Judul: Pengukuran kinerja Spam Filter menggunakan metode Naive Bayes Classifier Graham
Abstrak: Email spam has become a major problem for internet users and providers. After several failed attempt to filter spam based on heuristic approach such as black-listing or rule-based filtering, content-based filtering using naive Bayes classifier has become the standard for spam filtering today. However, the naive Bayes classifier exists in different forms. This research aims to compare two different forms of naive Bayes which are multinomial naive Bayes using boolean attribute and Graham version of naive Bayes which is popular among several commercial and open source spam filter applications. This research also compares performace of two different methods for data trainings which are train-everything (TEFT) and Train-on-Error (TOE). Finally, this research attempts to identify several hard-to-classify emails. The evaluation result showed that multinomial naive Bayes had better performances compared to Graham naive Bayes. The result also showed that TEFT successfully outperforms TOE in term of accuracy.
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Judul: Spam Filter Menggunakan Model Klasifikasi Multivariate Bernoulli dan Multinomial Naïve Bayes
Abstrak: Pertumbuhan pengguna email memicu peningkatan spam email sehingga diperlukan teknik spam filter. Model klasifikasi Naïve Bayes (NB) adalah salah satu metode supervised learning yang dapat digunakan untuk spam filter karena tingkat akurasi yang tinggi dan mudah diimplementasikan. Multivariat Bernoulli NB menggunakan atribut Boolean sedangkan Multinomial NB menggunakan frekuensi term, adalah dua model NB yang sering digunakan untuk fungsi klasifikasi. Pemilihan fitur ciri yang baik juga berpengaruh pada peningkatan akurasi klasifikasi. Penelitian ini mencoba memodelkan spam filter menggunakan model klasifikasi Multivariat Bernoulli dan Multinomial NB kemudian membandingkan akurasinya. Seleksi fitur chi-square dipilih dengan harapan dapat menghasilkan fitur ciri yang lebih baik. Model Multinomial NB tanpa seleksi fitur menghasilkan akurasi tertinggi sebesar 95.31%, sedangkan untuk tingkat akurasi terendah didapatkan pada model Multivariate Bernoulli tanpa seleksi fitur sebesar 89.69%. Seleksi fitur chi-square meningkatkan akurasi model Multivariate Bernoulli sebesar 3.31%, sedangkan Multinomial NB mengalami penurunan akurasi sebesar 1.98%.
Keyword: multinomial, multivariat bernoulli

Judul: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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