The Social Security Administering Body (BPJS) is a facility established by The government in providing services to citizens in The field of health welfare. The Spirit of cooperation in the utilization of health services which is very much currently a constraint in the budget is still insufficient in covering health services as a whole. For this reason, government policy is following with PERPRES No. 75 in 2019, the Government officially raised the BPJS Health contributions for 2020. The increase in BPJS Health contributions certainly caused a lot of comments. Namely Twitter, one of the social media that is used by the public to express disapproval or support for this government policy. This study, testing was carried out related to the prediction of comments from social media on community responses to the increase in BPJS Health contributions taken by the government. In the test carried out 3 (three) input algorithms. For every single algorithm including getting results through the K-NN method with an accuracy of 71.83% and AUC value of 0812, for the Naïve Bayes method produces an accuracy of 81.63% and AUC value of 0586. As for the C 4.5 method, the accuracy is 65.37% and the AUC value is 0628. While testing conducted through the Ensembles Vote method which combines the 3 algorithms above gives the best results with an accuracy of 80.10% and AUC value is 0871 for Twitter comment predictions.
Research on the performance of workers based on the determination of standard working hours for absences conducted by workers in a certain period. In disciplinary supervision, workers are expected to be able to provide the best performance in the implementation of work in accordance with predetermined working hours. The measurement of the level of discipline of admission hours for placement workers is carried out every working day, continuously and continuously. Attendance monitoring already uses online attendance by using data downloaded from the online attendance provider as the main data. In addition, data collection is done by filtering employee absentee data and supporting information on the categories that cause mismatches in meeting work schedules. Mobilization of workers according to location and working hours has been regulated in company regulations allowing the placement of workers in accordance with the residence so as not to affect the desired work results the company is still within reasonable limits and can be increased. The assessment of this study as a progression factor inhibiting the company in achieving company targets. From the results of the author's analysis of the prediction of employee delay factors using three algorithms, namely the C.45 algorithm accuracy = 79.37% and AUC value = 0.646, Random Forest Algorithm accuracy = 78.58% and AUC value = 0.807 while for the Random Tree algorithm accuracy = 76.26% and the AUC value = 0.610.
Keadaan dan tantangan pandemi pada awal tahun 2021 dengan telah ditemukannya vaksin atas virus Covid-19 tentunya diperlukan percepatan dalam pemberian vaksin kepada seluruh umat manusia di seluruh dunia. Di Indonesia, Pemerintah menggalakan program vaksinasi masal kepada seluruh Warga Negara Indonesia dengan melakukan percepatan vaksinasi di seluruh wilayah Indonesia sampai dengan saat ini. Berdasarkan hal tersebut diatas dipandang perlu melakukan analisa sentimen. Media sosial twitter dipilih sebagai salah satu sarana dalam analisas sentiman ini. Terdapat 1013 komentar positif dan negatif para pengguna twitter dengan kata kunci “vaksin” yang didapatkan untuk diproses terkait tanggapan masyarakat atas pelaksanaan vaksinasi masal yang dilaksanakan di Indonesia. Dengan menggunakan Algoritma Support Vector Machine (SVM) dan Naïve Bayes berbasis SMOTE dilakukan perbandingan pengujian atas komentar positif dan negatif tersebut. Dari proses pengujian tersebut didapatkan hasil akurasi dari algoritma SVM menggunakan teknik SMOTE didapatkan nilai akurasi =70.51% dan nilai AUC =0.827, sedangkan proses pengujian menggunakan algoritma Naïve Bayes dengan teknik SMOTE didapatkan nilai akurasi = 64.36% dan nilai AUC = 0.423. dari proses diatas, penggunaan Support Vector Machine berbasis teknik SMOTE memiliki akurasi yang lebih tinggi sehingga dapat digunakan untuk memberikan solusi terhadap analisis sentimen vaksinasi Covid-19.
Indonesia is developing one of the more efficient and effective Financial Technology (Fintek) support services innovations by using electronic documents. The Electronic Document provider business that is used as a reference and utilized by fintech companies is PrivyID. In this study, how is the commentary aspect of using the PrivyID application for digital signature services to become a legal electronic document. Web-based application platforms and mobile applications in the community are indispensable for the use of Electronic Documents developed by PrivyID as a service provider in business and personal transactions that are needed by the community. More in-depth research regarding the Prediction of PrivyID Application Comments in Its Use as an Electronic Document (e-doc) taken from 818 data of PrivyID application users. The research was conducted by combining 3 (three) algorithms (k-Nearest Neighbor, Na¨ıve Bayes, and C4.5) in the Ensembles Vote method which resulted in the best Prediction Comment value with an accuracy of 86.80.
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