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All Journal JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) SKANIKA: Sistem Komputer dan Teknik Informatika
Khoirunnisaa, Nabiilah
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Computer Science & IT Control & Systems Engineering Engineering

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Analisis Pola Faktor Penyebab Balita Stunting Pada Dinas Kesehatan Kota Bekasi Menggunakan Algoritma FP-Growth Khoirunnisaa, Nabiilah; Priatna, wowon; -, Rasim; Warta, Joni
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 7 No. 1 (2024): Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v7i1.4761

Abstract

Stunting is a common long-term nutritional problem among children under five, especially in developing countries, including Indonesia. Recently, the term stunting has become a popular topic of discussion while diverting attention from the problems of malnutrition and obesity. This study aims to determine the results of the formation of rules or association rules in determining the causal factors that most affect stunting in toddlers at the Bekasi City Health Office. The method that will be used in this research is the FP-Growth Algorithm. With the application of the FP-Growth Algorithm, accurate results can be obtained in determining the rules for determining the causal factors that most affect stunting in toddlers. The dataset has 4575 data and will be evaluated using the lift ratio. This research produces 26 rules, the best of which are Not Getting Exclusive Breastfeeding, Thinness, and Malnutrition, with a minimum support value of 0.5, a confidence value of 0.6, and a lift ratio of 1.17. Thus, applying the FP-Growth Algorithm in this study is effective because it achieves a lift ratio value of more than 1.
KLASIFIKASI TEKS ULASAN APLIKASI NETFLIX PADA GOOGLE PLAY STORE MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN SVM Khoirunnisaa, Nabiilah; Nabila Nastiti Kesuma, Kaylista; Setiawan, Septhiyanthi; Yunizar Pratama Yusuf, Ajif
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 7 No 1 (2024): Jurnal SKANIKA Januari 2024
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v7i1.3138

Abstract

Netflix is a subscription streaming platform that presents various shows, such as TV series, documentaries, and films, connected to a device connected to the internet. One of the most popular sites for streaming videos is Netflix, throughout the world and is now starting to apply data analysis and machine learning technology to improve its user services. Through the Google Play Store, users can submit various reviews about the Netflix application. It is possible to extract significant hidden information from this vast quantity of review data that is helpful for assessing an application's quality. Therefore this research aims to classify text reviews of the Netflix application by comparing the two algorithms applied, that is, Support Vector Machine (SVM) and Naive Bayes. With the aim of finding out which algorithm performs better in terms of accuracy. The dataset was obtained through the Google Play Store and applied to the scraping method, totaling 1000 reviews, and processed utilizing the Python programming language. Then the Netflix application review data that was obtained was divided into 70% train data and 30% test data. 82% of the accuracy results were obtained using the Naive Bayes approach., while the support vector machine (SVM) yielded 85% accuracy. It therefore demonstrates that support vector machines (SVM) are no more successful than the outcomes of applying the Naive Bayes method. (SVM).
Co-Authors -, Rasim Joniwarta Nabila Nastiti Kesuma, Kaylista Priatna , Wowon Setiawan, Septhiyanthi Yunizar Pratama Yusuf, Ajif