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All Journal METIK JURNAL Jurnal Info Sains : Informatika dan Sains
Muslimin B
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering

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IMPLEMENTASI SISTEM PAKAR DALAM MENDIAGNOSIS PENYAKIT MATA MENGGUNAKAN METODE BACKWARD CHAINING DAN DEMSTER SHAFER Muslimin B; Jamal Jamal
METIK JURNAL Vol 1 No 2 (2017): METIK Jurnal
Publisher : LP3M Universitas Mulia

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Abstract

Mata merupakan indera penglihatan oleh manusia yang sangat sensitive terhadap lingkungan, terutama yang berkaitan virus, bakteri, debu,dll. Selama ini proses evaluasi kesehatan mata hanya dilakukan dokter dengan keterbatasan waktu tertentu. Dengan menerapkan sebuah sistem pakar penyakit mata dapat meningkatkan proses pelayanan kesehatan mata dengan secepat mungkin berdasarkan pengetahuan dokter spesialis terhadap gejala-gejala yang dialami. Mendiagnosis penyakit mata merupakan sebuah sistem yang dapat mengidentifikasi berdasarkan rule-rule dan gejala yang di derita oleh pasien. Metode forward chaning merupakan metode yang dapat melakukan proses penelusuran rule keterkaitan antar gejala yang dialami pasien, serta menerapkan metode ketidakpasitian yaitu metode demster shafer dapat mengevaluasi nilai preferensi pengetahuan setiap gejala dan penyakit mata pasien.
Random Forest Analysis In Classifying Orange Quality Data Suci Ramadhani; Muslimin B; Ida Maratul Khamidah
Jurnal Info Sains : Informatika dan Sains Vol. 14 No. 02 (2024): Informatika dan Sains , Edition, June 2024
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/infosains.v14i02.4420

Abstract

The quality of oranges is important to determine selling value. However, citrus quality assessments are often subjective and inconsistent, which can impact consumer satisfaction and market efficiency. In the agricultural industry, especially in citrus commodities, there are difficulties in classifying fruit quality accurately and efficiently, which has an impact on the assessment and determination of market prices. Given the importance of citrus quality in the agricultural and food industries, there is an urgent need for objective and efficient methods for classifying citrus quality. Inappropriate classification can cause economic losses for farmers and distributors, as well as reduce consumer satisfaction with product quality. As a solution, this research proposes the use of the Random Forest method to classify orange quality data. The method used in this research involved collecting orange quality data, including characteristics such as color, texture, and size. This data is then analyzed using the Random Forest algorithm. The Random Forest method is used to process orange quality data, by utilizing features such as color, size and skin texture. This model is trained using historical data to predict fruit quality. The research results show that the Random Forest method successfully classifies citrus quality data with high accuracy, demonstrating its potential as an effective tool for future citrus quality assessment by proving its effectiveness in supporting decisions in the agricultural sector.
Co-Authors Ida Maratul Khamidah Jamal Jamal Suci Ramadhani