System Development for Learning Process Monitoring in Private Lesson Institution Using Codeigniter Framework

The monitoring information system is one of the main functions in managing student data in Generasi Cerdas private lesson. This study provides the results of an analysis and design of a monitoring information system that can be used to facilitate services to students in monitoring including grades and student absence. This application developed using UML Design and PHP Programming with CodeIgniter Framework and use MySQL Database. CodeIgniter is an open source framework in the form of a PHP framework with an MVC model (Model, View, Controller) for building dynamic websites. The resulting application has been able to provide information of teacher, student, student value and student absence data to assist in processing student and teacher data in the Generasi Cerdas private lesson, so that the institution has a faster, more efficient monitoring information system and easier to use.

Analysis of Factors that Affect the Success of E-Learning Implementation of STMIK BI Balikpapan

E-learning in higher education is a technique to improve learning and teaching experience, and as a tool to educate students through digital media, with or without the guidance of their instructors. STMIK BI Balikpapan has been using it since 2015, but its implementation has not been as optimal as expected. The research aims to identify the factors that influence the success of the application of e-learning in STMIK BI Balikpapan by referring to the model adopted from TAM (Technology Acceptance Model) and TOE (technological, organizational and environment). The research respondents were 94 people. Data were collected through questionnaires and analyzed using the Structural Equation Model (SEM) through the Smart PLS program. The results showed that of the four hypotheses tested, one hypotheses had significant influence (habits) and the other three hypotheses were not significant (connections, motivation and facility).

COMPATIBILITY OF SELECTION OF STUDENT DEPARTMENTS USING k-NEAREST NEIGHBOR AND NAÏVE BAYES CLASSIFIER IN INFORMATICS PRIVATE VOCATIONAL SCHOOL, SERANG CITY

Selection of majors by prospective students when registering at a school, especially a Vocational High School, is very vulnerable because prospective students usually choose a major not because of their individual wishes. And because of the increasing emergence of new schools in cities and districts in each province in Indonesia, especially in the province of Banten. Problems experienced by prospective students when choosing the wrong department or not because of their desire, so that it has an unsatisfactory value or value in each semester fluctuates, especially in their Productive Lessons or Competencies. To provide a solution, a departmental suitability system is needed that can provide recommendations for specialization or major suitability based on students' abilities through attributes that can later assist students in the suitability of majors. The process of classifying the suitability of majors in data mining uses the k-Nearest Neighbor and Naive Bayes Classifier methods by entering 16 (sixteen) criteria or attributes which can later provide an assessment of students through this test when determining the majors for themselves, and there is no interference from people. another when choosing a major later. Research that has been carried out successfully using the k-Nearest Neighbors method has a higher recall of 99%, 81% accuracy and 82% precision compared to the Naïve Bayes Classifier whose recall only yields 98% while the accuracy and precision is the same as the k- Nearest Neighbors.

WEB-BASED SCHEDULING APPLICATION AND MOTION SENSOR USING ARDUINO MEGA

The purpose of this research is to find out how to make a web application that can control electronic devices in the building, find out how to make motion sensor circuits with Arduino Mega, electronic devices can be on or off, and find out which one is more efficient in using web applications and sensors. motion on the building. The results of this study indicate that the creation of a web scheduling application that can control the needs of building electronic equipment: webserver (hosting), internet connection, Ethernet shield, Arduino mega, relay module, and the use of motion sensors with Arduino Mega. which can adjust the sensitivity and time delay of signaling when there is the movement of a human object. The use of Ethernet shield and motion sensor each has advantages and disadvantages. It would be better if the two components were combined.

Development of Student Associations Information System at Universitas Pembangunan Nasional Veteran Jakarta

Universitas Pembangunan Nasional Veteran Jakarta (UPN Veteran Jakarta) is one of the public universities which views student associations to play an important role in student self-development. Student’s self-development can be realized if students participate in every activity. but a lot of problems that occur because the process related to student association is still done manually without using an information system, where students have to come to campus to take care of all the needs to hold an activity. So that we need a system that aims to improve services to student associations as well as facilitate the management of existing student associations data and can increase the credibility of UPN Veteran Jakarta itself. It is called SIWA. It is expected to minimize errors that occur and manage business processes that exist in each student association. So that the benefits generated later, it is hoped that information on Real Estimate of Cost, submission of activity proposals, accountability reports and annual reports can be managed properly. Besides that, it can also support a paperless culture in the college environment. This information system will be built based on a web-based system and its development will use the waterfall method.

Prediction of Electrical Energy Consumption Using LSTM Algorithm with Teacher Forcing Technique

Electrical energy is an important foundation in world economic growth, therefore it requires an accurate prediction in predicting energy consumption in the future. The methods that are often used in previous research are the Time Series and Machine Learning methods, but recently there has been a new method that can predict energy consumption using the Deep Learning Method which can process data quickly for training and testing. In this research, the researcher proposes a model and algorithm which contained in Deep Learning, that is Multivariate Time Series Model with LSTM Algorithm and using Teacher Forcing Technique for predicting electrical energy consumption in the future. Because Multivariate Time Series Model and LSTM Algorithm can receive input with various conditions or seasons of electrical energy consumption. Teacher Forcing Technique is able lighten up the computation so that it can training and testing data quickly. The method used in this study is to compare Teacher Forcing LSTM with Non-Teacher Forcing LSTM in Multivariate Time Series model using several activation functions that produce significant differences. TF value of RMSE 0.006, MAE 0.070 and Non-TF has RMSE and MAE values of 0.117 and 0.246. The value of the two models is obtained from Sigmoid Activation and the worst value of the two models is in the Softmax activation function, with TF values is RMSE 0.423, MAE 0.485 and Non-TF RMSE 0.520, MAE 0.519.

Online System on Monitoring and Feedback for Education

This study reported the staging of process on developing a mobile application for real-time data management information system on monitoring and feedback in early childhood education, it can help tracking child care and education and assist teacher in monitoring and feedback on child services. A study was carried out to gather necessary information through data mapping, in-depth interviews with key stakeholders, document reviews, application development, direct entry in the field using mobile development, application testing and analysis that was conducted on for 253 respondents. To obtain a full picture on early childhood education, data on child growth and education shall be mapped and linked in one application. We introduce a mobile app to systematically compile the individual as well as group data across different aspects of child life, ranging from child education. Using a tablet PC or mobile phone, data could be easily entered at any time by the person. Due to still poor infrastructure at the grass root level, the system also allows a safety store offline that could automatically link to server when network connection is available. The immediate data entry will provide real-time data report that could be accessed by any relevant stakeholders at any levels to response accordingly. However, to avoid misuse of data, the access will also be restricted with a secured login system. Based on the study, this application is easily applicable for real-time monitoring and evaluation on early child education.

Implementation of Social Network Analysis in the Spread of Natuna Issues on Twitter

Currently, the growth of internet users has been accompanied by the development of applications that support interaction among users, which is called social media. One of the popular social media in society today is twitter. Data on Twitter can be presented in a graph structure visualization in nodes that represent actors and edges that represent relationships between actors. In an effort to find the most influential actors and actors who interact the most in spreading the Natuna topic on social media twitter, an analysis will be carried out using the Social Network Analysis method using the Degree Centrality approach. The data used in this study were taken from December 20, 2019 at 00.00 WIB to January 7, 2020 at 10.00 WIB consisting of 71,477 nodes and 147066 edges. The results of this study can be concluded that the @susipudjiastuti account is the most influential actor and plays an important role in social networking because the @susipudjiastuti account is the most linked account with 29755 links. Meanwhile, the @ shaktia704 account was the most active account during the data collection period, which reached 259 links.

K-Means Cluster Analysis of Sex, Age, and Comorbidities in the Mortalities of Covid-19 Patients of Indonesian Navy Personnel

Covid-19 disease is still ongoing. It is necessary to do intensive research related to age, sex and congenital diseases so that management can be better planned. The research was conducted using data from Indonesian Navy personnel and their families, retired Indonesian Navy and their families. This study used k-means clustering for data grouping of Indonesian Navy personnel based on age, sex and congenital disease characteristics. The results of the k-means cluster clustering show that the k = 2 cluster has not been able to provide an explanation of the relationship between age, sex and comorbidity with the risk of death due to Covid-19. However, in the cluster with k = 3, it turns out that deaths due to Covid-19 are related to old age, men, even though there is no congenital disease. Meanwhile, using the k = 4 cluster, it is increasingly clear that deaths due to Covid-19 are closely related to old age, both men and women, with comorbidities.

Optimization of Support Vector Machine Method Using Feature Selection to Improve Classification Results

The performance of the organizations or companiesare based on the qualities possessed by their employee. Both of good or bad employee performance will have an impact on productivity and the impact of profits obtained by the company. Support Vector Machine (SVM) is a machine learning method based on statistical learning theory and can solve high non-linearity, regression, etc. In machine learning, the optimization model is a part for improving the accuracy of the model for data learning. Several techniques are used, one of which is feature selection, namely reducing data dimensions so that it can reduce computation in data modeling. This study aims to apply the method of machine learning to the employee data of the Bank Rakyat Indonesia (BRI) company. The method used is SVM method by increasing the accuracy of learning data by using a feature selection technique using a wrapper algorithm. From the results of the classification test, the average accuracy obtained is 72 percent with a precision value of 71 and the recall value is rounded off to 72 percent, with a combination of SVM and cross-validation. Data obtained from Kaggle data, which consists of training data and testing data. each consisting of 30 columns and 22005 rows in the training data and testing data consisting of 29 col-umns and 6000 rows. The results of this study get a classification score of 82 percent. The precision value obtained is rounded off to 82 percent, a recall of 86 percent and an f1-score of 81 percent.