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All Journal Jurnal Ilmiah Teknik Informatika dan Komunikasi Intellektika: Jurnal Ilmiah Mahasiswa
Arifin Yusuf Permana
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Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Social Sciences

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Penerapan Data Mining Dalam Analisis Prediksi Kanker Paru Menggunakan Algoritma Random Forest Arifin Yusuf Permana; Hari Noer Fazri; M.Fakhrizal Nur Athoilah; Mohammad Robi; Ricky Firmansyah
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 3 No. 2 (2023): Juli : Jurnal Ilmiah Teknik Informatika dan Komunikasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v3i2.472

Abstract

Lung cancer is one of the one of the leading causes of death in the world. From this data there are several categories of people who are positive and negative for lung cancer, Here the researcher will display information on the exact number of people who contracted lung cancer from the data, and in this study using the Random Forest algorithm because Random Forest This research uses the Random Forest algorithm because Random Forest has a data set selection process. Has a data set selection process. to improve the performance of classification model. With feature selection, Random Forest can certainly work efficiently on big data with complex parameters, which will greatly facilitate the classification of positive and negative lung cancer patients. Observations will be a reference for analyzing the prognosis of lung disease. Observation will be a reference for analyzing the prognosis of lung disease here how the application of data data mining techniques on the prediction analysis of lung cancer analysis and how performance of the random forest algorithm in predicting lung cancer.by applying data mining techniques and has been tested using a survey dataset of lung cancer survey dataset and using software called Rapidminer toanalyze and predict positive patients with lung cancer It was concluded that the It is concluded that the Random Forest algorithm that has obtained the greatest accuracy obtained accuracy results worth 90.61% with an AUC value of 0.941.
Identifikasi Kualitas Visual Rempah Ekspor Indonesia Menggunakan Deep Learning Berbasis CNN Arifin Yusuf Permana; Ifani Hariyanti
Intellektika : Jurnal Ilmiah Mahasiswa Vol. 3 No. 5 (2025): Intellektika : Jurnal Ilmiah Mahasiswa
Publisher : STIKes Ibnu Sina Ajibarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59841/intellektika.v2i5.3228

Abstract

Indonesia is the world's leading producer of spices, but it still faces challenges in manual visual quality assessment, which is inconsistent. This study aims to develop a spice quality classification system using a Deep Learning approach based on Convolutional Neural Networks (CNN). Data was collected through digital images of five types of spices (cloves, cardamom, cinnamon, pepper, and nutmeg) classified into two categories: good and bad. The dataset was then processed and used to Train the CNN model using Tensorflow. The model architecture consists of several convolution, pooling, and dense layers, and is integrated into a web-based prototype application using Streamlit. Evaluation results show that the model achieves high Accuracy of 98.86% (Training), 98.45% (Validation), and 98.45% (Testing). The prototype application can provide automatic Predictions of spice quality through a simple and responsive interface. The results of this study indicate that CNN is effective in identifying the visual quality of spices and can serve as an objective, efficient technological solution that supports the enhancement of Indonesia's spice export competitiveness.
Co-Authors Hari Noer Fazri Ifani Hariyanti M.Fakhrizal Nur Athoilah Mohammad Robi Ricky Firmansyah