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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA JURTEKSI Jurnal Mantik IICS JME
Arie Satia Dharma
Institut Teknologi Del
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Social Sciences

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COMPARISON FEATURE EXTRACTION USING ARTIFICIAL NEURAL NETWORK ALGORITHM ON SMOKER PREDICTION Dharma, Arie Satia; Pardede, Cynthia Veronika; Sitorus, Jonggi Vegas
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 10 No. 4 (2024): September 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i4.2933

Abstract

Abstract: The habit of smoking is dangerous because of the addictive substances that make cigarettes addictive. Its addictive nature poses a significant risk, affecting personality with stress, depression and nervous disorders. Body factors that indicate smoking include blood sugar levels, dental caries, and hemoglobin. To address this, research has been conducted with focused efforts to understand and address the risks associated with smoking and its impact on overall health. This research aims to choose the best method for predicting smokers by using feature selection techniques. The feature selection algorithms uses for that are Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and Genetic Algorithm (GA) to select optimal attributes and uses the k-fold cross validation technique as the validation of the Artificial Neural Network algorithm. The data includes various parameters such as age, height, weight, vision, blood pressure, cholesterol, triglycerides, hemoglobin, AST, ALT, GTP, gender, dental caries and tartar. Hearing ability, urine protein content, and tartar were selected. The results showed that using the Analysis of Variance method showed higher accuracy (77.101%) compared to the Genetic Algorithm method (74.64%) and the Recursive Feature Elimination method (76.08%). Selection of relevant attributes increases the predictions and insights of the Artificial Neural Network model about the effects of smoking on health.            Keywords: artificial neural network; analysis of variance; genetic algorithm; recursive feature elimination; smoker prediction  Abstrak: Kebiasaan merokok berbahaya karena adanya zat adiktif yang membuat rokok menjadi ketagihan. Sifatnya yang membuat ketagihan menimbulkan risiko yang signifikan, mempengaruhi kepribadian dengan stres, depresi, dan gangguan saraf. Faktor tubuh yang mengindikasikan kebiasaan merokok antara lain kadar gula darah, karies gigi, dan hemoglobin. Untuk mengatasi hal ini, penelitian telah dilakukan dengan upaya terfokus untuk memahami dan mengatasi risiko yang terkait dengan merokok dan dampaknya terhadap kesehatan secara keseluruhan. Penelitian ini bertujuan untuk memilih metode terbaik dalam memprediksi perokok dengan menggunakan teknik seleksi fitur. Metode seleksi fitur yang digunakan adalah Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), dan Genetic Algorithm (GA) untuk memilih atribut yang optimal dan menggunakan teknik k-fold cross validation sebagai validasi algoritma Artificial Neural Network. Data tersebut mencakup berbagai parameter seperti umur, tinggi badan, berat badan, penglihatan, tekanan darah, kolesterol, trigliserida, hemoglobin, AST, ALT, GTP, jenis kelamin, karies gigi dan karang gigi. Kemampuan pendengaran, kandungan protein urin, dan karang gigi dipilih. Hasil penelitian menunjukkan bahwa penggunaan metode Analysis of Variance menunjukkan akurasi yang lebih tinggi (77,101%) dibandingkan dengan metode Genetic Algorithm (74,64%) dan metode Recursive Feature Elimination (76,08%). Pemilihan atribut yang relevan meningkatkan prediksi dan wawasan model Jaringan Syaraf Tiruan tentang dampak merokok terhadap kesehatan. Kata kunci: artificial neural network; analysis of variance; genetic algorithm; prediksi perokok; recursive feature elimination
Co-Authors Arlinta Christy Barus Evi Rosalina Silaban H. Justin Manurung Hana Maria Siahaan Hatigoran, Andreas Humasak T. A. Simanjuntak Oktavi Yanty Silitonga Pardede, Cynthia Veronika Pardosi, Chantika Nadya Serebella Purba, Daniel Somanta Samuel Indra Gunawan Situmeang Saragih , Yosua Giat Raja Silaen, Zan Peter Sitorus, Jonggi Vegas Sitorus, Judah Michael Parluhutan Tambunan, Veronika Tampubolon, Lily Andayani