Aplikasi Case Based Reasoning Untuk Identifikasi Serangan Hama Pada Tanaman Jeruk

Silmina, Esi Putri; Wardoyo, Retantyo

doi:10.14710/transmisi.20.3.96-104

Cited by 6 publications

(5 citation statements)

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“…Penelitian sistem pakar diagnosa penyakit zika dengan menerapkan metode Case Base Reasoning, dimana metode ini dapat digunakan hanya sebagai mencari nilai kemiripan untuk mencari nilai kepastian bahwa pasien tersebut terdiagnosa atau tidak maka digunakan metode Certainly Factor [9]. Penggunaan metode Case Based Reasoning dan Certainly Factor maka perhitungan nilai CF mendiagnosa penyakit zika memiliki persentase tingkat keyakinan 97%, pasien tersebut dinyatakan pasti terkena penyakit zika [10].…”

Section: Pendahuluanunclassified

Sistem Pakar Metode Case Based Reasoning untuk Mengidentifikasi Penyakit Psoriasis

Syahputra

2021

JSisfotek

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Proriasis is a type of chronic disease of the human skin.problem of psoriasis At the end of the day, theis becoming more interesting because the main cause of this disease has not been found, which has only been found while the cause of psoriasis is genetics. Because the cause is not known for sure, this disease is difficult to cure. Although this disease is not contagious and life-threatening to sufferers, it can damage internal organs if not handled properly. This study aims to determine the level of accuracy in identifying psoriasis in humans. There are several types of symptoms that refer to psoriasis. Furthermore, the data is processed manually with themethod Case Based Reasoning and continued by using a-based expert system software website. The processing stage is to use theprocess, which retrieve is a process of finding the similarities between new cases and existing cases in the knowledge base. The results of the data processing are continued with the calculation of the level of accuracy. The result of testing this method is that there are 100% of the 12 test data. Based on the accuracy of the identification results of this system, this study is very precise in the level of identifying the level of accuracy of psoriasis in humans. Expert testing system has been able to identify thedisease psoriasis specific. Through thismethod Case Based Reasoning , the level of accuracy that can be obtained is quite accurate and can help skin and genital specialists in improving accuracy in identifyingdiseases Case Based Reasoning in humans.

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Section: Pendahuluanunclassified

Sistem Pakar Metode Case Based Reasoning untuk Mengidentifikasi Penyakit Psoriasis

Syahputra

2021

JSisfotek

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“…Untuk menghitung nilai similaritas maka digunakan pendekatan Euclidean Distance. Pendekatan ini dipilih karena cocok dengan karakteristik atribut yang digunakan pada basis kasus serta berdasarkan beberapa penelitian terdahulu mampu memberikan akurasi yang sangat baik, yakni 99,1% pada penelitian yang berjudul Aplikasi Case Based Reasoning untuk Identifikasi Serangan Hama pada Tanaman Jeruk (Silmina and Wardoyo, 2018) dan sebesar 94,83% pada penelitian berjudul Case Based Reasoning untuk Diagnosis Penyakit Jantung (Wahyudi and Hartati, 2017).…”

Section: Pendahuluanunclassified

Case based Reasoning Prediksi Waktu Studi Mahasiswa Menggunakan Metode Euclidean Distance dan Normalisasi Min-Max

Wahanani

Swari

Akbar

2020

JTIIK

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Salah satu penyebab dari lamanya waktu tempuh mahsiswa di Jurusan Informatika UPN “Veteran” Jawa Timur adalah sullitnya memantau kemajuan studi mahasiswa secara seksama, mengingat jumlah mahasiswa yang cukup banyak serta pihak akademik belum memiliki metode yang akurat untuk memetakan mahasiswa yang diprediksi akan mengalami keterlambatan dalam penyelesaian studinya. Melalui perkembangan teknologi informasi yang berkembang pesat saat ini, maka sangat dimungkinkan untuk membuat sebuah sistem yang mampu memprediksi kemungkinan keterlambatan kelulusan mahasiswa melalui penggunaan berbagai metode komputasi yang ada. Salah satu pendekatan yang dapat digunakan untuk membuat sebuah sistem prediksi kelulusan adalah menggunakan pendekatan populer yang digunakan dalam pembuatan sistem cerdas (intelligent system) yaitu case based reasoning (CBR). Langkah-langkah yang dilakukan pada penelitian ini adalah melakukan pengumpulan dan memasukkan data kasus pada basis kasus, melakukan praprosesing yakni normalisasi atribut yang akan digunakan dalam perhitungan similartitas antar kasus menggunakan normalisasi min-max, implementasi CBR menggunakan metode Euclidean Distance, serta melakukan pengujian pada 141 data kasus. Dari sisi perhitungan akurasi, sistem mampu memberikan nilai akurasi paling tinggi sebesar 100% pada pada pengujian berdasarkan predikat kelulusan, sedangkan berdasarkan ketepatan waktu, sistem mampu memberikan akurasi tertinggi dengan nilai 85,71%, dan sistem mampu memberikan nilai akurasi tertinggi sebesar 71,43% pada pengujian berdasarkan massa studi. Untuk pengujian presisi, sistem mampu mengasilkan nilai terbesar berturut-turut sebesar 90,90%, 43,33%, dan 100%. Sedangkan pada pengujian sensitivitas, sistem berturut-turut mampu menghasilan nilai sebesar 90,90%, 40,48%, dan 100%. Hasil pengujian ini tentunya sangat bergantung dari basis kasus yang dimiliki, oleh sebab itu perbaikan dan peningkatan jumlah kasus yang dimiliki diharapkan mampu meningkatkan performa sistem rekomendasi. AbstractOne of the reasons for the length of study time for students of the Informatics study program of UPN "Veteran" Jawa Timur is the difficulty of monitoring the progressy, given the large number of students and academics do not have an accurate method to map students who are predicted to experience delays. It is possible to create a system that is able to predict the possibility of student graduation delay through the use of various existing computational methods. One approach that can be used to create a graduation prediction system is to use the popular approach namely case based reasoning (CB). The steps taken in this study are collecting and entering case data, normalizing the attributes using min-max normalization, implementing CBR using the Euclidean Distance, and system testing in 141 data case. System is able to provide the highest accuracy value of 100% in testing based on the predicate of graduation, while based on timeliness, the system is able to provide the highest accuracy value with a value of 85.71%, and the system is able to provide the highest accuracy value of 71.43%. on testing based on the study period. For precision testing, the system was able to produce the largest values of 90.90%, 43.33% and 100%, respectively. Whereas in the sensitivity test, the system was able to produce values of 90.90%, 40.48%, and 100% respectively. The results of this test are of course very dependent on the basis of cases that are owned, therefore improvements and an increase in the number of cases owned are expected to be able to improve the performance.

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“…Case Based Reasoning merupakan metode yang dalam pendekatannya berbeda dengan pendekatan utama pad AI (Artificial Intelegence) [15] Masalah akan baru bisa dikatakan mirip 100 % dengan kasus lama apabila nilai similaritas dari e (p,q) sama dengan 1 sedangkan tidak similaritas jika nilai e (p,q) sama dengan 0. Angka similaritas antara 0 sampai dengan 1 [11]. Dalam penelitian untuk mendiagnosa penyakit pada kucing yang mengkombinasikan CBR dengan metode Certainty factor didapatkan kesimpulan bahwasanya hasil akan bisa mencapai 90%.…”

Section: Pendahuluanunclassified

Identifikasi Karakteristik Anak Berkebutuhan Khusus Menggunakan Metode Case Based Reasoning (Studi Kasus di Sekolah Luar Biasa Negeri 1 Linggo Sari Baganti)

2020

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Children with special needs are children who have different characteristics and limitations in ability. This child with special needs is called Tunagrahita. Developmental impairment is classified into three categories namely mild, moderate and severe. This study aims to help the process of identifying the characteristics of mental retardation experienced by children. This study uses the Case Based Reasoning (CBR) method to identify children with special needs using the data of the mentally disabled children in SLBN 1 Linggo Sari Baganti. Similarity results were 51.92% for moderate developmental impairment, 17.5% for mild developmental impairment and 8% for severe developmental impairment. Calculations are performed using Visual Basic Net 2010.

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Aplikasi Case Based Reasoning Untuk Identifikasi Serangan Hama Pada Tanaman Jeruk

Cited by 6 publications

References 0 publications

Sistem Pakar Metode Case Based Reasoning untuk Mengidentifikasi Penyakit Psoriasis

Sistem Pakar Metode Case Based Reasoning untuk Mengidentifikasi Penyakit Psoriasis

Case based Reasoning Prediksi Waktu Studi Mahasiswa Menggunakan Metode Euclidean Distance dan Normalisasi Min-Max

Identifikasi Karakteristik Anak Berkebutuhan Khusus Menggunakan Metode Case Based Reasoning (Studi Kasus di Sekolah Luar Biasa Negeri 1 Linggo Sari Baganti)

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