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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Ilma, Hafizah
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Analisis Sentimen terhadap Isu Politik Calon Presiden Indonesia 2019 berdasarkan Opini Netizen dari Twitter Menggunakan Metode Klasifikasi Support Vector Machine Jati, Wahyu Pratama; Lutfi, Ahmad Zainul; Ilma, Hafizah; Widodo, Edy
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2019: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (582.451 KB)

Abstract

Dalam penelitian ini bertujuan untuk mengetahui bagaimana analisis sentimen Terhadap isu politik calon presiden Indonesia 2019 berdasarkan opini netizen dari Twitter dan juga mengetahui hasil klasifikasi sentimen isu politik calon presiden indonesia 2019 menggunakan metode klasifikasi Support Vector Machine. SVM adalah metode machine learning yang bekerja atas prinsip Structural Risk Minimization (SRM) dengan tujuan menemukan Hyperplane terbaik yang memisahkan dua buah class pada input space. Hasil dari penelitian ini didapatkan bahwa pada calon presiden Jokowi mengalami sentimen negatif lebih besar dari pada sentimen positif. Tweet Negatifi sebanyak 387 dari 742 tweet terhadap calon presiden Jokowi, sedangkan calon presiden Prabowo mendapatkan sentimen positif lebih banyak. Diketahui tweet positif kepada calon presiden Prabowo sebesar 273 tweet dari total 475 tweet. Dan hasil klasifikasi menggunakan Support Vector Machines terhadap sentimen Isu Politik Calon Presiden Indonesia 2019 menyatakan bahwa akurasi SVM terhadap calon presiden Jokowi sebesar 0.98 atau 98 %, nilai akurasi SVM terhadap calon presiden Prabowo sebesar 0.99 atau 99 %.
ASSOCIATION RULES IN RANDOM FOREST FOR THE MOST INTERPRETABLE MODEL Ilma, Hafizah; Notodiputro, Khairil Anwar; Sartono, Bagus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (450.868 KB) | DOI: 10.30598/barekengvol17iss1pp0185-0196

Abstract

Random forest is one of the most popular ensemble methods and has many advantages. However, random forest is a "black-box" model, so the model is difficult to interpret. This study discusses the interpretation of random forest with association rules technique using rules extracted from each decision tree in the random forest model. This analysis involves simulation and empirical data, to determine the factors that affect the poverty status of households in Tasikmalaya. The empirical data was sourced from Badan Pusat Statistik (BPS), the National Socio-Economic Survey (SUSENAS) data for West Java Province in 2019. The results obtained are based on simulation data, the association rules technique can extract the set of rules that characterize the target variable. The application of interpretable random forest to empirical data shows that the rules that most distinguish the poverty status of households in Tasikmalaya are house wall materials and the main source of drinking water, house wall materials and cooking fuel, as well as house wall materials and motorcycle ownership.
Co-Authors Bagus Sartono Edy Widodo Jati, Wahyu Pratama Lutfi, Ahmad Zainul