Sinkron : jurnal dan penelitian teknik informatika
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Face Detection in Complex Background using Scale Invariant Feature Transform and Haar Cascade Classifier Methods
No full textFace detection is a process by a computer system that can find and identify human faces in digital images or videos. One of the main challenges faced in the face detection process is the complex background. Complex backgrounds, such as many color combinations in the image, can interfere with the detection process. To overcome this challenge, this research uses a combination of two methods: Scale Invariant Feature Transform (SIFT) and Haar Cascade Classifier. Scale Invariant Feature Transform (SIFT) is a method used in image processing to identify and describe unique features in an image. The SIFT method looks for keypoint descriptors in images that can be used as a reference in comparing different images. After the keypoint descriptor is found with SIFT, the Haar Cascade Classifier method is used to detect faces in the image. Haar Cascade Classifier is a practical algorithm for object detection in images. After facial features are extracted with these two methods, the results are compared with the K-Nearest Neighbor (KNN) approach. This research involves the introduction of 28 color images with complex backgrounds. The results of combining these two methods produce an accuracy of 81.75%. This shows that combining these two methods effectively overcomes complex background challenges in face detection
An IoT-Enabled Smart Energy Management System to Improve Energy Efficiency in University Laboratory
No full textEfficient energy management remains a challenge in university laboratories. The aim of this study is to develop and deploy an Internet of Things (IoT) system that can automatically adjust electronic devices to improve the efficiency of power consumption in the university laboratory. An advanced system is urgently needed to support sustainable and effective energy management. A smart IoT-based energy management system can improve energy efficiency, reduce operating costs, and reduce negative environmental impacts. The system enables both human and automated control of lighting and air conditioning using scheduling and occupancy detection. This development-based research uses a product design methodology that includes both software and hardware. The software product in this research is an IoT platform website for managing IoT devices. The essential processes include defining project goals and requirements, designing a system architecture, creating a user experience design, developing and integrating components, and conducting tests. The system is successful in automatically monitoring and controlling electronic devices based on certain parameters such as scheduling and presence detection. The system meets most of the specified functional and non-functional requirements as demonstrated in experiments, although it is somewhat limited by hardware limitations. Ultimately, the system increases the energy efficiency of the laboratory and thus successfully fulfills the research goal. This innovative project could be a blueprint for other smart energy control efforts
A Modification Depth First Search (DFS) Algorithm for Troubleshoot Rotating Equipment Diagnosis
No full textRotating Equipment has a role in the industrial production process. There are times when the equipment that is being operated has trouble. Operators have difficulty dealing with problems of rotating equipment due to limited knowledge. To solve this problem we must have an expert with knowledge and experience. Based on this, the problem is building an expert system application to diagnose troubleshoot on rotating equipment which aims to transfer the knowledge that an expert has into the computer so that operator can find out what problems occur. This paper use Depht First Search (DFS) method, namely inward tracing techniques and Forward Chaining, namely the inference method that uses reasoning where to test a hypothesis starts from a fact. This system is equipped with an expert menu for knowledge management, so that experts can add, edit, and delete knowledge. The results showed that DFS and Forward Chaining are very suitable for diagnosing troubleshooting on Rotating Equipment. Based on the reasoning of the experts in their field and adjusted to the symptoms experienced by equipment so that the type of damage is found. It can also assist operators in diagnosing troubleshoot on Rotating Equipment so that operators can take preventive action to prevent further damage to the equipment
Comparison Of Exponesial Smoothing With Linear Regression Predicting Amount Of Goods Sales
No full textA trading business is a business that operates in the sales sector with the aim of obtaining maximum profits through sales activities. To be able to sell efficiently, a prediction system is needed, so that there is no excess or shortage of inventory and the sales process can run smoothly. Human limitations in solving prediction problems without using tools that apply prediction methods are one of the obstacles in finding the right prediction value. Therefore, we need a prediction system that can help find accurate and fast values. So the problem formulation is how to design and build a sales prediction system using exponential smoothing and linear regression methods, then compare the two and find out which method is the best, both of which use periodic data prediction models. The data collection method used is secondary data from previous research and journals, as well as combining library study methods, namely information obtained from books, references and scientific works related to predictions. The tool used to build applications is MS-Visual Studio 2010 and WEB based syste
Sentiment Analysis Of Indonesian State Army Police Neutrality Sentiment Towards The 2024 Election On X Using The Support Vector Machine Algorithm
No full textThe accompanying goals are created: One method for figuring out the order of feeling examination in the balance of military police towards the 2024 political decision depends on popular assessment in SVM technique in arranging opinion investigation in the balance of military police towards the 2024 races in light of 2024 general assessment in X. In a leading examination, the stages utilized are the exploration system. This was finished to coordinate the exploration stages. The technique of this examination is quantitative. An exploration area is where a specialist completes research, particularly in catching peculiarities or examination that really happens at the exploration area to get precise and genuine examination information. The consequences of the testing did were to decide the capacity of the framework that had been made to complete feeling investigation on opinion towards the lack of bias of the TNI and Polri during the political race Research begins with compiling, specifically determining the points to be discussed. The subject of this research is the execution of message mining in testing the balance of military police feelings towards the 2024 political decisions in X using the Help vector machine1 algorithm. Tweet Information Collection,In this review, scientists utilized 800 tweet information.. The consequences of the opinion examination did will be introduced as a disarray framework, where through the disarray network and characterization report the degree of exactness of the exploration that has been completed can be determined.It is trusted that the aftereffects of this assessment can give a thorough image of the public's discernment on Twitter with respect to the lack of bias of the TNI and Polri in sorting out races
Evaluation of Cluster Models for Creating Profiles of Home Buyers
No full textThe property industry in Indonesia is currently a dynamic and continuously evolving field, in line with rapid economic growth and urbanization. Shifts in lifestyle patterns, infrastructure development, and changes in government policies have had a significant impact on how properties are marketed in Indonesia. With a growing population and increasing purchasing power, the Indonesian property market is becoming more complex. Therefore, strategies are needed to segment consumer groups for effective marketing in the housing sector. This research will delve deeper into consumer segmentation in home selection, a technique that divides consumer diversity into distinct groups based on characteristics and behavior. By using an extensive dataset involving demographic data such as location, age, gender, occupation, and many other variables, clustering algorithms can uncover complex patterns to determine consumer segments in their home selection. The algorithms to be used for this study are K-Means clustering, the Gaussian Mixture model, and Hierarchical clustering. By using these three data clustering models, we can determine which algorithm produces the most ideal results for customer profiling. The results demonstrate that the K-Means algorithm outperforms the others in accurately identifying distinct consumer segments, hence producing customer profiles. Therefore, customer profiling can also be used by the marketing division as a tool to aid in promotions in order to better understand their target audience, hence creating a successful marketing campaign
Analysis of Indonesian Netizen Sentiment on Platform X Regarding the Arrival of Refugees in Indonesia Using the Multinominal Naive Bayes Method
No full textThis research aims to analyze the sentiments of Indonesian netizens regarding the arrival of Rohingya refugees in Indonesia using the Multinomial Naive Bayes method. Sentiment analysis was carried out on comments obtained from platform X. The data collection technique used the crawling method to extract comments from platform X users regarding the issue of the arrival of Rohingya refugees. The tool used for crawling is Google Collab. The data analysis process includes sentiment labeling, data preprocessing (case folding, stopword removal, tokenizing, stemming), and classification using the Multinomial Naive Bayes method. The research results show that the majority of Indonesian netizens' sentiments regarding the arrival of Rohingya refugees in Indonesia are negative, with a percentage of 81%. Positive sentiment reached 8%, while neutral sentiment was 11%. The Multinomial Naive Bayes method produces an accuracy of 82.5% in classifying netizen sentiment. The tools used to process the data are the Orange Data Mining application version 3.36.2 It is hoped that this research can contribute to the development of computer science, especially in the fields of Text Mining, Natural Language Processing, Machine Learning and Artificial Intelligence (AI). It is also hoped that this research will provide benefits to parties related to handling the Rohingya refugee problem in Indonesia, such as the government, humanitarian organizations, mass media, academics, the general public, and other researchers
Implementation of Zero Trust Security in MSME Enterprise Architecture: Challenges and Solutions
No full textThis research examines the implementation of Zero Trust Security in Enterprise Architecture in Micro, Small, and Medium Enterprises to improve cybersecurity. The background of this research focuses on the increasing cyber threats faced by MSMEs and their limitations in adopting advanced security systems. The purpose of this study is to evaluate the effectiveness of Zero Trust Security in protecting MSME data and information systems from internal and external threats, as well as identifying challenges and solutions in its implementation. The research method used is a case study on several MSMEs with a qualitative and quantitative approach involving in-depth interviews, surveys, and secondary data analysis. The results showed that the implementation of ZTS significantly improved information system security in MSMEs, with a 45% reduction in security incidents after ZTS adoption. In addition, ZTS was also shown to increase cybersecurity awareness among MSME employees. The main challenges identified include the need for adequate training, changes in organizational culture, and budget limitations. To overcome these challenges, this study recommends the adoption of continuous training strategies, increased cybersecurity awareness, and the utilization of affordable yet effective security solutions. The conclusion of this study confirms that Zero Trust Security is an effective and efficient approach to improving the cybersecurity of MSMEs. However, further research is recommended to explore the application of Zero Trust Security in various other industry contexts and to develop more affordable solutions for MSMEs with limited resources
Analysis of Malnutrition Status in Toddlers Using the K-MEANS Algorithm Case Study in DKI Jakarta Province
No full textMalnutrition in children is a serious health issue in various regions, including DKI Jakarta Province, which affects the physical and cognitive development of children. This research aims to classify malnutrition status in children using the K-Means algorithm, focusing on cases in DKI Jakarta. The objective is to identify patterns of malnutrition prevalence across different regions, serving as a basis for more effective interventions. The data used in this study includes the percentage of children with severely stunted, stunted, and normal nutritional status across six districts/cities in DKI Jakarta. The results of K-Means clustering show that Central Jakarta has the highest prevalence of severely stunted (10.50%) and stunted (13.01%) status, while West Jakarta has the lowest prevalence of severely stunted (4.62%) and stunted (10.22%) status. The solution offered by this research is the grouping of regions based on malnutrition prevalence, allowing for the identification of areas requiring priority intervention. The analysis results indicate that DKI Jakarta can be classified into several clusters based on malnutrition prevalence. The cluster with the highest malnutrition prevalence includes Central Jakarta, while the cluster with the lowest malnutrition prevalence includes West Jakarta and the Thousand Islands. The implementation of K-Means in this research provides an efficient approach to identifying groups of regions that need more attention in combating malnutrition in children. In conclusion, this research can serve as an important reference for policymakers in formulating more effective and efficient intervention strategies in DKI Jakarta, as well as inspire similar studies in other regions with different population characteristic
Designing an Used Goods Donation System to Reduce Waste Accumulation Using the WASPAS Method
No full textThis research aims to build a website application-based selection system for recipients of used goods donations using the WASPAS method. This system is designed to assist in the efficient and fair distribution of used goods to recipients in need. The WASPAS method is applied to calculate the preference value (Qi) for each alternative donation recipient based on predetermined criteria. The analysis results show that "Alternative-01" is the best alternative with the highest Qi value (1.866), while "Alternative-02" has the lowest Qi value (1.713). The significant difference in Qi values between these two alternatives indicates a clear difference in preferences. The weight (w) given to each criterion plays an important role in forming the preference value (Qi). Therefore, careful consideration needs to be taken in determining the weight of each criterion to ensure that the final decision is in line with expectations. The WASPAS method has proven to be effective in the selection system for recipients of used goods donations. The advantage of this method lies in its ability to handle multi-criteria problems and uncertain data. By applying the WASPAS method, the decision-making process can be carried out more quickly, accurately and objectively. Although the WASPAS method provides a strong basis for decision making, it is also necessary to consider other relevant factors, both quantitative and qualitative. This will ensure that the final decision taken is the best decision and in accordance with the research objectives