IMPLEMENTASI METODE FORWARD CHAINING PADA SISTEM PAKAR UNTUK MENDIAGNOSA PENYAKIT TANAMAN SELADA
DOI:
https://doi.org/10.31000/jika.v9i4.14585Abstrak
Penelitian ini bertujuan mengembangkan sistem pakar untuk mendiagnosis penyakit pada tanaman selada menggunakan metode forward chaining, yang diimplementasikan berbasis web. Penelitian dilakukan di Wara Hidroponik, Kota Palopo, di mana petani menghadapi kendala dalam mengidentifikasi penyakit tanaman secara akurat akibat ketergantungan pada metode manual dan keterbatasan keahlian. Sistem dirancang untuk memberikan diagnosis cepat dan tepat dengan mencocokkan gejala aturan yang telah ditetapkan, serta menyediakan solusi penanganan guna mengurangi kerugian hasil panen. Metode Research and Development (R&D) diterapkan dengan tahapan model Waterfall: analisis sistem, desain, implementasi, dan pengujian. Pengujian black box mengonfirmasi fungsionalitas, kemudahan penggunaan, dan akurasi sistem, dengan hasil menunjukkan kelayakan tinggi (skor rata-rata 3,98 dari ahli) dan kepuasan pengguna (skor rata-rata 4). Studi ini menyimpulkan bahwa sistem efektif mendukung petani dalam diagnosis penyakit, meningkatkan produktivitas dan keberlanjutan pertanian.Referensi
Amna, A., Asry, L., Asri, R., Dewi, R., & Mahendra, T. (2024). Pengembangan Sistem Informasi Penjualan Hasil Pertanian Berbasis Web. Jurnal Teknik Informatika dan Terapan, 2(02), 26–33.
Appi, W. T. (2024). Pertumbuhan Dan Produksi Selada (Lactuca Sativa L.) Pada Berbagai Jenis Media Tanam Dan Air Kelapa Fermentasi Pada Hidroponik Sistem Wick. Skripsi Universitas Hasanuddin Makassar, 15(1), 37–48.
Ayuningtyas, D. P., & Rositawati, F. (2025). Pemanfaatan AI dalam Smart Farming untuk Mencapai SDGs 2 (Zero Hunger) di Indonesia. ANTASENA: Governance and Innovation Journal, 3(1), 176–190. https://doi.org/10.61332/antasena.v3i1.325
Berutu, L. H., Tantawi, A. R., & Wardani, D. K. (2023). Analisis Perbandingan Perkembangan Penyakit Bercah Daun (Cercospora capsici) pada Tanaman Cabai Merah (Capsicum annuum L) di Dataran Tinggi dan Dataran Rendah selama Musim Hujan Studi Kasus di Kabupaten Karo dan Deli Serdang. Paspalum: Jurnal Ilmiah Pertanian, 11(2), 261. https://doi.org/10.35138/paspalum.v11i2.597
Dani. (2022). Respon Pertumbuhan Tanaman Selada Merah (Lactuca sativa var. lollo rosa) Dengan Pemberian Urin Sapi Dan Urin Kelinci Yang Terfermentasi. Skripsi Universitas Islam Negeri Raden Intan Lampung, 5(1)(2527–845), 132–139.
Faisal, F., Opitasari, O., & Mufti, A. (2024). Sistem Pakar Pendiagnosa Penyakit Mata Dengan Metode Forward Chaining. Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi), 8(01), 132–137. https://doi.org/10.30998/semnasristek.v8i01.7146
Indonesia, B.-S. (2022). Produksi Tanaman Sayuran Menurut Provinsi dan Jenis Tanaman. https://www.bps.go.id/id/statistics-table/3/ZUhFd1JtZzJWVVpqWTJsV05XTllhVmhRSzFoNFFUMDkjMw==/produksi-tanaman-sayuran-menurut-provinsi-dan-jenis-tanaman--2022.html?year=2022
Irvanka, D. S. (2023). Keragaan dan Kelayakan Usahatani Selada Hijau Dengan Sistem Hidroponik. Skripsi Universitas Siliwangi, 6.
Javandira, C., Pratiwi, N. P. E., Ramdhoani, Widyastuti, L. P. Y., & Yuniti, I. G.A.D. (2023). Pengenalan Penyakit Busuk Batang pada Tanaman Jeruk di Desa Awan Kecamatan Kintamani. Nusantara Community Empowerment Review, 1(2), 61–67. https://doi.org/10.55732/ncer.v1i2.957
Jumiono, A., Judianto, L., Apriyanto, Suryanto, A., Nuriadi, Fanani, M. Z., & Rusliyadi, M. (2024). Pengantar Ilmu Pertanian. PT. Shonpedia Publishing Indonesia.
Marsoni, A., Pramudita, A. M., Muzakki, F., & Robo, E. M. (2024). Literatur Review : Pendekatan Random Forest Untuk Klasifikasi Penyakit Busuk Akar Pada Tanaman. Buletin Ilmiah Ilmu Komputer dan Multimedia Volume, 2(3), 560–567.
Maulina, D., & Wulanningsih, A. M. (2020). Metode Certainty Factor Dalam Penerapan Sistem Pakar Diagnosa Penyakit Anak. Journal of Information System Management (JOISM), 2(1), 23–32. https://doi.org/10.24076/joism.2020v2i1.171
Naslia, & Lakani, I. (2024). Efektivitas Bakteri Bacillus Sp. Terhadap Pectobacterium Carotovorum Penyebab Penyakit Busuk Lunak Pada Tanaman Sawi ( Brassica Juncea L). Tadulako University, 12(5), 1328–1337.
Palopo, B. P. S. (2020). Pengaruh Sumber Daya Alam, Jumlah Tenaga Kerja Dan Jumlah Penduduk Terhadap Kemandirian Keuangan Daerah. In Galang Tanjung (Nomor 2504).
Pugu, M. R., Riyanto, S., & Haryadi, R. N. (2024). Metodologi Pertanian. PT. Shonpedia Publishing Indonesia.
Qoyim, Z. (2024). Pengaruh Berbagai Kombinasi Media Tanam Dan Dosis Pupuk Npk Terhadap Pertumbuhan dan Hasil Tanaman Selada (Lactuca Sativa L). Skripsi Universitas Islam Balitar, 1–23.
Ramadhani, A. P., & Fadillah, R. (2024). Teknik Pengendalian Hama Dan Penyakit Tanaman Vanili (Vanilia Planifola Andrews) Di Kebun Dinas UPTD Provinsi Nusa Tenggara TimurSintalydiawati, A., Fitriyanti, D., & Liestiany, E. (2024). Uji Efektivitas Daun Sirih Dalam Menghambat Pertumbuhan Layu Bakte. Seminar Nasional Kontribusi …, 58–65. https://prosiding.flmunhanri.org/index.php/senaskonsi/article/view/47%0Ahttps://prosiding.flmunhanri.org/index.php/senaskonsi/article/download/47/23
Saputra, G. E., Simbolon, R. F., Hanindia, M., & Swari, P. (2024). Sistem Pakar Untuk Mendiagnosis Penyakit Infeksi Saluran Pernapasan Akut (ISPA) Menggunakan Algoritma Dempster Shafer. Seminar Nasional Informatika Bela Negara (SANTIKA) ISSN, 4, 6–13.
Sari, J. A., & Diana, B. A. (2024). Dampak Transformasi Digitalisasi terhadap Perubahan Perilaku Masyarakat Pedesaan. Jurnal Pemerintahan dan Politik, 9(2), 88–96. https://doi.org/10.36982/jpg.v9i2.3896
Septiani, E. R., Rozaki, Z., Wulandari, R., & Suryani, C. A. (2022). Transformasi Digital di Pertanian dengan Peran Proaktif Generasi Muda. Seminar Nasional Agrbisnis, 103–108.
Sintalydiawati, A., Fitriyanti, D., & Liestiany, E. (2024). Uji Efektivitas Daun Sirih Dalam Menghambat Pertumbuhan Layu Bakteri Ralstonia solanacearum Pada Tanaman Terung. Jurnal Proteksi Tanaman Tropika, 7(1), 770–779. https://doi.org/10.20527/jptt.v7i1.2399
Ulfa, H., Maulana, H., Fimawahib, L., & Erwis, F. (2024). Akurasi Citra Image Penyakit Daun Kentang berdasarkan Citra Sehat, Citra Early Blight, dan Citra Late Blight Menggunakan Convolutional Neural Network ( CNN ). Riau Journal of Computer Science, 10(2), 167–174
Unduhan
File Tambahan
Diterbitkan
Terbitan
Bagian
Lisensi
License and Copyright Agreement
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- That it is not under consideration for publication elsewhere,
- That its publication has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Copyright
Authors who publish with International Journal of Advances in Intelligent Informatics agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.Â
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
Licensing for Data Publication
International Journal of Advances in Intelligent Informatics use a variety of waivers and licenses, that are specifically designed for and appropriate for the treatment of data:
Open Data Commons Attribution License, http://www.opendatacommons.org/licenses/by/1.0/ (default)
Creative Commons CC-Zero Waiver, http://creativecommons.org/publicdomain/zero/1.0/
Open Data Commons Public Domain Dedication and Licence, http://www.opendatacommons.org/licenses/pddl/1-0/
Other data publishing licenses may be allowed as exceptions (subject to approval by the editor on a case-by-case basis) and should be justified with a written statement from the author, which will be published with the article.
Open Data and Software Publishing and Sharing
The journal strives to maximize the replicability of the research published in it. Authors are thus required to share all data, code or protocols underlying the research reported in their articles. Exceptions are permitted but have to be justified in a written public statement accompanying the article.
Datasets and software should be deposited and permanently archived inappropriate, trusted, general, or domain-specific repositories (please consult http://service.re3data.org and/or software repositories such as GitHub, GitLab, Bioinformatics.org, or equivalent). The associated persistent identifiers (e.g. DOI, or others) of the dataset(s) must be included in the data or software resources section of the article. Reference(s) to datasets and software should also be included in the reference list of the article with DOIs (where available). Where no domain-specific data repository exists, authors should deposit their datasets in a general repository such as ZENODO, Dryad, Dataverse, or others.
Small data may also be published as data files or packages supplementary to a research article, however, the authors should prefer in all cases a deposition in data repositories.