Stroke is a disease caused by brain tissue damage because of blockage in the cerebrovascular system that disrupts body sensory and motoric systems Stroke disease is one of the highest death cause in the world. Data collection from Electronic Health Records (EHR) is increasing and has been included in the health service big data. It can be processed and analyzed using machine learning to determine the risk group of stroke disease. Machine learning can be used as a predictor of stroke causes, while the predictor clarifies the influence of each cause factor of the disease. Our contribution in this research is to evaluate Feyn Qlattice machine learning models to detect the influence of stroke disease's main cause features. We attempt to obtain a correlation between features of the stroke disease, especially on the gender as a feature, whether any other features can influence the gender feature. This research utilizes 4908 data of the disease predictor using the Feyn Qlattice model. The result implies that gender highly impacts age and hypertension on stroke disease causes. Autorun in Feyn Qlattice model was run with ten epochs, resulting in 17596 test models at 57s. Query string parameter that was focused on age and hypertension features resulted in 1245 models at 4s. An increase of accuracy was found in training metrics from 0.723 to 0.732 and in testing metrics from 0.695 to 0.708. Evaluation results showed that the model is reasonably good as a predictor of stroke disease, indicated with blue lines of AUC in training and testing metrics close to ROC's left side peak curve.
Stroke is a disease caused by brain tissue damage because of blockage in the cerebrovascular system that disrupts body sensory and motoric systems Stroke disease is one of the highest death cause in the world. Data collection from Electronic Health Records (EHR) is increasing and has been included in the health service big data. It can be processed and analyzed using machine learning to determine the risk group of stroke disease. Machine learning can be used as a predictor of stroke causes, while the predictor clarifies the influence of each cause factor of the disease. Our contribution in this research is to evaluate Feyn Qlattice machine learning models to detect the influence of stroke disease's main cause features. We attempt to obtain a correlation between features of the stroke disease, especially on the gender as a feature, whether any other features can influence the gender feature. This research utilizes 4908 data of the disease predictor using the Feyn Qlattice model. The result implies that gender highly impacts age and hypertension on stroke disease causes. Autorun in Feyn Qlattice model was run with ten epochs, resulting in 17596 test models at 57s. Query string parameter that was focused on age and hypertension features resulted in 1245 models at 4s. An increase of accuracy was found in training metrics from 0.723 to 0.732 and in testing metrics from 0.695 to 0.708. Evaluation results showed that the model is reasonably good as a predictor of stroke disease, indicated with blue lines of AUC in training and testing metrics close to ROC's left side peak curve.
Information systems and technology develops very rapidly and have significant impact on all areas. The information technology not only to use in the business sector, but also the public sector, one of which is an higher education institutions. The efficiency and effectiveness of process information using information systems will only occur if the technology is received by individuals in the organization. This Study is a research which discusses factors that affecting lecturers, students, and stafts acceptance of academic information systems. 200 respondens was participated in this study. Technolgy Acceptance Model was used as adoption model. In addition to testing the variables, this study also examined whether TAM theory can be used to determine user acceptance SIAK-SHB in STIKes Harapan Bangsa. Data analysis was performed with the approach of Structural Equation Modeling. Based on the results we concluded that the Actual System Use is influenced by behavioral intention to use. Behavioral Intention to Use is influenced by Perceived Usefulness and Perceived Usefulness is influenced by Perceived Easy of Use. ABSTRAKSistem informasi dan Teknologi informasi berkembang dengan sangat pesat dan berdampak signifikan terhadap segala bidang. Pemanfaatan teknologi informasi tidak hanya pada pemanfaatan sektor bisnis, tetapi juga sektor publik yang salah satunya adalah lembaga perguruan tinggi. Efisiensi dan efektifitas proses informasi dengan menggunakan sistem informasi hanya akan terjadi apabila teknologi tersebut diterima oleh individu dalam organisasi. Tesis ini merupakan hasil riset yang akan membahas mengetahui faktorfaktor apa yang sajakah yang mempengaruhi penerimaan dosen, mahasiswa dan staf akademik dalam menggunakan Sistem Informasi Akademik STIKes Harapan Bangsa. Sejumlah 200 responden ikut berpartisipasi dalam penelitian ini. Adapun model adopsi teknologi yang digunakan adalah model Technolgy Acceptance Model. Selain menguji variabel, penelitian ini juga menguji apakah teori TAM dapat digunakan untuk mengetahui penerimaan pengguna SIAK-SHB di Stikes Harapan Bangsa. Analisis data dilakukan dengan pendekatan Structural Equation
Uji Kompetensi Mahasiswa Program Profesi Dokter Gigi (UKMP2DG) merupakan exit exam bagi para mahasiswa yang telah selesai menempuh pendidikan profesi dokter gigi. Meski peserta yang tidak lulus UKMP2DG dapat mengulang ujian di periode-periode berikutnya, para retaker ini dihadapkan pada batasan masa studi maksimal sebelum dinyatakan drop out. Selain itu semakin lama jeda antara penyelesaian studi dan waktu ujian dikhawatirkan berpengaruh pada penguasaan terhadap ilmu yang telah dipelajari. Penelitian ini bertujuan membangun model yang dapat digunakan untuk memprediksi kelulusan calon peserta UKMP2DG melalui data-data pendukung. Penerapan algoritma C4.5 menghasilkan model dengan tingkat akurasi 81,40%, presisi 82,84% dan recall sebesar 95,82%. Hasil penelitian ini dapat dimanfaatkan oleh Institusi Pendidikan Dokter Gigi (IPDG) untuk mengambil langkah-langkah khusus dalam mempersiapkan calon peserta yang diprediksi akan tidak lulus dalam ujian yang akan ditempuh.
One of the problems that often occurs in school administration is the late payment of tuition fees. Therefore, it is necessary to evaluate the education payment process so that in the future the payment process can run in an orderly and disciplined manner. This study aims to create a cluster model for grouping student administration payments. This type of research is quantitative using the K-Means Clustering method to classify payment data based on 2 variables, namely the time of payment and the income of students' guardians carried out in private elementary schools in Semarang. The data used in this study is payment data for the 2019/2020 academic year, which totals 1,933 records, covering transactions from 419 students. Determining the number of clusters is calculated using the elbow method, the best clusters obtained from the data used are 3 clusters, namely clusters 0, 1 and 2. Our findings show that cluster 2 has the largest percentage of early monthly administration payments, namely 52.5%, the percentage is on time the highest was in cluster 1, namely 74.8%, and the highest percentage of late payments was in cluster 0, namely 28.6%. The results of the analysis show that the main factor for late payments is not the guardian's income but other external factors, as evidenced by the highest percentage of late payments in cluster 0, where the average income of student guardians is = 10,000,000.
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