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All Journal Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Jurnal Teknik Informatika dan Teknologi Informasi
Ahmad Nur Ihsan Purwanto
Universitas Ary Ginanjar
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Social Sciences Other

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OPTIMIZING HALAL ZONE LAYOUTS IN INDUSTRIAL ESTATES FOR WAREHOUSING AND DISTRIBUTION: A SPATIAL MODELING APPROACH UTILIZING QGIS Fajar Azhari Julian; Ahmad Nur Ihsan Purwanto; Muhammad Roihan Zainuddin
Multidiciplinary Output Research For Actual and International Issue (MORFAI) Vol. 5 No. 5 (2025): Multidiciplinary Output Research For Actual and International Issue
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/morfai.v5i5.4319

Abstract

This study develops a comprehensive spatial modeling framework for optimizing halal zones dedicated to warehousing and distribution within industrial estates in Indonesia. Employing Geographic Information System (GIS) tools—particularly QGIS—the research integrates buffering, overlay, and zoning analyses to identify and evaluate potential halal-compliant industrial areas based on accessibility, land suitability, and contamination risk. Data from West Java’s industrial corridors reveal four viable sites (KM42, KM62, KM71B, and KM72A), with KM72A demonstrating superior strategic positioning due to high connectivity, low contamination risk, and ample capacity. The proposed model conceptualizes halal zones as integrated spatial entities designed to ensure strict product segregation, traceability, and adherence to Islamic principles. The study bridges theoretical constructs of halal assurance with pragmatic spatial planning methodologies, offering a replicable GIS-based approach to halal industrial zoning. From a policy perspective, it provides actionable insights for governments and industry actors to strengthen halal logistics infrastructure, thus supporting Indonesia’s ambition to become a global halal hub. The fusion of geospatial analytics and halal governance in this study represents a strategic advancement toward transparent, efficient, and sustainable halal industrial ecosystems.
Tren dan Perkembagan Supervised versus Unsupervised Learning Muhammad Naufal Ammr Dzakwan; Fadillah Dani Prawoto; Ahmad Nur Ihsan Purwanto
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 2 (2025): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5742

Abstract

The rapid development of artificial intelligence (AI) in the last decade has driven the widespread adoption of machine learning in various sectors, from healthcare and finance to manufacturing and education. Two main approaches dominate: supervised and unsupervised learning. Supervised learning leverages labeled data to build predictive models with high accuracy and strong generalization capabilities, but its application is often hampered by the need for expensive, time-consuming, and error-prone labeling processes. In contrast, unsupervised learning operates on unlabeled data to identify latent patterns, clustering, and novel data representations. This approach excels in large-data scale and exploration efficiency, but faces challenges in interpreting results, validating performance, and implementing them in real-world systems. This article presents a recent literature review (2020–2024) based on searches in Scopus, IEEE Xplore, and Google Scholar using the keywords “supervised learning,” “unsupervised learning,” “machine learning trends,” and “hybrid approaches.” The results show a significant trend toward integrating hybrid approaches such as semi-supervised learning, self-supervised learning, and a combination of transfer learning approaches, aiming to overcome the limitations of each paradigm. Furthermore, research gaps were identified regarding the need for robust evaluation methods for unsupervised learning, the development of adaptive models for dynamic data, and increased transparency in the learning process. Critically, this article highlights the importance of an interdisciplinary approach in designing hybrid algorithms that are not only efficient and accurate, but also interpretive and reliable for data-driven decision-making. Recommendations for future research directions focus on the creation of a standardized evaluation framework, the integration of AI with edge computing technology, and the exploration of the potential of hybrid learning in the context of real-time data.  
Co-Authors Fadillah Dani Prawoto Fajar Azhari Julian Muhammad Naufal Ammr Dzakwan Muhammad Roihan Zainuddin