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All Journal Jurnal Ilmiah Informatika Komputer Jurnal Riset Informatika Jurnal Mantik Jusikom: Jurnal Sistem Informasi Ilmu Komputer INTI Nusa Mandiri Profitabilitas Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Teknik Komputer dan Informatika (JTeKI)
Zulia Imami Alfianti
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Languange, Linguistic, Communication & Media

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ANALISIS KANKER PARU-PARU MENGGUNAKAN ALGORITMA LOGISTIC REGESSION DAN RANDOM FOREST Alfianti, Zulia Imami; Ginabila, Ginabila; Fauzi , Ahmad; Pratiwi, Risca Lusiana
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 1 (2026): EDISI 27
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i1.7063

Abstract

Kanker paru-paru merupakan salah satu jenis kanker dengan tingkat kematian tertinggi di dunia, yang disebabkan oleh faktor gaya hidup seperti merokok dan konsumsi alkohol, serta faktor genetik. Mengingat deteksi dini konvensional memerlukan waktu dan biaya besar, penelitian ini mengusulkan pendekatan Machine Learning yang lebih efisien untuk memprediksi risiko penyakit. Menggunakan algoritma Logistic Regression dan Random Forest pada dataset Survey Lung Cancer yang berisi 309 responden dengan 16 variabel gaya hidup dan kesehatan , penelitian ini melibatkan tahapan data understanding, data preparation (termasuk encoding dan scaling), modeling, dan evaluation. Hasil analisis menunjukkan performa yang sangat baik untuk kedua algoritma dengan nilai Akurasi 96,77% dan nilai Presisi, Recall, serta F1-score mencapai 0,9833. Meskipun metrik utama identik, perbandingan kurva ROC menunjukkan bahwa model Random Forest (AUC = 0,958) sedikit lebih unggul dari Logistic Regression (AUC = 0,917). Berdasarkan analisis, faktor usia (AGE) teridentifikasi sebagai variabel paling berpengaruh terhadap risiko kanker paru-paru, diikuti oleh konsumsi alkohol, alergi, dan tekanan sosial7. Hasil ini diharapkan menjadi referensi dalam pengembangan sistem prediksi dan deteksi dini berbasis Machine Learning.
SENTIMENT ANALYSIS OF COSMETIC REVIEW USING NAIVE BAYES AND SUPPORT VECTOR MACHINE METHOD BASED ON PARTICLE SWARM OPTIMIZATION Alfianti, Zulia Imami; Gunawan, Deni; Amin, Ahmad Fikri
Jurnal Riset Informatika Vol. 2 No. 3 (2020): June 2020 Edition
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v2i3.113

Abstract

Sentiment analysis is an area of ​​approach that solves problems by using reviews from various relevant scientific perspectives. Reading a review before buying a product is very important to know the advantages and disadvantages of the products we will use, besides reading a cosmetic review can find out the quality of the cosmetic brand is feasible or not be used. Before consumers decide to buy cosmetics, consumers should know in detail the products to be purchased, this can be learned from the testimonials or the results of reviews from consumers who have bought and used the previous product. The number of reviews is certainly very much making consumers reluctant to read reviews. Eventually, the reviews become useless. For this reason, the authors classify based on positive and negative classes, so consumers can find product comparisons quickly and precisely. The implementation of Particle Swarm Optimization (PSO) optimization can improve the accuracy of the Support Vector Machine (SVM) and Naïve Bayes (NB) algorithm can improve accuracy and provide solutions to the review classification problem to be more accurate and optimal. Comparison of accuracy resulting from testing this data is an SVM algorithm of 89.20% and AUC of 0.973, then compared to SVM based on PSO with an accuracy of 94.60% and AUC of 0.985. The results of testing the data for the NB algorithm are 88.50% accuracy and AUC is 0.536, then the accuracy is compared with the PSO based NB for 0.692. In these calculations prove that the application of PSO optimization can improve accuracy and provide more accurate and optimal solutions.
Co-Authors Ahmad Fauzi Amin, Ahmad Fikri Dede Mustomi Eko Yulianto Fauzi , Ahmad Fauzi Ahmad Muda Ginabila Gunawan, Deni KOMALASARI, YULI Laetussaadah, Lela Mochammad Abdul Azis Pratiwi, Risca Lusiana Siti Fauziah Sugiono