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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Emerging Statistics and Data Science Journal
Hablinawati, Laela
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mathematics

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Analisis Sentimen Pengguna Twiter terhadap Perubahan Kebijakan Skripsi sebagai Syarat Wajib Kelulusan menggunakan Metode Naïve Bayes Classifier Hablinawati, Laela; Dzikrullah, Abdullah Ahmad
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7746

Abstract

The Minister of Education, Culture, Research, and Technology, Nadiem Makarim, has issued a policy to abolish theses, dissertations, or final papers as mandatory graduation requirements for undergraduate and postgraduate students in universities. The requirement to write a thesis is still enforced in most universities in Indonesia to obtain a bachelor's degree. The advancement of information system technology and the ease of accessing social media have caused news to spread rapidly. This policy has sparked pros and cons among the public, including on the social media platform X (formerly Twitter). Some people agree with it, considering that it can reduce the burden on students and increase the relevance of higher education to the needs of the job market. However, others argue that abolishing theses could lower the quality of university graduates and that the replacement could be even more burdensome. The purpose of this research is to understand Twitter users' sentiments towards the policy of abolishing theses as a graduation requirement and to determine the accuracy of the Naïve Bayes Classifier in classifying these sentiments. The data used consists of 656 tweets, which were processed through several stages, including cleaning, case folding, normalization, stopword removal, tokenizing, and stemming. The data was then labeled using a lexicon-based approach, resulting in 353 negative labels and 273 positive labels. The data was subsequently weighted using TF-IDF for the classification process. The dataset was split into training and testing data with a ratio of 90:10. After classification, the study found that the Naïve Bayes Classifier successfully categorized sentiments with an accuracy of 76%.
Analisis Sentimen Pengguna Twiter terhadap Perubahan Kebijakan Skripsi sebagai Syarat Wajib Kelulusan menggunakan Metode Naïve Bayes Classifier Hablinawati, Laela; Dzikrullah, Abdullah Ahmad
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7746

Abstract

The Minister of Education, Culture, Research, and Technology, Nadiem Makarim, has issued a policy to abolish theses, dissertations, or final papers as mandatory graduation requirements for undergraduate and postgraduate students in universities. The requirement to write a thesis is still enforced in most universities in Indonesia to obtain a bachelor's degree. The advancement of information system technology and the ease of accessing social media have caused news to spread rapidly. This policy has sparked pros and cons among the public, including on the social media platform X (formerly Twitter). Some people agree with it, considering that it can reduce the burden on students and increase the relevance of higher education to the needs of the job market. However, others argue that abolishing theses could lower the quality of university graduates and that the replacement could be even more burdensome. The purpose of this research is to understand Twitter users' sentiments towards the policy of abolishing theses as a graduation requirement and to determine the accuracy of the Naïve Bayes Classifier in classifying these sentiments. The data used consists of 656 tweets, which were processed through several stages, including cleaning, case folding, normalization, stopword removal, tokenizing, and stemming. The data was then labeled using a lexicon-based approach, resulting in 353 negative labels and 273 positive labels. The data was subsequently weighted using TF-IDF for the classification process. The dataset was split into training and testing data with a ratio of 90:10. After classification, the study found that the Naïve Bayes Classifier successfully categorized sentiments with an accuracy of 76%.
Peramalan Nilai Tukar Petani di Daerah Istimewa Yogyakarta Menggunakan Metode ARIMA: Peramalan Nilai Tukar Petani di Daerah Istimewa Yogyakarta Menggunakan Metode ARIMA Hablinawati, Laela; Nugraha, Jaka
Emerging Statistics and Data Science Journal Vol. 2 No. 1 (2024): Emerging Statistics and Data Science Journal
Publisher : Statistics Department, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/esds.vol2.iss.1.art9

Abstract

Indonesia merupakan negara agraris karena letaknya berada di iklim tropis, yang mempunyai potensi besar dan sumber daya alam yang melimpah untuk pertanian. Memiliki lahan pertanian yang luas, menjadikan pertanian sebagai sektor penting dalam perekonomian Indonesia. Untuk meningkatkan kesejahteraan petani, digunakan perhitungan Nilai Tukar Petani (NTP) sebagai indikatornya. NTP adalah perbandingan antara indeks harga yang diterima petani dengan harga yang dibayarkan oleh petani. Peramalan nilai NTP di masa depan karena penting bagi kesejahteraan petani. Penelitian ini bertujuan untuk memperkirakan Nilai Tukar Petani di Daerah Istimewa Yogyakarta selama satu tahun ke depan menggunakan metode Autoregressive Integrated Moving Average (ARIMA) berdasarkan data time series dari Januari 2019 hingga Desember 2022. Model terbaik yang digunakan adalah ARIMA (0,2,1) dengan hasil peningkatan rata-rata NTP sebesar 101,0448 dengan MAPE sebesar 0,581443 atau 5,81443% dan tingkat akurasi sebesar 99,41856%.
Co-Authors Dzikrullah, Abdullah Ahmad Jaka Nugraha