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All Journal Infomans: Jurnal Ilmu-ilmu Informatika dan Manajemen Governance IT Adoption and Technology Advance
Esa Firmansyah
Universitas Sebelas April
Computer Science & IT

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Arsitektur Mikroservis untuk Interoperabilitas Data: Transformasi Integrasi Layanan Smart Village ke Smart City Esa Firmansyah; Alif Gumelar Syah Moeslim; Rani Rahmayani
Governance IT Adoption and Technology Advance Vol. 1 No. 1 (2026)
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/govita.v1i1.10508

Abstract

Smart Village implementations often face data silo challenges due to the lack of efficient integration between village-level systems or city government systems (Smart City), limiting cross-level data synchronization and utilization. This study aims to design a flexible, scalable, and reliable Smart Village to Smart City data interoperability architecture at the architectural integration level to support population administration, licensing, and social assistance services. The proposed methodology adopts a software engineering approach based on the Software Development Life Cycle (SDLC), focusing on requirements analysis, domain-based service decomposition, and microservices architecture design grounded in modern Service-Oriented Architecture (SOA) principles. The proposed architecture integrates synchronous communication through REST APIs managed by an API Gateway and asynchronous communication through Apache Kafka as a message broker to enable real-time or near real-time data update events in distributed environments. The design results indicate that the microservices-based approach with API Gateway and Kafka enhances interoperability, reduces direct coupling between systems, and provides greater flexibility for village service development without disrupting the stability of central systems at the district or city level. Therefore, this architecture has the potential to serve as an adaptive integration framework for the digital public service transformation from Smart Village to Smart City.
Analisis Komparatif Model Regresi Linier dan Polinomial pada Small Dataset untuk Prediksi Timbulan Sampah: Indonesia Listia Silviani; Esa Firmansyah; Beben Sutara
Infoman's : Jurnal Ilmu-ilmu Informatika dan Manajemen Vol. 19 No. 2 (2025): Infoman's
Publisher : LPPM & Fakultas Teknologi Informasi UNSAP

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Abstract

Bandung Regency faces severe waste management challenges, generating an average daily volume of 1,300 tons without possessing an independent landfill facility. Consequently, accurate data-driven prediction is crucial for strategic infrastructure planning. However, the application of machine learning algorithms at the regional level is often significantly constrained by the scarcity of historical data (small datasets), which introduces high risks of overfitting and statistical bias. This study aims to evaluate prediction modeling strategies using a limited annual dataset (n=4) spanning from 2021 to 2024, sourced from Open Data Jabar. The methodology compares Linear Regression and Second-Degree Polynomial Regression algorithms by employing a full training approach combined with descriptive validation based on the Rate of Change (RoC) analysis. The results indicate that while Polynomial Regression achieves superior statistical performance with an R-squared of 0.9958 and an RMSE of 3,144.59 tons, trend analysis reveals clear signs of overfitting, as the model predicts an implausible deceleration in future waste growth that contradicts demographic realities. Conversely, Linear Regression (R2 = 0.9756) provides more stable and consistent trend estimates. Therefore, this study recommends Linear Regression as a more robust model for policy planning under limited data conditions, projecting waste generation to reach 467,964.01 tons in 2025, serving as an early warning for urgent capacity expansion.
Co-Authors Alif Gumelar Syah Moeslim Beben Sutara Listia Silviani Rani Rahmayani