Sinkron : jurnal dan penelitian teknik informatika
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Inventory Information System Using Fifo And Holt Winters Multiplicative Methods
No full textIn the era of society 5.0, information technology plays an important role in daily life, company operations, and management. Medium-scale stores such as Nisrina Mart require an inventory control process to determine the number of products to be restocked quickly and accurately. However, what happened was the opposite, shop owners experienced a lot of losses because the inventory control process carried out manually had the potential to experience inaccurate data and material losses. Based on these problems, this research tries to propose the development of an inventory information system for reporting incoming and outgoing goods using the First In First Out (FIFO) method. Stock forecasts based on previous sales data are generated to project future stock needs using the Holt-Winters Multiplicative trend moment method. The software development model uses a waterfall which includes requirements, design, implementation, testing, and maintenance. The test results of the Holt Winter multiplication method show a prediction error rate of 0.27. Meanwhile, the level of accuracy in predicting goods sales is 73%. The implementation of this information system is expected to provide convenience in stock monitoring, reduce prediction errors, and increase the accuracy of product data analysis reports to support more effective and efficient management decision-making
Publication Trend of Public Sentiment Towards Indonesia Government Policies
No full textThere are 167 million social media users in Indonesia. Some of these users express their opinions on social media known as public opinion. Public sentiment is the classification of public opinion into several classes. Understanding public sentiment through some public policies can benefit the government. Publication trends can be a stepping stone to deeply understanding a research topic. No research was conducted on the publication trend of public sentiment toward Indonesian government policies on social media. This study aims to explore publication trends in the area of public sentiment toward Indonesia government policies on social media using bibliometric analysis. The Scopus database is used to gather abstracts and keywords, funding details, citation information, bibliographical information, and other information. Search document terms used are "public", "sentiment", "social media", "government", governance," and "policy" rolled within the article title, abstract, and keywords. Research publication trends were visualized using VOSViewer co-occurrence keyword analysis, which resulted in seven clusters from all the collected literature. The research trend is climbing significantly in 2018–2021, but decreasing in 2022. The University of Indonesia is the institution that produces the most documents and IOP Conference Series on Earth and Environmental Science is the publication place that publishes the most documents. Decision trees, random forests, logistic regression, naïve bayes, support vector machines and long-short-term memory are part of the machine learning algorithms recycled and Twitter is the most used social media platform
Rainfall Monitoring Using Aloptama Automatic Rain Gauge And The Network Development Life Cycle Method
No full textExamining 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
Sentiment Analysis of the Indriver Online Ojek Application using the Naïve Bayes Classifier Method
No full textAccording 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 9
Analysis of Performance Comparison between K-Nearest Neighbor (KNN) Method and Naïve Bayes Method in Reward for Honda Motorcycle Salesman Tour
No full textHonda 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
No full textIn 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
Performance Comparison of ARIMA, LSTM, and Prophet Methods in Sales Forecasting
No full textThe development of the business world that is growing rapidly today resulted in tighter competitiveness between fellow business actors. One of the businesses that has sprung up in the market today is the bakery business. Currently, bread is one of the food needs in Indonesia that is great demand by children to the elderly, which is often used as breakfast or snack. One of the companies that produces white bread is the Bandung White Bread Factory. The number of sales at this factory continues to increase every month based on total sales data recorded since 2021. With the increasing number of sales at this factory, the factory often experiences stock shortages and cannot meet customer demand. Therefore, in this study, a model has been developed to forecast the sales of white bread using the ARIMA, LSTM, and Prophet methods. The results of the study showed that the ARIMA method (1,0,2) had the best performance compared to the LSTM and Prophet methods, because the ARIMA method (1,0,2) produced the smallest error accuracy value, namely with a MAPE value of 4.548%, an MSE value of 2248.0822, and an RMSE value of 47.4139
Prediction of Student Performance Based on Behavior using E-Learning During the Covid-19 Pandemic using Support Vector Machine
No full textThe COVID-19 crisis has profoundly impacted many sectors globally, including education, necessitating the shift from traditional in-person learning to independent or online learning through various digital platforms. The integrity of e-learning can be ensured by leveraging e-learning behavioral data. The objective of this research is to develop a novel data model to navigate the educational challenges of the COVID-19 era. Previous studies employed the Support Vector Machine (SVM) technique to predict student performance in an e-learning setting, yet they failed to contrast different SVM kernels and their outcomes. In contrast, this study uses SVM and compares three types of kernels: Radial, Polynomial, and Linear. The dataset used for this research was procured from X-API-Edu-Data. The SVM technique was utilized in a unique way to process the data, which comprised 17 variables and 40 observations. Notably, all 17 variables were character variables, with only four being numeric. Two variables, Raisedhands and Discussion, were selected for analysis due to their key role in effective learning and their association with student performance in an e-learning environment. The evaluation of the model was performed using the Topic variable, which represents the subjects in the dataset. The research findings revealed a marked improvement in accuracy compared to earlier studies. Among the three SVM kernels tested - Radial, Polynomial, and Linear, the Polynomial kernel demonstrated superior accuracy with a score of 0.9979. Therefore, the Polynomial model was deemed most appropriate for analyzing the Topic variable. In conclusion, the study indicates that the application of the e-learning method, specifically during the COVID-19 pandemic, proved highly effective in forecasting student performance
Building the Future of the Apparel Industry: The Digital Revolution in Enterprise Architecture
No full textUsing qualitative methodology, this study investigates the effects that the digital revolution in corporate architecture has had on the apparel industry. In this article, digital technologies, like AI, big data analytics, and the Internet of Things, are the main points of emphasis. They have revolutionized business and operational practices, as well as marketing strategies in the sector. According to the findings of this study, the implementation of advanced technologies significantly contributes to the enhancement of operational efficiency, the introduction of innovative products, and the enhancement of the competitiveness of businesses. The research also highlights the impact that digital transformation has had on sustainability and personalization in the clothing production industry. It demonstrates that adopting an enterprise architecture that is aligned with digital technologies not only increases operational efficiency but also strengthens innovative and competitive capacity. Furthermore, this research acknowledges the significance of ethically responsible and transparent business practices in this digital era, as well as taking into consideration the effects that digital transformation has on society and the environment. The findings of this study provide industry stakeholders with a strategic perspective that can be utilized in the formulation of adaptive business strategies, the exploitation of opportunities, and the facing of challenges in the ever-changing business environment that is associated with the digital er
Sentiment Analysis of the 2024 Indonesia Presidential Election on Twitter
No full textThis analysis enables the identification and a deeper understanding of the positive and negative sentiments reflected in online conversations, providing a comprehensive view of the direction of public support and preferences regarding presidential candidates. Sentiment analysis through machine learning can manage extensive sentiment data, ensuring time efficiency, and enhancing accuracy in swiftly and comprehensively comprehending people's opinions and preferences. With these advantages, machine learning-based sentiment analysis has gained popularity as an effective choice for understanding people's perspectives, preferences, and responses to various issues and events. Therefore, this research focuses on sentiment analysis regarding public opinions on the 2024 presidential election. The method employed in this research is the SVM algorithm with Word2Vec feature extraction. The researcher is interested in conducting a study related to sentiment analysis of the 2024 Indonesian Presidential election using the Support Vector Machine algorithm because of its high accuracy compared to other algorithms. The use of feature extraction aims to improve the performance and effectiveness of the algorithm, and Word2Vec is chosen because it can represent contextual similarity between two words in the generated vectors, enabling concise and improved text classification based on context. The results of this research indicate the best performance at 80:20 ratio with a precision score of 88,94%, Recall 93.08%, F1-score 90,43% and accuracy of 90,75%. This study's results outperform prior research using the SVM method, which achieved an 82,3% accuracy