The design of strategies to increase the potential benefits of an organization is very important for renewal by implementing modern strategies. Smart infrastructure is a digital system that functions to improve performance, welfare, and increase cost efficiency and resource consumption. Previous research shows a significant increase in smart infrastructure which is influenced by the ability of the community. This study aims to analyze the success of implementing a renewal strategy for Smart Infrastructure for employees at university which we can assess from the performance of the university employees. Primary data was collected through questionnaires with a sample of 40 respondents which was then processed quantitatively by ANOVA test and LSD test using the Statistical Package for the Social Sciences (SPSS). The results showed that the percentage rate accepted was 78%, so that the implementation of a smart infrastructure system could increase employee productivity in university.

This study aims to predict whether the patient deserves to be inpatient or outpatient by comparing several machine learning techniques, namely, logistic regression, decision tree, neural network, random forest, gradient boosting. The research method uses three stages of research, namely data collection, data preprocessing, and data modeling. Implementation of program code using google colab and python programming language. The dataset used as the research sample is Electronic Health Record Predicting data. Based on the accuracy results generated in this study, the use of the Neural Network machine learning algorithm to predict hospitalization decisions for patients has proven to be a machine learning algorithm that has the highest accuracy rate reaching 74, 47% compared to other comparison machine learning algorithms, namely logistic regression, decision tree, neural network, random forest, gradient boosting.

The development of distribution and market segmentation has become the company's background in improving business processes. The purpose of this research is to analyze the business processes of beverage companies using Business Process Management (BPM) modeling and improvised based on six core element management. In the analysis process, it is found that there is no stock forecasting system in forecasting sales stock that must be fulfilled. The results of the study show that the Business Process Management model is improved with the addition of a stock forecasting system, so that business processes become more controlled with the presence of a product stock inventory forecasting system in the company.

Pertumbuhan teknologi yang sangat pesat tentunya sangat mempengaruhi berbagai bidang. Berbagai pekerjaan sehari hari pun juga terbantu dengan adanya teknologi. Selain dampak positif yang diperhatikan dampak negatif pun juga perlu diwaspadai  yaitu perihal keamanan dokumen. Bidang kesehatan merupakan contoh yang bidang berhubungan dengan privasi. Salah satunya yaitu dokumen rekam medis. Tidak sedikit kasus kebocoran data pasien yang terjadi. Peristiwa yang kurang diinginkan seperti ini dapat terhindarkan dengan melakukan pengamanan dengan Kriptografi. Terdapat berbagai macam kriptografi salah satunya yaitu AES (Advanced Encryption Standard). Berdasarkan uraian tersebut penelitian ini bertujuan untuk melakukan simulasi pengamanan  dokumen rekam medis pada salah satu klinik dengan menggunakan metode AES. Hasil simulasi menunjukan bahwa algoritma AES dapat menjadi rekomendasi bagi perlindungan data. Dengan melalui proses enkripsi data akan diubah menjadi bentuk yang tidak dimengerti dan harus diproses menggunakan proses dekripsi agar bisa kembali dimengerti. Keuntungannnya adalah tidak semua orang bisa mengakses dokumen tersebut.