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The purpose of this study was to compare the sealing ability of gray mineral trioxide aggregate (GMTA), white MTA (WMTA), and both white and gray Portland cement as furcation perforation repair materials. A total of 120 human mandibular first molars were used. After root canal obturation and preparation of furcal perforations the specimens were randomly divided into four groups of 25 teeth each. In groups A, B, C, and D furcation perforations were filled with WMTA, GMTA, white Portland cement, and type II Portland cement, respectively. Ten teeth were used as positive controls with no filling materials in the perforations and 10 teeth with complete coverage with two layers of nail varnish were used as negative controls. A protein leakage model utilizing 22% bovine serum albumin (BSA) was used for evaluation. Leakage was noted when color conversion of the protein reagent was observed. The controls behaved as expected. Leakage was found in the samples from group A (WMTA), group B (GMTA), and in the two other groups (white and gray Portland cement). There were no statistically significant differences between GMTA and WMTA or white and gray Portland cement, but significant differences were observed between the MTA groups and the Portland cement groups. It was concluded that Portland cements have better sealing ability than MTA, and can be recommended for repair of furcation perforation if the present results are supported by other in vivo and in vitro studies.

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Tujuan dari Penelitian ini adalah Mengoptimalkan algoritma Naïve Bayes dengan seleksi fitur Forward Selection untuk dapat meningkatkan hasil akurasi atau tingkat keberhasilan yang didapatkan dari prediksi pembayaran kredit.Data yang akan digunakan dalam penelitian ini berasal dari Bank XY yang berada di Gorontalo. Data yang diperoleh berkaitan dengan semua aspek dari nasabah kredit termasuk informasi pribadi dari nasabah. Desain eksperimen dalam penelitian ini menggunakan dataset nasabah kredit.sedangkan analisi yang digunakan adalah Model algoritma Naïve Bayes dengan seleksi fitur Forward Selection. Prediksi tingkat kelancaran pembayaran kredit menggunakan algoritma Naïve Bayes berbasis Forward Selection mampu memprediksi kelancaran pembayaran kredit ke depannya hal ini terbukti dengan perolehan nilai akurasi Naive bayes berbasis Forward Selection mampu mencapai nilai akurasi 71,97 %.