Islamic boarding school is one of the Islamic education in Indonesia. Reform and reconstruction of Islamic education and its institutions need to be carried out, especially observing the development of the global world which requires every Islamic educational institution to continue to improve itself if it does not want to be abandoned by its devotees. There are still or not a few public perceptions that say that students have an image that is not positive enough in the entrepreneurial world. Another problem faced is the fate of the santri after 'study' from the Islamic boarding school. Employment opportunities for them are very small, especially if they do not have a general education provision. This research is a field research using a qualitative approach. The data in this study are primary data obtained from the Tarbiyah Islamic Boarding School and secondary data in the form of books that support this research. Methods of data collection using observation, interviews and documentation. The results showed that the Tarbiyah Islamiyah Islamic Boarding School in Cinyawang Village, Patimuan District, applied entrepreneurship to its students. Without eliminating its main characteristics as a boarding school, namely expertise in the field of religion. With this entrepreneurship, it encourages an economic independence in the Tarbiyah Islamic Boarding School environment. The impact in the development of entrepreneurship is not only felt by the students. But it also has an impact on the economy of Islamic boarding schools, alumni of students and also the surrounding community.

In data mining, we can use symptoms suffered by patients for a reference in classifying positive and negative Covid-19 patients using data mining. Random Forest and logistic regression are two data mining algorithms with high accuracy, precision, and sensitivity in data classification. This study compares the random forest and the logistic regression algorithm - where we use the lasso and ridge regulations - on classifying positive and negative Covid-19 patients based on their symptoms. From 5434 data used in the data set, the evaluation results show that the random forest algorithm is the best in terms of accuracy, precision, and sensitivity compared to other algorithms, while the logistic regression algorithm with ridge regulation is the worst. The random forest algorithm is the most reliable in classifying patients with positive Covid-19, while the logistic regression algorithm with ridge regulation is the least reliable. Also, the random forest algorithm is the most reliable in classifying patients with negative Covid-19, while the logistic regression algorithm with lasso regulation is the least reliable. Keywords: classification;covid-19;data mining;logistic regression;random forest. Abstrak: Dalam data mining, kita dapat menggunakan gejala yang diderita pasien sebagai acuan dalam mengklasifikasikan pasien positif dan negatif Covid-19 menggunakan data mining. Random forest dan logistic regression adalah dua algoritma data mining yang memiliki akurasi (accuracy), presisi (precision), dan sensitivitas (recall) tinggi dalam klasifikasi data. Penelitian ini membandingkan algoritma random forest dan logistic regression - di mana kami menggunakan regulasi lasso dan ridge - dalam mengklasifikasikan pasien positif dan negatif Covid-19 berdasarkan gejalanya. Dari 5434 data yang digunakan dalam data set, hasil evaluasi menunjukkan bahwa algoritma random forest adalah yang terbaik dalam hal akurasi, presisi, dan sensitivitas dibandingkan dengan algoritma lainnya, sedangkan algoritma logistic regression dengan regulasi ridge adalah yang terburuk. Algoritma random forest paling andal dalam mengklasifikasikan pasien positif Covid-19, sedangkan algoritma logistic regression dengan regulasi ridge merupakan algoritma yang paling tidak tidak dapat diandalkan. Selain itu, algoritma random forest paling andal dalam mengklasifikasikan pasien dengan Covid-19 negatif, sedangkan algoritma logistic regresssion dengan regulasi lasso merupakan yang paling tidak dapat diandalkan. Kata kunci: covid-19;data mining;klasifikasi;logistic regression;random forest.

Patient’s symptoms could be used as features in Covid-19 classification. Using multi layer perceptron, the classification uses data set that contains patient’s diagnosis which has Covid-19 symptoms dan processes the data set to see if the patient is Covid-19 positive or not. This paper compare four activation function such as identity, logistic, ReLu and tanh and combined them with optimizer such as L-BFGS-B, SGD and Adam. Using 5-fold and 10-fold cross validation technique to get the accuracy, F1, precision and recall values, the result that we get is that logistic function with L-BFGS-B optimizer and ReLu function with L-BFGS-B optimizer are the best combinations. The logistic function with SGD optimizer, ReLu function with Adam optimizer and tanh function with Adam optimizer are the worst combinations according to their accuration values. The logistic function with SGD optimizer is the worst combination according to its F1 value. The logistic function with SGD optimizer and tanh function with L-BFGS-B optimizer are the worst combinations according to their precision values. The logistic function with SGD optimizer, ReLu function with Adam optimizer and tanh function with Adam optimizer are the worst combinations according to their recall values.            Keywords: activation function, covid-19; multi layer perceptron; optimizer algorithm  Abstrak: Diagnosa gejala yang dialami pasien dapat digunakan sebagai fitur dalam klasifikasi penderita Covid-19. Dengan multi layer perceptron, klasifikasi dilakukan menggunakan data set yang berisi hasil diagnosa pasien yang memiliki gejala Covid-19 dan selanjutnya diolah untuk  melihat apakah memang pasien tersebut menderita Covid-19 atau tidak. Penelitian ini membandingkan fungsi aktivasi identity, logistic, ReLu dan tanh yang dikombinasikan dengan algoritma optimasi L-BFGS-B, SGD dan Adam. Hasil evaluasi cross validation menggunakan 5-fold dan 10-fold digunakan sebagai dasar menentukan kombinasi yang terbaik dan terburuk, dengan hasil yang menunjukkan bahwa kombinasi fungsi logistic dengan optimasi L-BFGS-B dan fungsi ReLu dengan optimasi L-BFGS-B merupakan kombinasi terbaik. Kombinasi fungsi logisctic dengan optimasi SGD, fungsi ReLu dengan optimasi Adam dan fungsi tanh dengan optimasi Adam merupakan yang terburuk dari nilai accuracy. Kombinasi fungsi logistic dan optimasi SGD merupakan kombinasi terburuk dari nilai F1. Kombinasi fungsi logistic dengan optimasi SGD dan fungsi tanh dan optimasi L-BFGS-B merupakan yang terburuk dari nilai precision. Kombinasi fungsi logisctic dengan optimasi SGD, fungsi ReLu dengan optimasi Adam dan fungsi tanh dengan optimasi Adam merupakan kombinasi terburuk dari nilai recall. Kata kunci: algoritma optimasi; covid-19; fungsi optimasi; multi layer perceptron

Rice is an intriguing research topic, particularly in computer vision fields, because it is a staple food consumed in many parts of the world. Different rice varieties can be classified using the rice grain image based on their textures, sizes, and colors. To extract features from rice images, we used two popular pre trained convolutional neural network models, Inception V3 and VGG 16. The extracted features are then used as transfer learning in six variations of multilayer perceptron models, using rectified linear units as the activation function and adaptive moments as the loss function. The results show that the VGG 16 network performs better than the Inception V3, with 0.5% higher accuracy, precision, and recall value. Also, using the VGG 16 network produces a lower misclassification percentage, compared to the Inception V3 network, with a difference of 2.6%.