Hypertension is a major risk factor for cardiovascular disease which is a major cause of death worldwide. The purpose of thisstudy was to determine the effect of water administration of celery powder (Apium graveolens) on blood pressure reductionin hypertensive patients. Celery (Apium graveolens) contains Apigenin which can prevent narrowing of the arteries(vasodilation) and Pthalides which can relax the artery muscles or relax blood vessels (vasorelaxation). This research usesfield observation method with a case study approach. Pre-treatment and post-treatment design. The results of the study of casestudies 1, 2, 3 and 4 showed an average result of a decrease in systolic pressure of 57.5 mmHg and diastolic of 25 mmHg.Study 5 as a control obtained results in lower blood pressure reduction. The conclusion of this study is the provision of watersteeping powder of celery simplicia influences in the form of decreased blood pressure and improved quality of life of patientswith hypertension.
Commodity prices forecasting is one of the business functions to estimate future demand based on past data trend. This study aims to implement a trial and error technique of the constant (alpha α) value in the exponential smoothing method. Dealing with confusion that often researchers find in selecting an alpha (α) value among exponential smoothing families, which suits characteristics of the investigated case. As selection of the constant value precisely contributes to reduce the forecasting deviation. This paper used the alpha (α) value in the range 0,1 to 0,9 and utilized the mean absolute percentage error (MAPE) and Mean Absolute Error (MAE) as the parameter to know the grade of prediction. In data training, the authors used Single Exponential Smoothing (SES) and Brown’s Double Exponential Smoothing (B-DES) as methods to compare the results of prediction. It is addressed that forecasting with alpha (α) 0,1 is the most optimal values for Single Exponential Smoothing (SES) in this case with margin error 0,00036 of MAPE and 16,84 of MAE.
Pendidikan karakter merupakan hal penting yang banyak mendapat perhatian di era sekarang ini. Keberadaan pendidikan karakter dinilai penting untuk dilaksanakan, mengingat akhir-akhir ini banyak dijumpai peristiwa-peristiwa yang tidak sesuai dengan nilai karakter yang baik. Di sana-sini sering terjadi pelanggaran norma, baik norma agama, kesusilaan, kesopanan, dan norma hukum. Sebagai contoh kecil saja, penyalahgunaan narkotika dan obat-obat terlarang. Kehadiran pendidikan karakter diharapkan dapat meminimalkan terjadinya perilaku menyimpang terhadap nilai-nilai karakter. Berbekal nilai-nilai karakter yang baik seseorang diharapkan akan memiliki wawasan, sikap, dan perilaku sesuai dengan nilai-nilai karakter tersebut. Nilai-nilai karakter untuk membentuk perilaku moral yang baik perlu dilakukan sejak usia dini. Harapannnya nilai karakter yang diinternalisasikan sejak usia dini akan berdampak pada hasil yang optimal dalam pembentukan karakter anak ketika ia dewasa. Pentingnya pendidikan karakter sejak usia dini ini didasari alasan bahwa di masa usia dini terdapat fase usia emas yang sayang untuk ditinggalkan. Pada fase ini sel-sel otak anak berkembang secara optimal. Untuk dapat mencapai perkembangan yang optimal, maka perlu diberikan stimulus yang tepat di segala aspek perkembangan, termasuk di dalamnya adalah karakter anak.
The purpose of this study is to produce a model that can assist in determining prospective new students of STMIK AKBA who receive scholarships. The algorithm used is decision tree and nave Bayes to classify the graduation of prospective recipients of the Indonesian Smart Card (KIP) scholarship. Based on the results of the classification of the decision tree algorithm with the confusion matrix, the accuracy value is 44.12% and the F1-Score is 34.48%. If you use the Naive Bayes algorithm, it produces an accuracy value of 76.47% using data on diploma scores and average report cards. Furthermore, for accuracy without using diploma value data and the average report card is 79.41%. The results of this study show that nave Bayes has a better performance even though it does not use diploma scores and average report cards. Measurement of the results of the nave Bayes classification with the confusion matrix showed low sensitivity with values of 66.67% and 58.33% for the first and second scenarios. Based on the evaluation results, the Naïve Bayes algorithm has a better performance than the Decision Tree algorithm in classifying KIP scholarship recipients.
The development of the times and technology produces innovations that facilitate human activities, one of which is for librarians to control and maintain library rooms. This study aimed to implement a noise detection automation tool and a sound monitoring system based on the Arduino nano 33 BLE sense microcontroller in a library room. The Arduino processed the data from the sound sensor through the Edge Impulse platform. Suppose a sound with a value exceeds 0.6 and takes more than 10 seconds. In that case, the warning "QUIET, PLEASE!" will be displayed, and a sound warning will also be issued via a buzzer. The sound warning can be monitored to turn on and off the buzzer through the application. When the librarian wants to turn off the buzzer, they can click the button to turn off the speaker; on the other hand, if they want to keep the buzzer on, they can click the button to turn on the speaker.
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