cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Irpan Adiputra pardosi
Contact Email
irpan@mikroskil.ac.id
Phone
+6282251583783
Journal Mail Official
sinkron@polgan.ac.id
Editorial Address
Jl. Veteran No. 194 Pasar VI Manunggal,
Location
Kota medan,
Sumatera utara
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 1,259 Documents
 119   120   121   122   123 
Comparative Study on Machine Learning Algorithms for Code Smell Detection U, Hayya; Saputri, Theresia Ratih Dewi
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.v10i1.15439

Abstract

Detecting code smells is crucial for maintaining software quality, but rule-based methods are often not very adaptive. On the other side, existing machine learning studies often lack large-scale comparisons on modern datasets. The goal of this research is to comprehensively compare the performance of various machine learning algorithms for multi-label code smells classification in terms of effectiveness and efficiency. The dataset used in this research is SmellyCode++, containing more than 100,000 samples. Seven models: Logistic Regression, Linear SVM, Naive Bayes, Random Forest, Extra Trees, XGBoost, and LightGBM combined with Binary Relevance were trained on data balanced using random undersampling and multi-label synthetic minority over-sampling. The performance of each model was evaluated using the F1-Macro, Hamming Loss, and Jaccard Score metrics. A non-parametric statistical analysis was also conducted to validate the findings. The experiment found that ensemble-based models statically significantly outperformed the linear and probabilistic models. The performance among the top ensemble models was found to be statistically equivalent. With this statistical equivalence in accuracy, computational efficiency measured with training time became the critical tiebreaker. BR_RandomForest, BR_XGBoost, and BR_ExtraTrees proved highly efficient, while BR_LightGBM was significantly slower. This study concludes that BR_RandomForest offers the best overall trade-off in providing top tier accuracy combined with excellent computational efficiency, making it a robust choice for practical applications.
Comparative Analysis of XGBoost, KNN, and SVM Algorithms for Heart Disease Prediction Using SMOTE-Tomek Balancing Yuliana, Yuliana; Robet, Robet; Hoki, Leony
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.v10i1.15469

Abstract

Heart disease remains one of the leading causes of death worldwide, making early detection crucial for improving patient outcomes. This study aims to evaluate and compare the performance of several machine learning algorithms in detecting heart disease using the 2015 BRFSS dataset, which includes responses from 253,680 individuals. The three algorithms examined are Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The data preprocessing steps involved feature encoding, class imbalance handling using the Synthetic Minority Over-sampling Technique combined with Tomek Links (SMOTE-Tomek), and hyperparameter tuning through RandomizedSearchCV. The models were assessed on a hold-out validation set using several metrics, including accuracy, Receiver Operating Characteristic-Area Under the Curve (ROC-AUC), F1-score, precision, and recall. The results demonstrated that XGBoost achieved the highest performance, with an accuracy of 94%, a ROC-AUC score of 0.98, and an F1-score of 0.94. In comparison, KNN achieved an accuracy of 87% (ROC-AUC 0.95), while SVM attained an accuracy of 79% (ROC-AUC 0.86). These findings suggest that XGBoost is a robust model for large-scale heart disease classification and holds potential for implementation in clinical decision support systems.
Comparison of IndoBERT and SVM Performance in Sentiment Analysis of Digital Education Platforms Br Sembiring, Aldina Bonaria Siva; Robet M.Kom; S.Kom., S.A.B., M.M, Leony Hoki,
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.v10i1.15472

Abstract

Sentiment analysis on user-generated reviews is essential for understanding the quality and effectiveness of digital education platforms. This study compares the performance of Support Vector Machine (SVM) and IndoBERT in classifying sentiments from Ruangguru user reviews. The original dataset contains 111,838 reviews, from which a stratified sample of 10,000 entries was selected for experimentation to maintain class proportion. Text preprocessing applied standard/light normalization (case folding and light cleaning, handling URLs/users/hashtags and repetition) without stopword removal to preserve polarity cues. Auto labels are validated on 139 manually annotated samples (accuracy 0.763, Cohen’s κ 0.644), indicating reliable yet imperfect alignment. To ensure a fair, leakage-safe comparison, we use a fixed 20% standard test split for all models; within the remaining data, 10% is used for validation, and IndoBERT checkpoints are selected based on validation macro-F1 (early stopping). The SVM baseline combines word- and character-level TF-IDF with class-balanced LinearSVC and grid search, achieving accuracy 0.888 and macro-F1 0.543, strong on positives but limited for the neutral class. IndoBERT yields more balanced performance: the class-weighted variant attains the best macro-F1 0.601 (accuracy 0.857), while the baseline reaches the highest IndoBERT accuracy (0.867) with macro-F1 0.596. These results show that Transformer models provide a more balanced trade-off under severe imbalance, whereas SVM remains a competitive accuracy-oriented baseline. In practice, platforms should prioritize macro-F1, use optimized IndoBERT when minority opinions matter, and invest in expanded manual labeling and advanced imbalance handling to improve neutral detection further.
IoT Sensor Data Analysis for Early Fire Detection Using Dynamic Threshold Br Tarigan, Widia; Robet, Robet; Tarigan, Feriani Astuti
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.v10i1.15478

Abstract

Early fire detection using Internet of Things (IoT) technology plays a vital role in minimizing potential material losses and casualties. Conventional systems generally still rely on static thresholds that are less adaptive to environmental dynamics, leading to high false alarm rates and delayed detection. This study proposes a dynamic threshold approach based on a hybrid method of Fuzzy Logic–Random Forest–Adaptive Z-Score and compares it with the static threshold method. Testing was conducted using publicly available secondary datasets, and the algorithms were implemented and tested in Jupyter Notebook. Evaluation was performed using accuracy, false alarm rate (FAR), detection time, F1-score, precision, and recall metrics. The test results show that the dynamic threshold method provides better performance with an increase in accuracy from 59.5% to 74.8%, a decrease in FAR from 31.1% to 14.3%, and a reduction in detection time from 21 seconds to 0 seconds. In addition, the F1-score increased from 0.459 to 0.638, precision from 0.473 to 0.716, and recall from 0.446 to 0.575. These results show that the dynamic threshold approach is more adaptive and reliable in IoT-based fire detection systems than conventional static threshold methods.
Tourism Destination Recommendation Using Blockchain Technology and MCDM Approach Sanjaya, Irfan; Azimah, Ariana; Hindarto, Djarot; Sani, Asrul
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.v10i1.15482

Abstract

The rapid advancement of digital tourism services has revolutionized how travelers search and select destinations, yet privacy and trust issues remain major challenges in centralized recommendation systems. User data such as preferences, location history, and feedback are often stored on centralized servers, making them vulnerable to data breaches and manipulation. This research proposes a Blockchain-Driven Multi-Criteria Decision Making (MCDM) Approach to develop a privacy-preserving and trustworthy tourist recommendation system. The proposed framework integrates blockchain technology to ensure secure, transparent, and immutable data management, while MCDM techniques such as the Analytic Hierarchy Process (AHP) and TOPSIS are employed to evaluate and rank tourist destinations based on multiple criteria, including popularity, cost, safety, accessibility, and sustainability. The blockchain layer enforces decentralized data verification through smart contracts and cryptographic consensus, ensuring that user privacy is protected without sacrificing system transparency. The experimental results indicate improved recommendation accuracy, reduced privacy risks, and enhanced user trust compared to conventional systems. The proposed model achieved 12.5% higher recommendation accuracy and 30% lower privacy risk compared to centralized models. This study demonstrates that combining blockchain and MCDM can effectively support transparent and fair decision-making in digital tourism, offering a scalable and secure foundation for next-generation recommendation systems.
Blockchain and SVM Integration for Distributed DDoS Attack Detection Hia, Septua Ginta Putra; Hayati, Nur; Hindarto, Djarot; Sani, Asrul
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.v10i1.15483

Abstract

Rapid developments in information technology have increased dependence on network services, but have also triggered an increase in cyber threats such as Distributed Denial of Service (DDoS). These attacks can paralyze systems by flooding servers with simultaneous fake traffic. Conventional rule-based detection methods are now less effective in dealing with dynamic attack patterns, requiring an adaptive approach based on machine learning. This research develops a Support Vector Machine (SVM) model enhanced with Blockchain technology to improve accuracy and data security in detecting DDoS attacks. The dataset used is CICDDoS2023 from the Canadian Institute for Cybersecurity, which contains various variants of modern DDoS attacks. The research stages include data pre-processing, training the SVM model using the RBF kernel, and integrating Blockchain with training data hash recording through a smart contract using Remix Ethereum to ensure data integrity. Performance evaluation was carried out using accuracy, precision, recall, and F1-score metrics based on the confusion matrix results. The integration of SVM and Blockchain showed an increase in security and detection accuracy compared to conventional SVM models. This approach not only improves the reliability of the DDoS attack detection system, but also creates a transparent and tamper-proof data validation mechanism. The research results are expected to contribute to the development of adaptive, decentralized network security systems with a high level of confidence in attack detection results.
A Blockchain-Assisted Neural Network Model for Flood Detection and Data Integrity Assurance Melanza, Fattan Rezky; Hindarto, Djarot; Wedha, Bayu Yasa; Sani, Asrul
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.v10i1.15487

Abstract

Flooding is one of the most frequent natural disasters and has substantial impacts on social, economic, and environmental conditions. Therefore, early detection plays a critical role in minimizing potential damage and supporting effective disaster response. This study proposes a Flood Detection System Using an Artificial Neural Network (ANN) with Blockchain-Based Data Integrity, which integrates predictive analytics and secure data management in a unified framework. The ANN model processes multisource environmental data such as satellite imagery, rainfall intensity, water level fluctuations, and soil moisture obtained from Google Earth Engine (GEE). Training is conducted using a sigmoid activation function and backpropagation algorithm to identify spatial and temporal patterns associated with flood-prone areas. The resulting classification outputs are stored in a blockchain ledger to ensure immutability, transparency, and protection against unauthorized data modification. Experimental evaluations demonstrate that the proposed hybrid approach achieves an accuracy of 95.82%, supported by precision, recall, and F1-score values that indicate consistent model performance across varying environmental conditions. The integration of blockchain provides verifiable and tamper-proof documentation of ANN predictions and related metadata. Overall, this research contributes a reliable, secure, and technically robust method for early flood detection, offering valuable support for data-driven decision-making in disaster mitigation and environmental risk management.
Music-Structure Segmentation in Balinese Gamelan (Tabuh Lelambatan) with SSM, Checkerboard Novelty, and HMM Pertiwi, Ni Nyoman Sucianta; Ariana, Anak Agung Gde Bagus; Meinarni, Ni Putu Suci; Willdahlia, Ayu Gede; Ariantini, Made Suci
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.v10i1.15494

Abstract

This study aims to automatically segment the musical structure of Balinese gamelan by combining the Self-Similarity Matrix (SSM) method, the Checkerboard Novelty kernel, and Hidden Markov Models (HMM). Balinese gamelan has a complex musical structure that is cyclical and based on a colotomik system, requiring an adaptive analytical approach to repetitive patterns and transitions between musical sections. The research data consists of 30 Tabuh Lelambatan gamelan audio recordings obtained from public digital sources and validated through expert annotation to produce ground truth. The segmentation process was carried out through feature extraction using Constant-Q Transform (CQT), SSM formation to detect acoustic similarity patterns, application of the checkerboard kernel to mark transitions between segments, and temporal sequence modeling using HMM to refine boundary detection. System performance evaluation was carried out by comparing the segmentation results with ground truth using precision, recall, and F1-score metrics. The test results showed an average macro precision value of 0.998, a recall of 0.705, and an F1-score of 0.818, indicating that this method is capable of detecting the main boundaries of musical structures with high accuracy and consistent stability. However, the model still tends to miss gradual micro transitions. This research contributes to the field of Music Information Retrieval (MIR) and supports efforts to preserve traditional Balinese music through data-based analysis and the development of music computing technology.
Evaluation of Machine Learning Algorithm for Automatic Assessment of School Students' English Essay Ali, Andi Nurfadillah; Hading, Muhaimin; Suryabuana, Andi Sahra
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.v10i1.15496

Abstract

The manual assessment of essays in English language learning often faces challenges related to objectivity and efficiency, especially on a large scale. With advancements in artificial intelligence technology, machine learning-based approaches have begun to be adopted to automate this process through Automated Essay Scoring (AES) systems. However, most existing AES models tend to rely solely on the final scores from the dataset without considering the structural quality of the writing, such as coherence between paragraphs. This study aims to evaluate the effectiveness of machine learning algorithms in assessing school students' essays by adding coherence features as predictor variables in a regression model. This approach uses linguistic feature representation techniques to explicitly build coherence indicators. The proposed model achieved a QWK improvement from 0.69 to 0.89 using SMOTE and coherence features. Meanwhile, human evaluation results showed that the pair of Rater 1 and Rater 2 achieved a QWK of 0.82, the pair of Rater 1 and Rater 3 scored 0.79, and the pair of Rater 2 and Rater 3 scored 0.81. These values indicate a high level of agreement among raters, suggesting that the assessment instrument used is stable. The main contribution of this study is introducing the coherence feature as an explicit predictor in the AES model, filling the gap not provided by standard datasets and proving that coherence improves model accuracy. This research provides practical benefits such as speeding up the evaluation process, reducing teachers' workload, and improving the objectivity and consistency of assessment in language education and evaluation.
ELECTRE-Based Decision Support Model for LPG Base Location Optimization Putri, Ida Ayu Putu Calista Kencana; Sudipa, I Gede Iwan; Ariana, Anak Agung Gede Bagus; Yanti, Christina Purnama; Ekayana, Anak Agung Gde
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.v10i1.15500

Abstract

The kerosene to LPG (Liquefied Petroleum Gas) 3 kg conversion program since 2007 has successfully improved household energy efficiency, but equitable access to bases in remote areas is still an obstacle. In Tabanan Regency, Bali, eight villages do not have access to 3 kg LPG bases, making it difficult for the community to obtain LPG at the Highest Retail Price (HET) and timely supply. This research develops a decision-making model using the ELECTRE method to recommend optimal base locations based on a case study of four villages: Pupuan Sawah, Dalang, Mundeh, and Belatungan. The model integrates 15 criteria including population density, infrastructure accessibility, existing base distance, and the presence of public facilities with a multi-stakeholder approach. The model is expected to be a tool for LPG agents and policy makers in determining the optimal base location and supporting equitable distribution of subsidized energy.

Page 121 of 126 | Total Record : 1259

 119   120   121   122   123 

Filter by Year

2016 2026


Reset
Filter By Issues
All Issue Vol. 10 No. 1 (2026): Article Research January 2026 Vol. 9 No. 4 (2025): Articles Research October 2025 Vol. 9 No. 3 (2025): Article Research July 2025 Vol. 9 No. 2 (2025): Research Articles April 2025 Vol. 9 No. 1 (2025): Research Article, January 2025 Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024 Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024 Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024 Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024 Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023 Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023 Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023 Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023 Vol. 6 No. 4 (2022): Article Research: Volume 6 Number 4, October 2022 Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022 Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022 Vol. 6 No. 1 (2021): Article Research Volume 6 Issue 1: January 2021 Vol. 5 No. 2 (2021): Article Research Volume 5 Number 2, April 2021 Vol. 5 No. 2B (2021): Article Research October 2021 Vol 4 No 2 (2020): SinkrOn Volume 4 Number 2, April 2020 Vol. 5 No. 1 (2020): Article Research, October 2020 Vol. 4 No. 1 (2019): SinkrOn Volume 4 Number 1, October 2019 Vol. 3 No. 2 (2019): SinkrOn Volume 3 Number 2, April 2019 Vol 3 No 2 (2019): SinkrOn Volume 3 Number 2, April 2019 Vol. 3 No. 1 (2018): SinkrOn Volume 3 Nomor 1, Periode Oktober 2018 Vol 3 No 1 (2018): SinkrOn Volume 3 Nomor 1, Periode Oktober 2018 Vol. 2 No. 2 (2018): SinkrOn Volume 2 Nomor 2 April 2018 Vol. 2 No. 1 (2017): SinkrOn Volume 2 Nomor 1 Oktober 2017 Vol. 1 No. 2 (2017): SinkrOn Volume 1 Nomor 2 April 2017 Vol. 1 No. 1 (2016): SinkrOn Oktober Volume 1 Edisi 1 Tahun 2016 More Issue