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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Tadbir Peradaban PASCIDEV: Pasundan Social Science Development
Syahirah, Dayini
Unknown Affiliation
Computer Science & IT Economics, Econometrics & Finance Social Sciences

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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.
Implementation of Association Rule Mining Using the FP-Growth Algorithm on Non-Procedural Indonesian Migrant Worker (PMI) Data in South Sulawesi Syahirah, Dayini
Pasundan Social Science Development Vol. 6 No. 1 (2025): Pasundan Social Science Development (PASCIDEV)
Publisher : Doctoral Program of Social Science Pasundan University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56457/pascidev.v6i1.271

Abstract

This study examines patterns in non-procedural Indonesian Migrant Workers (PMI) data in South Sulawesi and utilizes them to strengthen data-driven prevention. The method used is association rule mining using the FP-Growth algorithm within the CRISP-DM framework implemented through Altair AI Studio software. Modeling is run based on a minimum support value of 30% with a minimum confidence of 80%) for the Makassar, Pare-Pare, and Palopo Immigration Office datasets. Patterns are retained if the lift value is > 1 and selecting the top 10 patterns for each dataset. The results show consistent frequent itemsets and association rules indicating a general pattern of non-procedural PMI dominated by adult males with destinations in Malaysia with illegal/undocumented issues. The findings can be used as a preventive measure in strengthening interviews and document verification in the passport issuance process and the Immigration fostered village program. The study confirms that the application of FP-Growth with support, confidence, and lift evaluations provides evidence-based insights relevant to a more targeted and effective non-procedural PMI prevention policy by the Immigration Office
Pemanfaatan Data Subject of Interest (SOI) Dalam Pengawasan Keimigrasian Di Indonesia Syahirah, Dayini
Jurnal Tadbir Peradaban Vol. 5 No. 3 (2025): Jurnal Tadbir Peradaban
Publisher : Prodi Manajemen STIE Hidayatullah Depok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55182/jtp.v5i3.711

Abstract

Penelitian ini menggunakan metode deskriptif kualitatif dengan pendekatan studi pustaka. Penelitian ini membahas pemanfaatan Subject of Interest (SOI) sebagai instrumen pengawasan keimigrasian di Indonesia. SOI memuat data Warga Negara Indonesia (WNI) dan Warga Negara Asing (WNA), mencakup identitas dan riwayat pelanggaran yang bersifat dinamis, sehingga informasi selalu relevan dan diperbarui. Sistem ini terintegrasi dengan SIMKIM dan Border Control Management (BCM), memungkinkan deteksi otomatis saat subjek melakukan permohonan paspor, visa, atau melewati Tempat Pemeriksaan Imigrasi (TPI), serta menampilkan notifikasi dan visualisasi status subjek melalui indikator warna sesuai tingkat risiko. Pemanfaatan SOI mendukung implementasi prinsip Selective Policy, memberikan dasar bagi pengawasan yang selektif, adaptif, dan berbasis bukti. Selain itu, sistem ini memperkuat pengambilan keputusan petugas Imigrasi dalam menilai kebutuhan pemeriksaan lanjutan atau langkah administratif tambahan, sehingga keamanan dan ketertiban nasional dapat dijaga secara lebih efektif. Hasil penelitian menegaskan pentingnya SOI sebagai alat analitik dan pencegahan dini dalam pengawasan keimigrasian modern
Co-Authors Martadireja, Okky Pratama Priati Priati, Priati