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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 1,196 Documents
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Customer Loyalty Classification Using KNN and Decision Tree for Sales Strategy Development Mukhlisin, Mukhlisin; Nugroho, Handoyo Widi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.15110

Abstract

Customer loyalty is a crucial element in maintaining business continuity in today’s competitive digital era. This study aims to classify customer loyalty levels based on sales and transaction behavior data using two supervised machine learning algorithms: K-Nearest Neighbor (KNN) and Decision Tree. The models were developed and evaluated using Python in the Google Colaboratory environment, utilizing a dataset of 250 customer records. The research process included data preprocessing, feature selection, normalization, data splitting, model building, and evaluation using accuracy, precision, recall, and F1-score metrics. Evaluation results showed that the Decision Tree algorithm delivered the best performance with 99.20% accuracy, 99.50% precision, 99.50% recall, and a 99.50% F1-score. Meanwhile, the KNN algorithm achieved 91.60% accuracy, 91.63% precision, 98.50% recall, and a 94.91% F1-score. These findings indicate that the Decision Tree model is more effective for classifying customer loyalty and can be implemented as a decision support tool for data-driven Customer Relationship Management (CRM) strategies.
Indonesian Public Sentiment Toward Electric Vehicles: Analysis of Social Media Data Saraswati, Ni Wayan Sumartini; Suryawan, I Wayan Dharma; Muku, I Dewa Made Krishna; Bisena, I Kadek Agus; Pramita, Dewa Ayu Kadek
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.15179

Abstract

The development of electric vehicles (EVs) in Indonesia has progressed significantly, supported by government subsidies for Battery-Based Electric Motor Vehicles. These subsidies have sparked mixed public reactions that some support them due to environmental benefits and pollution reduction, while others oppose them for various reasons. Social media platform X serves as a valuable source for gauging public opinion, though analyzing such data manually can be complex. To address this, sentiment analysis, particularly using the Support Vector Machine (SVM) method, offers an efficient solution. This study analyzes 23,031 Indonesian-language tweets from social media platform X, collected between October 2023 and July 2024, using SVM for sentiment classification. The best-performing model, with parameter C = 0.5 and without stemming, achieved an accuracy of 84.98%. The findings suggest that Indonesians generally view electric vehicles positively, with more favorable sentiments than negative ones. This study offers implications across methodological, industrial, and policy domains. Word cloud analysis further supports this, highlighting public support in areas such as pricing, infrastructure, and environmental impact. However, the study also identifies key concerns, including issues around subsidies, taxes, vehicle durability, battery types, and import regulations. Overall, the research provides meaningful insights into the diverse perspectives of Indonesian citizens regarding EVs, helping to inform future policy and development strategies.
ECG-Based Heart Rate Variability and KNN Classification for Early Detection of Baby Blues Syndrome in Postpartum Mothers Megawati, Citra Dewi; Asriningtias, salnan Ratih; Bima Romadhon Parada Dian; Teo Pei Kian; Sutawijaya, Bayu; Fransiska, Ratna Diana
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.14956

Abstract

Early detection of baby blues syndrome plays an important role in preventing postpartum emotional disturbances from developing into more serious mental health conditions. This study proposes a simple and non-invasive approach to identify early signs of baby blues in postpartum mothers by analyzing electrocardiogram (ECG) signals using the K-Nearest Neighbor (KNN) algorithm. The ECG data were gathered through wearable sensors and processed to extract heart rate variability (HRV) features such as RMSSD, SDNN, entropy, and energy. These features were then used to train and test a KNN classification model through a five-fold cross-validation process. KNN was chosen because it is easy to implement, does not assume any specific data pattern, and works well with small datasets like those commonly found in clinical settings. Its ability to group data based on similarity makes it suitable for recognizing subtle physiological changes linked to emotional stress. The model reached an accuracy of 87.5%, with strong precision and recall scores, showing its reliability in distinguishing mothers who show early symptoms of baby blues from those who do not. Among all features, RMSSD and SDNN had the highest impact, pointing to reduced parasympathetic activity in affected individuals. These findings suggest that combining HRV analysis with a straightforward machine learning approach like KNN offers a promising, low-cost solution for early emotional screening in maternal care, especially where resources are limited.
Research and Analysis of Exchange Sort Algorithm in Data Structure Purnomo, Rakhmat; Putra, Tri Dharma
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15005

Abstract

Exchange sort is different from bubble sort. Exchange sort compares an element with other elements in the array, and swaps elements if necessary. So there is an element that is always the center element (pivot). Here is its theoretical description: Comparison, the algorithm compares each element with its adjacent element. Then continue until all elements are compared. Swap: If the elements are in the wrong order (for example, in ascending order, if the left element is greater than the right), they are swapped. This swapping continues until all match numbers are swapped. Iteration, this process of comparing and swapping, is repeated for each pair of adjacent elements in the array. Looping, this process is repeated a number of times (traversing) the array until no more swapping is required, indicating that the array is sorted. It is concluded that for the six numbers in these three case studies, the iterations needed are 5 iterations each. The swaps counts needed are 7, for case study 1. The swap counts needed are 12 for case study 2 and the swap counts are 8 for case study 3. In this research and analysis, the order, all of them is descending, although it can be made ascending. In modern days, exchange sort plays a very important role in terms of sorting algorithms. This paper is only research and analysis. For novelty, the analysis is given with a clear step-by-step procedure of the algorithm.
Collective Intelligence for Cybersecurity: Federated Learning under Non-IID Conditions for Intrusion Detection Mohammed, Hutheifa Anwar; Awos Kh. Ali
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15017

Abstract

Cyber threats are becoming increasingly complex in cyberspace, which highlights the necessity for strong Intrusion Detection Systems (IDS). However, traditional centralized IDS methods have large problems with data privacy and scalability. Federated Learning (FL) is an intriguing new idea that lets multiple clients train a model together without sharing data directly, which keeps privacy intact. The proposed federated intrusion detection model develops and assesses FL models for detecting network intrusions, focusing on the important issue of non-independent and non-identically distributed (non-IID) data among clients. This work implements and compares two widely recognized FL algorithms, Federated Averaging (FedAvg) and Federated Proximal (FedProx), using a 1D Convolutional Neural Network (CNN) architecture specifically designed for tabular network traffic data. The authors utilize a Dirichlet distribution (α=0.1) to distribute the data among 10, 20, and 30 clients, thereby simulating non-IID conditions in the experiment. The authors thoroughly compare the performance of algorithms using two benchmark datasets: NSL-KDD and NF-Bot-Net-V2. The comparison reveals that while both FedAvg and FedProx achieve high detection rates on NSL-KDD, FedProx is more capable of maintaining stability and converging on the more complex NF-Bot-Net-V2 dataset, achieving an accuracy of 0.9953. The results highlight that FedProx is a more appropriate algorithm for implementing robust and privacy-preserving federated intrusion detection systems in statistically heterogeneous network environments found in the real world.
Real-Time Web-Based Ship Collision Risk Detection Using AIS Data and Collision Risk Index (CRI) Asana, I Made Dwi Putra; Widyantara, I Made Oka; Linawati, Linawati; Wiharta, Dewa Made; Wikananda, I Gusti Ngurah Satya
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15106

Abstract

The high density of maritime traffic in Indonesian waters, particularly in the Lombok Strait and Nusa Penida region, increases the risk of ship collisions, especially among vessels lacking adequate navigation systems. This study presents the development of a web-based system for real-time ship monitoring and collision risk assessment using Automatic Identification System (AIS) data. The system integrates a backend powered by FastAPI and MongoDB with a frontend built using React JS. AIS data is collected from a base station and processed to detect ship encounters using the DBSCAN clustering algorithm combined with Haversine distance to identify encounter detection. The risk assessment applies the Collision Risk Index (CRI) method by calculating DCPA (Distance to Closest Point of Approach) and TCPA (Time to Closest Point of Approach), allowing for graded risk categorization. Real-time risk notifications are delivered via WebSocket, and the interface includes interactive maps, ship detail views, and maritime weather information from the BMKG API. The system achieved high responsiveness, with an average detection time of 0.0075 seconds per ship and an end-to-end response time of approximately 61 milliseconds. Functional and usability tests show that the system effectively supports early detection of collision risks and improves maritime situational awareness. The proposed solution is scalable and applicable for maritime safety monitoring in busy sea routes, contributing to safer navigation and proactive decision-making.
Analyzing User Acceptance of NFJuara Mobile Application Using TAM and D&M IS Success Model Koim, Muhammad; Wasilah; Chairani; Sriyanto; Sri Lestari
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15209

Abstract

This study purposes to know how NFJuara application is accepted by the users in Nurul Fikri Lampung using the Technology Acceptance Model (TAM) Integrated with D&M IS Success Model. Data was collected by a validated questionnaire with inner model and outer model testing using PLS-SEM software SmartPLS. The type of data in this study is a quantitative approach. The number of samples collected was 143 respondents. Results of this research show that one of the hypotheses is rejected, that is, Service Quality (SEQ) does not affect Perceived Usefulness (PU) significantly. Besides that, this study shows that Perceived Usefulness (PU) and Perceived Ease of Use (PEU) affect as significant Acceptance of IT (AI) with R2=0.59 (Moderate) and β=0,36 (PUàAI), β=0,46 (PEUàAI). These findings imply that developers of NFJuara applications need to improve the service quality to increase acceptance, although overall NFJuara application is accepted by the user because they still feel the benefits and usefulness of the application. The contribution of this study lies in testing the technology acceptance model in the context of mobile learning, which enriches the literature on the adoption of application-based e-learning, as well as providing practical recommendations for application developers to enhance user experience.  
Enterprise Architecture for the Cruise Industry: A TOGAF-ADM and ArchiMate-Based Approach Watasendjaja, William; Chudra, Glenny; Yohannis, Alfa
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15218

Abstract

Despite its growth and resilience over the last decades, the cruise industry faces significant challenges in its strategic, operational, and technology domains. The unique complexity of the industry requires cruise companies to adopt a structured approach to enterprise transformation. To address this problem, this aims to provide an Enterprise Architecture (EA) blueprint for the cruise industry. Using a case study of a leading cruise line, CruiseX, this study analyzes the operational model of the cruise line and apply two industry-leading standards: The Open Group Architecture Framework (TOGAF) and the ArchiMate modelling language. This study applies the four core phases of TOGAF Architecture Development Method (ADM) from the initial phase of Architecture Vision (Phase A), through the definition of Business Architecture, Information System Architecture, and Technology Architecture (Phase B to D). The ArchiMate language is utilized to visualize the core business processes, information systems, and technology architecture. By using TOGAF ADM as the technical guidelines and ArchiMate as the modeling language, the result of this study is a blueprint of core business processes, application and data that support each business processes, and the underlying technology infrastructure, that provides a structured framework and serves as an actionable tool for implementing enterprise architecture in cruise industry. This research also extends the application of TOGAF and ArchiMate to the under-research cruise industry domain. The study’s limitations include the reliance on publicly available data, the limited scope of business processes, and the lacks of practitioner validation, suggesting clear directions for future research.
Integrating Bayesian Optimization into Ensemble Logistic Regression for Explainable AI-Based Customer Behavior Analysis Jeffry, Jeffry; Azis, Azminuddin I. S.; Kandakon, Elisabeth Tri Juliana
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15219

Abstract

Understanding customer behavior is a strategic factor in business decision-making, particularly within the automotive sector, where competition is intense and product variety is diverse. While previous studies often rely on limited demographic variables, such as age and gender, this research advances the field by integrating ensemble logistic regression with Bayesian Optimization for hyperparameter tuning and SHAP-based interpretability. The proposed model incorporates additional features beyond demographics, including vehicle category, product type, vehicle year, dealer branch, and transaction source, to enhance predictive accuracy. The methodology involves data preprocessing through encoding and cleaning, class balancing using SMOTE combined with undersampling, and stratified train-test splitting (80:20). Baseline Logistic Regression achieved an accuracy of 80%, ROC AUC of 0.89, precision of 0.47/0.96, recall of 0.84/0.79, and F1-scores of 0.59/0.89. By applying ensemble logistic regression with Bayesian Optimization, performance improved to 84% accuracy, ROC AUC of 0.92, precision of 0.51/0.98, recall of 0.83/0.84, and F1-scores of 0.63/0.92. SHAP analysis confirmed that the additional features significantly contribute to prediction outcomes. The novelty of this study lies in combining Ensemble Logistic Regression with Bayesian Optimization and SHAP explainability in the automotive domain, offering not only improved accuracy but also interpretability and fairness for business decision-making, providing actionable insights for targeted marketing strategies and product management. Future studies may incorporate broader behavioral and transactional variables to capture more nuanced customer decision patterns..
Frequent Pattern Mining for Cyberattack Detection Using FP-Growth on Network Traffic Logs Hamsar, Ali; Maulana, Fajar; Hendra, Yomei; Nasyuha, Asyahri Hadi; Aly, Moustafa H
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15221

Abstract

Cybersecurity threats have become increasingly complex, coordinated, and adaptive, creating significant challenges for traditional intrusion detection systems (IDS) that rely on static, signature-based mechanisms. These systems often fail to recognize novel, evolving, or multi-vector attacks that do not match predefined patterns. To overcome these limitations, this study proposes a data-driven framework that applies the Frequent Pattern Growth (FP-Growth) algorithm to analyze co-occurring events within network traffic logs. Using the CIC-IDS2017 benchmark dataset, which includes a wide range of real-world attack scenarios, network events were preprocessed and transformed into transactional data. This transformation enabled the efficient extraction of frequent itemsets and association rules without the computational burden of candidate generation. The experimental results show that the proposed method effectively uncovers meaningful attack correlations, such as brute force attempts preceding privilege escalation or malware infections leading to large-scale DDoS attacks. The model achieved a precision of 77.27%, recall of 70.83%, and F1-score of 73.91%, confirming its reliability in detecting sophisticated attack chains. A heatmap visualization was also generated to improve interpretability, allowing security analysts to quickly identify critical attack relationships. In conclusion, this research demonstrates that FP-Growth provides a scalable, interpretable, and computationally efficient approach to cyberattack detection, with potential integration into real-time IDS environments. Future work will focus on temporal sequence mining and hybrid models combining FP-Growth with machine learning to enhance adaptive, context-aware threat detection.

Page 113 of 120 | Total Record : 1196

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