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All Journal INFORMAL: Informatics Journal Proceeding of the Electrical Engineering Computer Science and Informatics KOMPUTIKA - Jurnal Sistem Komputer Bubungan Tinggi: Jurnal Pengabdian Masyarakat TEKIBA : Jurnal Teknologi dan Pengabdian Masyarakat Prosiding Konferensi Nasional PKM-CSR
Nova El Maidah
Universitas Jember
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Mechanical Engineering Nursing Public Health Social Sciences Other

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Analisis Sentimen Opini Publik Terhadap Program Vaksinasi Covid-19 Di Indonesia Pada Twitter Menggunakan Metode Naive Bayes Classifier Priza Pandunata; Kukuh Tri Winarno N; Yanuar Nurdiansyah; Nova El Maidah
INFORMAL: Informatics Journal Vol 7 No 3 (2022): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v7i3.34930

Abstract

The COVID-19 virus emerged in December 2019 in China and actively spread throughout the world including Indonesia in early 2020. Its spread is very fast and has caused millions of deaths. Therefore, the Indonesian government is actively holding a COVID-19 vaccination program to prevent the spread of the virus and make the public immune to the virus. But the program invites pros and cons among the community. Twitter is one of the social media that is famous for being a medium of opinion from the general public. The process of sentiment analysis can find and solve problems based on public opinion on social media such as Instagram. The classification method used in this research is Naïve Bayes Classifier. The dataset can be obtained from data crawling process using Google Collabs and python programming language. The total dataset obtained is 2000. The data the labelled as positive, neutral, or negative. The labelling process result showed 1579 positive data, 277 negative data, and 144 neutral data. Then pre-processing is carried out on the data that has been labeled before, also word weighting process using TF-IDF. After that modelling is carried out using Naïve Bayes Classifier and the last process is evaluation-testing. The high accuracy of the result from fourth experiment which compare 90% data training with 10% data testing produce 86% accuracy. While the result of sentiment test show that positive sentiment more than negative sentiment and neutral sentiment.
Prediksi Harga Cabai Rawit di Provinsi Jawa Timur Menggunakan Metode Fuzzy Time Series Model Lee Vida Komaria; Nova El Maidah; Muhammad Ariful Furqon
Komputika : Jurnal Sistem Komputer Vol 12 No 2 (2023): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v12i2.10644

Abstract

ABSTRACT - East Java is the province with the most significant amount of chili pepper production in Indonesia based on data from BPS in 2021 which is around 41.75. Chili pepper is a commodity that high price fluctuations that will impact several parties, so a mechanism is needed to predict the price of chili pepper to become a consideration in decision making. Lee's fuzzy time series method can be used to predict time series stationary or non-stationary data. The research was conducted using historical data on the price of red and green chili peppers in East Java Province from April 2017 to February 2023 with a weekly data period of 307 data. The Z1 and Z2 values used to get the smallest error results are Z1 = 950 and Z2=400 for red chili peppers while for green chili peppers values the Z1 and Z2=100. The error value of forecasting red chili pepper prices is MAE = 4,469.04 RMSE = 6,138.64 MAPE = 13.09% (good MAPE value category) and the error value for green chili pepper is MAE = 1,486.15 RMSE = 2,211.06 and MAPE = 6.72% (very good MAPE value category). Keywords – forecasting; Lee’s fuzzy time series; chili pepper price; MAPE; Python
Co-Authors Achmad Maududie Agfianto Eko Putra Alanfansyah Ghozy Santoso Amalia, Karina Nine Diksy M Firmansyah Dini Kurniawati Erika Neferia Laurentina Furqon, Muhammad Ariful Hanny Rasni, Hanny Hardja, Ivan Haris Rafi Ivan Hardja Kukuh Tri Winarno N Kurniawan Dwi Yulianto M. Sadid Tafsirul Hadi MHD. Reza M.I. Pulungan Mochammad Febri Hariyadi Mohammad Zarkasi Noni Namida Oliviani Oktalia Juwita Pandunata, Priza Panggih Pawenang Priza Pandunata Rizky Berlia Oktaviandi S Slamin Vida Komaria Yanuar Nurdiansyah