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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Jurnal Eksplora Informatika KOMPUTIKA - Jurnal Sistem Komputer Jurnal Linguistik Komputasional Jurnal Teknik Informatika (JUTIF) Jurnal Amplifier: Jurnal Ilmiah Bidang Teknik Elektro dan Komputer
Faurina, Ruvita
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Journal : Jurnal Teknik Informatika (JUTIF)

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OPTIMIZATION OF DISEASE PREDICTION ACCURACY THROUGH ARTIFICIAL NEURAL NETWORK (ANN) ALGORITHMS IN DIAGNESE APPLICATION Faurina, Ruvita; Gazali, M. Jumli; Herani, Icha Dwi Aprilia
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1182

Abstract

This research aims to enhance the accuracy and speed of diagnoses in the Diagnese application by implementing the ANN algorithm for disease prediction. The dataset used for experimentation was featuring binary data types, containing 131 symptoms used to predict 41 types of diseases. The Diagnese application assists patients in identifying diseases and finding suitable specialist doctors based on reported symptoms. To achieve this goal, researchers explored various machine learning algorithms, such as decision trees, SVM, Random Forest, Logistic Regression, and ANN. Through comprehensive analysis, the ANN algorithm outperforms other algorithms and showcases the best performance. The research results demonstrate that integrating this application can significantly improve diagnostic accuracy and speed, thereby potentially reducing treatment delays and enhancing patient health outcomes. The neural network model displayed exceptional accuracy across training, validation, and testing datasets, scoring 97%, 99%, and 95%, respectively. Overall, this study showcases the potential of implementing the ANN algorithm within Diagnese applications to elevate the accuracy and efficiency of disease diagnosis. The application of this model is expected to augment the efficiency and precision of the medical diagnosis process, enabling doctors to make more accurate decisions and provide more effective patient care.
FISH FRESHNESS PREDICTION WITH CONVOLUTIONAL NEURAL NETWORK METHOD BASED ON FISH EYE IMAGE ANALYSIS Mahendra, Robby; Faurina, Ruvita
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.1351

Abstract

The potential for fish resources in Bengkulu waters is abundant, but quality must be maintained for safety and selling value. Changes in the skin, eyes, gills and flesh of fish indicate a decrease in quality due to enzyme, chemical and bacterial activity. The process of sorting fish by fishermen or sellers is still often done manually, which is sometimes inaccurate due to limited vision. With advances in computing technology, classification algorithms are needed that can identify and differentiate between fresh fish and non-fresh fish. This research uses a Convolutional Neural Network with DenseNet201, VGG16, and InceptionV3 architecture. The dataset contains 880 Belato Alepes Djedaba fish eye images, with a ratio of 80:15:5 for train, validation, and test. DenseNet201 has the best performance compared to VGG16 and InceptionV3. Accuracy on DenseNet201 test data 98%, InceptionV3 95%, and VGG16 91%. The classification results of the best model using 8 images with various scenarios show that all images were successfully classified 100% correctly. This research makes a contribution to the field of fishery product processing technology which allows fish quality classification to be carried out quickly and accurately, as well as increasing efficiency in ensuring the quality of fish for consumption.
Co-Authors Agustian, Indra Arie Vatresia Daratha, Novalio Gazali, M Jumli Gazali, M. Jumli Hadi, Faisal Herani, Icha Dwi Aprilia Ida Ayu Putu Sri Widnyani Irnanda Priyadi Mahendra, Robby Prakoso, Dian S Rahma, Silvia Revanza, Dede Simanjuntak, Yanti Sitanggang, Evlin Sopran, Ahmad Utama, Ferzha Putra Wijaya, Gusman