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INDONESIA
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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Herbal Leaf Identification for Balinese Lontar Usada Knowledge Preservation Using YOLOv8 Object Detection I Nyoman Hary Kurniawan; Ngurah Indra Erawan; Made Sudarma
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.v10i3.16254

Abstract

Indonesia is a megabiodiversity country with more than 30,000 documented medicinal plant species. Much of this ethnobotanical knowledge is preserved in the Lontar Usada Bali, a traditional Balinese manuscript that records the medicinal uses of plants. However, preserving this knowledge is challenging due to the declining number of traditional practitioners and the difficulty of identifying medicinal plants in natural habitats. This study proposes a deep learning-based medicinal plant detection system using the YOLOv8 architecture to identify 12 classes of medicinal plant leaves in Taman Usada Bali. A total of 1,344 images containing 3,230 annotated leaf objects were collected under diverse lighting and background conditions. To improve model generalization, horizontal flipping, vertical flipping, rotation, Mosaic, and MixUp augmentations were applied. Five YOLOv8 variants (YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x) were evaluated using Precision, Recall, F1-score, mAP50, and mAP50–95 metrics. Experimental results showed that all models achieved high detection performance, with YOLOv8m obtaining the highest mAP50–95 score of 0.8676. However, Cost Benefit Analysis (CBA) using the Weighted Sum Model (WSM) identified YOLOv8n as the optimal model. Although YOLOv8m achieved the highest accuracy, YOLOv8n obtained the highest WSM score (2.6400) by balancing detection performance (mAP50–95 of 0.8464         ) and computational efficiency. With a 6 MB model size, 2.7 ms inference time, and 1.559 hours of training, YOLOv8n is suitable for real-time mobile and edge-computing applications. The novelty of this study lies in integrating Lontar Usada Bali taxonomy into a structured dataset, applying WSM for model selection, and enhancing detection robustness through Mosaic and MixUp augmentation.
Comparison of Chronos and Conventional Models: Predicting Machine Downtime using Time Series Hendri Mardani; Miftah Farid Adiwisastra; Yani Sri Mulyani
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.v10i3.16257

Abstract

This study analyzes the comparison between a pretrained transformer model (Chronos) and conventional models in predicting industrial machine downtime using time series data to achieve greater accuracy and efficiency for companies. More specifically, this research focuses on early detection before downtime occurs to reduce company losses in terms of both costs and product quality, and to ensure that Key Performance Indicator targets are met. Design/methodology/approach: The research methodology includes primary data collection, data preprocessing, and sequential data splitting (80% training, 10% validation, 10% testing) to prevent potential data leakage. Model evaluation is measured using the Mean Absolute Error loss function, focusing on the “handling machine” category, which yields 4,069 to 4,101 data rows after the preprocessing stage. Research showed that the conventional XGBoost model with tuning performed best, with the lowest Mean Absolute Error among the other models. XGBoost proved to be highly effective and was capable of outperforming advanced transformer-based models (such as Chronos), particularly when applied to a limited dataset of 4,069 data points. Conversely, transformer architectures like Chronos performed poorly on small datasets because they were designed for massive datasets. This study focuses on the application and evaluation of modern artificial intelligence technologies, specifically transformer architectures such as the Chronos model. Although previous similar studies have successfully predicted downtime accurately using conventional models (such as ARIMA, Random Forest, Support Vector Machine, and autoencoders), those earlier studies have not tested the effectiveness of transformer architectures in detecting machine downtime.
Development and Technical Performance Evaluation of Mobile Goods Delivery Tracking System Using User Acceptance Testing Fatmariani Fatmariani; Eka Hartati; Rendy Almaheri Adhi Pratama; Muhammad Jhonsen Syaftriandi; Wizayunifa Wizayunifa
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.v10i3.16260

Abstract

The rapid growth of digital logistics services has increased the demand for mobile-based parcel tracking systems capable of providing real-time, accurate, and accessible shipment information. Most previous studies have primarily focused on developing web-based applications and conducting functional testing, while studies integrating technical performance evaluation with user acceptance assessment remain limited. This study aims to evaluate the technical performance and user acceptance of a mobile-based parcel tracking system. The system was developed using the Waterfall software development model. Functional verification was conducted through Black Box Testing, while user acceptance was evaluated using User Acceptance Testing (UAT) involving 82 respondents, including administrators, couriers, members, and customer service personnel. The UAT instrument consisted of 15 assessment indicators measured using a five-point Likert scale. In addition, technical performance was assessed by measuring system latency, throughput, and mobile device resource utilization. The results demonstrate that all system functionalities operated according to user requirements. The application achieved an average latency of 1.50 seconds, throughput of up to 45 requests per minute, 18% CPU utilization, 145 MB RAM consumption, 4% battery usage per 30 minutes, and 2.8 MB of network data usage per session, indicating that the system performs efficiently and responsively on mobile devices. The UAT evaluation yielded an overall acceptance score of 87.43%, categorized as highly acceptable, confirming that users positively perceived the system in terms of usability, tracking information clarity, operational efficiency, and support for logistics delivery activities. These findings provide empirical evidence that integrating technical performance evaluation with user acceptance assessment offers a comprehensive framework for developing mobile-based parcel tracking systems that are responsive, resource-efficient, and aligned with operational requirements in digital logistics services.
Comparison of Naive Bayes and Support Vector Machine for Sentiment Analysis of BPJS Health Service Deactivation Vitriayanti Payung Allo; Marlinda Sanglise; Julius Panda Putra Naibaho
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.v10i3.16261

Abstract

The deactivation of BPJS Health services has become a topic of public discussion on social media, particularly on platform X, where users frequently express opinions regarding healthcare access and membership status. This study aims to analyze public sentiment toward the deactivation of BPJS Health services and compare the performance of Naive Bayes and Support Vector Machine (SVM) for sentiment classification. The dataset consisted of 8,357 tweets collected from social media X, of which 7,546 tweets were retained after preprocessing, including data cleaning, case folding, tokenizing, stopword removal, and stemming. TF-IDF and FastText were employed as text representation techniques, while model evaluation was conducted using 5-Fold Cross Validation, Grid Search Cross Validation for hyperparameter optimization, and a paired t-test for statistical significance analysis. Classification performance was measured using accuracy, precision, recall, and F1-score metrics. The results showed that negative sentiment dominated public opinion, accounting for 70.63% of the dataset, followed by neutral sentiment (26.48%) and positive sentiment (2.89%). The SVM model with TF-IDF achieved the highest performance, with an accuracy of 80.97%, precision of 80.16%, recall of 80.97%, and F1-score of 79.70%, outperforming Naive Bayes with TF-IDF (79.01%), Naive Bayes with FastText (64.26%), and SVM with FastText (80.31%). Furthermore, a paired t-test confirmed that the performance difference between Naive Bayes and SVM was statistically significant (p = 0.011). These findings indicate that SVM combined with TF-IDF is more effective for sentiment classification of high-dimensional social media text data and provide empirical evidence regarding the effectiveness of different text representation approaches for healthcare policy-related sentiment analysis.  
Comparative Scalability Analysis of Python Multiprocessing and OpenMP on Windows and WSL2 Achmad Fauzan; Agung Purwo Wicaksono; Elindra Ambar Pambudi
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.v10i3.16262

Abstract

The advent of multicore processors has made efficient parallel programming models increasingly important for CPU-bound workloads. Process-based and thread-based parallelism in native Windows and WSL2 has not been comprehensively compared, despite extensive research on Python multiprocessing, OpenMP, and Windows Subsystem for Linux 2 (WSL2). This paper presents a unified multicore scalability assessment of Python multiprocessing and OpenMP, based on a CPU-intensive floating-point benchmark. Experiments were performed on an Intel Core i9-9900 processor with 8 physical cores and 16 logical threads in different process and thread configurations. Performance was evaluated in terms of execution time, speedup, parallel efficiency, CPU utilization, and statistical reliability metrics from ten repeated executions. Results demonstrate OpenMP performance advantage over Python multiprocessing for all tested configurations. OpenMP with 16 execution units provided maximum speedups of 6.482 on Windows and 7.065 on WSL2, compared to 5.624 and 5.790 for Python multiprocessing. The highest CPU utilization was achieved by OpenMP on WSL2 (97.31%). The reliability analysis confirmed experimental consistency, with coefficient-of-variation values below 10% for all the considered platforms. In general, WSL2 also had slightly better scalability and processor utilization than native Windows. The results show that WSL2 is a suitable environment for multicore computing and that thread-based parallelism provides better scalability for CPU-bound workloads. This study provides a comprehensive perspective on multicore scalability across different parallel programming models and execution environments by integrating multiple performance and reliability metrics into a single benchmark framework.
An End-to-End Balinese Lontar OCR Framework Using Bayesian-Optimized Multiscale Retinex and MobileNetV3 Putu Ayu Febyanti; I Putu Agus Eka Darma Udayana; Aniek Suryanti Kusuma
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.v10i3.16271

Abstract

The preservation of Balinese lontar manuscripts has become increasingly important due to their cultural, historical, and religious significance, while physical degradation such as uneven illumination, faded ink, texture interference, and manuscript aging continues to reduce readability and complicate digital preservation efforts. This study proposes an end-to-end Optical Character Recognition (OCR) framework for degraded Balinese lontar manuscripts by integrating Bayesian-optimized image enhancement, adaptive preprocessing, morphology-based segmentation, domain-specific augmentation, and lightweight deep learning recognition using MobileNetV3. The proposed enhancement pipeline combines Multiscale Retinex, adaptive gamma correction, edge-preserving filtering, and hybrid binarization to improve character visibility under degraded manuscript conditions. Bayesian Optimization with Optuna and Tree-structured Parzen Estimator (TPE) was employed to automatically optimize enhancement parameters according to manuscript quality characteristics. Experimental results demonstrated substantial improvements in manuscript image quality, where Laplacian Variance increased from 306.7596 to 6685.7641, RMS Contrast improved from 28.976 to 83.9085, Michelson Contrast increased from 0.8238 to 1.0, and Ink Ratio Score improved from 0.6096 to 0.9847. The MobileNetV3-based OCR recognition model achieved a test accuracy of 80.52% and a best validation accuracy of 83.78% across 102 Balinese script classes. The proposed framework demonstrates that adaptive enhancement optimization combined with lightweight OCR recognition can provide robust and computationally efficient recognition performance for degraded historical manuscripts while supporting scalable digital preservation and mobile-oriented cultural heritage applications.
PRIVA: Selective Face Blurring Video App Using YOLOv8-Face and MobileFaceNet Muhammad Satrio; Mohammad Nasucha
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.v10i3.16273

Abstract

The increasing use of vlog videos on social media creates privacy risks because third-party faces are often unintentionally recorded and distributed without consent. Existing face blurring approaches generally apply uniform anonymization to all detected faces and do not provide an identity-selective mechanism that keeps the content creator visible while blurring other individuals. This study develops PRIVA, a desktop-based selective face blurring application that runs locally without an external AI server. The proposed pipeline integrates YOLOv8n-Face-960 for face detection, MobileFaceNet for face recognition using 512-dimensional embeddings, and Deep SORT for maintaining identity consistency across video frames. Face enrollment is performed through guided multi-pose webcam capture, while video evaluation is conducted on extracted YOLO analysis frames from five real vlog-like test videos. YOLOv8n-Face-960 achieved an overall detection precision of 95.02%, recall of 89.32%, and F1-score of 92.09%. The baseline comparison showed that YOLOv8n-Face-960 achieved a higher mean detection F1-score than MTCNN, while MobileFaceNet provided a smaller and faster recognition model than FaceNet for CPU-based local inference. For correctly detected face instances, PRIVA achieved a system precision of 99.45%, recall of 98.70%, F1-score of 99.08%, and accuracy of 98.50% in determining whether faces should be blurred or kept visible. Processing performance testing showed an average analysis speed of 4.83 FPS, average export speed of 70.05 FPS, and average processing ratio of approximately 2.40 times the original video duration. These results indicate that PRIVA can support practical local identity-selective face blurring for video privacy protection, although detection robustness remains important under low-light, crowded, distant, or partially occluded face conditions.
Evaluation of Green Marketing Strategy Using Fuzzy AHP-Based Decision Support System Marsono Marsono; Asyahri Hadi Nasyuha; Evi Rosalina Widyayanti; Meng-Yun Hadi Chung
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.v10i3.16277

Abstract

Growing environmental awareness among consumers has encouraged companies to integrate ecological considerations into their marketing activities. However, many firms still find it difficult to determine which green marketing strategy should be prioritised, because the decision involves multiple conflicting criteria and a high degree of subjective human judgment. This study designs and applies a decision support system based on the fuzzy analytic hierarchy process (FAHP) to evaluate and rank green marketing strategy alternatives for a consumer goods company. The decision problem was structured into a three-level hierarchy consisting of the main goal, five evaluation criteria, namely green product, green price, green promotion, green distribution, and green corporate image, and four strategy alternatives. Expert judgments were gathered through pairwise comparison questionnaires using linguistic variables that were converted into triangular fuzzy numbers. Chang’s extent analysis was applied to compute the fuzzy synthetic extent and the degree of possibility, and the priority weights were normalised and verified through a consistency check (CR = 0.095, below the 0.10 threshold). To strengthen the validity of the recommendation, the alternative ranking obtained from FAHP was cross-validated against the Simple Additive Weighting (SAW) and TOPSIS methods. The results indicate that green product is the most important criterion (0.327), followed by green promotion (0.277) and green corporate image (0.210), while sustainable packaging is identified as the most preferred strategy alternative (0.294). All three methods produced an identical ranking, and a sensitivity analysis confirmed that the ranking remained stable under reasonable variations in the criteria weights. The proposed decision support system, whose architecture and interface are also presented, offers a transparent, consistent, and reproducible tool that helps managers allocate resources to the most effective green marketing strategy.
Customer Complaint Classification at PT Pos Indonesia Manokwari Using Naive Bayes and Random Forest Rizhmaria Ester Vieta Saphira; Christian Dwi Suhendra; Lilis Indrayani
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.v10i3.16280

Abstract

Customer complaints represent an important source of information for evaluating service quality and improving organizational performance. However, the increasing volume of complaints received by PT Pos Indonesia Manokwari makes manual complaint classification inefficient and time-consuming. This study aims to compare the performance of Naive Bayes and Random Forest algorithms for customer complaint classification using the Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction method. The dataset consisted of 1,490 customer complaint records collected from the Customer Complaint Handling (CCH) system and categorized into twelve complaint classes. The research process included data cleaning, case folding, stopword removal, TF-IDF transformation, dataset splitting, model training, and performance evaluation. The classification models were evaluated using accuracy, precision, recall, F1-weighted score, F1-macro score, and 5-fold cross-validation. The experimental results showed that Random Forest achieved better performance than Naive Bayes. Random Forest obtained an accuracy of 87.92%, precision of 85.22%, recall of 87.92% an F1-weighted score of 86.30%, and an F1-macro score of 70.85%, while Naive Bayes achieved an accuracy of 84.90%, an F1-weighted score of 84.00%, and an F1-macro score of 48.41%. The cross-validation results produced an average accuracy of 71.81%. Although Random Forest achieved the highest hold-out accuracy, the cross-validation results indicate performance variation across different data partitions, which may be caused by class imbalance among complaint categories. These findings demonstrate that Random Forest is more effective for multiclass customer complaint classification and can support the development of automated complaint management systems at PT Pos Indonesia Manokwari.
Design of a Parking Area Detection System Based on ESP32-CAM and YOLOv8 Natalya Sibarani; Andreas Leonardo Sumendap; Abdul Zaid Patiran
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.v10i3.16282

Abstract

Conventional car parking management systems still face challenges such as manual vehicle monitoring, limited real-time monitoring, and non-automatic parking access control. This study aims to develop an Internet of Things (IoT)-based smart parking system using ESP32-CAM and the YOLOv8n method to detect four-wheeled vehicles in real time. The developed system consists of ESP32CAM as an image acquisition device, a Python program as a processing center, YOLOv8n as an object detection model, EasyOCR for reading license plates, and MySQL as a storage medium for detection results. This system is also equipped with vehicle distance estimation features, LED flash control, and automatic gate control. Based on testing on 20 four-wheeled vehicle samples in a limited test environment, the system successfully detected all tested vehicles and no vehicle detection errors were found. The system was able to read vehicle license plates using EasyOCR and control automatic gates based on the detection results. However, the accuracy of driver detection and OCR decreased in night conditions, to 40% and 60%, respectively. In addition, the FPS dropped from 18 FPS in the morning to 11 FPS at night. These results indicate that the system is capable of supporting real-time vehicle monitoring and parking access control, although its performance is still affected by lighting conditions, image quality, and the limitations of the ESP32CAM camera.

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