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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Journal of Mathematics, Computation and Statistics (JMATHCOS) Emerging Statistics and Data Science Journal
Dzikrullah, Abdullah Ahmad
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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%.
Pengelompokan Provinsi Berdasarkan Kualitas Jaringan Internet Dengan Metode Centroid Linkage Dzikrullah, Abdullah Ahmad
Journal of Mathematics, Computations and Statistics Vol. 5 No. 1 (2022): Volume 05 Nomor 01 (April 2022)
Publisher : Jurusan Matematika FMIPA UNM

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Abstract

After the current pandemic, the internet has shown its power as a medium for digital acceleration and economic transformation. Internet quality is absolutely an essential requirement in carrying out various activities. Indonesia has unequal internet quality, and only specific areas in Java and Sumatra have good quality. This study aims to classify 34 provinces in Indonesia based on five indicators of internet network quality. The cluster method can rank regions based on internet quality indicators. The centroid Linkage method is an object grouping method that has advantages in handling outlier data. This study resulted in four groups with low internet quality located in Papua and West Papua provinces. The contours and topography of the area are obstacles for the government to distribute quality internet.
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%.
Implementasi Uji Mann Whitney Data Pengamatan Automatic Weather Station (AWS) Digi dan Pengamatan Manual di Stasiun Meteorologi Bandar Udara Internasional Juanda Tahun 2021-2022 : Implementasi Uji Mann Whitney Data Pengamatan Automatic Weather Station (AWS) Digi dan Pengamatan Manual di Stasiun Meteorologi Bandar Udara Internasional Juanda Tahun 2021-2022 Sumbri, Ines Halida Hanum; Dzikrullah, Abdullah Ahmad
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.art6

Abstract

Aspek transportasi udara pada masa kini menjadi pilihan yang tepat bagi beberapa orang untuk menempuh jarak yang jauh maupun dekat dengan waktu yang efisien. Menurut publikasi BPS, Bandar Udara Internasional Juanda masuk ke dalam tiga besar bandara tersibuk nasional berdasarkan jumlah penumpangnya di tahun 2023. Berdasarkan aspek tersebut, kebutuhan akan adanya informasi yang akurat mengenai parameter meteorologi sangat membantu pilot dalam melakukan penerbangan. Pada analisis ini, akan membandingkan data pengamatan meteorologi yang diukur dengan alat AWS Digi dan pengukuran manual yang berasal dari BMKGSoft. Pengujian dilakukan dengan menggunakan metode Mann Whitney dan didapatkan hasil bahwa median pada parameter tekanan udara dan rata-rata suhu udara memiliki nilai yang sama, sedangkan pada parameter kelembapan udara rata-rata dan rata-rata kecepatan angin memiliki nilai median yang berbeda dengan pengamatan manual. Selain itu, pengujian akurasi mengenai data pengamatan otomatis dengan data pengamatan manual dihitung dengan RMSE (Root Mean Square Error), didapatkan nilai RMSE untuk variabel tekanan udara sebesar 0,5 rata-rata suhu udara sebesar 0,7, rata-rata kecepatan angin sebesar 2,7, dan yang terakhir adalah rata-rata kelembapan udara sebesar 6,2. Sehingga dapat ditarik kesimpulan bahwa pengamatan otomatis belum sepenuhnya bisa menggantikan pengamatan manual dan perlu pengecekan atau pemeliharaan berkala dalam menjalankan proses pengamatan otomatis melalui instrumen tersebut terutama pada beberapa komponen alat ukur yang memiliki tingkat akurasi sedang.
Co-Authors Hablinawati, Laela Sumbri, Ines Halida Hanum