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"One of the teaching method is called his jigsaw method that can make the math makes lesson more interesting and increase the result of the test. In this method the student study in group and they solve problems together so that the teaching and learning process will be : exploration , elaboration, confrimation can be seen and applied . and the knowledge that they had got can be fixed in their mind."

"This classroom action research was held at SMAN I Koto Baru Dharmasrya with the aim is to improving student's motivation in laering mathematic through cooperative learning with two stay- two stray model plus hula hoop as a media."

"Vocatinal school is one of the high schools that has been choen as priority for students after junior high school. But when they are not able to continue their study , they can make job field and invite others to work together. But in teaching and learning process, teachers still gate some problem. so, the teacher should more creative to apply more interesting methods for strategy. One of model is introduced by the writer, cooperative learning that is guessing word type. It is hoped that the model will help students."

"Demam Berdarah Dengue (DBD) masih menjadi masalah kesehatan yang utama di Indonesia.  Berdasarkan data dari Kemenkes RI, pada tahun 2022 jumlah insiden DBD dicatat sebanyak 131.265 yang mana sekitar 40% adalah anak-anak usia 0 sampai 14 tahun dengan jumlah kasus kematian mencapai 1.135 jiwa dengan 73% terjadi pada anak-anak usia 0 sampai 14 tahun. DBD disebabkan oleh virus dengue yang disebarkan melalui gigitan nyamuk Aedes aegypti  dan Aedes albopictus.. Selain faktor kebersihan lingkungan dan kebiasaan masyarakat, tingginya insiden DBD di Indonesia juga dipengaruhi oleh beberapa faktor iklim seperti curah hujan, temperatur, dan kelembapan. Memaksimalkan proses pencegahan DBD oleh pemerintah dan masyarakat dapat menekan tingginya kasus DBD di Indonesia. Salah satu cara untuk memaksimalkan proses pencegahan DBD adalah dengan melakukan prediksi jumlah insiden DBD yang akan terjadi kedepannya. Dengan mengetahui hasil prediksi jumlah insiden DBD, diharapkan masyarakat dan pemerintah dapat memaksimalkan proses pencegahan DBD. Pada tugas akhir ini, dilakukan prediksi jumlah insiden DBD menggunakan convolutional neural network dan extreme gradient boosting, dengan jumlah insiden sebelumnya dan faktor cuaca sebelumnya yang terdiri dari temperatur, curah hujan, dan kelembapan relatif sebagai variabel prediktor. Variabel prediktor yang digunakan ditentukan berdasarkan time lag dari masing-masing variabel prediktor terhadap jumlah insiden DBD menggunakan korelasi silang. Model convolutinal neural network dan extreme gradient boosting yang dibentuk dievaluasi dan dibandingkan berdasarkan nilai Root Mean Square Error (RMSE), Mean Absolute Error (MAE), dan waktu simulasi. Pada tugas akhir ini, convolutional neural network memberikan performa yang lebih baik dibandingkan dengan extreme gradient boosting berdasarkan nilai RMSE dan MAE dengan rata-rata 13,3586 untuk RMSE dan 9,2249 untuk MAE. Berdasarkan waktu simulasi, extreme gradient boosting memberikan performa yang lebih cepat dibandingkan convolutional neural network.

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Dengue Hemorrhagic Fever (DHF) remains a major health problem in Indonesia. Based on data from the Ministry of Health of Indonesia, in 2022, the number of DHF incidents recorded was 131,265, of which approximately 40% were children aged 0 to 14 years, with a total of 1,135 deaths, 73% of which occurred in children aged 0 to 14 years. DHF is caused by the dengue virus, which is transmitted through the bites of Aedes aegypti and Aedes albopictus mosquitoes. In addition to environmental cleanliness and societal habits, the high incidence of DHF in Indonesia is also influenced by several climate factors such as rainfall, temperature, and humidity. Maximizing the DHF prevention process by the government and the community can help reduce the number of DHF cases in Indonesia. One way to maximize the DHF prevention process is by predicting the future number of DHF incidents. By knowing the predicted number of DHF incidents, it is hoped that the community and the government can maximize the DHF prevention process. In this final project, the prediction of the number of DHF incidents is carried out using convolutional neural network and extreme gradient boosting, with the previous incident counts and previous weather factors consisting of temperature, rainfall, and relative humidity as predictor variables. The predictor variables used are determined based on the time lag of each predictor variable on the number of DHF incidents using cross-correlation. In this final project, the convolutional neural network outperforms extreme gradient boosting based on the RMSE and MAE values, with an average of 13.3586 for RMSE and 9.2249 for MAE. However, in terms of simulation time, extreme gradient boosting demonstrates faster performance compared to the convolutional neural network."

"This study aims to identify the effectiveness of cooperative learning of Jigsaw and STAD models at elementary school students. Specifically this study aims to (I) identify the learning activities of students in Jigsaw and STAD model, and (ii) identify the effect of cooperative learning on student outomes, and (iii) identify the effects of cooperative learning on social skill on elementary school students. Subjects are fourth grade elementary school student of SD Sambungan 01 and 02 in Sub District of Undaan in Kudus Regency, was randomly selected through cluster sampling technique includes two experimental groups. It consists of 20 students for STAD model and 21 students for Jigsaw model. Collecting the data in the form of student learning outcomesn conducted during the 2009.1 academic year through tests and non test. Data was analyzed using ANOVA and testing requirements through the normality test and homogeneity of variance. Data processing activities and skills gained through observation and then be processed through descriptive analysis. Research results revealed that the implementation of cooperative learning model of Jigsaw and STAD model was capable to improve student learning activities. While the use of both models show that students only skillful in capturing the concept. The value of the influence of process skills with Jigsaw model is 59.6% and with the STAD model is 55.5%. Average yield study showed significance differences that Jigsaw model is better than STAD model. This means that the Jigsaw model is more able to improve student learning activity compared with STAD model. It is concluded that the Jigzaw model is better than STAD model. Teachers are advised to apply Jigsaw method of cooperative learning model as an alternative to teaching in the classroom."

"one or factors that determine the success of education process in classroom is teacher. Teachers not only has role as teacher that transfer knowledge to the students but also as a model. The are various ways conducted by the techers to improve students study result."

"During three years latest, the achievement of non regular education students offering BB in Animal Development course relatively low, with average gain C+ (57,5-67,5) because of the learning method used could activate students not well. For surpass that condition, the learning innovation that can increase student’s activity were needed. This expansion purposed to increase motivation and achievement of the students offering BB in Animal Development course by applying of jigsaw’s cooperative learning . The design used in this expansion is classroom action research in two cycles that applied in five and six meeting time in even semester 2007/2008. The student’s motivation were observed by perceived their interest, activity, effort, concentration and efficiency working during the learning process. The results indicate the increasing of good category’s classical learning motivation from 60.74 at 1st cycle to 77.78 at the 2nd , and the classical achievement from 62.96% to 85.19%. Its can concluded that the applied of jigsaw’s cooperative learning can improve student’s motivation and achievement. "

"Tren kenaikan frekuensi dan severitas klaim untuk klaim asuransi kendaraan bermotor menyebabkan dibutuhkannya metode otomatisasi baru untuk memprediksi probabilitas seorang pemegang asuransi kendaraan akan mengajukan klaim jika diberikan data historis mengenai pemegang asuransi tersebut, agar perusahaan asuransi dapat memilah dan memproses lebih lanjut para pemegang polis yang kemungkinan mengajukan klaimnya tinggi. Masalah ini dapat diselesaikan dengan berbagai metode, salah satunya dengan machine learning, yang mengkategorisasikan masalah tersebut sebagai masalah supervised learning. Volume data yang besar dan banyaknya kemungkinan adanya missing values pada data pemegang asuransi menjadi dua aspek yang mempengaruhi pemilihan model machine learning yang tepat. XGBoost merupakan model gradient boosting machine learning baru yang dapat mengatasi missing value dan volume data besar sehingga XGBoost diklaim merupakan metode yang tepat untuk digunakan pada masalah tersebut. Dalam skripsi ini akan diaplikasikan metode XGBoost kepada masalah ini, dan akan dibandingkan hasilnya dengan berbagai metode machine learning lainnya, seperti AdaBoost, Stochastic Gradient Boosting, Random Forest, Neural Network, dan Logistic Regression.

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The increasing trend of claim frequency and claim severity for auto-insurance result in a need of new methods to predict whether a policyholder will file an auto-insurance claim or not, given historical data about said policyholder, so that insurance industries can further process policyholders with high claim probability. This problem can be solved with many methods, one of which is machine learning, which categorizes this problem as a supervised learning problem. The high data volume and the existence of missing values on a policyholders historical data are aspects that the chosen machine learning model must be able to handle. XGBoost is a novel gradient boosting machine learning problem that is able to inherently handle missing values and high volume of data, which should make the model suitable for this problem. In this thesis, XGBoost will be applied to this problem, and its performance will be compared by other machine learning models, such as AdaBoost, Stochastic Gradient Boosting, Random Forest, Neural Network, and Logistic Regression."

"Economics is one of social subjects learnt by vocational high school students.Therefore , cooperative integrated reading and composition (CJRC) could improve the students learning result at the second grade TN 1 SMK negeri 1 Padang panjang."

"Penelitian ini bertujuan untuk meneliti efektifitas pembelajaran kooperatif tipe jigsaw yang diberikan melaluimetode eksperimen untuk meningkatkan belajar berdasar regulasi diri pada mahasiswa serta prestasi belajar siswa. Skala Belajar Berdasar Regulasi Diri dikembangkan oleh Pintrich et al. (1991) yang mengungkap profil pembelajar aktif. Penelitian ini sangat penting untuk dikembangkan mengingat perubahan pola pembelajaran di pendidikan tinggi dari pembelajarn berpusat pada Dosen ke pembelajaran berpusat pada mahasiswa (student active learning). Subjek penelitian ini adalah mahasiswa tahun pertama program diploma Fakultas Ilmu Keperawatan di Semarang yang terbagi dalam dua kelompok yaitu kelompok Kontrol sejumlah 33 mahasiswa dan kelompok perlakuan sejumlah 34 mahasiswa. Berdasarkan hasil analisis diketahui bahwa ada pengaruh pembelajaran kooperatif tipe jigsaw terhadap belajar berdasar regulasi diri, namun pengaruh padaregulasi strategi belajar tidak ditemukan.

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The main purpose of this study was to examine effectiveness of cooperative learning type jigsaw to enhance self regulated learning. Experiment design is used by implementing jigsaw classroom for improving self regulated learning. Data were collected by self regulated learning scale adapted from MSLQ (developed by Pintrich et al., 1991) to know the student?s profile of active learning. As known, the changes of paradigm in instruction from teacher or lecture center to student active learning have many problems. One of the causes of this condition is most of lecture still use traditional models of instruction that led lecture more active than students. Sixtyseven student from diploma degree in nurse faculty of Sultan Agung Islamic University involved in this study. Then, subject divided equally into two groups, 33 student for control group and 34 student for experiment group. The result showed that cooperative learning type jigsaw has significantly effect to self regulated learning. Jigsaw learning can improve student motivation significantly, however, the effect on the regulation of learning strategies was not found."