Judul: Identifikasi Penyakit Tanaman Kubis Menggunakan Gaussian Filter dan Wavelet Transformation
Abstrak: Decreased levels of cabbage production in Indonesia is caused by agricultural barriers such as diseases. Diseases on cabbage is difficult to identify. One solution that can be done is by using a digital image processing techniques. The use of digital image processing techniques is to facilitate the identification of cabbage diseases. This study uses Gaussian Filter and Wavelet Transformation to identify the type of cabbage diseases based on the image of its leaves. Gaussian filter method is applied to reduce noise in the image of diseased cabbage. Wavelet Transformation method is used as a feature extraction technique to the image. This study only focused on three types of cabbage diseases namely, leaf spot Alternaria, Black rot, and Downy mildew. In this study, identification of cabbage disease showed an accuracy of 85%. This suggests that the combination of these two methods can be applied to the identification of an image of cabbage diseases.
Keyword: cabbage diseases, gaussian filter, wavelet transformation

Judul: Analisis Penggunaan Filter Wavelet pada Identifikasi Penyakit Tanaman Akasia (Acacia crassicarpa) Berbasis Citra Digital
Abstrak: Acacia crassicarpa merupakan salah satu jenis Spermatophyta di Indonesia. Pemanfaatan tanaman akasia yang cukup banyak seperti pembuatan kertas, pembuatan dekorasi rumah dan furnitur, menyebabkan akasia banyak dibudidayakan. Perlunya pengendalian yang tepat agar kualitas tanaman tetap terjaga terutama dari penyakit. Seiring perkembangan teknologi, aplikasi otomatis untuk mendeteksi penyakit tanaman terutama berbasis citra telah hadir dan berkembang cukup pesat. Penelitian ini bertujuan untuk meningkatkan kemampuan aplikasi identifikasi penyakit tanaman berbasis citra khususnya pada penyakit tanaman akasia. Aplikasi tersebut menggunakan metode Discrete Wavelet Packet Transform (DWPT) dalam mengekstraksi ciri penyakit tanaman. Fokus penelitian ini adalah pemilihan wavelet filter untuk metode DWPT pada aplikasi identifikasi penyakit daun tanaman akasia. Wavelet filter yang digunakan yaitu Daubechies, Coiflet, Biorthogonal, dan Reverse Biorthogonal. Pemilihan kernel terbaik didasarkan pada hasil classifier support vector machine (SVM). Model dengan akurasi tertinggi terdapat pada wavelet filter Reverse Biorthogonal yaitu rbio1.1 dan Biorthogonal yaitu bior1.3 sebesar 92.68%.
Keyword: Acacia crassicarpa, Discrete Wavelet Packet, Transform, filter wavelet

Judul: Sintesis dan Karakterisasi Membran Polistirena Terfluorinasi untuk nnnnnnnDirect Methanol Fuel Cell
Abstrak: One of common energy resources is direct methanol fuel cell (DMFC), which use methanol as fuel. The important component in DMFC is proton exchange membrane. The study used fluorinated polystyrene membrane (PSf) as proton exchange membrane through fluorination process. PSf membrane was made with PSf concentration of 5%, 10%, and 15 %. Fluorination has caused change of membrane physical properties with presence of pores in the membranes. The best water uptake was shown by PSf 5% that is 11.07%. Membrane activation increased the proton conductivity from 0.52 × 10-7 S/cm to 0.54 × 10-7 S/cm for polysryrene (PS) and 0.44 × 10-7 S/cm to 0.69× 10-7 S/cm for the PSf. Qualitative test of methanol permeability showed good result with dry underneath the surface of membrane because methanol did not pass through the membrane. In DMFC system, proton conductivity of the PSf membrane is higher than that of PS membrane, while variation of conductivity from PSf membranes is caused by gaps at the PSf membranes.
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