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Cyber Security in Indonesian Higher Education Institutions: Lessons Learned from Recent Cyber Attacks Marpaung, Jonathan Nahum
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 1 (2025): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i1.876

Abstract

Cyber security is paramount to the sustainability of higher education institutions as more institutions move their information system to the cloud and allow stakeholders to access their information technology services from on-campus WIFI and mobile devices. This study aims to understand Indonesian higher education's current cyber security landscape by analyzing recent cyber-attacks that hit all sectors, notably higher education. Recent news articles and government reports related to cyber-security were analyzed using document analysis. This study found that data theft attacks were the number one cyber threat that higher education institutions faced, followed by attacks on institutional websites, social media pages, and personal mobile devices. This study found that the nature of recent cyber-attacks was consistent with the assessment posed by previous literature that stakeholders’ recent level of ability in cyber-security is not where it is supposed to be. The potential impact of future cyber-attacks on institutions and stakeholders is significant, underscoring the importance of this new understanding, which can help institutions prepare their stakeholders better to mitigate such threats.
Evaluation of user satisfaction in the public sector’s Lelang Indonesia application using sentiment analysis and text mining Hidayat, Nurul; Marpaung, Jonathan Nahum
Manajemen dan Bisnis Vol 24, No 2 (2025): September 2025
Publisher : Department of Management - Faculty of Business and Economics. Universitas Surabaya.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24123/mabis.v24i2.863

Abstract

This study used sentiment analysis and text mining on the user reviews of the Lelang Indonesia mobile application, specifically focusing on the ratings and reviews on the Google Play Store for the past six years. Sentiment analysis was performed by manually categorizing the data according to user review scores (positive or negative) and subsequently classified using the Support Vector Machine technique to assess accuracy levels. Various text mining approaches were employed to extract details regarding the issues or problems encountered by users. The study found that numerous Lelang Indonesia mobile application users encounter barriers that adversely influence their evaluation of public service quality, as seen by the attitude expressed in their reviews. The analysis of these reviews revealed several concerns, mainly regarding technical applications and service business procedures that require attention and follow-up from institutions. The study's findings can offer institutions a means to evaluate the quality of auction services delivered via mobile applications and serve as a basis for proactively enhancing these services to align with user expectations. This study enhances the understanding of mobile application-based public service satisfaction evaluation, enabling public institutions to evaluate these services in real-time by analyzing user sentiment from reviews.
The Influence of Digital Transformation on Risk-Taking in Commercial Banks in Indonesia Using Text Mining Mardhika, Bimo Anugrah Putra; Marpaung, Jonathan Nahum
Eduvest - Journal of Universal Studies Vol. 5 No. 9 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i9.51379

Abstract

This research examines the impact of digital transformation on banking risks, specifically focusing on credit risk (NPL), liquidity risk (LDR), and insolvency risk (Z-score). Employing a quantitative method, the study constructs a digital transformation index using text-mining techniques applied to annual reports of Indonesian commercial banks. The analysis utilizes Ordinary Least Squares (OLS), Fixed Effects (FE), and the System Generalized Method of Moments (SYS-GMM) on a dataset comprising 59 commercial banks in Indonesia from 2018 to 2024. The results reveal that digital transformation significantly raises credit risk. In contrast, its effects on liquidity and insolvency risks are statistically insignificant, suggesting potential improvements in credit evaluation through the use of enhanced data and technological tools. Additionally, the study demonstrates the utility of the SYS-GMM model in addressing endogeneity concerns in dynamic panel data. These findings can help regulators understand the strategic role of digital implementation and innovation in enhancing risk management and financial stability within the commercial banking sector.