dari implementasi metode MOORA pada sistem pendukung keputusan penentuan prioritas penanganan genangan banjir berbasis web. Kriteria yang digunakan pada penelitian ini meliputi kedalaman genangan, luas genangan, lama genangan, dan frekuensi genangan kriteria kerugian ekonomi, kriteria gangguan sosial dan fasilitas pemerintah, kriteria kerugian dan gangguan transportasi, kriteria kerugian pada daerah perumahan, serta kriteria kerugian hak milik dan pribadi. Metode MOORA adalah salah satu metode yang diperkenalkan oleh Brauers dan Zavadskas pada tahun 2006. Berdasarkan penelitian yang telah dilakukan disimpulkan bahwa Kelurahan Rawa Makmur mendapatkan rangking 1 dengan nilai Yi 0.235556 dan rangking 30 yaitu Kelurahan Betungan dengan nilai Yi 0.13033. Hasil pengujian keakuratan sistem yang mendapatkan akurasi sebesar 100% dan pengujian black box dengan akurasi 100%. Kata Kunci: Banjir, Sistem Pendukung Keputusan, Metode MOORA.
Implementasi YOLOv11 Untuk Deteksi Penyakit Tanaman Padi Berdasarkan Citra Daun Alifyandra Akbar, Farrel; Sari, Julia Purnama; Oktoeberza, Widhia KZ
Rekursif: Jurnal Informatika Vol 13 No 2 (2025): Volume 13 Nomor 2 November 2025
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v13i2.43876

Abstract

Rice (Oryza sativa) is a strategic commodity for food security in Indonesia, yet it is highly vulnerable to diseases such as bacterial leaf blight (blight), blast, and tungro, which can significantly reduce productivity. Early detection of these diseases through manual observation by farmers is often inaccurate and slow. This study aims to implement the YOLOv11 algorithm, a deep learning-based approach, to detect rice plant diseases from leaf images with high accuracy. The research method follows the CRISP-DM (Cross Industry Standard Process for Data Mining) framework, encompassing business understanding, data collection, data preparation, modeling, and evaluation. The dataset consists of 500 rice leaf images classified into three disease categories. The data was processed through augmentation and resizing to balance class distribution and standardize image dimensions. The YOLOv11 model was trained with parameters set at 100 epochs, an image size of 224x224 pixels, and a batch size of 32. Evaluation results demonstrate that the model achieved 95% accuracy, with average precision and recall exceeding 95%. The confusion matrix revealed excellent classification performance, particularly for tungro disease (100% accuracy). The model also proved efficient in prediction, with an inference time of 8.2 milliseconds per image. In conclusion, this research confirms the effectiveness of YOLOv11 for rice disease detection based on leaf images. Recommendations for future development include expanding dataset diversity, integrating the model into mobile applications, and conducting field tests to validate real-world performance. Keywords: YOLOv11, rice disease detection, deep learning, leaf image, computer vision.
Co-Authors Aan Erlansari Adhadi Kurniawan Afdhal Kurniawan Mainil Afriani, Annisa Afriwilda, Mayang T. Alfareza, Muhammad Yusril Alifyandra Akbar, Farrel Andesta, Gena Ayub Sugara Baladewa, Aulia Salsabyla Belladona, Meilani Desi Andreswari Didi Suryadi Doloksaribu, William Tiodo Fitria, Laily Fuji Astuti Funny Farady Coastera, Funny Farady Haikal Fikri, Muhammad Hanisa, Eiza Nur Huda, Widya Nurul Jufrise, Siska Khairul Amri Rosa, Muhammad Lubis, Bustanuddin Lubis, Bustanudin Makhrian, Andi Makhrian, Andy Malay, Aliyah Salsabila Melia Eka Daryati Monica, Exca Wella Novrian, Willi Oktoeberza, Widhia KZ Pasaribu, Bryan Putra, Yusran Panca Putri, Lusiya Melda Putri, Tiara Eka Rahman, Muhammad Farchan Al Romiza, Naziha Salsabila, Elvina Santoso, Ahmad Yassar Mahdy septa, nickyastria Setiawan, Devid Frianda Simanjutak, Saud Martua Sri Wahyuni, Ade Surapati, Alex Tama, Syella Deniecha Ade Utama, Ferzha Putra Vasiguhamiaz, Azvadennys