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Dengue fever disease is one of the public health problems in Indonesia growing rapidly and spreading widely. Dengue fever disease is caused by the dengue virus. The virus is spread by species mosquito Aides Aegypti and Aedes Alboctipus as primer vector as well as Aedes polynesiensis, Aedes scutellaris and AE (Finlaya) niveus as secondary vector. According to data from WHO, Pacific Asean bore 75 percent from dengue burden in the world during 2004 and 2010, while Indonesia is reported as the biggest second country with dengue fever disease cases between 30 endemic countries. One way to have a better understanding of the problem is by identifying the model based on the known data and do prediction. It is expected that the government would take action based on the prediction result. To solve this problem, the researcher using Bat Algorithm and artificial neural network backpropagation. In order to solve this problem. This paper purposes Bat Algorithm and Artificial Neural Network – Backpropagation to identify the spreading model. The purpose of the identification system using neural network backpropagation is to identify the ODE model of spreading dengue fever disease based on actual data. The first process is the estimation of parameters model using bat algorithm, with inquiring for a numeric solution from the ODE model spread of dengue fever disease as an objective function. The second process is model identification using artificial neural networks and the last, prediction of dengue fever spreading. T Based on the simulation result using dengue fever disease data start from January 2013 until December 2017 the MSE is 0.008 for identification process and 0,1728 for prediction process whereas The MSE value in validation process result is 0.1089 for identification process and 0.1617 for prediction process.

AbstrakMenyiapkan tenaga kerja yang mampu berpikir analitis menjadi salah satu tugas institusi pendidikan, baik perguruan tinggi maupun sekolah menengah atas. Kegiatan pengabdian kepada masyarakat bertujuan ini untuk meningkatkan kompetensi guru Matematika SMA dalam menyusun instrumen evaluasi pembelajaran berbasis Thinking Analysis, yaitu penyusunan soal kategori Higher Order Thinking Skills (HOTS).Kegiatan ini bekerjasama dengan mitra yaitu MGMP Matematika SMA/MA se-Kabupaten Tuban. Rangkaian kegiatan dimulai dengan penugasan awal untuk peserta berupa penyusunan soal HOTS. Kegiatan berikutnya yang merupakan kegiatan utama kegiatan pengabdian masyarakat ini adalah pelatihan penyusunan soal kategori HOTS dan penelaahan seluruh soal HOTS yang telah dikumpulkan oleh peserta yang diikuti oleh 30 peserta guru Matematika SMA di Kabupaten Tuban didampingi oleh tim instruktur. Selanjutnya kegiatan telaah soal oleh tim reviewer terhadap soal yang telah disusun oleh setiap peserta. Rangkaian kegiatan ini ditutup dengan evaluasi atas keseluruhan pelaksanaan kegiatan serta hasil penyusunan soal. Berdasarkan hasil evaluasi secara keseluruhan tampak bahwa kompetensi peserta dalam penyusunan soal HOTS mengalami peningkatan jika dibandingkan dengan sebelum mendapatkan pelatihan. Disamping itu, peserta lebih percaya diri dalam menyusun soal kategori HOTS. Luaran kegiatan ini salah satunya berupa e-book yang merupakan kumpulan seluruh soal yang telah disusun oleh peserta sebagai referensi bagi guru SMA Matematika se-Kabupaten Tuban guna penyusunan soal selanjutnya. Kata kunci: HOTS, Kompetensi, Instrumen evaluasi pembelajaran, Matematika

Cholesterol is a lipid (fat) produced by the liver and is required to build and maintain cell membranes. Cholesterol is also important for the metabolism of fat soluble vitamins. This important lipid is found in human blood. Excess cholesterol (high cholesterol) can cause health problems such as being a factor of coronary heart disease that responsible for the heart attacks, liver or kidney disease. Observation of iris pattern can detect several types of diseases, one of which is high cholesterol. The purpose of this research is to detect whether someone is exposed to high cholesterol or not, through iris images based on firefly algorithm, simulated annealing, and radial basis function. Firefly algorithm and simulated annealing are used in the unsupervised learning process in radial basis function neural networks. The stages of high cholesterol detection process are images processing namely grayscale process, thresholding, histogram equalization, segmentation, and detection process is using radial basis function neural network. The percentage success rate of the recognition pattern of iris images for detecting high cholesterol is 89%.

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