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All Journal JUTIM (Jurnal Teknik Informatika Musirawas) International Journal Of Computer, Network Security and Information System (IJCONSIST)
Ariansyah, Fery Almas
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Computer Science & IT Control & Systems Engineering Engineering Other

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KLASIFIKASI PNEUMONIA PADA CITRA X-RAY PARU-PARU MENGGUNAKAN ALGORITMA NAIVE BAYES Ariansyah, Fery Almas; Kartini, Kartini; Maulana, Hendra
JUTIM (Jurnal Teknik Informatika Musirawas) Vol 9 No 1 (2024): JUTIM (Jurnal Teknik Informatika Musirawas) JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jutim.v9i1.2243

Abstract

Penelitian ini menguji efektivitas metode ekstraksi ciri Local Binary Model (LBP) – Gray Level Co-occurrence Matrix (GLCM) dan algoritma klasifikasi Naive Bayes dalam mengenali citra X-ray paru-paru yang menunjukkan adanya pneumonia. Tantangan utamanya adalah meningkatkan akurasi diagnosis penyakit melalui analisis citra X-ray. Tujuan dari penelitian ini adalah untuk menerapkan pendekatan yang efektif untuk mengklasifikasikan citra X-ray paru-paru ke dalam tipe pneumonia dan normal menggunakan ekstraksi ciri LBP-GLCM dan metode Naive Bayes. Selain itu, penelitian ini bertujuan untuk mengevaluasi efektivitas klasifikasi pneumonia pada citra X-ray paru-paru menggunakan metode LBP-GLCM dan Naive Bayes. Eksperimen dilakukan dengan menggunakan kumpulan data citra X-ray paru-paru, terutama berfokus pada 100 dan 500 gambar, serta memvariasikan jumlah data eksperimen. Hasil eksperimen menunjukkan akurasi tertinggi yang dicapai dengan variasi pengujian sebesar 30%, dengan tingkat akurasi sebesar 93% pada dataset 100 gambar dan 86% pada dataset 500 gambar. Metode LBP-GLCM dan Naive Bayes berkontribusi signifikan terhadap diagnosis medis dengan tingkat akurasi yang memuaskan, sehingga dapat membantu meningkatkan efisiensi praktik klinis sehari-hari.
Stock Price Prediction Using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) Methods Riza Akhsani Setyo Prayoga; Ariansyah, Fery Almas; Daffa, Muhammad Falikhuddin; Laqma Dica Fitrani; Masti Fatchiyah Maharani; Angga Lisdiyanto; Angkawidjaja , Steven
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.158

Abstract

This research aims to improve the accuracy of stock price prediction through the application of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) methods, focusing on stocks from the Composite Stock Price Index (CSPI) referred to as the IDX Composite. The research process includes comprehensive steps, including data collection and preprocessing, dataset creation with emphasis on stock closing prices, and division of the dataset into training and test data. The LSTM and GRU models were designed with a recurrent layer and a Dense layer and then trained for 100 epochs with a batch size of 32. Model evaluation was performed by comparing key metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE) on the test set. The EPOCH-RMSE graph provides an overview of the changes in the RMSE value during training. The best result of the LSTM model was achieved at the 96th epoch with RMSE 40.36, MSE 1385.97, and MAE 30.09, while GRU achieved peak performance at the 92nd epoch with RMSE 37.33, MSE 908.29, and MAE 25.42. In conclusion, GRU can be considered as a more effective option in predicting JCI stock prices based on performance evaluation using various metrics such as RMSE, MSE, and MAE.
Co-Authors Angga Lisdiyanto Angkawidjaja , Steven Daffa, Muhammad Falikhuddin Kartini Kartini Laqma Dica Fitrani Masti Fatchiyah Maharani Maulana, Hendra Riza Akhsani Setyo Prayoga