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All Journal Journal of Dinda : Data Science, Information Technology, and Data Analytics Maritime Park: Journal Of Maritime Technology and Socienty Jurnal Riset & Teknologi Terapan Kemaritiman
Bin Abdul Hadi, Abdul Razak
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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Optimalisasi Penentuan Rute Terbaik Pelayaran Berbasis Analisis Big Data untuk Efisiensi Bahan Bakar Kapal Laut Setiawan, Ariyono; Bin Abdul Hadi, Abdul Razak; Widyaningsih, Upik; Pamungkas, Anjar
Jurnal Riset & Teknologi Terapan Kemaritiman Vol. 3 No. 1 (2024)
Publisher : Departemen Teknik Sistem Perkapalan, Universitas Hasanuddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25042/jrt2k.062024.05

Abstract

Penelitian ini bertujuan untuk mengoptimalkan rute pelayaran dengan menerapkan big data analytics guna meningkatkan efisiensi bahan bakar. Dengan memanfaatkan data real-time dan historis, studi ini mengidentifikasi rute paling efisien untuk meminimalkan konsumsi bahan bakar tanpa mengorbankan efektivitas operasional. Berbasis pada teori logistik maritim, analitik big data, dan efisiensi bahan bakar, penelitian ini menggabungkan model optimasi rute, prakiraan cuaca, serta analisis kinerja kapal untuk mendukung pengambilan keputusan navigasi. Selain itu, dampak regulasi IMO MARPOL Annex VI, khususnya EEDI dan SEEMP, turut dipertimbangkan dalam upaya optimalisasi efisiensi energi. Metode yang digunakan adalah pendekatan campuran, yang mengombinasikan analisis kuantitatif dari data AIS, laporan cuaca, serta catatan konsumsi bahan bakar dengan algoritma pembelajaran mesin untuk optimasi rute. Analisis korelasi Pearson mengevaluasi hubungan antara kecepatan, jarak, waktu tempuh, dan konsumsi bahan bakar. Studi kasus digunakan untuk memvalidasi model yang dikembangkan. Hasil penelitian menunjukkan bahwa konsumsi bahan bakar sangat dipengaruhi oleh kecepatan kapal, dengan kecepatan lebih tinggi meningkatkan konsumsi bahan bakar. Korelasi negatif ditemukan antara waktu tempuh dan konsumsi bahan bakar harian, menunjukkan bahwa pelayaran lebih lambat dapat meningkatkan efisiensi. Studi ini menekankan pentingnya pemrosesan data real-time dalam penyesuaian rute berdasarkan cuaca, kemacetan, dan efisiensi energi. Penelitian ini menawarkan pendekatan inovatif berbasis data dalam perencanaan rute, berbeda dari metode tradisional yang mengandalkan bagan statis dan pengalaman. Integrasi big data dalam logistik maritim dapat mengurangi emisi, menekan biaya, serta meningkatkan keberlanjutan operasional.
Unveiling Risk Patterns of Disability Progression A Clustering Based Transition Matrix Analysis Using Indonesian National Data Setiawan, Ariyono; Bin Abdul Hadi, Abdul Razak; Faller, Erwin; Wibawa, Aji Prasetya
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i2.1868

Abstract

This study investigates the progression of disability severity from "some difficulty" to "a lot of difficulty" using a transition matrix framework. It aims to identify risk patterns and classify severity clusters based on national survey data from Indonesia between 2010 and 2023. The study draws on the theory of functional limitation progression, which assumes that individuals with mild disabilities face varying probabilities of developing severe limitations depending on contextual and demographic factors. It also incorporates clustering theory to group similar progression behaviors. We utilize 20,604 data points from multiple disability types (cognitive, hearing, mobility, etc.). The transition rate is computed as the ratio of individuals with "a lot" difficulty to the total with "some" and "a lot" difficulty. Statistical analyses include descriptive summaries, Pearson correlation, and K-Means clustering via the FASTCLUS procedure. Heatmaps are generated to observe annual and typological patterns. The average transition rate is 66.77%, with a maximum of 99.6% in some subgroups. Three distinct severity clusters emerged, centered at 31.27%, 58.62%, and 82.20%. Transition rate negatively correlates with "some difficulty" prevalence (r = –0.45, p < .0001), indicating progressive concentration of severity in smaller populations. Heatmaps reveal consistent risk escalation over time, especially in cognitive and self-care disabilities. This study enables policy actors to stratify intervention priorities and monitor disability risk more accurately using dynamic, data-driven indicators. This is the first study in Indonesia to apply a large-scale transition matrix combined with clustering to map functional disability progression. It offers a novel quantitative method to uncover hidden severity patterns and informs future decision-support systems for inclusive health planning.
Multivariate Risk Analysis of Echotoxic Chemicals of Ballast Water Chemicals Based on PCA and DSS Using ECOTOX GISIS Data Setiawan, Ariyono; Widyaningsih , Upik; Pamungkas, Anjar; Bin Abdul Hadi, Abdul Razak; Dewi, Deshinta Arrova
Maritime Park: Journal of Maritime Technology and Society Volume 4, Issue 3, 2025
Publisher : Department of Ocean Engineering, Faculty of Engineering, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62012/mp.vi.44925

Abstract

This study proposes a multivariate risk classification model for ballast water treatment chemicals by integrating global datasets—ECOTOX (U.S. EPA) and GISIS (IMO). Using Principal Component Analysis (PCA), we analyze 37 substances based on acute toxicity (LC50), chronic toxicity (NOEC), and bioaccumulation potential (BCF). The aim is to provide a practical, data-driven tool to support ecological compliance, early warnings, and regulatory prioritization in maritime chemical management. Results show that 43.24% of substances fall into the high-risk category, while only 8.11% are low risk. PCA effectively reduces dimensionality, explaining 73.63% of variance with just two components. High-risk chemicals such as Dibromoacetic acid and Dichloroacetonitrile exhibit low NOEC and high BCF values—indicating significant ecotoxic potential, often underregulated. Some commonly used oxidants also reveal hidden chronic toxicity, suggesting gaps in current risk frameworks post-BWM Convention. We construct a risk-scoring matrix and chemical heatmap to visualize ecotoxic profiles, enabling real-time risk ranking and decision support. Unlike previous studies that focus solely on toxicity thresholds or narrative reviews, this approach integrates empirical data with decision logic to aid Port State Control (PSC) and IMO policy design. The method is replicable and adaptable to other maritime pollutants, especially in the ASEAN context, enhancing smart port readiness and ecological safeguarding.
Co-Authors Aji Prasetya Wibawa Dewi, Deshinta Arrova Faller, Erwin Pamungkas, Anjar Setiawan, Ariyono Widyaningsih , Upik Widyaningsih, Upik