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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Pendas : Jurnah Ilmiah Pendidikan Dasar JATI (Jurnal Mahasiswa Teknik Informatika) TEMATICS : TECHNOLOGY MANAGEMENT AND INFORMATICS RESEARCH JOURNALS Inkubis: Jurnal Ekonomi dan Bisnis Innovative: Journal Of Social Science Research International Journal of Multidisciplinary Approach Research and Science
Martadireja, Okky Pratama
Politeknik Imigrasi
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Education Immunology & microbiology Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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TWITTER SOCIAL NETWORK ANALYSIS AND SENTIMENT IDENTIFICATION OF “VAKSIN BOOSTER” KEYWORD Romdendine, Muhammad Fahrury; Martadireja, Okky Pratama; Danutirta, Alif Sofa; Sulasno, Mitsal Shafiq
TEMATICS: Technology Management and Informatics Research Journals Vol 6 No 2 (2024): TEMATICS: Technology ManagemenT and Informatics Research Journals
Publisher : Polteknik Imigrasi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abstract. The low acceptance level and limited coverage of booster vaccines, despite their critical importance for public health, highlight the need for deeper insights into societal perceptions and behaviors. Social networks, as a significant medium for information dissemination, offer a valuable opportunity to understand public discourse and identify influential factors. This study leverages graph topology analysis to map and analyze the dynamics of vaccine-related discussions within social networks. By identifying key individuals who play pivotal roles in spreading booster vaccine information, the analysis reveals the structure and flow of information within the network. Furthermore, sentiment analysis indicates that neutral interactions dominate these discussions, followed by negative and positive sentiments. Notably, the neutral content largely pertains to travel procedures, which aligns with the "mudik" tradition during the data collection period. These findings provide a framework for understanding the sociotechnical landscape of vaccine acceptance and offer actionable insights for designing targeted, effective strategies to enhance booster vaccine uptake.
IMPLEMENTASI DATA MINING PADA ANALISIS KARAKTERISTIK PELANGGAN: SYSTEMATIC LITERATURE REVIEW Marclyna Nau, Maria; Nurul Fathya, Vita; Pratama Martadireja, Okky
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13725

Abstract

Pada masa ini, salah satu hal yang menjadi kunci dari transisi perkembangan zaman adalah pengolahan data. Data menjadi krusial karena berbagai pengaruh yang dimilikinya seperti pada prediksi, analisis, pengambilan keputusan, penetuan kebijakan, terciptanya inovasi, efisiensi, peningkatan mutu pelayanan, dan masih banyak lagi. Berangkat dari hal ini, salah satu metode pengolahan data yang umum digunakan adalah data mining yang bertujuan untuk menambang dan menggali informasi pada data di berbagai sektor baik pemerintahan, perusahaan, pendidikan, kesehatan, dan lain sebagainya. Tinjauan literatur sistematis ini akan mencari tau secara spesifik metode dan algoritma seperti apa yang digunakan dalam implementasi data mining pada analisis pelanggan. Pada penelitian ini, dilakukan tinjauan terhadap penelitian-penelitian terdahulu yang memiliki keterkaitan dengan implementasi data mining ini sendiri. Dalam tinjauan literatur sistematis ini akan dilakukan filterisasi pada referensi artikel terkait hingga menemukan referensi yang dapat menjadi acuan dalam membantu melakukan analisis pelanggan dengan metode dan algoritma yang tepat serta melihat seberapa efektif penggunaan data mining pada analisis karakteristik pelanggan. Hasil penelitian ini meninjau bahwasannya algoritma K-means adalah algoritma yang umum digunakan serta terbukti efektif dalam penggunaannya.
Association Rule Mining across Multiple Domains: Systematic Literature Review Syahirah, Dayini; Priati, Priati; Martadireja, Okky Pratama
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15227

Abstract

This Systematic Literature Review (SLR) synthesizes 50 studies published between 2020 and 2025 that applied Association Rule Mining (ARM) across multiple domains, using the PRISMA 2020 framework. The review examines application areas, algorithm choices, implementation tools, parameter settings, and emerging trends. Results indicate that transportation and market analysis are the most prominent domains, followed by healthcare, manufacturing, and governance, with smaller contributions from tourism, agriculture, energy, and the environment. Apriori remains the most widely used algorithm due to its simplicity, FP-Growth is preferred for efficiency, and hybrid or modified approaches are adopted to address scalability issues. Python dominates as the primary implementation tool, alongside RapidMiner and R-Studio, with parameter thresholds generally adapted to dataset size and domain-specific needs. The novelty of this review lies in providing a cross-domain synthesis of ARM, filling the gap left by prior reviews that were limited to specific fields or algorithms. This broader perspective reveals temporal trends and recurring challenges, particularly scalability and interpretability, while identifying opportunities such as integration with deep learning, real-time ARM, and cross-domain adaptation. By offering a structured overview of developments in ARM, this study contributes both conceptual insights and practical guidance, serving as a reference for optimizing applications and informing future research directions.
The Effect of Auto Gate Systems on The Traveler Profiling System at Soekarno-Hatta International Airport Martadireja, Okky Pratama; Romdendine, Muhammad Fahrury
Innovative: Journal Of Social Science Research Vol. 4 No. 6 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i6.16282

Abstract

The growing numbers of international flights have resulted in the improvement of immigration processes at the airports. Therefore, self-service biometric gates have been developed to guarantee more speed and security at the border. In this paper, the authors evaluate the effects of implementing auto gate systems at Soekarno-Hatta International Airport, specifically looking into the effect of operational efficiencies, privacy, and data security. This study uses a qualitative approach to literature analysis. The author uses previous studies, Government papers, and industry documents to identify the mechanisms that facilitate effective implementation and acceptance of these systems and their relationship to privacy and data security issues. Implementation of the proper auto gate systems might ease immigration processes and encourage traveling around the world, but addressing internal aspects like perception, data safety, and even human beings will always be vital. The findings from this analysis recommend an increase in the data protection models in place, enhancing the accuracy of the systems and making the procedures more open.
PERAMALAN JUMLAH PERMOHONAN PASPOR : SYSTEMIC LITERATURE REVIEW Wibowo, Besar Tri; Vita Nurul Fathya; Okky Pratama Martadireja
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 10 No. 02 (2025): Volume 10, Nomor 02 Juni 2025 t
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v10i02.23499

Abstract

The mobility of society, influenced by globalization, continues to increase, with fewer limitations on travel distances. People around the world can travel to other countries easily and quickly. Cross-border movement requires a travel document in the form of a passport as an individual's identification when entering and leaving a country. The number of passport applications varies each year. Typically, public interest in obtaining a passport rises during certain seasons, such as the Hajj season or long holidays. To anticipate uncertainties in passport application numbers, this study aims to explore which forecasting methods can be used to predict the number of passport applications within a specific timeframe. This research employs a survey approach by reviewing scientific journals or articles published between 2020 and 2025. Through this study, we can identify the types of methods used in similar research. Based on the findings, the most commonly used approach is the Autoregressive Integrated Moving Average (ARIMA), while the approach with the highest research accuracy is the Fuzzy Time Series Model Cheng, achieving an accuracy of 99.55%.
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 PENETRATION TESTING PADA KEAMANAN WEBSITE Lamintang Ramadhan; Nurul Maharani Piranti; Okky Pratama Martadireja
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 10 No. 04 (2025): Volume 10 No. 04 Desember 2025 In Published
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v10i04.33554

Abstract

Advances in internet technology pose major challenges in maintaining website security against various cyber threats. This Systematic Literature Review (SLR) aims to identify and analyze the methods and tools used in website security testing with a focus on penetration testing. This study was conducted by screening 1,911,363 initial articles obtained from various academic repositories, such as IEEE Xplore, Springer Link, Crossref, Semantic, and Google Scholar, resulting in 32 articles that met the inclusion criteria based on their relevance and quality. The results of the study show that the penetration testing approach is dominated by various security frameworks, including OWASP, ISSAF, OSSTMM, and PTES, which are used to evaluate and strengthen web system security. In addition, this study reveals that tools such as Acunetix, Nmap, Metasploit, Burp Suite, and SQLmap are often used to detect and exploit security vulnerabilities. Several studies also highlight the integration of machine learning technology in detecting attacks such as SQL Injection and Cross-Site Scripting (XSS), which indicates a new trend in website security testing. The conclusion of this study emphasizes the need for a more adaptive approach to penetration testing, combining automation techniques and manual analysis to improve the effectiveness of threat detection and mitigation. The results of this study can serve as a reference for academics and cybersecurity practitioners in choosing the optimal strategy to secure website systems from evolving threats.
SYSTEMATIC LITERATURE REVIEW ON QR CODE IMPLEMENTATION FOR GOVERNMENT INVENTORY MANAGEMENT SYSTEMS Geraldo Nathaniel; Gunawan Ari Nursanto; Okky Pratama Martadireja
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 10 No. 04 (2025): Volume 10 No. 04 Desember 2025 In Published
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v10i04.33668

Abstract

This study aims to systematically review various literature related to the design and development of a web-based office stationery (ATK) management application utilizing QR-Code technology. The research focuses on the implementation of this system at the Class I Non-TPI Immigration Office in Bekasi as an effort to support the efficiency of APBN (State Budget) utilization. This study collects, filters, and analyzes articles, journals, and other academic sources relevant to the topic to provide a comprehensive overview of the benefits, challenges, and methodologies for designing digital systems in government environments. The review results indicate that a QR-Code-based system can reduce recording errors, accelerate administrative processes, and enhance transparency in budget usage. Additionally, the implementation of this technology supports the digital transformation of bureaucracy, aligning with the government's budget efficiency policies. The findings from the reviewed literature provide a theoretical foundation for the proposed system's development and highlight the importance of integrating information technology into ATK inventory management to optimize operational efficiency and budget utilization.
Passport Application Data Mining Implementation at The Muara Enim Non-TPI Class II Immigration Office Piranti, Nurul Maharani; Khalish, Fadhlulrahman; Martadireja, Okky Pratama
Inkubis : Jurnal Ekonomi dan Bisnis Vol. 8 No. 1 (2026): INKUBIS Jurnal Ekonomi Dan Bisnis
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/inkubis.v8i1.141

Abstract

Background: The Class II Non-TPI Muara Enim Immigration Office processes thousands of passport applications annually. However, existing data utilization remains limited to administrative recording and routine reporting, constraining its strategic value for service planning. Objective: This study aims to identify meaningful patterns in passport application data and determine the optimal cluster structure using the K-Means algorithm to support evidence-based decision-making. Methods: Therefore, this study was conducted to apply data mining techniques using the K-Means clustering method to analyze passport application patterns based on data from 2022 to 2024, with attributes such as gender, travel purpose, type of application, type of passport, and year of submission, implemented through Python with the support of Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn libraries. Results: The results show that passport applicants can be grouped into several clusters with specific characteristics — for instance, clusters dominated by applicants of productive age with purposes of working or performing Umrah, as well as other clusters consisting mainly of applicants traveling for tourism or education; moreover, the analysis reveals spikes in applications during certain periods, such as before the Hajj season and long holidays. Conclusion: Thus, it can be concluded that applying K-Means clustering provides added value beyond conventional data processing, enabling administrative data to be transformed into informative and predictive patterns that support resource planning, staff allocation, and more effective data-driven service policies.
Co-Authors Cakra Trinata Danutirta, Alif Sofa Geraldo Nathaniel Khalish, Fadhlulrahman Lamintang Ramadhan Marclyna Nau, Maria Nursanto, Gunawan Ari Nurul Fathya, Vita Nurul Maharani Piranti Piranti, Nurul Maharani Priati Priati, Priati Romdendine, Muhammad Fahrury Rosmaya, Mila Sulasno, Mitsal Shafiq Syahirah, Dayini Vita Nurul Fathya Wibowo, Besar Tri