Irman Hermadi

Digital identity verification in e-governance faces a trilemma between security, scalability, and regulatory compliance with Indonesia's Personal Data Protection Law (UU PDP). To resolve this, in this paper, we propose the ZMC-Framework, a blockchain-based hybrid architecture integrating Zero-Knowledge Proofs (ZKPs) for privacy-preserving verification and Merkle Trees for efficient, scalable data integrity on-chain. Its core innovation is a Legal Proof Protocol with 3+1 parameter augmentation, which cryptographically binds static identifiers to a user-controlled secret, ensuring compliance with UU PDP (data minimization) and UU ITE (authentication integrity) while aligning with key controls of the international ISO/IEC 27001:2022 standard. Evaluated on Polygon Mainnet, the framework demonstrates 29.9% lower operational costs for batch verifications and 50% better storage efficiency compared to pure ZKP systems. These results validate a practical solution to the verification trilemma, providing a secure, scalable, and legally sound foundation for public service identity management in Indonesia's digital governance ecosystem.

This study aims to develop an accurate and efficient ocean data prediction model to tackle the challenges posed by climate change and complex oceanographic dynamics. The main goal is to use long short-term memory (LSTM) networks along with genetic algorithms (GA) to predict four key ocean factors at once: sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS), and chlorophyll-a (Chl-a). An experimental quantitative approach is employed, utilizing satellite data from the Banda Sea region. This approach involves time series modeling using LSTM, which is optimized by GA for hyperparameters such as the number of neurons and batch size. The results show that the combined LSTM-GA model greatly improves prediction accuracy and successfully identifies seasonal trends and irregular changes in all variables, even when there is a lot of noise. Tests reveal that the optimal configuration varies for each variable, and the GA optimization process can expedite model convergence by as little as 10 epochs. These findings underscore the effectiveness of integrating evolutionary techniques in training deep learning (DL) models for ocean data. The implications of this research include potential applications in adaptive ocean monitoring systems, early warning initiatives, and data-driven planning in marine resource management.

This study examines the influence of Social Media Marketing Activities (SMMA) dimensions on Purchase Intention through Brand Equity and Customer Engagement among premium smartphone consumers. Therefore, this research aims to analyze the effects of Entertainment, Interaction, Trendiness, Electronic Word-of-Mouth (E-WOM), and Advertising on Brand Equity and Customer Engagement, as well as their implications for Purchase Intention. This study employed a quantitative cross-sectional design using purposive sampling. Data were collected from 175 iPhone users in the Greater Jakarta area through structured online questionnaires measured using a five-point Likert scale. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The results indicate that Interaction and E-WOM significantly influence both Brand Equity and Customer Engagement, while Entertainment, Trendiness, and Advertising show limited influence on Brand Equity. Furthermore, Customer Engagement demonstrates the strongest direct effect on Purchase Intention, exceeding the influence of Brand Equity. The mediation analysis also confirms that Customer Engagement acts as the dominant mediating variable in the relationship between SMMA and Purchase Intention.The novelty of this study lies in the introduction of the concept of Interactive Community-Based Brand Valuation, which emphasizes that brand value in digital environments is increasingly shaped through consumer interaction and engagement rather than conventional promotional activities. The findings imply that companies should shift their digital marketing strategies from content-oriented promotion toward engagement-driven communication and community interaction to enhance consumer purchase intention in highly competitive digital markets.

The combination of satellite and Machine Learning (ML) approaches is emerging as a new paradigm in crop height estimation, facilitating informed decision-making. This study aims to identify key factors influencing sugarcane height prediction and compare the performance of single satellite data with data fusion. The proposed height estimation model was designed with the help of satellite data from Sentinel-2 and Landsat 9. A data fusion technique was used to extract vegetation, morphological, and meteorological indicators from three land plots in 2023 and 2024. The collected data were preprocessed using the Interquartile Range (IQR). Forward chaining was utilized to split data into training and testing subsets (40:20, 50:20, 60:20, 70:20, and 80:20). Feature importance analysis was performed to identify features contributing to sugarcane height prediction. The selected features were used as inputs for three ML models: Random Forest (RF), Extreme Gradient Boosting (XGB), and Support Vector Regression (SVR). The results of sugarcane height estimation indicate that the use of multi-source data fusion (vegetation indicators, morphology, and meteorology) with XGB achieved the best and most robust performance, with a coefficient of determination (R2) of 0.997, a Root Mean Square Error (RMSE) of 6.254 cm, and a Mean Absolute Percentage Error (MAPE) of 3.097%. The findings demonstrate a strong potential of real-time remote sensing-based height estimation for sugarcane, supporting precise monitoring and data-driven decision-making.

Election results recapitulation requires stakeholder participation, data integrity, and auditability. However, electronic systems remain largely centralized and provide limited support for multi-stakeholder involvement, traceable audit trails, and verifiable record integrity. This study formalizes the recapitulation process as a rule-based state-transition model and proposes a consortium Blockchain architecture as a parallel recapitulation channel. Built on Hyperledger Fabric, the network comprises peer nodes representing election officials, supervisors, and witnesses, and is governed by smart contracts with policy-driven endorsement. The model encodes stakeholder roles, cryptographic document binding through IPFS content identifiers and hash verification, and rule-based state validation, with emphasis on arithmetic consistency and plenary time-window constraints. The artifact is evaluated through regulatory compliance mapping and computational testing. The results show that the model enables multiparty verification, preserves auditable transaction history, and maintains verifiable linkage between recapitulation records and supporting documents, while constraining accepted state transitions to defined arithmetic and procedural rules.

The implementation of e-government systems in public sector organizations frequently encounters adoption and sustainability challenges, particularly in employee performance and incentive management platforms. This study evaluates the success of implementing the Civil Servant Income Supplementary Information System (TPP) in the Karangasem Regency Government using the Human, Organization, Technology Fit (HOT-Fit) model. The research addresses low system adoption and operational inefficiencies by examining the alignment between human factors (user satisfaction and system usage), organizational factors (structural support), and technological factors (system, information, and service quality), and their impact on net benefits. Data were collected through questionnaires distributed to system users and analyzed using descriptive statistics and Structural Equation Modeling (SEM) with SmartPLS. Findings indicate that organizational and human factors significantly influence perceived system benefits, while technological aspects—particularly reliability and ease of use—require improvement. The study identifies key adoption barriers, including insufficient training, weak managerial support, and suboptimal system features. Theoretically, this research enriches the application of the HOT-Fit model within the Government-to-Employee (G2E) context in Indonesia. Practically, it provides evidence-based recommendations for local governments to strengthen organizational support, enhance user capacity, and refine system design to ensure fair, transparent, and timely performance-based remuneration.

Transformasi digital dalam pendidikan merupakan keniscayaan, namun penerapannya di sekolah menengah di Indonesia masih menghadapi tantangan terkait infrastruktur, literasi digital SDM, dan resistensi organisasi. Penelitian ini menganalisis strategi optimal PT Kunci Transformasi Digital dalam digitalisasi manajemen mutu pendidikan sekolah menengah di Jawa Barat. Dengan pendekatan campuran (kuantitatif dan kualitatif), 66 responden dari sekolah klien terlibat dalam survei dan wawancara. Analisis yang dilakukan mencakup Importance-Performance Analysis (IPA), klasterisasi kesiapan digital, SWOT, GAP, IFE, EFE, IE Matrix, TOWS, dan QSPM. Hasilnya, mayoritas sekolah pada tahap "cukup siap" hingga "siap digital", dengan kekuatan pada integrasi platform dan pelatihan SDM. Strategi prioritas adalah pelatihan SDM berbasis klaster kesiapan, inovasi fitur platform berbasis kebutuhan pengguna, dan penguatan ekosistem kolaborasi. Studi ini merekomendasikan pelatihan berjenjang, kolaborasi aktif, inovasi adaptif, serta advokasi regulasi demi optimalisasi digitalisasi pendidikan menengah.

Pelabuhan merupakan simpul penting dalam sistem logistik nasional. Namun, kinerja operasional pelabuhan di Indonesia masih belum optimal, yang berdampak pada tingginya biaya logistik. Penelitian ini bertujuan untuk mengoptimalkan kinerja operasional Terminal Petikemas Koja di Pelabuhan Tanjung Priok dengan pendekatan handling capacity. Fokus utama penelitian adalah pelayanan kapal, khususnya kecepatan bongkar muat yang dipengaruhi oleh penggunaan alat bongkar muat seperti Quay Container Crane (QCC), Rubber Tyred Gantry Crane (RTG), dan Head Truck(HT). Analisis dilakukan dengan mengumpulkan data operasional dari Terminal Operation System selama periode 2020–2024, dan dilakukan simulasi regresi linear berganda serta optimasi menggunakan metode linear programming untuk menentukan komposisi alat yang paling efisien. Hasil penelitian menunjukkan bahwa komposisi optimal untuk mencapai Vessel Operating Rate (VOR) tertinggi adalah 5 QCC, 17 RTG, dan 42 HT. Model regresi yang dihasilkan menunjukkan bahwa peningkatan VOR berkontribusi signifikan dalam menurunkan waktu kerja dermaga (Berth Working Time/BWT), yang berdampak langsung pada pengurangan biaya tambat kapal. Penelitian ini memberikan rekomendasi strategis untuk pengelolaan fasilitas terminal secara optimal demi mendukung efisiensi logistik nasional.

Aglaonema plants have become an important element in people's lives, serving as home decorations and sources of income. Aglaonema cultivation is increasingly popular, opening up business opportunities for farmers. This plant is not just a hobby but a horticultural commodity with high economic value. This research aims to analyze the difficulties faced by Aglaonema farmers, from seedling to marketing. This research uses a constructivist paradigm and qualitative methods, involving 20 informants from among farmers, traders, buyers, extension workers, and farmer organizations. Data were collected through interviews, focus group discussions, and observations, with triangulation to validate the information. Data analysis was conducted using the NVivo application. The research results show that the main difficulties faced by Aglaonema farmers include unpredictable price fluctuations, limited varieties, weak communication among farmers, traditional seedling methods, and a lack of information about seed supply. These findings emphasize the need for information sharing between farmers and stakeholders to improve productivity and quality in agribusiness, in line with consumer demands and technological advancements, which can be implemented through a knowledge management system platform in the Aglaonema agribusiness supply chain.