Judul: Prediksi Penyakit Paru Menggunakan Algoritme Voting Feature Intervals 5 dengan Bobot Fitur Tidak Seragam
Abstrak: Penyakit paru mempunyai gejala yang sangat mirip misalnya pada Bronkitis dan Tuberkulosis Paru, gejala tersebut adalah batuk, batuk darah, sesak nafas, sakit dada, badan lemah, nafsu makan berkurang, berat badan turun, berkeringat malam walaupun tanpa kegiatan. Pada penelitian ini algoritme VFI5 melakukan klasifikasi dua jenis penyakit paru yaitu Bronkitis dan Tb Paru. Pengumpulan data dilakukan dengan proses wawancara pada pasien baru di poli paru Rumah Sakit Umum Daerah Pasar Rebo. Pertanyaan yang diberikan berupa gejala umum penyakit paru yang telah dikonsultasikan dengan dr. Tri Novianti, MARS dan dilakukan proses pelatihan dan pengujian menggunakan algoritme Voting Feature Intervals 5. Penggunaan algoritme VFI 5 dalam mengklasifikasi Bronkitis dan Tb Paru cukup memberikan hasil yang baik dan miripnya gejala pada kedua penyakit ini dapat dibuktikan setelah dilihat dari selang-selang yang dihasilkan oleh setiap gejala pada pelatihan dan pada normalisasi akhir seluruh percobaan.
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Judul: Optimalisasi algoritme voting feature intervals 5 menggunakan algoritme genetik pada data tuberkulosis paru
Abstrak: We proposed a genetic algorithm to optimize VFI5 classification algorithm and to get the best feature weights in lung tuberculocis data. This research used a uniform value for this features weights which equals to one. The accuracy obtained was 83%. Genetic Algorithm (GA) is used to optimize VFI5 by determining the optimal weights for each feature. GA will combine each weight and search the best combination to get an optimal solution. In this research, GA can find the optimal weight feature VFI5 classification algorithm in lung tuberculocis data. The optimal feature weights are “the blood cough with average weight 0.91, limp with average weight 0.82, lost of appetite with average weight 0.63, body weight decrease with average weight 0.62, fever and perfiration with average weight 0.62”. in this reseached, 3-fold cross validation was used to divide data into traning and testing and obtained 95% accuracy in each fold. In conclucions, this research has provided with and accurate model to predict TB and Non TB data instances.
Keyword: VF15 classification algorithm, Genetic algorithm, Tubercolosis paru, Bogor Agricultural University, Institut Pertanian Bogor, IPB

Judul: Elektrodeposisi Film Tipis Semikonduktor Cu2ZnSnS4 (CZTS) pada Substrat Kaca ITO
Abstrak: Semiconductor Cu2ZnSnS4 (CZTS) thin film that can be used as absorber layers on solar cell module was made via electrodeposition method. CZTS thin film was electrodeposited on the surface of a silicate glass coated with a thin layer of indium tin oxide (ITO). Deposition process was done using potentiostatic mode at room temperature with a potential of 1.05 V for 30, 45, and 60 minutes, followed with annealing the film at 2 different temperatures, 500 ºC using furnace and 180 ºC using hot plate, both treatments for 45 minutes in N2/H2S atmosphere. The X-ray difractograms were analyzed using Match 2 program and showed that the CZTS thin films had kesterit crystalline structure, with a low percentage of CZTS phase. The thin film also contained secondary phases such as Cu2S, ZnS, and SnS. The obtained CZTS thin films had a thickness between 1.039 and 1.676 μm. Absorption study showed that the band gap energy of CZTS thin films which was annealed using furnace ranged from 1.50 to 1.52 eV, whereas that annealed using the hot plate was not observed. The band gap energy value showed that CZTS thin films can be used as sunlight absorber layers on solar cell. The formation of CZTS crystal film on this investigation was effectively performed using furnace annealing process at a temperature of 500 ºC with deposition time of 45 minutes.
Keyword: Thin film, Electrodeposition, Cu2ZnSnS4