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All Journal Management Studies and Entrepreneurship Journal (MSEJ) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Equivalent: Jurnal Ilmiah Sosial Teknik
Sfenrianto Sfenrianto
Universitas Bina Nusantara
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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INFORMATION SYSTEM SECURITY EVALUATION USING COBIT 5 FRAMEWORK Lilis Griffith Toyner; Sfenrianto Sfenrianto
Journal of Information System Management (JOISM) Vol. 4 No. 2 (2023): Januari
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/joism.2023v4i2.992

Abstract

Most companies use information technology to develop their business. But there are things to note, some threats can occur and cause losses. Undesirable events hinder the achievement of company goals and strategies. PT XYZ believes that information security is important in all business activities. Threats that can compromise information security. Information is an important asset for PT XYZ. Therefore, it is necessary to evaluate or measure the controls and activities that have been implemented to protect company data/information. Evaluation in this paper uses the COBIT 5 Framework which focuses on Manage Security Services (DSS05). Keywords: Evaluation, COBIT 5, Manage Security Services, Capability Level
Sosial Media Marketing Strategi Analisis Dan Implementasi Untuk Meningkatkan Keterlibatan Pelanggan (Studi Kasus Pada PT. XYZ) Della Hernita Putri; Sfenrianto Sfenrianto
Management Studies and Entrepreneurship Journal (MSEJ) Vol. 4 No. 5 (2023): Management Studies and Entrepreneurship Journal (MSEJ)
Publisher : Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/msej.v4i5.1804

Abstract

Social media plays a significant part in marketing promotion techniques in the modern, digitalized corporate world. Because of its rapid growth, it is important to note social media's influence on marketing for both small and large businesses, using social media as a marketing tool necessitates the owner to develop customer connection with their follower(s) or member(s). Posting content to social media must have a clear concept and strategy to be successful, not only for promoting the business but also it can increase sales and dig up more information about their follower(s) by doing various kind of interactions and activities. This study attempted to investigate and explore the impact of Social Media Marketing Strategy Analysis and Implementation to increase Customer Engagement at PT.XYZ. This study uses a social media marketing strategy framework by Tuten & Solomon, and a qualitative method which data collection is done by interview and observation. The result showed that with maximizing the features and applying the concept social media marketing strategy, can increase the customer engagement and in the sales conversions as well.
Sentiment Analysis Comparison of Two E-Commerce Platforms Using Random Forest, Support Vector Machine, Logistic Regression, and IndoBERT Theresia Vania Davita Suyana; Sfenrianto Sfenrianto
Equivalent: Jurnal Ilmiah Sosial Teknik Vol. 8 No. 2 (2026): Equivalent: Jurnal Ilmiah Sosial Teknik
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jequi.v8i2.324

Abstract

Background: The rapid growth of e-commerce in Indonesia has generated massive volumes of user-generated reviews. A critical mismatch exists between numerical star ratings and the actual sentiment expressed in review texts, creating unreliable signals for platform management and highlighting the need for text-based sentiment analysis. Objective: This study aims to analyze user review sentiments towards two leading e-commerce platforms in Indonesia using Machine Learning and Deep Learning approaches. Methods: The analysis process was conducted through the CRISP-DM stages, including data cleaning, labeling, model training, and performance evaluation. Two types of labeling were used, namely Rating-Based and VADER-Based, to compare the accuracy levels of sentiment classification. Four models were applied: Random Forest, Support Vector Machine (SVM), Logistic Regression, and IndoBERT. VADER labeling was adapted for Indonesian through preprocessing with an Indonesian-English translation layer. Results: Based on the evaluation results, the IndoBERT model showed the best performance on e-commerce X, with an accuracy of 0.96 using VADER-based labeling. Meanwhile, for e-commerce Y, Random Forest achieved an accuracy of 0.81 using VADER labeling. These results indicate that IndoBERT's Transformer architecture with contextual embeddings enabled superior understanding of Indonesian semantic nuances. Random Forest's advantage on e-commerce Y (627 samples per label) reflects a lower overfitting risk compared to deep learning models on small datasets. Conclusion: This study demonstrates the effectiveness of combining IndoBERT and VADER in Indonesian sentiment analysis and can serve as a reference for the e-commerce industry to improve service quality and customer satisfaction strategies.
Co-Authors Della Hernita Putri Lilis Griffith Toyner Theresia Vania Davita Suyana