Heroes' Day is one of important days for Indonesia, is a day that commemorates one of the most important historical events, especially for the Indonesian people. The independence of the threatened Indonesian nation could be defended by heroes who sacrificed their lives against the invaders, where the incident coincided on November 10, 1945. but there are still many young people today who still do not understand the importance of their hero's struggle on that day, and consider Heroes' Day an ordinary holiday. The serious game is one of the game genres that is commonly used to provide learning about a topic by using games as learning media. By utilizing games as learning media, it will be easier for youth to understand the events of November 10 directly. The game is designed as a first-person shooter game developed using Unity with players playing the role of fighters against invaders on November 10, 1945. After playing, players will be given a series of questionnaires that contain events that occurred in the game and provide value to the game application. from the results of the questionnaire, the value obtained from the questionnaire was 69 and the value of the aspects of the game was 3.37.
As the number of human populations increases and the economy becomes more advanced, people's awareness of health increases. This can increase the number of patient visits if the community will visit for treatment, therefore it is necessary to pay special attention from the health center to carry out readiness in the fulfillment of facilities and service support equipment, such as services in the outpatient registration place where registration documents must be adjusted to the number of existing patients, if the documents are lacking or have not been made, there can be long queues or accumulation of patients which leads to inadequate service. For this reason, the public health center must carry out careful planning activities, one of which is by conducting forecasting activities in order to overcome these problems.This study compares the best method among the 2 time series methods, then the forecasting results will be compared with the actual data to find which forecasting is the best.The final results showed the MAPE value of the arima method for Direct Patient Visits data was worth 22.55% while the Referral Patient Visits were valued at 47.40% with the Moderate/Feasible category, the Holwinters method for Direct Patient Visits data was worth 7.90% while the Referral Patient Visits were worth 11.90% with the excellent category.can be said that the smallest error value is Holtwinters from Direct Patient Visit data with MAPE 7.90% and from Referral Patient Visit data with MAPE 11.90%. Which is where it is said to be an excellent forecasting category
Parkinson's disease is a progressive and relatively common neurodegenerative disorder in the central nervous system where sufferers can have difficulty moving. This disease has a high mortality rate in the world of around 9.3 million in 2021. Meanwhile, in Indonesia, it is estimated that as many as 12,980 people die every year due to Parkinson's cases. This increase in cases of death is due to the lack of information about the initial symptoms and dangers of the disease, besides it is important to know how to prevent it early. Early detection of Parkinson's disease can prevent symptoms of a certain age thereby increasing life expectancy. The existence of a computer-based system for diagnosing Parkinson's disease is called a classification system where the system applies the Machine Learning method. This study aims to compare the performance of algorithms in the classification system of people with computer diseases. In this study, it used methods in Machine Learning such as K-NN, Multi Layer Percepteron (MLP), Linear Regression and Support Vector Machine (SVM). The data set in this study was obtained using the Weka application. The dataset used was Parkinson's Disease data totaling 195 rows of data taken from the UCI Machine Learning Repository Datasets. The results of the experiment based on the four algorithms showed that the poor performance was the Multi Layer Percepteron approach to regression data with an RSME value of 0.459. Meanwhile, the k-Neural Network Algorithm is a good classification technique forParkinson's problem with an RMSE value of 0.1895.
Seiring bertambahnya jumlah populasi manusia dan keadaan perekonomian yang semakin maju, maka kesadaran masyarakat terhadap kesehatan semakin meningkat. Hal ini dapat meningkatkan jumlah kunjungan pasien yang harus diiringi dengan kesiapan pihak balai kesehatan masyarakat dalam pemenuhan fasilitas dan alat penunjang pelayanan, peralatan yang dibutuhkan di bagian tempat pendaftaran rawat jalan diantaranya dokumen yang harus disesuaikan dengan jumlah pasien. Oleh karena itu pihak balai kesehatan masyarakat harus melakukan kegiatan perencanaan yang matang salah satunya dengan melakukan kegiatan peramalan (Forecasting) agar pelayanan tetap berjalan dengan baik.Penelitian ini melakukan peramalan menggunakan metode ARIMA, Single Exponential Smoothing Dan Holt-Winters yang sangat cocok untuk mengolah data yang bersifat time series seperti pada kunjungan pasien rawat jalan. Data dari pasien rawat jalan yaitu kunjungan pasien selama 5 tahun dari Januari 2014 sampai dengan Desember 2018 yang dimana data rawat jalan ini di ambil dari jumlah total kunjungan pasien rawat jalan dari kategori – kategori data kunjungan : Umum, Klinik, BPJS, Non BPJS (SKM), Non BPJS (Gakinda) Daftar pasien baru dan Daftar pasien yang lama kemudian diprediksi tingkat kunjungan pasien selama 2 tahun berikutnya yaitu dari Januari 2019 sampai dengan Desember 2020. Penelitian ini membandingkan metode yang paling terbaik diantara ke 3 metode time series tersebut, Selanjutnya hasil peramalan akan dilakukan perbandingan dengan data sebenarnya untuk melihat akurasi dan mencari peramalan mana yang paling baik. Hasil akhir menunjukkan Nilai MAPE dari metode ARIMA untuk data kunjungan pasien bernilai 22.55%, metode Single Exponential Smoothing bernilai 9.74% dan metode Holt- Winters bernilai 7.90%. dapat dikatakan nilai error yang terkecil adalah Holt-Winters dari data kunjungan pasien dengan MAPE 7.90% yang di mana dikatakan sebagai kategori peramalan yang sangat baik dengan menghasilkan nilai total Forecast = 53894.2 dengan rata-rata perbulan = 2245.59 untuk peramalan 2 tahun kedepannya. hasil rata-rata perbulan ini dijadikan acuan jumlah penggunjung yang datang untuk tiap bulannya kisaran 2245 orang setelah itu tahap terakhir yang dilakukan ialah membuat suatu perancangan strategis menggunakan teknik analisis SWOT yang di kombinasikan dengan hasil prediksi menggunakan metode Holt-Winters di dapatkan sebuah kesimpulan yaitu dapat membuat dokumen yang baru dikisaran 2245 dokumen/orang terkhususnya data pasien rawat jalan untuk permasalahan solusi yang terjadi di balai kesehatan masyarakat.
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