Judul: Sintesis Zeolit X Dan Nanokomposit Zeolit/TiO2 Dari Kaolin Dengan Sekam Padi Sebagai Sumber Silika
Abstrak: Zeolite X was synthesized from kaolin originated from Bangka Belitung and was added with silica from rice husk ash. Zeolite X was synthesized because it has many benefits, including as an adsorbent. Kaolin was used as raw material due to its high content of silica and alumina, which were 48.03% and 35.50% respectively. Meanwhile, silica from rice husk ash was used in the form of sodium silicate. Zeolite was synthesized through hydrothermal process inside a polypropylene bottles, by mixing metakaolin, the calcined form of kaolin heated at 700 °C for 6 hours, with sodium silicate and NaOH to activate the major components of Si and Al in the kaolin. Hydrothermal processes were carried out at 100 °C for 24 hours with NaOH concentrations of 1.5 N and 2.5 N. The concentration of NaOH used was resulted in certain type of zeolite obtained. Zeolite X was obtained when the addition 2.5 N NaOH with NaP1 and Faujasite as impurities. On the other hand, NaOH 1.5 N gave NaP1 zeolite. Modification of zeolite to nanocomposite zeolit/TiO2 was shown to have better capability, that was adsorption-photocatalytic property due to its ability to degrade methylene blue under ultraviolet light.
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Judul: Sintesis Zeolit dan Nanokomposit Zeolit/TiO2 dari Abu Layang dan Waterglass serta Uji Adsorpsi dan Fotodegradasinya
Abstrak: Coal fly ash contains 20.74% Si and 4.09% Al, rendering its possibility to be synthesized to zeolite. Zeolite was synthesized by smelting at 550 °C, and continued with hydrothermal method at 90 °C for 6 hours. The weighed fly ash was melted with solid NaOH and various amounts of waterglass at 550 °C for 1 hour, which were coded ZK1 for 1.0 g of waterglass added, ZK2 (1.5 g), ZK3 (2.5 g), ZK4 (5.0 g), and ZK5 (7.5 g). The results indicated that the zeolite has the character as type P1. ZK1 zeolite had the highest crystallinity (65%), therefore it was used further to synthesize zeolit/TiO2 nanocomposite. The nanocomposite was formed to improve the zeolite characters, especially adsorption-photodegradation capacity. The synthesized zeolite and the nanocomposite were tested for their adsorption capacities. The results showed that the ZK1 has the highest adsorption capacity, i.e. 147 mg/g. Optimization was performed on ZK1 and zeolit/TiO2 nanocomposite to compare their adsorption capacity and to determine their optimum conditions for adsorption. The optimum concentration of adsorbate that can be adsorbed by ZK1 was obtained at 400 ppm using 0.02 g adsorbent, with the adsorption time of 3 hours. Zeolite/TiO2 nanocomposite has optimum concentration of 250 ppm with the weight of adsorbent of 0.01 g, and the adsorption time of 1 hour. The nanocomposite zeolite was able degrade methylene blue under ultraviolet radiation for 6 hours at λ=365 nm.
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Judul: Prediction of Survival in Heart Failure Patients with  Machine Learning Method Logistic Regression, Support Vector Machine, and  Random Forest
Abstrak: Kardiovaskular merupakan gangguan pada jantung dan pembuluh darah seperti coronary heart disease. Penyakit ini setiap tahunnya membunuh sekitar 17 juta orang di seluruh dunia dengan penyakit utamanya adalah gagal jantung dan infark miokard. Pendekatan dalam prediksi kelangsungan hidup pasien gagal jantung umumnya dengan menggunakan metode New York Heart Association(NYHA) dengan empat kelas utamanya, tetapi metode ini gagal memprediksi fitur dasar dan gagal mencapai akurasi yang tinggi. Penelitian ini menggunakan pendekatan machine learning dengan tiga model prediksi yaitu regresi logistik, support vector machine, dan random forest. Model random forest mempunyai kinerja paling baik yang disusul oleh regresi logistik, kemudian support vector machine. Setiap model memberikan tiga fitur kuat utama dalam memprediksi variabel target yaitu time, ejection fraction, dan serum creatinine. Pengambilan keputusan hasil simulasi prediksi dilakukan dengan melihat kelas terbanyak yang muncul pada hasil prediksi yang diberikan oleh setiap model dengan urutan randomforest, regresi logistik, kemudian support vector machine.
Keyword: cardiovascular, machine learning, logistic regression, support vector machine, random forest