Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal Sistemasi: Jurnal Sistem Informasi PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science Jurnal Testing dan Implementasi Sistem Informasi
Savra, Daffa Takratama
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
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.
Aspect-based Sentiment Analysis of Public Opinions on Integrated Islamic Schools using Lexicon based and Machine Learning Approaches Muttakin, Fitriani; Savra, Daffa Takratama
Sistemasi: Jurnal Sistem Informasi Vol 15, No 3 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i3.5848

Abstract

This study aims to examine public perceptions of Integrated Islamic Schools through aspect-based sentiment analysis by integrating Latent Dirichlet Allocation, Lexicon-Based approach, and Deep Neural Networks. LDA is employed to extract topic structures that represent the semantic context of public reviews, Lexicon Based method is used for sentiment analysis, while DNN infers sentiment orientation based on the extracted representations. This approach seeks to combine the strengths of probabilistic topic modeling and deep learning to obtain a more comprehensive understanding of public opinion. The analysis was conducted on a collection of 2,280 online reviews, which after preprocessing resulted in 1,438 reviews processed using the LDA–DNN combination. The results demonstrate that this approach is capable of identify in opinion dimensions in a more contextual manner and enhancing the interpretability of the analysis outcomes. Empirical evaluation shows that the proposed model achieved an accuracy of 63.89% for aspect classification and 93.06% for sentiment classification, outperforming the K-Means–LSA and K-Means–PCA approaches, which achieved 45.14% and 31.94% accuracy for aspect classification and 92.36% accuracy for sentiment classification, respectively. These findings confirm the superiority of probabilistic topic modeling in capturing complex semantic relationships and provide a methodological contribution to the development of sentiment analysis in the context of integrated Islamic education.
Co-Authors Estanto, Baihaqi Hilmi Fayyad, Muhammad Fauzi Halim, Fandi Rahmat Kurniawan, Viki Megawati - Muttakin, Fitriani Pratama, Muhammad Ripaldo Suryani Suryani Tama, Naufal Safiq Yuda, Afi Ghufran