Annisa Novtariany
Universitas Nusa Mandiri

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Analisis Sentimen Twitter Terhadap Penggunaan Artis Korea Selatan Sebagai Brand Ambassador Produk Kecantikan Lokal Ristyani Slamet; Windu Gata; Annisa Novtariany; Khairunisa Hilyati; Febri Ainun Jariyah
INTECOMS: Journal of Information Technology and Computer Science Vol 5 No 1 (2022): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v5i1.3933

Abstract

Twitter sebagai platform yang digandrungi masyarakat terbukti dengan data yang menunjukkan bahwa Indonesia peringkat lima di dunia dengan banyak pengguna mencapai 18.4 juta. Banyak pengguna mengutarakan pendapat di dalam Twitter. Skincare lokal kini sedang merajai industri kecantikan di Indonesia. Beberapa brand kecantikan yang menggunakan artis korea sebagai brand ambassador. Berdasarkan hal tersebut, maka dilakukan analisis sentimen menggunakan algoritma Support Vector Machine (SVM) dan naïve bayes untuk mengetahui bagaimana sentimen para pengguna terhadap penggunaan artis korea sebagai brand ambassador produk kecantikan lokal. Data yang digunakan merupakan komentar masyarakat twitter sebanyak 317 data yang terdiri 266 komentar positif dan 21 komentar negatif. Hasil terbaik diperoleh pada SVM dengan nilai akurasi 83.60% dengan precision 83.86% dan recall 99.62%.
Exploring feature selection techniques on Classification Algorithms for Predicting Type 2 Diabetes at Early Stage Mila Desi Anasanti; Khairunisa Hilyati; Annisa Novtariany
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4419

Abstract

Predicting early Type 2 diabetes (T2D) is critical for improved care and better T2D outcomes. An accurate and efficient T2D prediction relies on unbiased relevant features. In this study, we searched for important features to predict T2D by integrating ML-based models for feature selection and classification from 520 individuals newly diagnosed with diabetes or who will develop it. We used standard machine learning classifications, such as logistic regression (LR), Gaussian naive Bayes (NB), decision tree (DT), random forest (RF), support vector machine (SVM) with linear basis function, and k-nearest neighbors (KNN). We set out to systematically explore the viability of main feature selection representing each different technique, such as a statistical filter method (F-score), an entropy-based filter method (mutual information), an ensemble-based filter method (random forest importance), and a stochastic optimization (simultaneous perturbation feature selection and ranking (SpFSR)). We used a stratified 10-fold cross-validation technique and assessed the performance of discrimination, calibration, and clinical utility. We attained the highest accuracy of 98% using RF with the full set of features (16 features), then used RF as a classifier wrapper to select the important features. We observed a combination of SpFSR and RF as the best model with a P-value above 0.05 (P-value = 0.26), statistically attaining the same accuracy as the full features. The study's findings support the efficiency and usefulness of the suggested method for choosing the most important features of diabetic data: polyuria, gender, polydipsia, age, itching, sudden weight loss, delayed healing, and alopecia.