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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA KLIK: Kajian Ilmiah Informatika dan Komputer
Eki Saputra
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Cybersecurity Supply Chain Risk Management Using NIST SP 800-161r1 Rahmi Aulia Astri; Muhammad Jazman; Syaifullah; Eki Saputra
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 6 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i6.799

Abstract

Supply chain security issues were related to the product life cycle in an information system so it can harm the success of a company. Nowadays, there has been a paucity of analytical and decision-support tools used to analyze security supply chains. The purpose of this research was to determine the maturity level of supply chain risk management so that the research results can provide mitigation and optimize decision support to minimize supply chain risk in a company. The stages of this research started with a literature study, identification of the problem, data collection, and data analysis. Data collection was carried out using a questionnaire with a Likert scale referring to NIST SP 800-161r1. Data analysis was performed using descriptive statistics to describe the maturity level of cyber security supply chain risk management. The results showed that the level of maturity in cybersecurity supply chain risk management using NIST SP 800-161 was at level 3, namely the Defined level. These findings provide recommendations for companies to improve the contingency plan aspect because it had a score with the lowest gap, especially in every product change activity carried out in the system
Analisis Perbandingan Pembelajaran Online Dan Offline Terhadap Mahasiswa UIN SUSKA Riau Menggunakan Naive Bayes Nur Asiah; Idria Maita; Rice Novita; Eki Saputra; Arif Marsal
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6701

Abstract

Online lectures have become a common method used in education to deliver course materials to students. However, the exucution of Online lectures is not always smooth and often faces various challenges. One of the main issues is the limited internet access frequently experienced by students, particularly in regions where internet connectivity is limied, making it difficult for them to parcipate in lecturesnseamlessly. Addtionally, some students encounter difficulties in time management and self-motivation for independent learning. This research aims to analyst the conditions and issues that arise during the implementation of Online lectures and compare them with the traditional Offline lecture delivery at UIN SUSKA Riau. The Naïve Bayes algorithm is applied for the analysis, with a focus on Accuracy, Precision, Sensitivity, and specificity. The findings and analysis using this algorithm demonstrate a remarkable accuracy rate of 66,67%, precision rate of 70%, sensitivity rate of 77,78% and specificity rate of 50%. By looking at the results obtained, the Naïve Bayes method was successfully used in analyzing comparisons of Online and Offline learning for students of UIN SUSKA Riau.
Sentimen Analisis Terhadap Fitur Tiktok Shop Menggunakan Nave Bayes dan K-Nearest Neighbor Sandi Saputra Hasibuan; Angraini Angraini; Eki Saputra; Megawati Megawati
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7238

Abstract

One of the features introduced by the TikTok platform is TikTok Shop, where sellers and buyers can interact easily through the live broadcast feature and promote products within the TikTok application without having to switch to other applications, as well as offering products at very cheap prices. However, this causes dissatisfaction from small traders who find it difficult to compete. Opinions about the TikTok Shop feature have created various responses from the public. This research aims to analyze public sentiment towards the TikTok Shop feature using Twitter as a data source. Applying the Naive Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms and dividing data using 10-Fold Cross Validation. Labeling was carried out using the clustering method using the K-Means algorithm, divided into three categories positive, negative and neutral. The addition of qualitative data analysis techniques with thematic analysis method in this research aims to find patterns or themes in the data. The labeling results show that 75% of the total data expresses negative sentiment towards the TikTok Shop feature. And the results of the thematic analysis found that the main theme, namely "Inappropriate regulations", covers 32% of the data, with a total of 754. This research concludes that the KNN method is superior to NBC, with better accuracy, precision and recall. This is in line with previous research that KNN is superior to nave Bayes, but other research shows the opposite where nave Bayes is superior to KNN. Further research can be carried out to improve the performance of these two algorithms in sentiment analysis, for example by using more sophisticated preprocessing methods, more representative feature extraction, or more efficient optimization techniques.
Co-Authors Angraini Angraini Arif Marsal Megawati Megawati Muhammad Jazman Nur Asiah Rahmi Aulia Astri Rice Novita Sandi Saputra Hasibuan Syaifullah