Journal of Computer Networks, Architecture and High Performance Computing
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Increasing Student Interest in Learning through the Implementation of the K-Nearest Neighbor Algorithm in Classifying Learning Preferences at SMAN 1 Kraksaan
Get PDFThis research examines the effectiveness of implementing the K-Nearest Neighbor (KNN) algorithm in classifying student learning preferences and its impact on increasing interest in learning at SMAN 1 Kraksaan. The main aim of the research is to optimize learning methods through personalization based on individual student preferences. The study involved 560 students of SMAN 1 Kraksaan, with data including variables of age, gender, academic grades, daily study time, attendance and participation in class. The KNN algorithm is used to classify students' learning preferences into visual, auditory, kinesthetic, and reading/writing categories. The learning method is then adjusted based on the results of this classification. The results show that the KNN algorithm is able to classify student learning preferences with an accuracy of 80.36%. After implementing personalized learning methods, there was a significant increase in students' interest in learning, with an average increase of 1.76 points on a 10-point scale. Paired t-test analysis showed a statistically significant difference between interest in learning before and after intervention (p < 0.0001). This research concludes that the implementation of the KNN algorithm in classifying learning preferences can help increase students' interest in learning effectively. These findings emphasize the importance of personalization in education and demonstrate the potential of integrating machine learning in the pedagogical process to improve learning outcomes
Application Of The Weighted Product (WP) Method In The Selection Of Prospective New Employees Using Assessment Indicators At PT. Delta Sukses Sejahtera
Get PDFIn running its business, the success of a company is largely determined by the quality of its employees. To obtain quality employees who meet the required qualifications, companies must carry out a selection process in recruiting qualified new employee candidates. PT. Delta Sukses Sejahtera is a private company that operates in the field of recruitment services. This recruitment service serves companies by taking over the job of recruiting candidates. Each assessment is taken into account and considered according to the company's needs. So far, the selection process for prospective employees has experienced difficulties because they are still comparing the test results of prospective employees one by one to determine potential new employees. This process takes a long time. Apart from that, the old employee selection system created an element of subjective assessment. So it is necessary to build a decision support system using the Weighted Product (WP) method as an alternative solution, so that it runs effectively and reduces the occurrence of subjective assessments. Weighted Product (WP) is a method in a decision making system that connects attribute ratings using a multiplication technique, where the rating for each attribute needs to be raised to the power of the relevant attribute weight before calculation, this process is similar to the normalization stage. Based on the results of this research, it shows that from the five orders of candidates for recruiting new employees, it can be seen that the scores obtained by each prospective new employee are not too far apart from first to fifth. In the first and fifth places, they have similarities in the final psychological and educational criteria. Where the first and fifth psychological test criteria have almost perfect scores
Implementing Dynamic Systems Development Method for a Web-Based System to Evaluate Child Health and Growth
Get PDFThe Simpang Gambir UPTD Community Health Center has developed an innovative digital system to monitor the growth of toddlers. Previously, the recording of toddler growth data was done manually, often leading to data loss or damage. This new system is designed to address these issues and provide a more efficient and accurate solution. The system not only facilitates health center staff and posyandu cadres in monitoring toddler development but also assists them in creating digital growth reports. With this system, toddler growth data can be accessed quickly and easily, facilitating decision-making regarding child health management. One of the key features of this system is its ability to track toddler growth based on weight-for-age charts. This feature allows health workers to easily identify toddlers with nutritional problems and promptly provide necessary interventions. Additionally, the system is equipped with a fast data search feature, enabling staff to easily find specific toddler growth data. The development of this system utilizes the Dynamic System Development Method (DSDM), allowing for a structured and efficient development process. With this method, the system can be developed rapidly and in accordance with user needs
Application of the Naive Bayes Method for Determining the Quality of Crude Palm Oil (CPO) at PTPN 2 Sawit Seberang
No full textThe palm oil industry is a vital pillar of Indonesia's economy, with Crude Palm Oil (CPO) as one of its leading commodities. The quality of CPO significantly impacts its competitiveness and market price internationally. PTPN 2 Sawit Seberang, as a prominent CPO processing company, faces challenges in consistently maintaining product quality. Key factors affecting CPO quality include moisture content, free fatty acids, and impurity levels, which are difficult to manage manually. To address these challenges, this study applies the Naive Bayes method as an efficient and fast classification tool for determining CPO quality. Naive Bayes was chosen for its simplicity in probability calculations and its ability to handle data classification with reasonable accuracy. The data used in this study include moisture content, free fatty acids, and impurity levels measured between February and June 2024. The data was split into training data (80%) and testing data (20%) and analyzed using RapidMiner software. The results show that the Naive Bayes method achieved an accuracy rate of 66.6%, with precision and recall values of 50% each. Although the accuracy could be improved, the application of this method has significantly enhanced the efficiency of determining CPO quality. Thus, the implementation of the Naive Bayes method in determining CPO quality at PTPN 2 Sawit Seberang is an effective step towards improving operational efficiency, classification accuracy, and decision-making quality related to product standards, ultimately supporting the company's competitiveness in the global market
Determining Superior Classes Based on Academic Grades at SMK Karya Pembaharuan with the K-Means Clustering Method
Get PDFDalam lingkungan pendidikan, pengelompokan k-means dapat membantu sekolah menemukan kelas terbaik berdasarkan nilai akademik siswa. Dengan mengelompokkan siswa berdasarkan nilai akademik, sekolah dapat lebih mudah mengidentifikasi kelompok siswa yang memiliki nilai akademik tinggi, sedang, dan rendah. Kemudian penelitian yang digunakan adalah Semua objek dalam satu cluster memiliki karakteristik yang sama , tetapi setiap cluster memiliki karakteristik yang berbeda. Novi dan Ade Mubarok menulis jurnal pada tahun 2021 yang berjudul “Penerapan Algoritma K-Means Untuk Menentukan Kelas Unggulan Pada Smp Pelita Bandung” yang menyimpulkan bahwa SMP Pelita Bandung membutuhkan 3 cluster. Setelah peneliti melakukan eksperimen, mereka dapat menghasilkan 3 cluster, yaitu cluster 0 merupakan cluster dengan nilai rata-rata terendah yang akan masuk ke dalam kelas C sebanyak 42 siswa, pada cluster 1 dengan nilai rata-rata sedang akan masuk ke dalam kelas B sebanyak 37 siswa, sedangkan pada cluster 3 dengan nilai rata-rata siswa, sedangkan pada cluster 3 dengan nilai rata-rata tertinggi akan masuk ke dalam kelas A sebanyak 40 siswa. Hasil penelitian ini menunjukkan bahwa terdapat 6 siswa dalam kategori tinggi, 24 siswa dalam kategori sedang, dan 14 siswa dalam kategori rendah. Evaluasi terhadap hasil pengelompokan menunjukkan hasil yang cukup baik, dengan nilai Davies Bouldin Index (DBI) sebesar 1,180 yang mendekati angka 0
Design a Desktop-Based Load and Customer Calculation Application Information System (SIAPEL)
Get PDFIn today's technological developments, many people are using technology to make work easier, as is PT. PLN Persero Customer Service Implementation Unit (UP3) Tasikmalaya. Several parts of this company, especially the section for recording expenses and customers by the PDKB Team, still use manual methods, namely by calculating using a calculator. This method is very risky, especially as it has the potential for errors in calculations or writing of the recorded numbers. Given these problems, a desktop-based load and customer calculation application information system (SIAPEL) was built. Information system solutions for related load and customer data calculation applications (SIAPEL) so that the results obtained are faster and more accurate. By making direct observations or observations, actively communicating with related fields through the interview process, and looking for research materials that support building an application as a solution to the problems faced. The load and customer calculation application information system (SIAPEL) is an application that can calculate load and customer data and can store the data as a form of company archive. The system development used is waterfall with stages or processes carried out sequentially from the system. The software used to build the load and customer calculation application information system (SIAPEL) is NetBeans 8.2, Java Development Kit 1.8, and MySql. Users can process load calculation data and process customer calculation data. And users can print reports from data that has been entered into the system database. With SIAPEL, it is hoped that it can reduce the risk of information errors and make it easier for users to calculate, store and process data. And it can be used as a more effective way to process data compared to using manual method
Implementation of K-Means Clustering in Recognizing Crime Hotspots and Traffic Issues Through GIS
Get PDFThe challenge of accurately identifying instances of crime and traffic issues has rendered the precise localization thereof difficult, thereby impeding the populace's access to information concerning areas of high risk and safety. Employing a Geographic Information System (GIS)-based mapping system utilizing the K-means clustering method, spatial data pertaining to crime and traffic concerns are grouped. The primary objective is to aid in the identification of high-risk areas concerning crime and traffic matters. The methodology employed in this study revolves around the application of the K-means clustering method to categorize spatial data relevant to crime and traffic issues. K-means clustering represents a non-hierarchical cluster analysis technique designed to partition data into multiple groups based on spatial similarities. Research findings elucidate that through the utilization of the K-means clustering method, three distinct sets of clusters predicated upon the intensity of crime and traffic issues emerge. Consequently, from these clustering outcomes, districts and specific locales falling within each cluster, denoted as moderately vulnerable (C1), vulnerable (C2), and highly vulnerable (C3), can be delineated. This system is poised to furnish recommendations to pertinent authorities for addressing areas exhibiting heightened intensity levels while concurrently facilitating the generation of reports and dissemination of information to the public via a dedicated website pertaining to areas at elevated risk of crime and traffic issues
Application of The Support Vector Machine Algorithm for Timely Student Graduation Prediction Based on Streamlit Web at The Faculty of Informatics Engineering Nurul Jadid University
Get PDFUniversities must provide good education so that they can produce good graduates.There are many factors that influence student graduation rates, one of the problems faced by an educational institution, especially at universities, whether state or private, is finding predictions of student graduation rates on time.One of the technological advances currently available is a system that can predict whether students will graduate on time or not. One of the machine planning algorithms that can be used is the Support Vector Machine.The results of this research were carried out by predicting the on-time graduation rate of students at Nurul Jadid University, Faculty of Engineering, Informatics Study Program. By using the Support Vector Machine method, this research used testing data of 20% of the data from 612 student data with the same 7 attributes. The data obtained 123 data which resulted in 72 student data being on time, 45 student data being late, 4 student data being correct. time and 2 students' data was late. From the results, the accuracy of the training data was 94%, while the results of the accuracy of the testing data received a score of 95%. And based on the validity test of the Support Vector Machine algorithm, the presentation results obtained were Accuracy levels of 96%, Recall 98%, and Precision 94% from 123 testing data. Next, the model is deployed using Streamlit. Streamlit is an open source Python-based framework designed to help developers build interactive web-based programs in the fields of data science and machine learning. The accuracy rate is very good, this shows that SVM can be applied to predict student graduation rates
Design of Goods Inventory Information System Using Visual Basic .Net (Case Study: CV. Barokah Medan)
Get PDFCV Barokah Medan is a company engaged in the distributor of beverage powder. This company was founded in 2019. Currently, the inventory process at CV Barokah Medan is still carried out conventionally, namely by using ledger records and Microsoft Excel tools. By using the current system, it takes a lot of time to search for goods because of the large number of items, then the problem that occurs is that there is data redundancy and notebooks and inventory data files are damaged or lost so that the checking process takes a long time and is mistaken in the data recording process. Efforts to overcome these problems in this study the authors are interested in creating an inventory information system using the visual basic .net programming language at CV. Barokah Medan. Visual basic .net is visual basic that is re-engineered for use on the .NET platform so that applications created using visual basic .NET can run on computer systems supported by the windows operating system. The results of this study indicate that the proposed inventory information system at CV. Barokah Medan can be used to store and process data in the warehouse, namely item data reports, supplier data reports, and inventory reports per day, per month and per year. So that it can support the CV. Barokah Medan to achieve higher productivity, and save costs, energy, and time in collecting, retrieving and managing inventory data, as well as displaying information data quickly and accurately
Implementation of The Apriori Algorithm in Managing Stock Items at Drl.Rumahan Retail
Get PDFDrl.Rumahan is a retail store that sells a variety of motorcycle lamp modifications. Drl.Rumahan is still struggling with determining stock levels and understanding customer purchases. Additionally, they are not utilizing transaction data as a valuable information source. Without leveraging this data, Drl.Rumahan will fall behind its business competitors and lose customers because the products they seek are unavailable. This situation will inevitably become a significant problem if it continues. This study aims to utilize sales transaction data as valuable information and identify customer purchasing patterns from the sales transaction data. The algorithm used is the Apriori algorithm to identify purchasing patterns from the transaction data set. The results of this study identified the three highest rules: if someone buys a pass beam switch, they will buy a shroud with a support value of 5.8% and a confidence value of 47.6%; if someone buys a shroud, they will buy a pass beam switch with a support value of 5.8% and a confidence value of 45.5%; and if someone buys a shroud, they will buy a relay with a support value of 5.2% and a confidence value of 40.9%. These results can inform business strategy decisions by increasing the inventory of products that form rules and serve as a guide for promotional product packages for products that have rules above the minimum support and minimum confidence