An Early Detection Method of Type-2 Diabetes Mellitus in Public Hospital

Diabetes is a chronic disease and major problem of morbidity and mortality in developing countries. The International Diabetes Federation estimates that 285 million people around the world have diabetes. This total is expected to rise to 438 million within 20 years. Type-2 diabetes mellitus (T2DM) is the most common type of diabetes and accounts for 90-95% of all diabetes. Detection of T2DM from various factors or symptoms became an issue which was not free from false presumptions accompanied by unpredictable effects. According to this context, data mining and machine learning could be used as an alternative way help us in knowledge discovery from data. We applied several learning methods, such as instance based learners, naive bayes, decision tree, support vector machines, and boosted algorithm acquire information from historical data of patient’s medical records of Mohammad Hoesin public hospital in Southern Sumatera. Rules are extracted from Decision tree to offer decision-making support through early detection of T2DM for clinicians.

AUGMENTED REALITY MEASUREMENT SEDERHANA MENGGUNAKAN OS ANDROID (ARealSure)

Aplikasi pengukuran telah banyak digunakan di sistem IOS maupun android. Namun, aplikasi pengukuran berbasis android tidak sebaik dengan aplikasi pengukuran berbasis sistem IOS. Beberapa orang masih meragukan keakuratan aplikasi ini karena terkadang hasil pengukuran tidak akurat dan masih banyak eror atau bug. Tujuan dari kegiatan ini adalah untuk mengembangkan aplikasi pengukuran berbasis android dengan tingkat keakuratan 90%. Apikasi pengukuran ini menerapkan AR (Augmented Reality). Proses pembuatan aplikasi menggunakan software android studio, library dan ARcore. Untuk Bahasa pemrograman yang digunakan yaitu Java Output. Pertimbangan menggunakan perangkat tersebut aplikasi pengukuran untuk smartphone berbasis Android dapat mengukur atau mengetahui dimensi benda bangun datar dan bangun ruang dengan satuan cm dan hasil pengukuran yang akurat. Kata Kunci: Augmented Reality, Android, Aplikasi, Pengukuran, Akur

An Empirical Investigation of Different Classifiers, Encoding, and Ensemble Schemes for Next Event Prediction Using Business Process Event Logs

There is a growing need for empirical benchmarks that support researchers and practitioners in selecting the best machine learning technique for given prediction tasks. In this article, we consider the next event prediction task in business process predictive monitoring, and we extend our previously published benchmark by studying the impact on the performance of different encoding windows and of using ensemble schemes. The choice of whether to use ensembles and which scheme to use often depends on the type of data and classification task. While there is a general understanding that ensembles perform well in predictive monitoring of business processes, next event prediction is a task for which no other benchmarks involving ensembles are available. The proposed benchmark helps researchers to select a high-performing individual classifier or ensemble scheme given the variability at the case level of the event log under consideration. Experimental results show that choosing an optimal number of events for feature encoding is challenging, resulting in the need to consider each event log individually when selecting an optimal value. Ensemble schemes improve the performance of low-performing classifiers in this task, such as SVM, whereas high-performing classifiers, such as tree-based classifiers, are not better off when ensemble schemes are considered

Lombok earthquake, one year later: housing sector recovery

The series of strong earthquakes that hit the province of West Nusa Tenggara (NTB) on 29 July 2018 (M6.4), 5 August 2018 (M7.0), 9 August 2018 (M6.2) and 19 August 2018 (M6.5 and M6.9) has caused many casualties, injuries as well as damages in the housing sector. The disaster emergency was stated on July 29th, 2018 and was extended several times up to August 25th, 2018. A transition period from an emergency situation to recovery was declared starting from August 26th, 2018 to February 26th, 2019, while the rehabilitation and reconstruction (R & R) phase was started from February 27th. , 2019. In the R & R program, the Government has established a policy of building earthquake-resistant houses with a self-managed system through the formation of community groups (POKMAS) consisting of house owners, supported by technical and administrative facilitators. There are many models of earthquake resistant houses, both those proposed by the government and the private sector that are approved by the government. A Presidential Instruction (Inpres) Number 5 Year 2018 was issued on 23 August 2018 to provide guidance on the housing sector reconstruction. An investigation to study and document the lessons learnt from the housing reconstruction process is conducted in Lombok Island, covering the issues of program management, institutional set up and coordination, project administration, technical design, quality assurance and community participation. It was found that there were so many house building models proposed to the community by various sponsors, which cause confusion to the affected community. The study also found that the house reconstruction program has been delayed during the process, as one year after the earthquake, there were only about 52 thousands houses which have been completed, and 80 thousands other are still in progress, compared to the total of more than 237 thousand affected houses to be repaired and rebuilt. The study is expected to reveal various impeding issues and propose solutions for expediting the process. It is also expected that the result of the study can be used as a reference for future post-disaster housing sector recovery program in other places

Proceedings of the 3rd International Conference on Community Engagement and Education for Sustainable Development

This proceeding contains articles on the various ideas of the academic community presented at The 3rd International Conference on Community Engagement and Education for Sustainable Development (ICCEESD 2022) organized by the Universitas Gadjah Mada, Indonesia on 7th-8th December 2022.  ICCEESD is a biannual forum for sharing, benchmarking, and discussing HEI’s activities in developing Education for Sustainable Development towards community engagement. Education for Sustainability as a teaching strategy for resolving community challenges through formal, informal, or non-formal education is expected to benefit from various community service best practices by academics, researchers, and students. The 3rd ICCEESD has “Strengthening Education for Sustainability Towards Better Community Engagement” as its theme this year. It is expected that the 3rd ICCEESD will provide a forum for the presenters and participants to exchange best practices, policies, and conceptual implementation of Education for Sustainability towards better community engagement and explore ideas to address community needs.  Conference Title: 3rd International Conference on Community Engagement and Education for Sustainable DevelopmentConference Theme: Strengthening Education for Sustainability Towards Better Community EngagementConference Acronyms: ICCEESD 2022Conference Date: 7th-8th December 2022Conference Location: Grand Rohan Jogja Yogyakarta, IndonesiaConference Organizer: Universitas Gadjah Mada, Indonesi