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All Journal JUSIFO : Jurnal Sistem Informasi Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Pendidikan dan Teknologi Indonesia
Siregar, Syafril
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Computer Science & IT Education Engineering

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Analisis Manajemen Risiko Teknologi Informasi Pada Sistem TBS Dengan Metode ISO 31000 di PT XYZ Rahayu, Sherly; Saputra, Eki; Luthfi Hamzah, Muhammad; Fronita, Mona; Siregar, Syafril
Jurnal Pendidikan dan Teknologi Indonesia Vol 4 No 12 (2024): JPTI - Desember 2024
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.551

Abstract

Penelitian ini mengeksplorasi manajemen risiko informasi dalam sistem pembelian Tandan Buah Segar (TBS) di PT XYZ dengan menerapkan metode ISO 31000. Metode yang ini mencakup identifikasi risiko melalui observasi langsung, wawancara dengan karyawan, dan penyebaran kuesioner kepada responden yang terlibat dalam proses pembelian sistem TBS ini. Delapan risiko yang teridentifikasi, di mana 4 risiko dikategorikan sebagai tinggi, yaitu  server down, gagal update, petir, dan listrik padam, sementara 4 risiko lainnya tergolong sedang, yaitu kebakaran, human error, data corrupt, dan koneksi jaringan terputus. Dengan dua risiko utama , yaitu server down dan gagal update, yang masing-masing memiliki nilai RPN 25. Penelitian ini merekomendasikan langkah-langkah mitigasi, seperti pemeliharaan rutin terhadap server dan jaringan, pelatihan rutin untuk karyawan, serta pengadaan sumber daya cadangan untuk mengurangi kemungkinan dan dampak dari risiko yang teridentifikasi. Diharapkan, penerapan langkah-langkah mitigasi ini dapat meningkatkan kualitas sistem pembelian TBS, serta memberikan kontribusi signifikan bagi perusahaan dalam mengelola risiko secara efektif. Hasil penelitian ini juga dapat menjadi acuan bagi perusahaan lain yang menghadapi masalah serupa dalam manajemen risiko sistem informasi.
Comparative Analysis of Naïve Bayes Classifier and Support Vector Machine for Multilingual Sentiment Analysis: Insights from Genshin Impact User Reviews Aprinastya, Rachell; Jazman, Muhammad; Syaifullah, Syaifullah; Rahmawita, Medyantiwi; Siregar, Syafril; Saputra, Eki
JUSIFO : Jurnal Sistem Informasi Vol 10 No 2 (2024): JUSIFO (Jurnal Sistem Informasi) | December 2024
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v10i2.24876

Abstract

This study evaluates the performance of Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC) algorithms in analyzing user sentiments regarding the popular online game Genshin Impact. The dataset comprises reviews sourced from the Google Play Store and App Store in both Indonesian and English, reflecting linguistic diversity. Preprocessing techniques such as tokenization, stemming, and Term Frequency-Inverse Document Frequency (TF-IDF) were employed to enhance data quality. Sentiments were classified as positive, neutral, or negative using TextBlob-assisted processes. Results demonstrate that NBC outperformed SVM across all metrics, with an average accuracy of 71% compared to 63%. Notably, sentiment analysis on English datasets consistently achieved higher accuracy than on Indonesian datasets, emphasizing the challenges posed by the linguistic complexity of Indonesian. This research underscores the critical role of language-specific adaptations in improving machine learning algorithms for multilingual sentiment analysis. The findings provide actionable insights for optimizing user engagement through enhanced game feedback mechanisms.
Analisis Perilaku Pengguna SAMSAT Digital Nasional Menggunakan Metode TAM danTPB Intan, Sri; Fronita, Mona; Hamzah, Muhammad Luthfi; Saputra, Eki; Anofrizen, Anofrizen; Siregar, Syafril
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 4 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i4.44362

Abstract

This study focuses on the challenge of low user behavioral intention towards the SIGNAL app, which is an important issue in digital technology adoption. To investigate this issue, research was conducted using a comprehensive framework integrating TAM and TPB methods, with six hypotheses proposed. Data were analyzed using multiple linear regression on questionnaires completed by a sample of SIGNAL users. The results of the analysis showed a significant relationship between perceived usefulness and ease of use and users' attitude towards the app, as well as a strong relationship between users' attitude and behavioral intention to use the app. These findings signify that the more users perceive tangible benefits and ease of use of the app, the more positive their attitude, which in turn drives SIGNAL's adoption intention. However, several hypotheses were not supported by the results of the analysis, including the absence of significant relationships between perceived benefits and users' behavioral intentions, as well as between subjective norms and behavioral intentions. This suggests that although users realize the benefits of the app, social factors and perceived individual control are not significant in determining usage intentions. The implications of these findings provide guidance for digital platform developers to design more effective interventions to increase the use of SIGNAL, as well as facilitate behavior change in the community.
Co-Authors Anofrizen Anofrizen Aprinastya, Rachell Eki Saputra Hamzah, Muhammad Luthfi Intan, Sri Jazman, Muhammad Mona Fronita, Mona Muhammad Luthfi Rahmawita, Medyantiwi Sherly Rahayu Syaifullah Syaifullah