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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTECOMS: Journal of Information Technology and Computer Science Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Khairunisa Hilyati
Universitas Nusa Mandiri
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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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.
Sentimen Analisis Masyarakat Indonesia Terhadap Presiden Rusia Pada Komentar Media Berita Online Ihud Hafid; Windu Gata; Khairunisa Hilyati; Valianda Farradillah Hakim; Sri Rahayu
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 7 No 1 (2023): JANUARY-MARCH 2023
Publisher : KITA Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v7i1.698

Abstract

Russia's invasion of Ukraine was criticized by various parties, including from Indonesia. The attitude shown by the Indonesian government is not the same as the response of the Indonesian people based on various comments on online news media pages. Comments by online news readers are used as an assessment of the Russian President who is involved in the conflict between Russia and Ukraine in the form of sentiment analysis. This study succeeded in obtaining data as many as 352 comments from one of the online news media, the data had previously gone through the cleansing stage to eliminate duplication. To get basic information on comments, Text mining and Text Pre-Processing become an important part of the process. The algorithm used in this research is the Naive Bayes (NB) and Support Vector Machine (SVM) classification algorithm which is optimized using Particle Swarm Optimization (PSO). The two algorithms were tested and gave the result that PSO-based SVM got the best accuracy, which was 79.90% and AUC 0.901.
Analisis Sentimen pada Komen Twitter Pawang Hujan Mandalika dengan Support Vector Machine (SVM) dan Naïve Bayes Rahmat Satria Buana; Windu Gata; Alda Zevana Putri Widodo; Hendra Setiawan; Khairunisa Hilyati
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 7 No 2 (2023): APRIL-JUNE 2023
Publisher : KITA Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v7i2.705

Abstract

Indonesia is a country with a majority Muslim population, but Indonesia is also rich in culture influenced by previous religions such as Hinduism and Buddhism. The rain handler by Muslims is considered shirk, but on the other hand also considers this ancestral culture that has always existed. With the advancement of human technology, it is easier to express opinions or opinions regarding a topic that is currently being discussed, for example, regarding the rain handler who acted at the Mandalika Circuit some time ago through social media. Twitter is one of the social media that is used as a forum to accommodate these opinions. In this study, the CRISP-DM (Cross Industry Standard Process for Data Mining) data mining methodology was used with Rapid Miner Version 9.10 with the Support Vector Machine (SVM) classification method and nave Bayes with SMOTE to improve accuracy. The application of the SVM method is 97.71 % with AUC 0.997 Positive Comments, and Using Naïve Bayes, the accuracy obtained is 93.41% accuracy with AUC 0.973 Positive Comments.
Co-Authors Alda Zevana Putri Widodo Annisa Novtariany Febri Ainun Jariyah Hendra Setiawan Ihud Hafid Mila Desi Anasanti Rahmat Satria Buana Ristyani Slamet Sri Rahayu Valianda Farradillah Hakim Windu Gata