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All Journal TEKNIK INFORMATIKA CESS (Journal of Computer Engineering, System and Science) AL-ISHLAH: Jurnal Pendidikan Jurnal Nasional Komputasi dan Teknologi Informasi Krea-TIF: Jurnal Teknik Informatika Journal of Applied Engineering and Technological Science (JAETS) Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Teknoif Teknik Informatika Institut Teknologi Padang
Elin Haerani
Universitas Islam Negeri Sultan Syarif Kasim Riau
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

 1 
Analisis sentimen larangan penggunaan obat sirup menggunakan algoritma naive bayes classifier Fitri Wulandari; Elin Haerani; Muhammad Fikry; Elvia Budianita
Computer Science and Information Technology Vol 4 No 1 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i1.4781

Abstract

The Indonesian government made a policy to stop consuming syrup as a form of prevention against acute kidney failure, which affects many people in Indonesia. However, the policy has caused a lot of comments from the public. These public comments can be found on YouTube, because YouTube has a large data source opportunity to be used as a research material. These comments can be processed directly without using a machine, but it is less effective and efficient. Thus, the comments are processed using machine learning methods. Based on the earlier research, the naive bayes classifier algorithm tends to be simple and easy to use. In addition, this algorithm also has a high accuracy. The amount of data used in this study is 1000 YouTube comment data related to videos regarding the policy of prohibiting the use of syrup medicine, the comments are divided into 2 category, which are positive class and negative class. The results of labeling 1000 comments obtained 704 negative comments and 296 positive comments. Based on the experiments conducted using python programming language, the highest accuracy was obtained at 74% in 70:30 data split. Furthermore, in the balanced dataset (296 positive and 296 negative comments), the highest accuracy was obtained at 64.70% with in 80:20 data split. These results represent that the naive bayes classifier algorithm is good enough at sentiment analysis about the policy of prohibiting the use of syrup drugs.
Pengelompokan pembagian zakat dengan menggunakan metode clustering k-means Alvin Alvin Anzaz Islami; Elin Haerani; Novriyanto; Alwis Nazir
Computer Science and Information Technology Vol 4 No 1 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i1.4804

Abstract

Zakat merupakan ibadah yang menyangkut harta benda. Zakat juga termasuk rukun islam yang ke empat yang memiliki tujuan menyucikan harta bagi setiap muslim dengan cara menyisihkan sebagian harta kekayaannya, jika telah mencapai waktu dan besaran jumlahnya diberikan kepada orang yang berhak menerimanya. Pengumpulan dan penyaluran zakat biasanya ditangani oleh Badan Amil Zakat (BAZ) yang ada disetiap wilayah Indonesia, salah satunya di Pekanbaru. Sesuai dengan peraturan ada dua tahap yang dilakukan dalam memberikan bantuan kepada para mustahik yaitu melakukan wawancara dan observasi lapangan, kemudian menentukan nominal bantuan yang diberikan dengan kategori Mustahik penerima bantuan zakat 1, zakat 2, dan zakat 3. Masalah yang sering dijumpai dalam penentuan calon penerima bantuan adalah cara dalam pemilihan Mustahik yang masih menggunakan cara manual, sehingga sering menimbulkan masalah seperti lamanya proses pemilihan dan terjadinya salah hitung sehingga hasil seleksi Mustahik menjadi kurang akurat. Untuk itu, perlu dibuat suatu analisis yang dapat mengolah data menjadi informasi. Data mining ialah proses untuk mengolah data menjadi suatu informasi dengan teknik statistik, AI, dan machine learning. Ada banyak metode dalam data mining. Pada penelitian ini menggunakan algoritma k-means clustering dan untuk pengujian menggunakan Davies Bouldin Index. berdasarkan pengujian menggunakan davies bouldin index (DBI) klaster 4 merupakan klaster terbaik dengan nilai 0.671, dimana jika nilainya semakin rendah maka akan semakin baik klaster tersebut
Co-Authors Abu Anwar Alga Prananda Alvin Alvin Anzaz Islami Alwis Nazir Amany Akhyar Damayanti, Elok Devi Julisca Sari Elvia Budianita Fadhilah Syafria Fahmi Rizal Febi Yanto Felina Amelia Fitra Lestari Ismail Marzuki Iwan Iskandar Jasril Jasril Kasman Rukun Khairul Amri Lola Oktavia Muhammad Affandes Muhammad Fikry Novriyanto Novriyanto Ramdaril Ramdaril Ramdaril Reski Mai Candra Robbi Nanda Said Nanda Saputra Samsi Hasan Siska Kurnia Gusti Siti Ramadhani Yayuk Wulandari Yusra, Yusra