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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 1,361 Documents
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Cost-Effective Big Data Orchestration via n8n Workflow Automation for Digital Health Transformation in Resource-Constrained Hospitals Wandi Purnama; Akhmad Unggul Priantoro
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16286

Abstract

Achieving compliance with Indonesia’s national SATUSEHAT health data mandate remains a complex hurdle for underfunded regional medical centers. The primary operational challenges stem from isolated departmental information architecture combined with the exorbitant licensing expenses of commercial middleware systems. To overcome these barriers, this study introduces a budget-friendly Big Data integration framework powered by n8n, an open-source, low-code workflow engine designed to dynamically unify disparate hospital environments. The methodology employs a Hadoop-based ecosystem and Apache Kafka for robust data ingestion, while n8n automates the Extract, Transform, Load (ETL) process to map raw clinical records into standardized HL7-FHIR JSON resources. Additionally, a lightweight Linear Regression model is applied as a low-compute operational optimization for dynamic batch-size prediction to prevent network overload during data transmission. Experimental results under a 72-hour continuous simulation on a single-core legacy server using 25,000 synthetic records demonstrate that the n8n-driven framework successfully sustains a throughput of 150 to 180 records per minute with a prediction error (RMSE) of 0.042. Furthermore, by eliminating proprietary software licensing fees and utilizing existing hardware, a comparative financial model indicates an estimated 85% reduction in the Total Cost of Ownership (TCO). Ultimately, this research provides a scalable technical blueprint for automating healthcare data integration, enabling under-resourced hospitals to achieve national interoperability mandates efficiently without compromising data integrity or financial stability.
Evaluation of YOLOv8n Performance for Real-time Human Detection on Autonomous Mobile Robots Alif Daffa Dziqy Riyansah; Febrian Hadiatna; Ratna Susana
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16287

Abstract

This study presents the implementation and evaluation of the You Only Look Once version 8 nano (YOLOv8n) algorithm for real-time human detection on an autonomous mobile robot. The proposed system is designed as an edge-computing-based surveillance solution for monitoring restricted or difficult-to-access areas. The hardware platform integrates a Raspberry Pi 4B for visual processing and an Arduino Mega 2560 for navigation control through serial communication. Human detection is performed using a night-vision camera, while obstacle avoidance is supported by three ultrasonic sensors. A custom dataset was collected under various human postures, object distances ranging from 1 to 10 meters, and different lighting conditions. The YOLOv8n model was trained using 300 epochs with an image resolution of 640 × 640 pixels. Experimental results demonstrate that the proposed system achieves reliable real-time performance under varying environmental conditions. Under lighting variation tests, the model achieved 100% precision, 93.5% recall, 96.6% F1-score, and 93.55% accuracy with an average processing speed of 24.30 frames per second. Distance-based testing produced 100% precision, 92.42% recall, 96.06% F1-score, and 92.42% accuracy at 23.2 frames per second. Furthermore, autonomous navigation experiments confirmed that the robot was capable of simultaneously detecting humans and avoiding obstacles with response times ranging from 2.4 to 3.2 seconds. These findings indicate that You Only Look Once version 8 nano (YOLOv8n)  provides an effective balance between detection accuracy, processing speed, and computational efficiency, making it suitable for deployment on edge-computing-based autonomous mobile robots.
Depression Detection on Indonesian Social Media Using Fine-Tuned IndoBERT and SVM Donny Amanullah Putra Rahman; Muhamad Akrom; Muhammad Naufal
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16294

Abstract

Depression has become a major mental health issue in Indonesia, where approximately 167 million of the country’s 273 million citizens actively use social media platforms such as X (Twitter). The informal writing style, code-mixing, and linguistic variability in Indonesian tweets create significant challenges for automated depression detection systems. This study evaluates a fine-tuned IndoBERT model combined with a Support Vector Machine (SVM) classifier for detecting depression-related indications from Indonesian-language tweets. A total of 10,082 Indonesian tweets were collected and labeled into two categories: Terindikasi Depresi and Tidak Terindikasi; after deduplication, 3,874 unique tweets were used for modeling. Two scenarios were compared: (1) a fine-tuned IndoBERT model, and (2) fine-tuned IndoBERT CLS embeddings with a linear SVM classifier. The fine-tuned IndoBERT model achieved 73.20% accuracy (AUC-ROC = 0.8212), while the hybrid approach achieved a marginally higher 73.71% accuracy (AUC-ROC = 0.8088); a McNemar’s test found this difference not statistically significant (p = 0.86). Both models outperformed five traditional TF-IDF-based baselines (best: 70.36%) on the same held-out test set. The hybrid model required only 0.01 MB of storage versus 475.24 MB for the full fine-tuned model. Given statistically equivalent accuracy, combining fine-tuned IndoBERT embeddings with SVM offers substantially lower storage requirements at no measurable cost in classification performance, making it a promising, resource-efficient approach for depression detection on Indonesian social media.
Tailscale-Based Overlay Network Architecture for Proxmox VE Fiqih Akbari
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16296

Abstract

Remote management of virtualized infrastructure introduces security risk when management services are exposed directly to the public internet. This risk is amplified when testbeds are intended to support sovereign edge computing workloads that require secure, isolated infrastructure. This study designs and evaluates a secure remote management architecture for a Proxmox VE node using a Tailscale overlay network and interface-specific firewall hardening, establishing a foundational infrastructure baseline for sovereign edge computing. The research follows Design Science Research supported by a network engineering evaluation procedure. The artefact was developed through problem identification, topology design, implementation, measurement, and evaluation. Data were collected from Tailscale status checks, Proxmox VE observation, ping latency testing, relay netcheck output, iptables verification, and external port scanning before and after firewall hardening. The Tailscale path achieved an average round-trip time of 0.434 milliseconds with zero packet loss, comparable to the public Internet Protocol path at 0.540 milliseconds with zero packet loss. Before hardening, public scanning detected management ports 22, 2222, and 8006. After applying interface-specific firewall rules, the external scan reported no open ports among the top 1000 ports, while private access to Proxmox VE through the Tailscale interface remained available. The proposed architecture demonstrates that overlay networking must be combined with firewall hardening to remove public management exposure without disrupting authorized remote administration. The result establishes a replicable foundational infrastructure baseline for sovereign edge computing, providing the first stage toward deployment of secure edge computing systems in resource-limited environments.
A Web-Based Personalized Diet Recommendation System Using Decision Tree for Food Suitability Classification Muhammad Farhansyah; Safitri Jaya
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16305

Abstract

Obesity and unhealthy eating patterns have become significant health concerns due to poor dietary habits and a lack of personalized nutritional guidance. Existing food recommendation systems often provide general recommendations without considering individual calorie and nutritional requirements. Therefore, this study aims to develop a web-based diet food recommendation system that integrates nutritional requirement calculations and Decision Tree-based food suitability classification. The system utilizes user information, including age, gender, weight, height, physical activity level, and diet goals, to calculate nutritional requirements through Body Mass Index (BMI), Basal Metabolic Rate (BMR) using the Mifflin-St Jeor method, and Total Daily Energy Expenditure (TDEE). A food dataset containing Indonesian foods and beverages was preprocessed and labeled using a rule-based approach based on macronutrient similarity scores. The Decision Tree algorithm was implemented to classify foods into suitable and unsuitable categories according to users’ nutritional requirements. Suitable foods were subsequently processed through a scoring mechanism and meal construction procedure to generate personalized meal plans. Experimental results showed that the Decision Tree model achieved an accuracy of 92.50%, precision of 78.26%, recall of 94.74%, and F1-score of 85.71%. System testing demonstrated that the developed features functioned properly and generated structured diet recommendations automatically. In conclusion, the proposed system can assist users in selecting foods according to their nutritional requirements and support healthier dietary planning.
Amplitude-Based Statistical Filtering Method for Resonance Localization in Low-Cost Acoustic Sensing Systems Feri Iskandar; Ibnu Anugrah; Deosa Putra Caniago; Yopy Mardiansyah; Galang Mario Alpindra
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16311

Abstract

Acoustic resonance experiments using closed organ pipes are widely used to study the relationship between sound frequency, wavelength, and air-column length. However, low-cost sensor-based systems often produce unstable acoustic signals contaminated by environmental noise, amplitude fluctuations, and sensor-response variability, making resonance identification difficult. This study aims to develop and evaluate an amplitude-based statistical filtering method to improve signal stability and determine the fundamental resonance position more accurately. The proposed method was evaluated using a closed organ pipe experiment integrated with an acoustic sensor and microcontroller-based data acquisition system. Acoustic signals were processed using amplitude-based statistical filtering to extract dominant resonance responses and improve resonance localization. Statistical evaluation was conducted to analyze signal stability and measurement accuracy. The results showed that the filtering process reduced the standard deviation from 9.24 cm in the raw dataset to 6.72 cm in the final resonance candidates, indicating improved resonance localization stability. The experimental resonance length obtained after filtering was 16.12 cm, while the theoretical resonance length was 16.75 cm, resulting in a relative error of 3.76%. These findings demonstrate that the proposed filtering method can improve resonance detection accuracy using a simple, practical, and computationally efficient approach suitable for low-cost educational laboratory systems.
Random Forest-Based Prediction of Self-Reported Headache Complaint Indicators Among College Students Using Daily Activity and IoT Sensor Data Adrian Halomoan Parerus Simbolon; Juliansyah Putra Tanjung; Renaldy Syahputra; Aditya Ahmad Pribadi
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16350

Abstract

Headache complaints among college students may be associated with daily activity patterns and environmental conditions. This study aimed to model self-reported headache complaint indicators using daily activity questionnaire data and Internet of Things environmental data without positioning the output as a clinical diagnosis. Environmental data were recorded using BME280, BH1750, and MQ-135 sensors, while daily activity data were collected using a self-report questionnaire. Sensor readings were aggregated by date and integrated with questionnaire responses to form 305 records from 59 respondents. Random Forest was optimized using Randomized Search CV and evaluated against Decision Tree and K-Nearest Neighbors under three feature scenarios Internet of Things features, daily activity features, and combined features. SMOTE was applied only to the training data, and model differences were assessed using the McNemar test and Wilcoxon signed-rank test. Random Forest achieved the highest overall performance in the daily activity questionnaire scenario, with 81.52% accuracy, 84.96% F1-score, and 85.32% mean cross-validation F1-score. In the combined scenario, Random Forest obtained 77.17% accuracy and 81.08% F1-score. Statistical testing showed significant differences only in selected McNemar comparisons, while Wilcoxon tests on cross-validation F1-scores were not significant across all comparisons. Daily activity data were more informative than date-level environmental sensor data in this dataset. The findings should be interpreted as exploratory numerical performance results rather than evidence of clinical causality or universal model superiority.
TinyML and MFCC Feature Extraction for Energy Efficient Automatic Air Purifier Control Mangasa Manullang; Ferawaty Ferawaty; Leonardo Angkasa; Winson Lim
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16352

Abstract

Classroom environments are highly susceptible to airborne disease transmission due to high occupant density and prolonged interaction times. Conventional mitigation strategies often rely on continuously operating air purification systems throughout building operational hours. This always-on approach guarantees continuous air circulation but results in massive and unnecessary electrical energy consumption, especially during idle periods or when biological contamination is absent. This research aims to design and implement an energy-efficient smart classroom system that automatically controls air purifiers based on real-time acoustic detection of sneeze events. The system utilizes Tiny Machine Learning embedded on an edge microcontroller with an onboard microphone. Audio datasets comprising sneeze, cough, and speech classes were processed using Mel-Frequency Cepstral Coefficients feature extraction at a 16 kHz sampling rate to optimize memory usage, followed by a neural network classifier training. The hardware prototype controls two air purifiers positioned for cross-ventilation, activating them for 15 minutes exclusively upon sneeze detection. The trained model achieved an overall accuracy of 97.5%, with a perfect precision rate in recognizing sneeze events. Field testing during an active class period demonstrated that the event-driven system consumed only 92.8 Watt-hours. Compared to the conventional continuous operation method, the automated system successfully reduced electrical power consumption by 71.4%. Implementing edge-based artificial intelligence for acoustic environmental monitoring provides a highly reliable approach to automated facility management, balancing health risk mitigation through optimal cross-ventilation with significant electrical energy conservation in smart classrooms. Future integration with low-power wireless modules is highly recommended to transmit event logs to a central dashboard, completing the sustainable facility management ecosystem.
Deep Learning-Based Classification of Cikadu Batik Motifs Using ResNet50 and MobileNetV2 Rizki Ripai; Fajar Mahardika; Fazar Sidik; Nurul Badriah; Angga Maulana Purba
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16368

Abstract

Batik motif recognition is essential for cultural heritage preservation and the digitization of traditional Indonesian textile knowledge. This study proposes a deep learning-based framework for the automatic classification of Cikadu Batik motifs from Tanjung Lesung, Banten — a regionally distinct batik pattern that has not been systematically studied in prior computational literature. Two convolutional neural network (CNN) architectures were implemented and comparatively evaluated under identical experimental conditions: ResNet50, a high-capacity model employing residual skip connections, and MobileNetV2, a lightweight model utilizing depthwise separable convolutions and inverted residual blocks. A curated dataset of 2,500 images spanning five motif classes was constructed through collaboration with local batik artisans, preprocessed via resizing (224×224), pixel normalization, and augmentation (rotation, zoom, horizontal flip, brightness adjustment), and partitioned using a stratified 70:15:15 split. Both models were trained with transfer learning from ImageNet weights, using the Adam optimizer (lr=0.0001), categorical cross-entropy loss, batch size of 32, and early stopping over 30 epochs. Model evaluation employed accuracy, precision, recall, F1-score, AUC-ROC, inference time, and parameter count. ResNet50 achieved 95.51% accuracy, 95.67% precision, 95.34% recall, 95.50% F1-score, and 99.56% AUC-ROC, with an inference time of 18.2 ms and 25.64 million parameters. MobileNetV2 achieved 92.13% accuracy, 92.28% precision, 91.98% recall, 92.13% F1-score, and 98.89% AUC-ROC, with an inference time of 8.7 ms and only 3.54 million parameters — approximately 7× lighter and 2× faster. These results empirically establish a clear accuracy-efficiency trade-off, with ResNet50 favored for accuracy-critical server-based systems and MobileNetV2 better suited for real-time mobile deployment. This study constitutes the first published benchmark for deep learning-based Cikadu Batik classification and provides a principled basis for architecture selection in regional batik recognition applications
A Regional Edge Cluster Approach for Reliable Connectivity in Mobile Healthcare IoT Systems Ali AL-ALLAWEE; Senan A. M. Alhasan
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16373

Abstract

Healthcare applications are known to be critical and time-sensitive nowadays. Recently, several healthcare applications have been deployed utilizing Internet of Things (IoT) technology due to its capability of management, efficiency of treatments and mobility which yield accordingly to improve patients’ health. Successful stories have been achieved between IoT-healthcare and Edge computing to support smooth mobility and near processing. However, patient mobility, especially with high speed transportation and intersection, leads IoT-healthcare devices to loss connections and/or process with latency. This paper proposes a Patient Mobility Control under Edge Computing (PMC-EC) mechanism that aims to guaranty and enhance the connectivity between patients (IoT Health sensors) and cloud by using edge computing paradigm in the middle. The objectives are achieved by utilizing cluster management strategies which arranged between cloud and edge computing servers. The mechanism is implemented in OpenStack cloud servers and evaluated by calculating the latency and throughput parameters in two different scenarios. The results, in sum, are collected with promised indication of latency and throughput values to provide acceptable performance.

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