The Effects of Skin Health Care Awareness on Practic Behavior of Women in Their 20s

This study was conducted to study skin health care awareness and practices for women in their 20s who are highly interested in skin health care. From December 1, 2020, to February 1, 2021, women in their 20s who are currently living in Jinju-si Gyeongsangnam-do and Gwangju, and the study were surveyed using the Internet and SNS, and a total of 300 copies were used as final analysis data. The specific questionnaire consisted of a total of 40 items. Data analysis was conducted using the SPSWIN 21.0 program. First, in the difference between skin health care awareness and skin health care practice behavior according to general characteristics, age ‘26-29 years old’, marriage status ‘unmarried’, final education ‘university student/graduation’, occupation ‘student’, and monthly beauty-related expenses ‘less than 10-130,000 won’ drinking alcohol ‘1-2 times a week’ was the highest, and for the smoking, non-smoking women were the highest. Regarding skin health care awareness according to general characteristics, the overall average was 3.82, ‘harmful factors’ appeared to be the highest, and for the skin health care practice behavior, the overall average was 3.43, showing the highest average in ‘cosmetics selection’, and the difference in skin health care awareness according to age, educational background, and smoking was significant. In response, in this study, it is intended to be used as basic data to help maintain and improve skin health by grasping problems such as awareness of skin health care and practice behavior of women in their 20s.

Analisis Hybrid Decision Support System dalam Penentuan Status Kelulusan Mahasiswa

Determination of graduation status is often faced by lecturers in every university. The facts show that many of the decisions still have a fairly high error rate in determining graduation status. This study aims to develop an analytical model in the process of determining student graduation using the Hybrid Decision Support System (DSS). The methods used in the analysis process are Analytical Hierarchy Process (AHP) and Technique for Others Preference by Similarity to Ideal Solution (TOPSIS). The performance of AHP can determine the value of the weight criteria and TOPSIS performs rankings to produce solutions in determining. The criteria indicators used to consist of Depth (C1), Material Breadth (C2), Answer Accuracy (C3), Fluency of Answers (C4), Scientific Attitude (C5), Logical Consistency of Content (C6), Authenticity (C7), Scientific Quality ( C8), Language (C9), and Writing (C10). The results of this study indicate that the Analytical Hierarchy Process (AHP) method provides a weighting value for each criterion with a fairly good accuracy rate of 85,86%. These results conclude that each criterion has a consistent level of relationship in determining student graduation. Based on the output of the TOPSIS analysis, the results presented can determine the student's graduation status correctly and accurately.

PkM UJI KOMPETENSI KEAHLIAN SISWA JURUSAN TEKNIK OTOMOTIF ALAT BERAT DI SMK RIGOMASI BONTANG

Expertise Competency Test (UKK) is part of government intervention in ensuring the quality of education in Vocational High School education units. One of the government's efforts to improve students' knowledge and skills, especially for Vocational High Schools, is by holding a skill competency test that determines student graduation. Competency testing is needed to determine a person's ability or competence according to professional standards. To be accepted to work in the world of work, a person must be competent, which is evidenced by, among other things, a competency certificate through a competency test. A person is said to have the competence (competent) in a particular field if he has the knowledge, skills, and attitudes to complete the job correctly by the demands of professionalism. The purpose of implementing this UKK is to measure the competency attainment of Rigomasi Vocational High School students at a certain level according to the expertise of heavy equipment automation engineering majors taken during the learning period at school

Klasterisasi Tingkat Masa Studi Tepat Waktu Mahasiswa Menggunakan Algoritma K-Medoids

The period of study on time is one of the parameters of a student's success in completing college to obtain a bachelor's degree. A student is said to have completed his studies on time if he is able to complete his studies less than or equal to the predetermined time. Academic Provides facilities to find out the estimated time of student graduation. By providing information on which students are included in the cluster, they can complete their studies on time and which students do not complete their studies on time. In this study, the data processed were data from students who had graduated in the previous year. Then the data is processed using rapidminer software. This study applies the K-Medoids algorithm in clustering. The result of testing this method is to determine the student clusters who can complete the study period on time and the student clusters who cannot complete the study period on time. This research is expected to contribute to the campus in evaluating the tendency of students to complete their studies on time or not. The results of the evaluation of performance can produce information for study programs, lecturers and students in making policies

Unpaid Tuition Balances at Community Colleges: An Exploratory Analysis Delinquent Tuition Debt and Graduation

The rising cost of higher education has led to increased tuition costs for students and their families, forcing more students to secure larger amounts of debt to finance their educational pursuits. Although scholars have explored how student loan debt accumulation influences higher education persistence and graduation, an unexplored area of higher education finance and debt is the relationship between unpaid tuition balances on community college student graduation. This analysis attempts to illuminate this gap by utilizing a unique institutional dataset with data from the National Student Clearinghouse to analyze the relationship between unpaid tuition balances and postsecondary graduation for community college students. Results suggest that having an outstanding tuition balance dramatically decreases the likelihood of graduation 3 years out from the unpaid balance. Implications for future research and practice are discussed.

Analisis Metode K-Nearest Neighbors (K-NN) Dan Naive Bayes Dalam Memprediksi Kelulusan Mahasiswa

In this research the author aims to apply the K-NN and Naive Bayes algorithms for predicting student graduation rates at Sekolah Tinggi Pariwisata (STP) Mataram, The comparison of these two methods was carried out because based on several previous studies it was found that K-NN and Naive Bayes are well-known classification methods with a good level of accuracy. But which one has a better accuracy rate than the two algorithms, that's what researchers are trying to do. The output of this application is in the form of information on the prediction of student graduation, whether to graduate on time or not on time. The selection of STP as the research location was carried out because of the imbalance between the entry and exit of students who had completed their studies. Students who enter have a large number, but students who graduate on time according to the provisions are far very small, resulting in accumulation of the high number of students in each period of graduation, so it takes the initial predictions to quickly overcome these problems. Based on the results of designing, implementing, testing, and testing the Student Graduation Prediction Application program using the K-NN and Naive Bayes Methods with the Cross Validation method, the result is an accuracy for the K-NN method of 96.18% and for the Naive Bayes method an accuracy of 91.94% with using the RapideMiner accuracy test. So based on the results of the two tests between the K-NN and Naive Bayes methods which produce the highest accuracy, namely the K-NN method with an accuracy of 96.18%. So it can be concluded that the K-NN method is more feasible to use to predict student graduation

Determining patterns of student graduation using a bi-level learning framework

The practice of data science, artificial intelligence (AI) in general, has expanded greatly in terms of both theoretical and application domains. Many existing and new problems have been tackled using different reasoning and learning methods. These include the research subject, generally referred to as education data mining (or EDM). Among many issues that have been studied in this EMD community, student performance and achievement provide an interesting, yet useful result to shaping effective learning style and academic consultation. Specific to this work at Mae Fah Luang University, the pattern of students’ graduation is determined based on their profile of performance in different categories of courses. This course-group approach is picked up to generalize the framework for various undergraduation programmes. In that, a bi-level learning method is proposed in order to predict the length of study before graduation. At the first tier, clustering is applied to derive major types of performance profiles, for which classification models can be developed to refine the prediction further. With the experiments on a real data collection, this framework usually provides accurate predictive outcomes, using several conventional classification techniques.

Pengembangan Model Untuk Prediksi Tingkat Kelulusan Mahasiswa Tepat Waktu dengan Metode Naïve Bayes

Many parameters affect the timeliness of student graduation, starting from the student's interest in certain majors, the type of class chosen, to the grades for each semester obtained. This is a determining factor in how students can graduate on time or not at the end of their education. So a model is needed to predict student graduation rates on time, using alumni data whose data is obtained from several universities in Palembang City. The model used is a Naïve Bayes algorithm which serves as a model for classification. The dataset used is alumni data that has been collected from several universities, while the attributes used are the Department, College, Class Type, Temporary IP Value from semester 1 to 4, graduation year, and college generation. Then from the attributes and models used, the researcher used the Python 3 programming language and the Jupyter Notebook tools to process the prepared dataset. Furthermore, the distribution of the dataset is divided by 70% for training data and 30% for testing data. To test the algorithmic process used by researchers using K-Fold Validation. The results of this study are the accuracy of the prediction model carried out, where the accuracy results obtained from the Python 3 programming language and the Naïve Bayes algorithm are 0.8103.

Peningkatan Konerja Metode SVM Menggunakan Metode KNN Imputasi dan K-Means-Smote untuk Klasifikasi Kelulusan Mahasiswa Universitas Bumigora

<p class="Abstrak">Salah satu permasalahan utama Universitas Bumigora adalah rasio antara mahasiswa yang masuk dengan mahasiswa lulus tepat waktu tidak seimbang, sehingga akan mengakibatkan penurunan penilaian akreditasi dikemudian hari. Salah satu indikator penilaian dalam proses akreditasi adalah rasio kelulusan mahasiswa. Data kelulusan mahasiswa yang tersimpan pada basisdata kampus, tetapi belum dimanfaatkan dengan maksimal. Dengan memanfaatkan data kelulusan mahasiswa dapat mengetahui pattern atau pola-pola mahasiswa yang lulus tepat waktu atau tidak, sehingga dapat minimalisir terjadinya mahasiswa yang drop out. Tidak hanya itu, pengambil keputusan dapat dimudahkan membuat kebijakan secara dini untuk membantu mahasiswa yang berpotensi drop out dan lulus tidak tepat waktu. Solusi yang ditawarkan pada penelitian ini adalah menggunakan teknik data mining. Salah satu metode data mining yang digunakan penelitian ini adalah metode SVM. Adapun tujuan penelitian ini adalah meningkatkan kinerja metode SVM untuk klasifikasi kelulusan mahasiswa Universitas Bumigora menggunakan metode KNN Imputasi dan K-Means-Smote. Penelitian ini terdiri dari beberapa tahapan yaitu pengumpulan data kelulusan mahasiswa, pra-pengolahan seperti penanganan nilai hilang menggunakan metode KNNI, penanganan ketidakseimbangan kelas menggunakan K-Means-Smote, klasifikasi menggunakan metode SVM. Tahapan terakhir adalah pengujian kinerja SVM berdasarkan akurasi, sensitivitas, spesifisitas, dan f-measure. Berdasarkan hasil pengujian yang telah dilakukan, integrasi metode KNNI, K-Means-Smote, dan SVM mendapatkan akurasi 83.9%, sensitivitas 81.3%, spesifisitas 86.6%, dan f-measure 83.5%. Penggunaan metode KNNI dan K-Means-Smote dapat meningkatkan kinerja metode SVM berdasarkan akurasi, sensitivitas, spesifisitas, dan f-measure. </p><p class="Abstrak"><strong><em><br /></em></strong></p><p class="Abstrak"><strong><em>Abstract</em></strong></p><p class="Abstrak"><em> One of the main problems of Bumigora University is the ratio between incoming students and students graduating on time is not balanced, so that it will result in a decrease in accreditation assessment in the future. One of the assessment indicators in the accreditation process is the student graduation ratio. Student graduation data stored in the campus database, but has not been maximally utilized. By utilizing graduation data, students can find out patterns or patterns of students who graduate on time or not, so as to minimize the occurrence of students who drop out. Not only that, decision makers can make it easier to make policies early to help students who have the potential to drop out and not graduate on time. The solution offered in this research is to use data mining techniques. One of the data mining methods used in this study is the SVM method. The purpose of this study is to improve the performance of the SVM method for the classification of Bumigora University graduation students using the KNN Imputation and K-Means-Smote methods. This research consists of several stages, namely the collection of student graduation data, pre-processing such as handling missing values using KNNI method, handling class imbalances using K-Means-Smote, classification the SVM method. The last stage is testing SVM performance based on accuracy, sensitivity, specificity, and f-measure. Based on the results of test that have been carried out, the integration of the KNNI, K-Means-Smote, and SVM method get an accuracy of 83.9%, sensitivity 81.3%, specificity 86.6%, and f-measure 83.5%. The use of KNNI and K-Means-Smote method can improve the performance of the SVM method based on accuracy, sensitivity, specificity, and f-measure. </em></p>