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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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Sentiment Analysis towards the 2024 Vice Presidential Candidate Debate Using the Support Vector Machine Algorithm Harahap, Raihan Rizieq; Furqan, Mhd.
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13903

Abstract

In today’s digital era, social media plays an important role in disseminating information and influencing public opinion. For instance, YouTube. At the 2024 Vice Presidential Debate, YouTube became a medium where people gave various comments. This study aimed to analyze public sentiment through comments on the 2024 Vice Presidential Debate on the Metro TV YouTube channel. This study used descriptive quantitative methods with the Support Vector Machine algorithm to identify various public comments. The results show that from the data experiment taken as many as 1012 data, 80% data training amounting to 809 data and 20% data testing amounting to 203 data is carried out. An accuracy of 82% was obtained with a precision value of 80%, a recall value of 87%, and an f1-score value of 83%. With a fairly high accuracy value, the support vector machine model can be said to be the right model to calculate the accuracy value in sentiment analysis.
Tanjung, Tegar Haryahya Classification of Heart Disease Using Support Vector Machine Tanjung, Tegar Haryahya; Furqan, Mhd
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13904

Abstract

Heart disease is a disease that has a high mortality rate, with more than 12 million deaths occurring throughout the world. Diagnosis of heart disease is very challenging due to the complex interdependence of several attribute factors. The problem that frequently encountered is the lack of accuracy in the classification process. Thus, a system is needed to carry out early diagnosis of heart disease. The structure of this research is to take a heart disease dataset from Kaggle. Then the data will be cleaned with preprocessing. The preprocessing process carried out is changing table names, checking missing values, and normalizing. 820 data will be trained using a Support Vector Machine and 205 data will be tested to find out how well the model can perform classification. The results of training and testing from a total of 1025 data will form a classification model. The model formed using the Support Vector Machine obtained confusion matrix results of 88 is True Positive data, 93 is True Negative data, 10 is False Positive data, and 14 is False Negative data. So the results of model training produce an accuracy of 88%.
Composite Performance Index in Decision Making for Social Assistance Wulandari, Andini; Fakhriza, M.
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13906

Abstract

The majority of the residents in this village, approximately 90%, worked as farmers or farm laborers. Given the economic conditions, social assistance became crucial in reducing social inequality and enhancing the welfare of vulnerable communities. The role of village governance was significant in improving community welfare. The Village Hall served as the center of village administration, managing various activities, including the distribution of social assistance. The Village Hall was responsible for ensuring that social assistance was distributed fairly and effectively to recipients according to prevailing policies. However, the Village Hall faced issues such as inefficiency and inequality in the distribution of social assistance. The process of selecting social assistance recipients was still conducted conventionally, where Village Hall staff collected data on the community based on certain criteria. This method was prone to errors in decision-making and incorrect distribution of assistance, such as recipients who did not actually qualify still receiving aid, while those in need often did not receive appropriate support. These issues were caused by a lack of thorough analysis. The village government needed to establish a decision-making system that was accurate and precise. The operation of this system included all steps of problem identification, selection of relevant information, and determination of the approach used for decision-making through to the resolution of the issues. To achieve accurate results, this research applied the Composite Performance Index method. The aim of this research was to create a decision support system (DSS) for selecting social assistance recipients in the village. This DSS was expected to help staff improve the speed of social assistance classification, avoid errors, and produce accurate decisions.
Rainfall Monitoring Using Aloptama Automatic Rain Gauge And The Network Development Life Cycle Method Nugroho, Kristiawan; Afandi , Afandi; Rokhayadi, Wakhid; Budiarto, Indri; Hermawan, Taufan
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13908

Abstract

Examining the role of rainfall data management in monitoring and reducing natural disasters. Between the observation post and the coordinating office of the Central Java Meteorology, Climatology and Geophysics Agency, there are problems in managing rainfall data. To increase the accuracy and efficiency of rainfall monitoring, the Central Java BMKG Coordinator has used various platforms that are considered very good, such as Grafana, Node-RED, Xampp, and MQTT. Previous research has shown that the use of the Automatic Rain Gauge (ARG) and the Network Development Life Cycle (NDLC) method is very effective in creating an accurate and reliable rainfall monitoring system. This research uses the NDLC model, which consists of analysis, design, prototype simulation, implementation, monitoring and management stages. It is hoped that the research results will help improve visual monitoring of rainfall in local areas and increase understanding of rainfall patterns, flood prediction, water resource management and mitigation measures. This will serve as a reference for governments and institutions working together to make decisions to avoid catastrophic climate change.
Machine Learning to Predict Student Satisfaction Level Using KNN Method and Naive Bayes Method Arfah, Dinda Julia; Masrizal, Masrizal; Irmayanti, Irmayanti
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13914

Abstract

This research aims to apply machine learning techniques in classifying student satisfaction levels at the Faculty of Science and Technology, using the K-Nearest Neighbors (KNN) and Naive Bayes methods. This method was chosen because of its ability to manage classification data and provide accurate predictions regarding student satisfaction with the faculty. It is hoped that this research will provide a deeper understanding of the factors that influence student satisfaction as well as the potential for developing a better evaluation system in the future. This research was carried out through structured stages, starting from selecting the right data to collect relevant information, designing the model by applying the KNN and Naive Bayes methods, to evaluating the performance of the model being built. The data used consisted of 110 student data, where the classification results showed that 104 students were satisfied and 6 other students were dissatisfied with the faculty. The evaluation process produced excellent accuracy, with the Test and Score results and confusion matrix showing an accuracy level exceeding 90%. In conclusion, this research succeeded in showing that the KNN and Naive Bayes methods were effective in classifying the level of student satisfaction at the Faculty of Science and Technology. The results obtained confirm that both methods are reliable in managing and analyzing student satisfaction data efficiently, and provide valuable insights for educational institutions to improve student services and experiences in the future
Using Fuzzy Tsukamoto Method In Forecasting The Amount Medication Requrementsat In The Hospital Bayhaqi, Abdullah; kurniawan, Rakhmat
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13926

Abstract

The pharmaceutical installation as one of the hospital service locations is an inseparable part of the hospital health service system which is oriented towards patient service, including pharmaceutical services needed by patients such as consumable medical equipment that is affordable for all levels of society and the provision of quality medicines. The problem that arises is the uncertain number of patients and the medicines needed by each patient are different and often the supply of medicines that are currently needed by the community is empty, while medicines that are less needed are in abundant stock. Mistakes in ordering medicines can cause shortages or excesses of medicine stock. They tend to only use estimates of the amount of remaining stock without any special methods being used. Even hospital pharmacies tend to buy too many medicines because of uncertain demand and fear of shortages. A method that can help in predicting the amount needed for medicines is by applying the fuzzy Tsukamoto method. The prediction process begins with testing drug data in 2022 to predict the amount needed for medicines in 2023 before finally the drug data for 2023 is used to predict the amount needed in 2024.The prediction process will use drug sales data in the form of the amount of inventory, the amount needed and remaining stock to build a prediction model that projects the amount of drug need in the year 2023. This approach will involve analyzing historical data and applying the Tsukamoto method to produce predictions of the amount needed for all drugs in the following year.
Real-Time Monitoring of Photovoltaic Panel Using Node-RED Raziah, Isyatur; Novandri, Andri; Away, Yuwaldi
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13929

Abstract

This research aims to design and implement an Internet of Things (IoT)-based monitoring system for Photovoltaic (PV) panels using Node-RED. The system can monitor critical parameters such as voltage, current, power, and the electrical energy produced by the PV panels in real-time. The data obtained from the PV panels is sent to a Node-RED server and visualized in the form of indicators and graphs on a dashboard. Statistical analysis calculates the daily average power and total energy produced. The results show that the proposed system can enhance monitoring efficiency and significantly benefit PV system maintenance and management. Users can quickly identify and address issues that may arise, such as panel performance degradation or system disruptions. Energy analysis and maintenance planning can be carried out by collecting historical data. This research supports the broader renewable energy development and provides an effective real-time PV system monitoring solution.
Sentiment Analysis of the Indriver Online Ojek Application using the Naïve Bayes Classifier Method Akbar, Muhammad Zidan; Muhammad Ikhsan; ika Zufria
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13934

Abstract

According to statista.com, there are 73.1 million online motorcycle taxi users in Indonesia and there are 68.1 million active online motorcycle taxi users in Indonesia especially in the province of North Sumatra, there are 43,811 online motorcycle taxi drivers. The Indriver online motorcycle taxi application is an international online transportation service that gives passengers and drivers the freedom to negotiate prices. Sentiment analysis analizes text to determine positive, negative, or neutral sentiments. The method commonly used in sentiment analysis is the Naive Bayes Classifier method. This research uses quantitative methods to analyze sentiment toward the InDriver online motorcycle taxi application by utilizing the Naïve Bayes Classifier algorithm. User review data is collected from reviews on the Google Play Store, then cleaned and converted into a format suitable for statistical analysis. To analyze sentiment towards the InDriver online motorcycle taxi application using the Naïve Bayes Classifier method, collecting review data and user comments using the Python library and the Visual Studio code application, carrying out preprocessing, TF-IDF weighting, dividing the data into 70% and 30%, after that conducting testing using naïve Bayes classifier algorithm, as well as carrying out evaluation using a confusion matrix. The results of calculating the level of accuracy using the Naïve Bayes method for sentiment classification can be said to be good, this can be seen from the accuracy results on a dataset of 1393 with a comparison of training data and test data of 7:3, obtaining an accuracy value of 76%, precision of 71% , recall of 81% and f1-score of 76%. The results of this research analysis produced superior positive sentiment totaling 677 and negative sentiment totaling 608 while neutral was 90
Analysis of Performance Comparison between K-Nearest Neighbor (KNN) Method and Naïve Bayes Method in Reward for Honda Motorcycle Salesman Tour Fawaz, Muhammad Ayyasi; Khairul , Khairul; Siahaan , Andysah Putera Utama
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13935

Abstract

Honda Indako Trading Coy Krakatau is a company in the automotive and spare parts industry. As the main dealer of Honda motorcycles and spare parts for North Sumatra and Aceh, the company faces challenges in boosting sales and maintaining employee loyalty. To address this, the company offers a reward salesman tour for employees who meet certain criteria. However, the current evaluation system is too simple and does not fully capture the quality of employees, especially their product knowledge and involvement in company campaigns. This study aims to solve these issues using data mining techniques, specifically the Naïve Bayes and K-Nearest Neighbors (KNN) methods. These methods were chosen for their accuracy and simplicity. The K-Nearest Neighbor method (K=11) showed an accuracy of 94.04%, a precision of 83.78%, and a recall of 96.87%, while the Naïve Bayes method showed an accuracy of 81.81%, a precision of 72.00%, and a recall of 81.25%.
Performance Analysis of AODV and DSDV Routing Protocols for UDP Communication in VANET Bintoro, ketut Bayu Yogha; Marchenko, Michael; Saputra, Rofi Chandra; Syahputra, Ade
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13938

Abstract

In high-mobility Vehicular Ad hoc Networks (VANETs), maintaining a low Packet Loss Ratio and a high Packet Delivery Ratio (PDR) under UDP communication is crucial. This study compares the performance of Ad hoc On-Demand Distance Vector (AODV) and Destination-Sequenced Distance-Vector (DSDV) routing protocols in vehicular communications and networking using Network Simulator 3 (NS3) simulations. The research employs a simulation-based approach, leveraging NS3 and SUMO to analyze these protocols across different VANET scenarios, including free flow, steady flow, and traffic jams over varying time intervals (300 to 700 seconds). Our findings demonstrate that AODV outperforms DSDV. AODV maintained an average Packet Loss Ratio of 98% and achieved higher throughput, while DSDV experienced higher packet loss and lower throughput. Additionally, AODV exhibited lower end-to-end delay and a higher Packet Delivery Ratio compared to DSDV. These results indicate that AODV is better suited for UDP communication in VANETs, offering lower packet loss, higher throughput, and reduced delays. The study further emphasizes that AODV is preferable for UDP communication in VANETs due to its superior performance metrics. There is potential for further research in vehicular communications, such as integrating advanced hybrid routing protocols and exploring the effects of different traffic densities, vehicle types, and real-world environmental conditions. By investigating these factors, future studies can enhance the reliability and efficiency of VANET communications, contributing to the advancement of intelligent transportation systems.

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