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Sinkron : Jurnal dan Penelitian Teknik Informatika
Published by Politeknik Ganesha Medan
ISSN : 2541044X     EISSN : 25412019     DOI : 10.33395/sinkron.v8i3.12656
Core Subject : Science,
Computer Science & IT
Scope of SinkrOns Scientific Discussion 1. Machine Learning 2. Cryptography 3. Steganography 4. Digital Image Processing 5. Networking 6. Security 7. Algorithm and Programming 8. Computer Vision 9. Troubleshooting 10. Internet and E-Commerce 11. Artificial Intelligence 12. Data Mining 13. Artificial Neural Network 14. Fuzzy Logic 15. Robotic
Arjuna Subject : Ilmu Komputer (semua kategori) - Visi Komputer dan Pengenalan Pola
Articles 1,290 Documents
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Implementing Fast Content Management to Accelerate Editorial Workflows in a Digital Newsroom Arifin, Nofri Yudi; Suri, Ghea Paulina; Atmojo, Tri Bowo; Indra, Indra; Riswandha, Muhammad Noval; Agustini, Sherly
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15911

Abstract

The rapid development of digital technology has significantly transformed the mass media industry, particularly in the production and distribution of news content. Local media organizations are increasingly required to publish news rapidly to meet the expectations of digital audiences, while still maintaining journalistic accuracy and quality. Batam Pos, as one of the leading local media outlets in the Riau Islands, faces challenges related to slow editorial workflows caused by conventional content management processes. This study aims to analyze the implementation of Fast Content Management (FCM) as a technical and managerial approach to accelerate digital news publication at Batam Pos. The research adopts a descriptive qualitative method, utilizing data collection techniques including direct observation of newsroom activities, in-depth interviews with editorial and technical staff, and literature review related to Content Management Systems (CMS) in digital media. The results indicate that the implementation of FCM shows observable improvement editorial efficiency through workflow automation, integrated collaboration features, and real-time content management. The publication cycle from writing to distribution becomes shorter without compromising content accuracy due to the presence of layered verification mechanisms within the system. This study concludes that Fast Content Management is an effective strategy for enhancing digital publication performance and can serve as a practical model for other local media organizations undergoing digital transformation.
Application of Two-Stream Late Fusion on EfficientNetV2 based on Transfer Learning to classify AI-generated paintings Rinaldi, Muhammad Kevin; Ernawati , Ernawati; Andreswari , Desi; Sari , Julia Purnama
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.v10i2.15814

Abstract

The rapid advancement of generative artificial intelligence (AI) has made synthetic digital paintings increasingly difficult to distinguish from human-made artworks, raising concerns regarding authenticity, copyright protection, and digital forensics. The main objective of this research is to develop a reliable and interpretable framework for distinguishing AI-generated paintings from human-created artworks by integrating visual and noise-based features. To address the limitations of conventional single-stream CNN models, this study proposes a Two-Stream Network with a Late Fusion strategy, combining a visual stream based on EfficientNetV2-S and a noise stream based on Xception with Spatial Rich Models (SRM).The proposed architecture processes semantic visual features and residual noise characteristics independently, followed by weighted decision-level fusion with a ratio of 0.7:0.3. Experiments were conducted using the AI-Artwork public dataset from Kaggle, consisting of 15,000 images with a data split of 64% training, 16% validation, and 20% testing. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC, ensuring a comprehensive assessment beyond accuracy alone. The results demonstrate that the proposed method achieves 98% accuracy, 98% precision, a 99% F1-score, and high discriminative capability compared to single-stream baselines. Model interpretability was analyzed using Grad-CAM to examine the contribution of each stream. Despite promising results, this study is limited by evaluation on a single dataset and static fusion weights, which may affect generalization to unseen generative models. Future work includes cross-dataset evaluation, adaptive fusion strategies, and exploration of lightweight architectures. Practically, this approach has potential applications in digital art authentication, forensic analysis, and content moderation systems, as well as supporting emerging policies for AI-generated content regulation and copyright protection.
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.
Retrieval-Augmented LLM-Based Empathetic Chatbot for Early Postpartum Depression Screening in Aceh Saffiera, Cut Amalia; Fiqey Indriati Eka Sari; Nur Amalia Hasma; Rizaki Akbar
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.v10i2.15841

Abstract

Postpartum Depression (PPD) remains a significant maternal mental health concern, particularly in low-resource settings where access to professional psychological services is limited. Although digital mental health tools have emerged to address this gap, most existing chatbot-based systems rely on rule-based interactions, offer limited personalization, and lack integration of structured clinical screening mechanisms. This study addresses the lack of culturally adapted, LLM-based empathic chatbots for postpartum mental health screening in low-resource Indonesian settings. We design and implement an AI-driven conversational chatbot that integrates a Retrieval-Augmented Generation (RAG) architecture with a Large Language Model (LLM) to enable context-aware, knowledge-grounded response generation. The system incorporates a Patient Health Questionnaire-9 (PHQ-9)–based screening module to support early identification of depressive symptoms and adaptive conversational support. An early-stage usability evaluation was conducted through a seven-day user interaction study involving 30 postpartum mothers in Aceh, with 12 participants completing the System Usability Scale (SUS). The system achieved an average SUS score of 85.63, indicating excellent perceived usability. While the evaluation focuses on usability rather than clinical effectiveness, the findings suggest that the proposed system demonstrates feasibility as a culturally adapted, scalable digital support tool for early postpartum mental health screening. Further studies with larger samples and long-term evaluation are required to assess clinical impact and sustained user engagement.
Integration of Chatbot and Complaint Website Using Agile Scrum with Load Testing and UAT Winasis, Galih Adi; Lutfina, Erba; Saraswati, Galuh Wilujeng
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.v10i2.15879

Abstract

This study investigates an integrated public complaint service that combines a non-AI, rule-based WhatsApp chatbot, a web-based administrative dashboard, and a RESTful API to improve early response, status traceability, and ticket-based two-way communication. The system was developed using an Agile Scrum approach, implementing the chatbot in Node.js, the backend services and dashboard in Laravel, and PostgreSQL as the centralized database, while real-time dashboard updates were delivered via WebSocket. Evaluation was conducted through User Acceptance Testing (UAT) for core functional flows and RESTful API load testing using Apache JMeter under gradual-load conditions (Typical Busy, Peak, Stress) and an extreme surge condition (Spike/Burst). The UAT results indicate that all core scenarios passed, covering ticket-based complaint submission, duplicate prevention via a one active ticket per WhatsApp number rule, administrator validation and routing, and real-time conversation synchronization within the ticket context. Under gradual-load conditions, all evaluated endpoints maintained a 0% error rate with sub-second average latency in the range of a few hundred milliseconds, indicating stable baseline behavior as workload increased progressively. Under Spike/Burst, the system remained error-free but latency increased, with average response times of 6,593 ms for create complaint, 18,010 ms for status tracking, 18,321 ms for chat message, and 14,308 ms for mixed load, with throughputs of 7.06 req/s, 2.62 req/s, 2.05 req/s, and 5.90 req/s, respectively. Overall, the results demonstrate end-to-end functional feasibility, stable baseline performance under gradual load, and a resilience boundary under extreme surge, motivating targeted optimization of synchronous processing, history retrieval, and payload serialization to improve Spike/Burst time responsiveness.
Finite-Key Analysis of BB84 and B92 QKD with Discrete Phase Randomization and Koashi Bound Oktaviansyah, Brenendra Putra; Sutojo, T.; Akrom, Muhamad
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.v10i2.15882

Abstract

Quantum Key Distribution (QKD) enables theoretically secure key exchange based on fundamental quantum principles such as the no-cloning theorem and Heisenberg’s uncertainty principle. However, practical implementations remain vulnerable to side-channel attacks caused by device imperfections, while many existing studies primarily analyze asymptotic security or isolated attack scenarios rather than realistic finite-key conditions. Unlike prior studies that focus on asymptotic or single-attack analyses, this work presents a comprehensive finite-key security evaluation of BB84 and B92 protocols under hybrid side-channel attacks using Discrete Phase Randomization (DPR) as a lightweight mitigation strategy and the Koashi bound for improved phase-error estimation in B92. Numerical simulations are performed using realistic system parameters with a finite-key size of 100 billion pulses across ten representative attack scenarios. The results show that applying DPR (M = 32) significantly suppresses phase-sensitive attack-induced errors, reducing the quantum bit error rate (QBER) from 11–50% to approximately 1.5–3.02%, thereby restoring practical secure key generation. B92 with the Koashi bound achieves secure transmission distance improvements from 181.6 km to 190.8 km without attacks and reaches 187.0 km under hybrid attacks with DPR, slightly exceeding BB84 in certain conditions. Peak secret key rates reach 0.1363 bit/pulse for BB84 and 0.0741 bit/pulse for B92. These findings demonstrate that non-orthogonal protocols can remain competitive under realistic finite-key constraints using practical mitigation techniques, although literature based induced QBER assumptions remain a limitation.
Multimodal Detection Models for Poultry Fraud Monitoring on Jetson Nano Atmoko, Rachmad; Perdana, Rizal Setya; Wijaya, Fariz Rizky; Setiawan, Akas Bagus
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.v10i2.15884

Abstract

This study defines an indoor commercial poultry-house scenario with no Global Positioning System (GPS) signal, variable bird density, illumination shifts, occlusion, and normal versus fraud episodes characterized as abnormal poultry population behavior (an unauthorized deviation between observed bird count and expected inventory baseline). We evaluate an unmanned aerial vehicle (UAV) to an edge-computing pipeline on Jetson Nano by comparing three models: You Only Look Once version 11 (YOLOv11) with red-green-blue (RGB) input, YOLOv11 with RGB and thermal late fusion, and a convolutional neural network (CNN) backbone with a support vector machine (SVM) classifier. The dataset contains 12,000 frames with synchronized RGB-thermal augmentation to preserve modality alignment. Evaluation covers mean Average Precision (mAP), precision, recall, F1-score, counting errors via mean absolute error (MAE) and root mean square error (RMSE), and edge metrics including frames per second (FPS), latency, and memory. YOLOv11 RGB+thermal records mAP@0.5 of 0.94 (Table 4a), MAE of 1.4, and RMSE of 2.0 (Table 4b), compared with YOLOv11 RGB at 0.91, 1.8, and 2.5 and CNN-SVM at 0.85, 2.6, and 3.4 (Table 4a-4b). For edge throughput, CNN-SVM reaches 28 FPS, while YOLOv11 RGB reaches 18 FPS and YOLOv11 RGB+thermal reaches 14 FPS (Table 8). As a scenario study, these metric-supported results indicate that YOLOv11 RGB+thermal is accuracy-first, CNN-SVM is speed-first, and YOLOv11 RGB is a balanced option for real-time poultry fraud monitoring.
Multi-Variable Agrometeorological Parameter Combination for Drought Early Warning System Using Hybrid SSA–AMBP Algorithm Rezaa, Syahrull; Muhammad Imam Dinata; Anggraini; Syaharuddin
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.v10i2.15885

Abstract

Drought is one of the hydrometeorological disasters that has a significant impact on the agricultural sector, water availability, and food security, thus requiring an accurate and adaptive early warning system. This study aims to develop and evaluate a drought Early Warning System (EWS) model based on a combination of multi-variable agrometeorological parameters using a hybrid approach of Singular Spectrum Analysis (SSA) and Adaptive Model-Based Prediction (AMBP). The agrometeorological data used includes rainfall, air temperature, humidity, solar radiation, wind speed, and other supporting variables processed in the form of monthly time series over a period of ten years. The SSA method is used to perform signal denoising and extract dominant components from the data, while AMBP is applied as an adaptive predictive model to generate SPI-6 drought index forecasts. Model performance is evaluated using RMSE and the coefficient of determination (R²) in the model training and evaluation phases. The results show that the hybrid SSA–AMBP model has the best performance compared to single methods, with an RMSE value of 0.149 and R² of 0.983 in the model training phase, and an RMSE of 0.176 and R² of 0.941 in the model evaluation phase. In addition, the 2026 prediction results show a seasonal pattern with indications of a moderate dry period from October to November. These findings indicate that the developed model demonstrates relatively high predictive accuracy and stability based on RMSE and R² evaluation metrics within the SPI-6 dataset used in this study, and it has the potential to serve as a conceptual basis for decision-support in drought risk mitigation, water resource management, and sustainable agricultural planning.
A Comparative Methodological Study of Automated Machine Learning for Multiclass Stunting Prediction Using Anthropometric Data Joharini, Joharini; Subekti, Agus
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.v10i2.15886

Abstract

Stunting remains a major public health challenge among children under five years old and requires reliable early screening to support timely nutritional interventions, particularly in resource-limited healthcare settings. However, many existing stunting prediction studies rely on complex socio-economic variables and manually selected machine learning models, which limits reproducibility and practical deployment. This study proposes an automated machine learning (AutoML)–based framework for multiclass stunting prediction using routinely collected anthropometric data. The prediction task is formulated as a multiclass classification problem encompassing normal growth, stunted, severely stunted, and above-normal nutritional status. The proposed framework integrates standardized preprocessing, systematic model comparison, stratified 10-fold cross-validation, and controlled hyperparameter optimization, evaluated under SMOTE and non-SMOTE preprocessing scenarios. Experimental results demonstrate that reliable multiclass prediction can be achieved without socio-economic variables. Under SMOTE preprocessing, the optimized k-Nearest Neighbors model improves minority-class sensitivity, increasing accuracy from 0.9806 to 0.9820 with an MCC of 0.9688, while under non-SMOTE conditions, Random Forest achieves robust performance with an accuracy of 0.9985 and an MCC of 0.9975 without resampling. Confusion matrix, ROC, and learning curve analyses confirm strong discriminative capability and stable generalization for both models. Overall, the findings indicate that the proposed AutoML-based framework provides a practical, scalable, and reproducible solution for early multiclass stunting screening using anthropometric data alone.

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