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Literasi Matematis dan Pemanfaatan Potensi Ekonomi Biru pada Nelayan Desa Gempolsewu Kabupaten Kendal. Muhamad Aris Sunandar; Nur Aliyyah Irsal; Sudibyanung Sudibyanung; Dani Iswahyuni
Jurnal Sosial Ekonomi Kelautan dan Perikanan Vol 21, No 2 (2026): Desember 2026 (Article in Press)
Publisher : Balai Besar Riset Sosial Eonomi Kelautan dan Perikanan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15578/jsekp.v21i2.19205

Abstract

Perikanan skala kecil merupakan pilar penting agenda blue economy yang inklusif. Namun, pengambilan keputusan nelayan sering dibatasi oleh numerasi yang rendah dan institusi pasar yang timpang. Penelitian ini bertujuan untuk mengukur literasi matematis nelayan pada domain formulate, employ, dan interpret  serta menganalisis keterkaitannya dengan pemanfaatan ekonomi biru melalui nilai tangkapan, struktur biaya, efisiensi, dan kesejahteraan Anak Buah Kapal (ABK). Desain mixed-methods menggabungkan tes literasi matematis kontekstual, wawancara mendalam, dan pemetaan institusi. Survei dilakukan pada 30 nelayan, sedangkan wawancara melibatkan 18 informan kunci. Hasil menunjukkan kelemahan persisten pada formulate dan interpret. Proporsi responden kategori rendah masing-masing 43,33% dan 46,67%, sementara employ didominasi kategori sedang (46,67%). Temuan ini berkaitan dengan friksi pasar seperti ketergantungan pada tengkulak, praktik lelang yang kurang transparan, dan koperasi yang tidak aktif. Kondisi tersebut menekan harga jual dan margin. Nilai per trip turun dari Rp4.650.000 pada musim puncak menjadi Rp865.000 pada musim peralihan. Temuan ini menegaskan pentingnya keputusan portofolio dan penentuan waktu penjualan. Kesejahteraan ABK juga rentan karena pendapatan bulanan masih berada di bawah standar upah minimum setempat. Selain itu, BBM menjadi komponen biaya terbesar. Karena itu, efisiensi konsumsi energi dan pencatatan biaya menjadi prioritas. Integrasi perangkat numerasi (logbook, pelatihan interpretasi harga, dan analisis titik impas) dengan reformasi kelembagaan berbasis komunitas (penguatan koperasi dan transparansi pasar) menawarkan jalur praktis menuju blue economy desa yang lebih adil dan berkelanjutan. Title: Mathematical Literacy and the Utilization of Blue Economy Potential Among Fishermen in Gempolsewu Village, Kendal Regency    Small-scale fisheries are a key pillar of an inclusive blue economy agenda. However, fishers’ decision-making is often constrained by low numeracy and unequal market institutions. This study aims to measure fishers’ mathematical literacy across the formulate, employ, and interpret domains and to analyze its relationship with blue economy utilization, as measured by catch value, cost structure, efficiency, and crew (ABK) welfare. A mixed-methods design combines a contextual mathematical literacy test, in-depth interviews, and institutional mapping. The survey involved 30 fishers, while interviews covered 18 key informants. The results show persistent weaknesses in formulating and interpreting. The proportions of respondents in the low category are 43.33% and 46.67%, while employ is dominated by the medium category (46.67%). These findings are associated with market frictions such as dependence on middlemen, less transparent auction practices, and inactive cooperatives. These conditions suppress selling prices and margins. Trip value declines from IDR 4,650,000 in the peak season to IDR 865,000 during the transition season. This underscores the importance of portfolio decisions and sales timing. ABK welfare is also vulnerable because monthly earnings remain below the local minimum wage standard. In addition, fuel is the largest cost component. Therefore, improving energy-use efficiency and cost record keeping becomes a priority. Integrating numeracy tools (logbooks, price interpretation training, and break-even analysis) with community-based institutional reforms (cooperative strengthening and market transparency) offers a practical pathway toward a fairer and more sustainable village-level blue economy. 
Blockchain-Enabled Multi-Agent Reinforcement Learning for Secure Decentralised Resource Allocation in 5G/6G Network Slicing Agustinus Suradi; Muhamad Aris Sunandar; Umna iftikhar
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 3 (2025): September: Global Science: Journal of Information Technology and Computer Scien
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i3.174

Abstract

The integration of blockchain technology with Multi-Agent Reinforcement Learning (MARL) presents a promising solution for optimizing resource allocation and ensuring security in decentralized network environments, particularly in 5G and 6G network slicing. This research proposes a model that combines the security features of blockchain with the adaptive, decentralized decision-making capabilities of MARL. Blockchain ensures the integrity and transparency of resource allocation by providing a secure, tamper-proof ledger for transaction validation, while MARL allows agents to dynamically allocate resources based on real-time network conditions. The simulation results demonstrate significant improvements in resource allocation efficiency, fairness among users, and resilience to cyberattacks. By combining these two technologies, the proposed model overcomes many of the challenges posed by traditional centralized systems and offers an enhanced, secure, and fair solution for resource distribution in future mobile networks. However, scalability remains a challenge, especially in large-scale networks where transaction processing and consensus overhead can create bottlenecks. Additionally, training complexity in MARL models presents computational challenges, particularly in highly dynamic network environments. The model's performance trade-offs, including the balance between high security and system overhead, are also discussed. Future research should focus on optimizing blockchain consensus mechanisms to improve scalability and enhancing MARL model training techniques to reduce computational costs and improve real-time decision-making. This integration holds significant potential for revolutionizing resource allocation in 5G and 6G networks, enabling more efficient, secure, and fair management of network resources in the increasingly complex and decentralized digital ecosystem