PENGEMBANGAN METODOLOGI UNTUK PENGEMBANGAN PERANGKAT LUNAK MULTIMEDIA

Classical software development methodologies have alwaysbeen taught in universities. Not so with the multimedia softwaredevelopment. The purpose of this paper is to provide amultimedia software development methodology for learning andother purposes. This methodology is the result of analysis frommethodologies that already exist. The final result of this paperis a new multimedia software development methodology in theearly stage.Key words : multimedia software development methodology,multimedia, multimedia development, methodologydeveloping

PERBANDINGAN METODE KLASIFIKASI RANDOM FOREST DAN SUPPORT VECTOR MACHINE TERHADAP DATASET RESIKO KANKER SERVIKS

Cervical cancer is a significant global health issue, representing a type of cancer that develops from the cells of the cervix. This research focuses on comparing the effectiveness of two classification methods, namely Random Forest (RF) and Support Vector Machine (SVM), in assessing the risk of cervical cancer. Utilizing relevant datasets, the study aims to identify the strengths and weaknesses of each method and evaluate their ability to provide predictions of cervical cancer risk. Through comparative analysis, it is anticipated that this research will offer valuable insights for the development of more efficient methods for assessing the risk of cervical cancer. The results of this study are expected to contribute to a deeper understanding of the performance comparison between Random Forest and SVM in the context of assessing the risk of cervical cancer, opening opportunities for the optimal application of classification methods in efforts for the prevention and early detection of this disease.Kanker serviks, merupakan salah satu masalah kesehatan global yang signifikan, kanker ini merupakan jenis kanker yang berkembang dari sel-sel leher rahim. Penelitian ini memfokuskan pada perbandingan efektivitas dua metode klasifikasi, yaitu Random Forest (RF) dan Support Vector Machine (SVM), dalam menilai risiko kanker serviks. Dengan menggunakan dataset yang relevan, penelitian ini bertujuan untuk mengidentifikasi keunggulan dan kelemahan masing-masing metode serta mengevaluasi kemampuan mereka dalam memberikan prediksi resiko kanker serviks. Melalui analisis perbandingan, diharapkan penelitian ini dapat memberikan wawasan yang berharga untuk pengembangan metode penilaian risiko kanker serviks yang lebih efisien. Hasil penelitian ini diharapkan dapat memberikan kontribusi pada pemahaman lebih lanjut tentang perbandingan performa antara Random Forest dan SVM dalam konteks penilaian resiko kanker serviks, membuka peluang untuk penerapan metode klasifikasi yang lebih optimal dalam upaya pencegahan dan deteksi dini penyakit ini