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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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ERP-BIM Integration Impact on Post-Commissioning Payment Approval: A PLS-SEM Approach I Gede Upeksa Negara; Asrul Sani
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.v10i3.16144

Abstract

The construction industry continues to face inefficiencies in post-commissioning payment approval, resulting in delayed settlements, disputes, and increased project costs. Although Enterprise Resource Planning (ERP) and Building Information Modeling (BIM) have individually improved project management and information handling, their integrated use for financial process optimization remains insufficiently explored. This study aims to examine the effect of ERP-BIM integration on post-commissioning payment approval performance in construction projects and to identify the most influential determinants. A quantitative approach was employed using a structured questionnaire distributed to 150 construction professionals with direct experience in ERP and BIM implementation. The collected data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS. The results show that System Integration Quality has the strongest effect on Data and Workflow Efficiency (β = 0.356; p < 0.001) and also significantly influences Payment Approval Performance (β = 0.301; p < 0.001). ERP Functional Maturity also demonstrates a strong positive effect on Data and Workflow Efficiency (β = 0.291; p < 0.001) and Payment Approval Performance (β = 0.274; p < 0.001). In addition, Data and Workflow Efficiency significantly affects Payment Approval Performance (β = 0.332; p < 0.001) and mediates the relationship between the exogenous constructs and the endogenous variable. The model explains 62.4% of the variance in Data and Workflow Efficiency and 71.3% of the variance in Payment Approval Performance. These findings indicate that ERP-BIM integration has substantial potential to improve financial workflow effectiveness and strengthen payment approval processes in construction projects.
A Measurement-Driven And Capacity-Aware Framework For 5G NR NSA Deployment Afrizal Yuhanef; Muhammad Putra Pamungkas; Herry Setiawan; Laras Itra Dini
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.v10i3.16154

Abstract

Conventional LTE-to-5G NR Non-Standalone (NSA) planning remains predominantly coverage-oriented and often fails to capture user-experienced throughput degradation under realistic traffic conditions. This limitation is critical in NSA architectures, where LTE acts as the anchor layer for control and mobility while 5G NR provides additional capacity. This study proposes a measurement-driven, throughput-centric spatial framework to identify LTE Capacity Bottleneck Zones (CBZ) as a basis for more realistic LTE-to-5G NR NSA deployment planning. The main novelty is the integration of a KPI-weighted RF Index with Kernel Density Estimation (KDE), DBSCAN spatial clustering, and fuzzy spatial zoning to generate throughput-aware capacity maps rather than purely coverage-based assessments. Drive-test measurements were conducted in Lubuk Alung District, Indonesia, under live LTE network conditions, yielding 8,355 radio KPI samples (RSRP, SINR) and 25,613 HTTP downlink throughput samples with geolocation. Statistical analysis using Pearson/Spearman correlation, polynomial regression, and Random Forest regression reveals consistently weak relationships between RSRP/SINR and throughput, indicating that radio-layer indicators alone provide limited explanatory power for user-experienced performance. The proposed framework classifies the study area into three spatial zones: LTE Stability Zone (28.99%), LTE Degradation Zone (63.02%), and LTE Capacity Bottleneck Zone (7.99%), where CBZs are characterized by acceptable radio conditions but localized throughput degradation. These findings enable a shift from coverage-centric evaluation toward targeted, throughput-aware capacity optimization for LTE-to-5G NR NSA deployment planning
A WebGIS-Based Location Analysis System for Disaster Mitigation in Bitung City Using Ray Casting and Haversine Formula Romeo Fernando Mikhael Dendeng; Kristofel Santa; Sondy C. Kumajas
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.v10i3.16164

Abstract

Indonesia is highly vulnerable to multi-hazard natural disasters, and Kota Bitung specifically faces high risks due to its geographical contours. Currently, the dissemination of disaster information by the Regional Disaster Management Agency (BPBD) relies heavily on conventional static maps. Most existing disaster WebGIS platforms focus merely on static visualization and lack an integrated system capable of instantly analyzing a user's coordinate status against multi-hazard spatial polygons while simultaneously providing location-based evacuation routing. To address this gap, this research aims to design and develop a responsive WebGIS that allows users to independently detect their risk status and logically find the nearest evacuation route. The system development utilizes the Rapid Application Development (RAD) method. The core engine integrates the Ray Casting algorithm to solve the Point-in-Polygon problem against disaster zone boundaries, and the Haversine Formula to calculate the nearest available evacuation point. Based on comprehensive evaluations, including accuracy testing, spatial distance validation, and 11 distinct black-box testing scenarios, the system successfully processed GPS-based coordinate inputs, handled polygon boundary edge-cases, and generated evacuation routes using the OpenSource Routing Machine (OSRM). Ultimately, the proposed system provides a functional prototype for location-based disaster risk analysis and evacuation point recommendation, serving as a foundational interactive instrument to support emergency preparedness in Kota Bitung.
Comparative Sentiment Analysis of GrabFood Reviews Using BiLSTM and BiGRU Jakasurya Siswoyo; Andreas Leonardo Sumendap; Lorna Yertas Baisa
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.v10i3.16165

Abstract

The exponential growth of user-generated reviews on digital platforms has made manual sentiment interpretation of Online Food Delivery (OFD) services increasingly impractical. GrabFood, operating within the Grab ecosystem, has accumulated over 16.1 million reviews on the Google Play Store, necessitating an automated and scalable approach to sentiment monitoring. Conventional labeling approaches, including star-rating proxies and lexicon-based annotation, are inadequate for capturing contextual nuance, negation, and informal linguistic patterns prevalent in Indonesian-language OFD reviews. Furthermore, limited research has systematically compared BiLSTM and BiGRU architectures within a transformer-assisted labeling framework for Indonesian OFD sentiment analysis. This study aims to implement RoBERTa-based automatic sentiment labeling and to comparatively evaluate BiLSTM and BiGRU models for three-class sentiment classification of GrabFood reviews. A corpus of 265,500 raw reviews was collected via web scraping, filtered to 17,709 reviews through rigorous preprocessing, and annotated using the w11wo/indonesian-roberta-base-sentiment-classifier. Random Oversampling was applied to address class imbalance. BiLSTM and BiGRU models were trained and benchmarked against Support Vector Machine (SVM) and Naïve Bayes baselines. BiLSTM achieved 86% accuracy while BiGRU attained 85%, both substantially outperforming SVM (82%) and Naïve Bayes (77%). However, BiGRU demonstrated superior convergence speed and more stable per-class performance, particularly on the neutral category (F1: 51% vs. 50%). Transformer-assisted automatic labeling combined with bidirectional recurrent architectures constitutes an effective and scalable pipeline for Indonesian OFD sentiment classification, with neutral sentiment remaining the primary classification challenge.
Classifying Student Academic Achievement from Limited Categorical Institutional Records: A Comparative Study of Naive Bayes, K-Nearest Neighbor, and Decision Tree Relita Buaton; I Gusti Prahmana; Siti Nur Azizah; Elisiya Putri; Windy Indah Sary Sinaga
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.v10i3.16166

Abstract

Student academic achievement prediction is an important application in Educational Data Mining (EDM) that supports proactive academic decision-making. This study investigates a specific and underexplored condition in the literature the classification of student academic achievement when the only available predictors are categorical institutional background attributes  without behavioral, attendance, or course-level data. This condition reflects data infrastructure limitations commonly found in Indonesian private higher education institutions. Three widely used classification algorithms Naive Bayes (BernoulliNB), K-Nearest Neighbor (KNN), and Decision Tree (CART) are compared against a majority class baseline through a five-stage preprocessing pipeline encompassing label normalization, cohort feature extraction, KNN k-value sensitivity analysis, and reporting of balanced accuracy and macro F1-score for fair evaluation under mild class imbalance. Results show that Decision Tree (depth=5) achieved the highest balanced accuracy (57.77%) and macro F1-score (57.51%), while Naive Bayes demonstrated the best generalization stability based on 10-fold cross-validation (60.07% ± 6.02%). All three models substantially outperformed the majority class baseline on balanced accuracy (+5–8 percentage points) and macro F1-score (+19–21 percentage points). Feature importance analysis identified IPS prior major background (15.6%) and the 2020 cohort (14.4%) as the most discriminative features. These findings provide evidence based algorithm selection guidance for data-constrained institutions and establish a reproducible performance benchmark for the categorical attributes only classification condition.
Image-Based Food Classification for Nutritional Information Estimation Using Deep Learning Sahrial Ihsani Ishak; Sri Dianing Asri; Bias Yulisa Geni; Okma Arnilia; Tri Widodo; Diva Maulana Ilham
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.v10i3.16170

Abstract

This study aims to develop an image-based food classification application integrated with nutritional information retrieval using a deep learning approach. The proposed system is designed to recognize food types from images and provide nutritional information based on an Indonesian food nutrition database. The method involves collecting a dataset of 8,248 images representing 38 categories of Indonesian traditional foods, performing image preprocessing and data augmentation, and developing a Convolutional Neural Network (CNN) model based on the MobileNetV2 architecture through transfer learning. Model performance was evaluated using a 3-fold stratified cross-validation strategy and measured using accuracy, precision, recall, and F1-score metrics. Experimental results showed that the proposed model achieved average accuracy, precision, recall, and F1-score values of 98.85%, 98.88%, 98.85%, and 98.85%, respectively, demonstrating robust and consistent classification performance across the validation folds. The trained model was subsequently deployed into a mobile application using TensorFlow Lite to support real-time food classification and nutritional information presentation. The main contribution of this study is the development of an end-to-end mobile system that integrates deep learning-based food classification with an Indonesian food nutrition database, enabling users to obtain calorie, protein, fat, and carbohydrate information quickly and conveniently for dietary monitoring and health awareness.  
Classification of Traditional Balinese Kites Using CNN for Cultural Preservation Ni Wayan Sumartini Saraswati; Eddy Hartono; Ketut Jaya Atmaja; Welda Welda; I Dewa Made Krishna Muku
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.v10i3.16181

Abstract

The digital preservation of cultural heritage has become increasingly important in sustaining local traditions amid rapid modernization. Balinese traditional kites represent a distinctive form of intangible cultural heritage with unique visual characteristics; however, their identification and classification are still largely based on subjective expertise. This research develops a Convolutional Neural Network (CNN)-based model for image classification to automatically recognize three primary types of Balinese traditional kites: Bebean, Janggan, and Pecukan. Beyond technical implementation, this research contributes to the development of a culturally specific visual dataset, addressing the limited representation of local heritage objects in mainstream computer vision research, which is predominantly based on global datasets of generic objects. A balanced dataset of 2,400 images was constructed and evaluated using 5-Fold Cross Validation to assess model stability and generalization capability. The proposed CNN model achieved an average validation accuracy of 91.5%, with balanced precision, recall, and F1-score across folds. Further evaluation on an independent test set of 282 images resulted in an accuracy of 87.94%, indicating a generalization gap of approximately 4%, which remains within an acceptable range. The results demonstrate that CNN-based classification can effectively support structured digital documentation of traditional kites. This study highlights the potential of computer vision not only as a technical tool, but also as a strategic approach to advancing data-driven cultural preservation and expanding AI applications within localized cultural contexts.
A Comprehensive Analysis of Heap Sort Algorithm for Efficient Sorting Using C++ Programming Language Rakhmat Purnomo; Tri Dharma Putra
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.v10i3.16185

Abstract

Sorting is a need in the computational system including in big data analysis, database management systems, and real time applications. Heap sort is an efficient, comparison-based sorting algorithm that visualizes an array as a binary tree and transforms it into a heap data structure (usually a max heap for ascending sort). The algorithm repeatedly takes the largest element from the root of the heap, swaps it with the last element, and thus reduces the heap size until the heap is sorted The algorithm repeatedly takes the largest element from the root of the heap, swaps it with the last element, and thus reduces the heap size until the heap is sorted. The steps of this algorithm: a. Create Max Heap: Convert the input array into a Max Heap. b. Sort: Swap the root element (the largest element) with the last element, decrease the heap size by 1, and then convert the new root element into a heap. c. Repeat step 2 until the heap is empty. C++ is a known programming language. In this journal we use C++ programming to sort unsorted array. The code is presented in the details. One thorough step by step simulation is given in real data with heap sort and the program is run. The analysis is given by 7 data, namely:  [13, 10, 30, 2, 6, 7, 9]. The result is a presented with sorted heap sort. With 7 datasets to be analysed, it is concluded that 6 swaps happened.
Two-Stage Framework Using IndoBERT for Sentiment Analysis of Tokopedia Reviews under Extreme Class Imbalance Ades Tikaningsih; Imam Tahyudin; Berlilana
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.v10i3.16187

Abstract

The rapid growth of the Indonesian e-commerce industry has generated a large volume of customer reviews for sentiment analysis, but the data distribution often suffers from extreme class imbalance. The review dataset exhibits a 97.6% dominance of the positive class, causing the single-stage transformer model to produce high accuracy that does not fully represent classification capability. The baseline model achieves a macro-averaged F1-score of 0.599, with a neutral-class recall of 26.3%. Approaches based on loss function adjustment, such as class-balanced loss, focal loss, weighted cross-entropy, and decision-threshold adjustment, are unable to fundamentally address this issue, yielding only limited performance improvements. This study proposes a two-stage classification approach that decomposes the multi-class classification task into two sequential binary classification stages using a BERT-based Indonesian-language transformer model (IndoBERT). The first stage separates the positive class from the non-positive class, while the second stage distinguishes between the neutral and negative classes in a more balanced decision space. The proposed approach achieves a macro-averaged F1-score of 0.761, representing a 16.2% improvement over the baseline and outperforming all loss-function-based methods. These findings suggest that, under conditions of extreme class imbalance, simplifying the decision space through gradual task decomposition is more effective than intervention at the loss-function level. Furthermore, error propagation analysis and qualitative evaluations demonstrate that this approach improves sensitivity to minority classes, although challenges remain in cases involving ambiguous expressions.
Hybrid Deep Learning Model for Coffee Leaf Disease Detection Using CNN DeiT Jepri Banjarnahor; Reclesia Br Harianja; Syafridatul maulidah; Nenda Sartika Manalu
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.v10i3.16200

Abstract

Coffee production plays a crucial role in the agricultural economy; however, its productivity is significantly affected by plant diseases that are difficult to detect at early stages. Accurate disease identification remains challenging due to subtle visual differences and high intra-class variability in leaf symptoms. To address this problem, this study proposes a hybrid deep learning framework that integrates Convolutional Neural Networks (CNN) and Data-efficient Image Transformers (DeiT) for automated coffee leaf disease classification. The proposed architecture leverages CNN to capture fine-grained local features, while DeiT models global contextual relationships through self-attention mechanisms, enabling a more comprehensive feature representation. The model is trained and evaluated on a dataset of 6,048 labeled images across four classes: Healthy, Rust, Red Spider, and Leaf Miner. Experimental results demonstrate that the proposed CNN–DeiT model outperforms baseline CNN and Transformer-based approaches, achieving an accuracy of 93.1%, an F1-score of 92.3%, and a ROC-AUC of 95.6%. Robustness analysis shows that performance degradation remains limited (1.6%–3.4%) under various perturbation conditions, while out-of-distribution evaluation indicates strong generalization capability with only a minor accuracy decrease. These findings confirm that the hybrid CNN–Transformer architecture effectively enhances classification performance, robustness, and generalization. This study contributes to the advancement of deep learning methodologies in agricultural image analysis by providing a robust and scalable framework for plant disease classification, with potential applications in precision agriculture and data-driven crop management.

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