The integration of blockchain into industrial environments promises secure and verifiable data exchange; however, existing permissioned blockchain (PBC) frameworks, such as Hyperledger Fabric and Quorum, impose overheads that are unsuitable for resource-constrained systems. This article introduces LowCapChain (LCC), a lightweight PBC designed for manufacturing nodes, such as programmable logic controllers, robotic arms, and smart sensors. LCC integrates Merkle ledger compression, elliptic-curve cryptography-based proof-of-membership, and delegated fault-tolerant consensus to enable secure operations without full ledger replication. Implemented in a robotic metal stamping facility using Raspberry Pi edge nodes, LCC achieves 964 transactions per second with 108 ms consensus latency and memory usage below 55 MB. Compared with Hyperledger Fabric, LCC reduces mean consensus latency by 61% and cryptographic overhead by up to 81%. Compared with Quorum, the reductions are 54% and 76%, respectively. Scalability tests confirmed near-linear throughput growth across 10-200 nodes, and fault tolerance experiments verified block finalization under validator failure. These findings establish LCC as an efficient architecture for embedded industrial systems, offering a pathway toward scalable Industry 4.0 adoption with energy savings inferred from reduced CPU cycles rather than directly measured power.
The Syattariyah Sufi order is one of the tariqas that played an important role in the development of Islam in Sumatra through networks of scholars and intellectual traditions. Syattariyah scholars functioned not only as practitioners of Sufism, but also as agents of da‘wah, community leaders, and guardians of local intellectual heritage. This study aims to analyze how scholarly networks shaped, strengthened, and reproduced the continuity of the order in Sumatra through teacher disciple relationships, the transmission of knowledge, and socio-religious roles. The method employed is a qualitative-historical approach with library research on Sufi manuscripts, genealogical chains of transmission (sanad), scholarly works, and academic literature. The findings show that the Syattariyah order developed as a regional network of scholars connecting Aceh, Minangkabau, and Palembang through traditional educational institutions, the circulation of manuscripts, and the legitimation of spiritual lineage. This article emphasizes the uniqueness of the Syattariyah order as an institutionalized socio-intellectual system that is adaptive to colonialism and modernity, and remains relevant in shaping local Islamic identity into the contemporary period.
The development of Indonesian civilization and Islam has been closely intertwined, evolving dynamically and progressively in response to domestic and global political developments, even prior to Indonesia’s emergence as an independent nation. In his 718-page book, Observing Islam in Indonesia, 1971–2023: A Study in Cultural Anthropology, Emeritus Professor Mitsuo Nakamura presents the findings of more than five decades of observing Islam in Indonesia through two major Islamic organizations, Muhammadiyah and Nahdlatul Ulama (NU). Nakamura’s independence as a scholar is evident in his candid and straightforward presentation of events and developments based largely on his own observations and experiences. The book provides an important perspective for understanding the dynamics of these two organizations and their evolving relationship with Indonesian government policies. Nevertheless, one of the book’s limitations lies in its compilation of previously published writings that originally appeared in different media outlets and at different points in time. This structure, while reflecting the breadth of Nakamura’s long-term observations, may result in some fragmentation in the presentation and analysis of the subject matter.
The growing use of Artificial Intelligence (AI) in education raises important questions for pesantren, where knowledge transmission has historically been grounded in sanad, talaqqi, and adab. This study examines how contemporary pesantren negotiate AI in relation to knowledge, religious authority, and Islamic educational traditions. Employing a qualitative multiple-case study design across three pesantren in West Java, Indonesia, the study involved 60 purposively selected participants: six kiai, six institutional administrators, twelve teachers (ustadz), twenty-four students (santri), six information technology staff, and six alums. Data were collected through semi-structured interviews with all participants, observation of educational practices, and document analysis, and were examined through within-case and cross-case analysis. The findings show that AI primarily supports information seeking, translation, clarification, summarization, and preliminary engagement with learning materials rather than replacing established modes of knowledge transmission. A recurring pattern of AI-assisted exploration followed by teacher-mediated epistemic validation demonstrates that informational accessibility does not automatically confer knowledge legitimacy. Sanad, talaqqi, and adab remain central to knowledge validation, while the growing accessibility of AI-generated religious information produces authority negotiation rather than displacing kiai and teachers. Institutionally, the study identified four non-linear and potentially coexisting response patterns: resistance, selective adaptation, integration, and transformation. Building on these findings, the study proposes the Pesantren AI Negotiation Model, which explains AI integration through three interconnected mechanisms (interpretive validation, authority verification, and ethical alignment). The study demonstrates that AI integration in pesantren is an epistemically and institutionally negotiated process in which technological affordances are selectively incorporated through established structures of knowledge, authority, and ethical formation.
Indonesia is a country with high seismic activity due to its location at the convergence of three major tectonic plates. This condition creates a strong need for earthquake pattern analysis and magnitude prediction to support disaster mitigation. This study aims to cluster earthquake data using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and to predict earthquake magnitude using the Random Forest algorithm optimized through hyperparameter tuning. The Indonesian earthquake dataset was obtained from Kaggle with a total of 92,887 valid entries. The DBSCAN clustering results revealed several active seismic zones, particularly in Sumatra, Java, Sulawesi, and Papua. The comparison of R² between the Baseline Random Forest and the Tuned Random Forest shows a significant improvement after the parameter tuning process. The Tuned Random Forest model achieves an R² value of 0.478, which is higher than the Baseline Random Forest's 0.442. This indicates that the tuned model is better able to explain the variance in the data and provides more accurate predictions.
