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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA IKRA-ITH ABDIMAS Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi TEPIAN Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) International Journal Software Engineering and Computer Science (IJSECS) Jurnal Pengabdian West Science
Januponsa Dio Firizqi
Universitas Pradita
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Public Health Social Sciences Other

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GOVERNANCE OF IOT DEVICES USING NODE-RED ORCHESTRATOR AND WEB-BASED DASHBOARD Atmodjo, Dwi; Harianja, Richad; Firizqi, Januponsa Dio; Kurniawan, Rido Dwi
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 5 (2023): JUTIF Volume 4, Number 5, October 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.5.1336

Abstract

High electricity bills are often caused by a lack of monitoring the use of electronic equipment in the household. One common problem is forgetting to turn off appliances that are not in use, which causes electricity to continue flowing and increases electricity bills. Currently, although there are several technologies that allow the control of appliances, their features are still limited, especially in lamps. This research focuses on developing Internet of Things (IoT) technology in the field of energy and control. One of the implementations is using a Raspberry Pi microcontroller to monitor the use of electrical energy in households. In order to make the use of IoT components more efficient and easier, a NODE-RED-based system is used which serves as a link between end users and IoT equipment. For monitoring IoT components, a web dashboard using the Laravel framework was also built. This allows users to view the components used and the activities that occur on their IoT equipment. High electricity bills are often caused by a lack of monitoring the use of electronic equipment in the household. One common problem is forgetting to turn off appliances that are not in use, which causes electricity to continue flowing and increases electricity bills. Currently, although there are several technologies that allow the control of appliances, their features are still limited, especially in lamps. This research focuses on developing Internet of Things (IoT) technology in the field of energy and control. One of the implementations is using a Raspberry Pi microcontroller to monitor the use of electrical energy in households. In order to make the use of IoT components more efficient and easier, a NODE-RED-based system is used which serves as a link between end users and IoT equipment. For monitoring IoT components, a web dashboard using the Laravel framework was also built. This allows users to view the components used and the activities that occur on their IoT equipment.
ANALYSIS AND PREDICTION OF HEALTH INSURANCE PREMIUM VALUE USING MACHINE LEARNING ALGORITHM Danica Recca Danendra; Januponsa Dio Firizqi
Multidiciplinary Output Research For Actual and International Issue (MORFAI) Vol. 5 No. 2 (2025): Multidiciplinary Output Research For Actual and International Issue
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/morfai.v5i2.2775

Abstract

Rising healthcare costs and administrative complexity in the health insurance sector underscore the need for an efficient predictive model to anticipate insurance premium prices. The study explores Machine Learning (ML) techniques to predict the value of health insurance premiums. Also, it aims to provide further insights to stakeholders to create strategies in premium pricing and risk management. This study uses the Kaggle.com datasets and a boosting regression algorithm to compare the accuracy and metric evaluation results in predicting the value of insurance premiums. Feature engineering techniques are applied to improve model performance, reduce over-fitting, and interpret the model to ensure the inclusion of relevant predictors by studying the strengths and limitations of each technique. They overcome this through feature selection, model interpret-ability, scalability, and generalization. Through this comprehensive review, the results of this study aim to provide valuable insights for practitioners, researchers, and policymakers, as well as facilitate informed decision-making in the context of determining the value of health insurance premiums through the use of ML methodologies.
An Enterprise Architecture Blueprint for HL7-Based RIS–PACS Integration Using TOGAF ADM: A Mobile Diagnostic Access Framework Purnama, Wandi; Indrajit, Richardus Eko; Firizqi , Januponsa Dio
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15825

Abstract

The shift to digital radiology is frequently hindered by data fragmentation and restricted mobility for healthcare professionals. This research addresses these deficiencies by developing an Enterprise Architecture (EA) blueprint for hospital radiology departments using the TOGAF ADM framework. The design emphasizes HL7 standards for administrative data transfer and DICOM protocols for imaging, featuring a web-based Mobile Viewer using the WADO protocol for remote high-resolution access. Architectural validation via gap analysis demonstrates that the proposed integration can theoretically eliminate manual data entry between HIS and RIS-PACS through an HL7 broker mechanism, potentially shortening clinical report turnaround times. However, this study is limited to architectural validation and does not include live clinical deployment. The proposed architecture offers a scalable roadmap for "film-less" environments, though further longitudinal studies are required to assess long-term clinical user satisfaction.  
Orchestrating the Circular Smart Palm Ecosystem: A Design Science Research Using TOGAF-Based Enterprise Architecture Hamdani, Ibnu; Indrajit, Richardus Eko; Firizqi, Januponsa Dio
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15828

Abstract

The global palm oil industry is currently confronting a sustainability trilemma involving production efficiency, regulatory compliance (e.g., EUDR), and environmental circularity. Existing supply chain models are characterized by fragmented information systems, where Operational Technology (OT) remains disconnected from Enterprise IT, resulting in traceability gaps and underutilized biomass waste. This study addresses these challenges by designing a comprehensive Enterprise Architecture (EA) for a Circular Smart Palm ecosystem. Adopting a Design Science Research (DSR) methodology, the research applies the TOGAF Architecture Development Method (ADM), supported by ArchiMate 3.1 modeling, to develop an integrated architectural blueprint. The proposed architecture consolidates blockchain-enabled traceability, digital export compliance, IoT-driven bioenergy valorization, and value-added kernel processing into a unified enterprise framework. The resulting architecture is qualitatively validated through architectural consistency analysis and gap assessment between the As-Is and To-Be states. From a theoretical perspective, this study contributes to Enterprise Architecture literature by extending EA as an integrative mechanism for circular bioeconomy implementation in sustainability-driven agribusiness ecosystems. The findings demonstrate that enterprise-level architectural integration can transform sustainability compliance from an operational constraint into a strategic enabler for traceability, circular value creation, and digital governance.
Multi-stream Revenue Model for AI-based Fintech Lending using Enterprise Architecture Framework Edwin Wiyanto Saputra; Richardus Eko Indrajit; Januponsa Dio Firizqi
TEPIAN Vol. 7 No. 2 (2026): June 2026
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v7i2.3657

Abstract

The contemporary Peer-to-Peer (P2P) lending industry is currently facing existential sustainability challenges, primarily driven by intense market saturation and the escalating risk of Non-Performing Loans (NPL). Conventional business models, which rely heavily on interest-based income, are proving increasingly fragile in this volatile financial landscape. Addressing these critical vulnerabilities, this study proposes a comprehensive Enterprise Architecture (EA) framework to transform Fintech platforms from traditional loan intermediaries into intelligent, data-driven financial orchestrators effectively integrated within a Smart Urban Ecosystem. Methodologically, the research adopts a systematic qualitative Design Science Research (DSR) approach, synthesizing the Business Model Canvas for strategic alignment and ArchiMate 3.1 standards for technical structural modeling. The study critiques the limitations of existing monolithic systems and introduces a novel multi-stream revenue model designed to diversify income sources. This model comprises three core pillars: AI-based Credit Scoring as a Service (CSaaS) to foster cross-platform interoperability and trust, Credit Behavior Analytics, which monetizes previously underutilized "dark data" for deeper risk insights, and Embedded Credit Scoring tailored for real-time, high-frequency transactional environments. Technically, the proposed architecture strategically decouples high-load AI computation from low-latency decisioning processes using a microservices approach, thereby resolving scalability bottlenecks often found in legacy systems. The findings demonstrate that transitioning to this modular "Fintech-as-a-Service" paradigm significantly mitigates financial risk by shifting reliance from uncertain loan interest to stable, fee-based revenue streams. This research provides a strategic blueprint for Fintech stakeholders, offering a pathway to long-term viability and competitive advantage in the emerging data-driven digital economy.
Artificial Intelligence-Based Driver Behavior Scoring for Improving Safety and Delivery Efficiency in Logistics Operations Ibnu Hamdani; Teddy Mantoro; Januponsa Dio Firizqi
TEPIAN Vol. 7 No. 2 (2026): June 2026
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v7i2.3667

Abstract

The logistics sector faces increasing challenges related to road safety and operational efficiency due to unsafe driving behavior, high accident rates, and inconsistent delivery performance. Traditional monitoring approaches are often reactive and limited in their ability to capture complex driving patterns in real time. This study proposes an artificial intelligence–based driver behavior scoring framework that integrates in-vehicle telematics, GPS route data, and in-cab monitoring to improve safety performance and delivery efficiency in logistics operations. The research utilizes historical and real-time vehicle data collected from onboard diagnostic systems, including speed, acceleration, braking patterns, and driving duration. Three artificial intelligence models Random Forest, Long Short-Term Memory (LSTM), and a hybrid CNN–LSTM were developed and evaluated to classify risky driving behavior and predict safety-critical events. Experimental results indicate that the hybrid CNN–LSTM achieved the best performance, reaching an accuracy of 96.1% and a mean absolute error of 0.054. A three-month pilot deployment in a logistics fleet environment further demonstrated practical benefits, with average driver safety scores improving from 78.4 to 89.7 and on-time delivery rates increasing from 91.2% to 96.5%. These findings highlight the effectiveness of multimodal driver behavior analytics in simultaneously enhancing road safety and logistics performance. The proposed framework provides actionable decision-support insights for fleet managers and contributes to the advancement of AI-enabled intelligent transportation and logistics systems.
Pelatihan Pemanfaatan Business Intelligence Untuk Visualisasi Data Menggunakan Tableau Pada Ekstrakurikuler Kewirausahan Di SMA Pahoa Afifah Trista Ayunda; Wahyu Tisno Atmojo; Master Edison Siregar; Hendra Mayatopani; Rido Dwi Kurniawan; Januponsa Dio Firizqi; Masriah Masriah; Qonita Azizah; Shabila Octavia
IKRA-ITH ABDIMAS Vol. 10 No. 2 (2026): IKRAITH-ABDIMAS Vol 10 No 2 Juli 2026
Publisher : Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikra-ithabdimas.v10i2.7194

Abstract

Teknologi visualisasi data telah menjadi bagian integral dari perkembangan bisnis modern, memungkinkan pemahaman yang lebih baik terhadap informasi yang kompleks dan mendukung pengambilan keputusan yang lebih efektif. Di era digital ini, pemanfaatan teknologi ini semakin penting, khususnya dalam konteks kewirausahaan, di mana pengelolaan dan analisis data menjadi keterampilan yang esensial. Namun, tingkat pemahaman siswa SMA terhadap konsep dan aplikasi teknologi visualisasi data dalam konteks kewirausahaan masih menjadi perhatian. Penelitian ini bertujuan untuk mengeksplorasi pemanfaatan teknologi Business Intelligence, khususnya Tableau, dalam memvisualisasikan data untuk mendukung pendidikan kewirausahaan di SMA Pahoa Tangerang. Melalui pendekatan observasi, pelatihan, dan metode kuantitatif menggunakan kuesioner untuk evaluasi, penelitian ini berupaya mengukur sejauh mana pemahaman siswa meningkat setelah pelatihan. Hasil penelitian menunjukkan bahwa penggunaan teknologi visualisasi data memberikan kontribusi signifikan terhadap pemahaman siswa, terutama dalam memanfaatkan data untuk menggambarkan informasi dalam bentuk gambar yang mudah dipahami. Hasil evaluasi menggunakan kuesioner menunjukkan skor 77,31%. Dengan demikian, penerapan teknologi visualisasi data, seperti Tableau, efektif dalam meningkatkan kompetensi siswa dalam bidang kewirausahaan dan pengelolaan data.
A Completeness-Driven Enterprise Architecture for Integrated Smart-Township Operations Using TOGAF ADM, ArchiMate, and the Zachman Framework Stephen Edlin; Erick Dazki; Handri Santoso; Januponsa Dio Firizqi; Richardus Eko Indrajit
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 3 (2026): DECEMBER 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i3.8489

Abstract

Master-planned townships represent a predominant model of urban expansion in Indonesia, each governed by an operational entity that concurrently coordinates residents, commercial tenants, estate-management teams, and third-party service providers. However, the operational systems supporting these developments remain predominantly fragmented—characterized by estate dues tracked on disparate spreadsheets, resident inquiries and incident reports handled via informal messaging channels, and gate access logged manually—depriving township operators of an integrated, auditable, and data-rich operational platform. Objective: This study establishes a completeness-driven enterprise architecture (EA) framework for integrated smart-township operations. Method: Grounded in Design Science Research, the architecture was developed using the TOGAF Architecture Development Method across Phases A to D, modeled in ArchiMate 3 using the Archi modeling tool, and systematically validated through cross-layer consistency verification, an As-Is versus To-Be gap analysis, and a Zachman 5W1H completeness assessment. Results: The Architecture Vision establishes end-to-end alignment between five stakeholder groups, six strategic operational goals, and their requisite resources. Three core processes—estate dues billing, facility booking with smart access, and resident service with security incident response—are traced from business workflows through application services to underlying cloud and cyber-physical infrastructure, with shared services providing modular integration boundaries. Technology-layer specifications formalize external payment gateways and IoT network connections. The Zachman completeness evaluation indicates that 20 of the 24 cells achieve full coverage, while the remaining four cells (three partial and one weak) converge exclusively within the temporal (When) dimension. Conclusion: The developed enterprise architecture provides an extensible, auditable blueprint for operator-managed townships with an empirically documented coverage profile, wherein the identified temporal gaps establish an explicit empirical trajectory for operational lifecycle scheduling and governance extensions.
Integrated Enterprise Architecture for Coffee Shop Supply Chain and Customer Interaction Using TOGAF ADM Sanky Yunliahni; Handri Santoso; Erick Dazki; Januponsa Dio Firizqi; Richardus Eko Indrajit
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16726

Abstract

Although the coffee shop sector continues to expand quickly, enterprise architecture research in this domain has generally treated the customer-facing point of sale and the upstream sourcing of coffee as two separate concerns, so that supply chain and customer interaction are rarely represented together. This study builds an integrated enterprise architecture model for a coffee shop enterprise that brings supply chain operations and customer interaction into a single design spanning three product lines: coffee beverage, non-coffee beverage, and food and snack sales. The study is conceptual and design-oriented; the enterprise modelled is a representative reference case reconstructed from operational patterns reported in earlier studies of independent coffee shops, not an empirically observed firm. The work follows the phases of The Open Group Architecture Framework Architecture Development Method, from the Preliminary Phase to Migration Planning, with ArchiMate as the modelling notation and the Business Model Canvas as the expression of the business logic. The findings show that the Architecture Vision phase connects three stakeholder groups and their strategic drivers to specific realizing components, namely integrated point-of-sale management, customer service management, and smart inventory management, while the Business Architecture phase shows that all three product lines share a recurring procurement-inventory-preparation-sales pattern and rely on a common supplier and point-of-sale application layer instead of separate systems. A preliminary analytical self-assessment of coverage and traceability indicates internal completeness, whereas expert validation remains future work. The model offers a reusable reference architecture for the digital transformation of small and medium coffee shop enterprises.
Enterprise Architecture for Multi-Sided E-Commerce Marketplaces: A Completeness-Driven TOGAF-Zachman Approach Stephen Edlin; Handri Santoso; Erick Dazki; Januponsa Dio Firizqi; Richardus Eko Indrajit
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16735

Abstract

Indonesian micro, small, and medium enterprises (MSMEs) are moving into digital commerce, but the move remains scattered. National statistics show that 94.76% of e-commerce businesses sell through instant-messaging applications and only 17.23% through a marketplace platform, while 48.03% still arrange delivery themselves and cash remains the dominant settlement method. Multi-sided marketplace platforms can consolidate this activity, but designing them is architecturally demanding. Existing enterprise architecture (EA) studies also seldom verify whether every fundamental descriptive dimension of the enterprise has been addressed. This study proposes a completeness-driven EA for a multi-sided e-commerce marketplace. It integrates the TOGAF Architecture Development Method (ADM) as the design process, ArchiMate 3 as the modeling language, and the Zachman Framework’s 5W1H ontology (What, How, Where, Who, When, Why) as an explicit completeness lens. Within a Design Science Research approach, the artifact is developed across TOGAF ADM Phases A to D and evaluated through architectural consistency checking, gap analysis, and a structured completeness assessment that maps every model element onto a perspective-by-aspect matrix under a stated population rule. Of the 24 cells assessed, 20 are fully populated, 3 are only partially populated, and 1 is weak. The What, How, Where, Who, and Why aspects are answered at all four perspectives, while the temporal When aspect accounts for every cell that falls short. The study contributes a reusable blueprint for marketplaces that advance SME digitalization, an escrow mechanism modeled end-to-end including its dispute path and regulatory driver, and a replicable procedure for assessing architectural completeness.
Co-Authors Abimanyu, Andiani Amalia, Zalfa Azzah Andrew Andrew, Andrew Aprikasari, Manda Atmodjo, Dwi Atmojo, Wahyu Tisno Ayunda, Afifah Trista Clarinda , Marcellina Aurelia Danica Recca Danendra Dazki, Erick Dwi Atmodjo Wismono Prapto Edwin Wiyanto Saputra Elizabeth Hiu , Jesslyn Juana Fangky Antoneus Sorongan Hamdani, Ibnu Handri Santoso Handri Santoso Handri Santoso Harianja, Richad Heni Rohmawati Hidayat, Nurul Ibnu Hamdani Masriah Masriah Master Edison Siregar Mayatopani, Hendra Panna, Ariya Pratama, Arif Purnama, Wandi Putra Setiawan, Valentinus Qonita Azizah Refgiufi Patria Avrianto Richardus Eko Indrajit Richardus Eko Indrajit Richardus Eko Indrajit Rido Dwi Kurniawan Rido Dwi Kurniawan Saiful Azhari Muhammad Sanky Yunliahni Shabila Octavia Stephen Edlin Stephen Edlin Subhi Basit, Muhamad Teddy Mantoro Wiryaatmaja , Christopher Louis WP, Dwi Atmodjo