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All Journal PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science Jurnal Testing dan Implementasi Sistem Informasi
Savra, Daffa Takratama
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Audit Tata Kelola Universitas Islam Negeri Sultan Syarif Kasim Riau Kuliah Kerja Nyata Sistem Menggunakan COBIT 2019 Yuda, Afi Ghufran; Savra, Daffa Takratama; Halim, Fandi Rahmat; Pratama, Muhammad Ripaldo; Tama, Naufal Safiq; Megawati, Megawati
Jurnal Testing dan Implementasi Sistem Informasi Vol. 2 No. 1 (2024): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v2i1.774

Abstract

Penelitian ini menggunakan framework Control Objectives for Information and Related Technology (COBIT) 2019. Tujuannya adalah untuk mengidentifikasi masalah-masalah yang ada dan memberikan rekomendasi perbaikan guna meningkatkan tata kelola teknologi informasi di LPPM. Penelitian ini melibatkan tahap perencanaan, pengumpulan data, dan analisis hasil. Hasil audit menggunakan COBIT 2019 menunjukkan bahwa sistem berada pada level 2 dengan kategori Large Achieved (L) untuk proses APO01, APO04, APO14, dan BAI03. Hal ini menandakan bahwa sistem berjalan dengan baik meskipun masih ada kekurangan dalam konsistensinya. Sementara itu, proses APO06 mencapai level 5 dengan kategori Fully Achieved (F), menunjukkan bahwa sistem telah terdefinisi dengan baik, namun perlu pengukuran kinerja untuk meningkatkan perbaikan yang berkelanjutan. Penelitian ini memberikan kontribusi dalam meningkatkan tata kelola teknologi informasi di LPPM Universitas Islam Negeri Sultan Syarif Qasim. Rekomendasi perbaikan yang diberikan dapat menjadi panduan bagi LPPM dalam mengoptimalkan penggunaan teknologi informasi dalam proses pemilihan lokasi Kuliah Kerja Nyata (KKN) Mahasiswa. Diharapkan dengan implementasi rekomendasi ini, efektivitas penggunaan website LPPM dapat ditingkatkan, sehingga masalah seperti server yang sering down, kesulitan dalam proses login, kurangnya integrasi database, dan user interface yang membingungkan dapat diatasi.
Sentiment Analysis of Towards Electric Cars using Naive Bayes Classifier and Support Vector Machine Algorithm Suryani, Suryani; Fayyad, Muhammad Fauzi; Savra, Daffa Takratama; Kurniawan, Viki; Estanto, Baihaqi Hilmi
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 1: PREDATECS July 2023
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i1.814

Abstract

The use of non-renewable energy sources causes a reduction in fossil fuel resources, and greenhouse gas emissions. Based on the 2020 Climate Transparency Report, G20 member countries are trying to minimize gas emissions according to the target of the Nationally Determined Contribution (NDC), that the transportation sector contributes 27% of air pollution. The solution to reduce greenhouse gas emissions is to start using electric cars. The change from conventional transportation to electric transportation is expected to reduce carbon emissions and dependency on fossil fuels. However, the transition from conventional transportation to electric transportation raises pros and cons for the people of Indonesia. Social media Twitter is a forum for sharing opinions. Twitter users can express opinions on a matter. This study uses the sentiment analysis method to determine public opinion on the use of electric cars in Indonesia. Sentiment classification was performed using the NBC and SVM Algorithms. The results of this study indicate the use of two different algorithms, namely the Naive Bayes Classifier and SVM with the highest accuracy in Naive Bayes with k = 2 and k = 9 is 88%, while the highest accuracy in SVM with k = 9 and k = 10 is 90%. Thus, SVM has better capabilities than Naive Bayes in this study.
Co-Authors Estanto, Baihaqi Hilmi Fayyad, Muhammad Fauzi Halim, Fandi Rahmat Kurniawan, Viki Megawati - Pratama, Muhammad Ripaldo Suryani Suryani Tama, Naufal Safiq Yuda, Afi Ghufran