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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Pendas : Jurnah Ilmiah Pendidikan Dasar JATI (Jurnal Mahasiswa Teknik Informatika) G-Tech : Jurnal Teknologi Terapan TEMATICS : TECHNOLOGY MANAGEMENT AND INFORMATICS RESEARCH JOURNALS Journal of Dinda : Data Science, Information Technology, and Data Analytics Innovative: Journal Of Social Science Research International Journal of Multidisciplinary Approach Research and Science Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Muhammad Fahrury Romdendine
Politeknik Imigrasi
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Public Health Social Sciences Other

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TINJAUAN SISTEMATIS ANALISIS SENTIMEN DENGAN METODE PRISMA (2021–2025) Bilkis Praharani; Priati Assiroj, Muhammad Fahrury Romdendine
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 10 No. 03 (2025): Volume 10 No. 03 September 2025 Terbit
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v10i03.34012

Abstract

This study presents a systematic review of sentiment analysis research conducted between 2021 and 2025 using the PRISMA method. From searches across three databases, namely Google Scholar, Semantic Scholar, and Garuda, a total of 12,089 articles were identified and then filtered down to 30 selected studies. The aim of this study is to identify the methods, algorithms, data sources, and accuracy levels used in sentiment analysis research. The findings indicate that the Naïve Bayes algorithm is the most widely applied, followed by SVM, while other algorithms such as KNN, Random Forest, Logistic Regression, and CNN were used only in limited cases. These findings highlight that sentiment analysis remains largely directed toward digital and social media issues, with classical algorithms such as Naïve Bayes and SVM continuing to be the main choices due to their ease of implementation and competitive accuracy.
SYSTEMATIC LITERATURE REVIEW: PENERAPAN K-MEANS CLUSTERING Albert Putra Pratama; Vita Nurul Fathya; Muhammad Fahrury Romdendine
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.v10i03.29785

Abstract

This study is a systematic literature review on the application of K-Means Clustering in various sectors using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach. The clustering technique, particularly K-Means, is widely used in big data analysis due to its simplicity and efficiency. Although this method is popular, the main challenges include determining the optimal number of clusters, handling outliers, and computational limitations when applied to large-scale data. This research analyzes sectors that apply K-Means Clustering, such as industry, education, healthcare, and finance. The findings of this study are expected to provide insights into the trends in the use of clustering methods and offer recommendations on the most suitable tools for applying clustering based on data characteristics .
PEMANFAATAN TEKNOLOGI INFORMASI DALAM KEAMANAN LABORATORIUM UNTUK MENGANTISIPASI PENYELUNDUPAN NARKOBA DI INDONESIA: THE UTILIZATION OF INFORMATION TECHNOLOGY IN PRISON SECURITY TO ANTICIPATE DRUG SMUGGLING IN INDONESIA Alif Shofa Danutirta; Muhammad Fahrury Romdendine
TEMATICS: Technology Management and Informatics Research Journals Vol. 7 No. 2 (2025): TEMATICS: Technology ManagemenT and Informatics Research Journals
Publisher : Polteknik Imigrasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52617/tematics.v7i2.851

Abstract

Penelitian ini bertujuan untuk menganalisis peluang pemanfaatan teknologi informasi dalam pencegahan penyelundupan narkoba di lembaga pemasyarakatan, dengan fokus pada integrasi sistem informasi dalam pengamanan. Metode penelitian menggunakan pendekatan kualitatif deskriptif berbasis data sekunder dari kebijakan internal, praktik keamanan, catatan insiden, dan studi terdahulu. Hasil penelitian menunjukkan bahwa pemanfaatan teknologi informasi seperti sistem CCTV berbasis kecerdasan buatan, pengendalian akses digital, dan integrasi manajemen data pengunjung memberikan kontribusi penting dalam pencegahan penyelundupan. Namun, hambatan utama masih ditemukan, berupa kurangnya integrasi sistem informasi, keterbatasan infrastruktur teknologi, rendahnya literasi digital petugas, ancaman keamanan siber, serta keterbatasan sumber daya. Kesimpulannya, transformasi digital dalam pengamanan lapas perlu menjadi prioritas melalui penguatan infrastruktur, peningkatan kapasitas SDM, serta kolaborasi antarlembaga untuk deteksi dini dan pencegahan. Penelitian ini berkontribusi pada pengembangan kebijakan dan sistem informasi pemasyarakatan yang lebih adaptif terhadap ancaman modern.
The Role AI in Securing Immigration Data: A Descriptive Study of Digital-Era Protection Policies Nurul Maharani Piranti; Mila Rosmaya; Muhammad Fahrury Romdendine; Cakra Trinata; Okky Pratama Martadireja
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.
Co-Authors Albert Putra Pratama Alif Shofa Danutirta Angga Aditya Permana Angga Aditya Permana Annisa Annisa Ardhi Baskara, Arya Bilkis Praharani Cakra Trinata Cakra Trinata Danutirta, Alif Sofa Dewanto, Rafi Dwi Handoko Putra Fatriani, Rizka Maftuh, Naufal Maharani Piranti, Nurul Mala Nurilmala Martadireja, Okky Pratama Mila Rosmaya Nursanto, Gunawan Ari Nurul Maharani Piranti Okky Pratama Martadireja Perdana, Analekta Tiara Sulasno, Mitsal Shafiq Vita Nurul Fathya Wilonotomo Wisnu Ananta Kusuma