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All Journal Pendas : Jurnah Ilmiah Pendidikan Dasar JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Abdimas Imigrasi (JaIM) TEMATICS : TECHNOLOGY MANAGEMENT AND INFORMATICS RESEARCH JOURNALS International Journal of Multidisciplinary Approach Research and Science
Cakra Trinata
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Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Immunology & microbiology Law, Crime, Criminology & Criminal Justice Social Sciences Other

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KAJIAN LITERATUR : METODE PENGUJIAN KEAMANAN SISTEM APLIKASI BERBASIS WEB Sapta Kusuma Azhari; Cakra Trinata; Besse Hartati
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 10 No. 03 (2025): Volume 10 No. 03 September 2025
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v10i03.29519

Abstract

This document discusses the importance of information system security in the digital era, where much data and information is stored in website-based applications. Information system security is crucial to prevent various threats, such as data manipulation, information theft, and sabotage. This study aims to explore security testing methods, especially Penetration Testing, to identify and address vulnerabilities in web-based applications. The methods used in this study include a search for relevant literature, with a systematic approach using the PRISMA method. The results of the study indicate that there are several methods that are often used in application security testing, including Penetration Test, OWASP ZAP, and Vulnerability Assessment.
The Role AI in Securing Immigration Data: A Descriptive Study of Digital-Era Protection Policies Piranti, Nurul Maharani; Rosmaya, Mila; Romdendine, Muhammad Fahrury; Trinata, Cakra; Martadireja, Okky Pratama
International Journal of Multidisciplinary Approach Research and Science Том 4 № 01 (2026): International Journal of Multidisciplinary Approach Research and Science
Publisher : PT. Riset Press International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59653/ijmars.v4i01.2083

Abstract

The digital transformation of immigration systems has introduced significant challenges to personal data protection, particularly from escalating cyber threats. Critical digital infrastructure, including SIMKIM, e-Visa, and autogate, handles sensitive data whose breach could severely compromise national security and individual privacy. This qualitative descriptive study analyzes Indonesia's immigration data protection policies and evaluates the potential of Artificial Intelligence (AI) as a strategic tool for cyber threat mitigation. Our findings, based on a comprehensive literature review, indicate that current legal frameworks are largely normative, lacking specific technical provisions for data protection within this sector. In contrast, AI technology shows immense promise for detecting system anomalies, enhancing audit capabilities, and preventing data breaches. Consequently, this study recommends implementing specific sectoral technical regulations, investing in digital human resource capacity, and deploying AI-driven systems to secure immigration data, thereby laying a foundation for a risk-based, adaptive digital security governance framework.
SYSTEMATIC LITERATURE REVIEW: PEMETAAN KARAKTERISTIK DENGAN DATA MINING MENGGUNAKAN ALGORITMA K-MEANS Rizqi Afif Izzuddin; Cakra Trinata; Okky Pratama Martadiredja
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 10 No. 04 (2025): Volume 10 No. 04 Desember 2025 In Order
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v10i04.33310

Abstract

K-Means is a non-hierarchical data clustering method based on data similarity, capable of grouping data into several clusters. In other words, data with similar characteristics are grouped into the same cluster, while data with differing characteristics are placed in separate clusters. The K-Means method can be applied to various types of data, including those in the governmental sector. Although the K-Means algorithm has been widely utilized, its application in government-related activities remains limited, often restricted to selection or recruitment processes. Moreover, the use of attributes in such studies needs to be expanded to achieve more optimal results. This study reviews several articles that implement the K-Means method in research related to public administration. Based on the findings, journals discussing the use of the K-Means algorithm for clustering in government contexts are proven to be relevant and beneficial for future research. It can be concluded that the K-Means method is a validated approach and can be effectively employed for clustering in the public sector. This method also offers advantages across various aspects of governance, benefiting stakeholders, the general public, and other administrative domains.
Co-Authors Aji, M.Bayu Krisna Anung Prabadhi, Isidorus Anung, Ishidorus Arief Febrianto Arwanto, Moch Miftah Mawardi Berlian, Theresya Besse Hartati Budy Mulyawan, Budy Dewanto, Rafi Dila Permatasari Fahrury Romdendine, Muhammad Galuh Boy Hertantyo, Galuh Boy Hilmy, Muhadzib Rezki Imadana Nicholas Saputra Marbun, Jecksen Jani, Salsabila Andi Karunia, Ferdyan Samuel Martadireja, Okky Pratama Mastur, Anida Sri Rahayu Nur Erlina, Novita Nurkumalawati, Intan Nursanto, Gunawan Ari Okky Pratama Martadiredja Pascalis Danny Kristi Wibowo Piranti, Nurul Maharani Pratikno, Raga Sakti Priati Assiroj Priati Priati, Priati Rizqi Afif Izzuddin Romdendine, Muhammad Fahrury Rosmaya, Mila Sapta Kusuma Azhari Tongam Gustaf Christoper Edwin