The decision on financing approval in sharia cooperatives has a high risk of the inability of customers to pay their credit obligations at maturity or referred to as bad credit. To maintain and minimize risk, an accurate method is needed to determine the financing agreement. The purpose of this study is to classify sharia cooperative loan history data using the Naïve Bayes algorithm, Decision Tree and SVM to predict the credibility of future customers. The results showed the accuracy of Naïve Bayes algorithm 77.29%, Decision Tree 89.02% and the highest Support Vector Machine (SVM) 89.86%.

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Coronavirus Disease 2019 (COVID-19) has become a pandemic in Indonesia as a non-natural disaster in the form of disease outbreaks which must be undertaken as a response. The Ministry of Health in the Republic of Indonesia published a guidebook for prevention and control of COVID-19 in its response efforts. This guideline is intended for health officials as a reference in preparing for COVID-19. This handbook contains early detection and response activities to identify conditions of PDP, ODP, OTG, or confirmed cases of COVID-19. The efforts made are adjusted to the world situation progress from COVID-19 which is monitored by the World Health Organization (WHO). From the results of documentation studies that have been carried out on the COVID-19 pandemic in Indonesia, there are several problems that must be resolved from the prevention of the disease outbreak COVID-19. Lack of knowledge and awareness of the general public in the prevention and control of COVID-19 is one of the factors increasing the spread of that virus in Indonesia. Furthermore, there are difficulties in carrying out surveillance, early detection, contact tracing, infection prevention or control, and risk communication or people empowerment. This is due to the lack of implementation and testing on artificial intelligence methods for COVID-19 diagnosis that can be used by the public. The purpose of this research is to make a diagnosis of surveillance classification which includes PDP, ODP, and OTG using the C4.5 algorithm. The results showed that the diagnosis of the COVID-19 surveillance category using the C4.5 algorithm was successfully modeled into a decision tree with PDP, ODP, and OTG classification. The testing process in a confusion matrix with 3 (three) classes produces an accuracy rate of 92.86% which is included in the excellent classification category.

Accurate accounting information system is one of accounting information systems used in the six companies in the city of Sukabumi. DeLone and McLean information system success model is a suitable model to measure the success of the application of information systems in an organization or com-pany. This study will analyze factors that measure the success of DeLone & McLean information sys-tems model to the users of the Accurate accounting information systems in six companies in the city of Sukabumi. The data collected from 37 respondents through surveys, is then analyzed using Partial Least Squares (PLS) available in SmartPLS 2.0 M3 software application. The results demonstrate that the quality of information and service quality does not have a significant effect on the usage variable, while other variables have significant effects in measuring the success of the use of Accurate accoun-ting information systems to the value of R-squares for use 0.57, 0.94 for user satisfaction and 0.94 for net benefit. In addition, the value of goodness of fit (GoF) was 0.72 or 72%, so the models are substantially enough to represent the research result. Keywords: success, information system, partial least squares, smartPLS. AbstrakSistem informasi akuntansi Accurate merupakan salah satu sistem informasi akuntansi yang menjadi pilihan untuk digunakan di enam perusahaan di Kota Sukabumi. Model Kesuksesan Sistem Informasi DeLone dan McLean adalah model yang cocok untuk mengukur keberhasilan dari penerapan sistem informasi pada sebuah organisasi atau perusahaan. Penelitian ini akan menganalisis faktor-faktor yang mengukur keberhasilan model kesuksesan sistem informasi DeLone & McLean terhadap pengguna sistem informasi akuntansi Accurate di enam perusahaan di Kota Sukabumi. Data dari 37 responden yang dikumpulkan melalui survei, kemudian dianalisis dengan Partial Least Squares (PLS) menggunakan perangkat lunak SmartPLS 2.0 M3. Hasil dari penelitian ini membuktikan bahwa kualitas informasi dan kualitas pelayanan tidak berpengaruh signifikan terhadap variabel penggunaan, sedangkan variabel lainnya teruji signifikan dalam mengukur keberhasilan penggunan sistem informasi akuntansi Accurate dengan nilai R-square 0,57 untuk penggunaan, 0,94 untuk kepuasan pengguna dan 0,94 untuk manfaat bersih. Selain itu, nilai goodness of fit (GoF) sebesar 0,72 atau 72%, sehingga mo-del dinyatakan telah sesuai secara substansial dalam merepresentasikan hasil penelitian.

Around the world, the adoption of digital health applications is growing very fast. The use of e-health laboratory systems is increasing while research on the factors that impact users to use e-health laboratory systems in Indonesia has not been done much. The objective of this study is to analyze the behavioral factors of e-health laboratory users. This study includes a survey conducted on Indonesian users, and data analysis was carried out thoroughly. Based on the Technology Acceptance Model, this research framework explores a combination of variables consisting of task-driven, technology-driven, human-driven, and adoption variables to form the model proposed in this study. This model was verified using the Structural Equation Modeling (SEM) method for factor analysis, path analysis, and regression. A total of 163 respondents were collected to evaluate this research model empirically and the level of this study were individuals. These three problems are all essential in affecting usage intentions in adopting an e-health laboratory system. Specifically, task technology fit, information quality, and accessibility show a direct effect on both perceived usefulness and perceived ease of use factors perceived by the user, and have an indirect influence on the adoption of an e-health laboratory system through these two factors. The design of an online laboratory system affects perceived ease of use and personal innovativeness factors affect the perceived usefulness that users feel when adopting a laboratory system, while task technology fit and personal innovativeness factors do not affect the perceived ease of use. However, overall technology characteristic and perceived usefulness followed by design are the main predictors of adopting an e-health laboratory system on e-health systems in Indonesia.

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The automated diagnosis of cervical cancer in Pap smear images is a difficult though extremely important procedure. In order to obtain reliable diagnostic information, the nuclei and their characteristics must be correctly identified and evaluated. However, the presence of inflammatory and overlapping cells in these images complicates the detection process. In this work, a segmentation algorithm is developed to extract the inflammatory cells and enable accurate nuclei detection. The proposed algorithm is based on the combination of gray level thresholding and the definition of a distance rule, which entails in the identification of inflammatory cells. The results indicate that our method significantly simplifies the nuclei detection process, as it reduces the number of inflammatory cells that may interfere.