—Fraud detection is the first step to preventing fraud committed by both individuals and organizations. The development of a high-performance classification model to detect fraud is an interesting topic in machine learning modeling. A finding of the best Bayesian and Naive Bayes classification models is a crucial issue because both models are simple and easily applied models in the fields of life and social sciences. This study aims to obtain the best performance of classification models developed based on probability concepts, namely Bayesian and Naive Bayes models. Adding a threshold value to the decision-making criteria of the two models is an effort expected to can find models that perform superiorly. Data on the auditing of fraudulent firms containing of 775 firms from various business sectors in Australia is used as a case study. The testing data consisting of 100 instances were taken by cluster random sampling with a proportion of 61 non-fraudulent and 39 fraudulent firms and the remaining instances as the training data. The best Bayesian model has an average accuracy of 84% obtained at a threshold value of 0.22. While the best Naive Bayes model has an average accuracy of 94% which is obtained in the 15 threshold values. Adding the threshold value has a significant impact on the performance of the Bayesian model, which can increase the average accuracy from 36% to 84%. On the other hand, the average accuracy of the Naive Bayes model only increased by 1%, from 93% to 94%. Performance measures Sensitivity, Specificity, F1 score, and ROC curve of the Naive Bayes model are also superior to the Bayesian model.

Latar belakang diadakannya Penelitian ini adalah rendahnya kompetensi guru sasaran Di SMP Negeri 9 Mataram dalam penyusunan Rencana Pelakssanaan Pembelajaran (RPP) yang baik dan benar yang berdampak kurang percaya diri dalam proses pembelajaran. Solusinya diadakan pendampingan baik secara kelompok maupun individu dalam penyusunan RPP yang baik dan benar. Tujuannya adalah untuk mengetahui efektifitas pelaksanaan pendampingan berbasis MGMP dalam upaya meningkatkan kompetensi guru dalam menyusun RPP yang baik dan benar, yang bermanfaat untuk meningkatkan profesionalisme sebagai kepala sekolah dan bagi guru untuk meningkatkan proses pembelajaran di kelas. Hipotesis tindakan: meningkatkan kompetensi guru guru sasaran SMP Negeri 9 Mataram semester satu tahun pelajaran 2018/2019 dalam menyusun RPP yang baik dan benar sesuai kurikulum 2013 (kurtilas). Penelitian ini dilaksanakan sebanyak dua siklus, masing-masing siklus dua kali pertemuan. Tahapan setiap siklus adalah perencanaan, pelaksanaan, pengamatan, dan refleksi. Indikator keberhasilan dalam penelitian ini adalah; 1) hasil observasi Kepala Sekolah maupun observasi guru selama proses pendampingan telah memperoleh skor rata-rata > 4,0, 2) hasil kerja guru dalam penyusunan RPP mencapai > 100% dengan nilai rata-rata > 70,00. Hasil penelitian pada siklus I observasi Kepala Sekolah rata-rata (3,30), observasi guru rata-rata (3,40) dan hasil kerja individual rata-rata nilai (65,47) dengan prosentase ketercapaian (0%). Pada siklus II observasi Kepala Sekolah rata-rata (4,40), observasi guru rata-rata (4,30) dan hasil kerja individual rata-rata nilai (82,40) dengan prosentase ketercapaian (100%). Indikator keberhasilan telah tercapai, penelitian di nyatakan berhasil dan dihentikan pada siklus II. Kesimpulan; pelaksanaan pendampingan dapat meningkatkan kompetensi guru sasaran SMP Negeri 9 Mataram dalam penyusunan RPP yang baik dan benar. Disarankan agar Kepala Sekolah lainnya melakukan penelitian sejenis dalam upaya peningkatan kompetensi guru, dan kepada guru mata pelajaran agar mampu menyusun RPP dengan baik dan benar.

This research is concerned with Bazilevic B1(α) on the unit disc D = {z| < 1}, related to the Lemniscate Bernoulli (LB), defined by kind of subordination for some positive alpha. There will be determined the Hankel determinant, especially the third Hankel determinant which B1(α) subordinates to LB, and we start with the case for starlike and convex functions, subset of B1(α). In this article we impeove the result of Kumar and Ravichandran.

Abstract Contour interval is elevation difference between two contour lines that are adjacent and parallel. Ground Contour interval is an important information that must be provided in a construction. Ground contour interval estimation problem could be viewed as a 3D trajectory estimation problem. One framework, called dead reckoning, is often used for motion (position and orientation) tracking of a mobile systems over time. Dead reckoning requires a precise 3D orientation estimation algorithm. Two competing algorithms are Direction Cosine Matrix and Madgwick’s. To ensure the 3D trajectory estimation gives an accurate result, these two algorithms must be carefully and thoroughly evaluated. This research is part of a complete research on the development of a ground contour estimation and only focuses on comparison between two 3D orientation estimation algorithm.