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Sejalan dengan penerapan ilmu komputer yang semakin meluas serta pesatnya perkembangan teknologi informasi, memberikan dampak positif pada bidang kesehatan saat ini. Dengan adanya perkembangan teknologi informasi pada bidang kesehatan, dapat meningkatkan pelayanan kesehatan menjadi lebih baik. Salahsatu implementasinya adalah untuk melakukan diagnosa penyakit paru-paru. Penyakit paru-paru menjadi hal yang penting untuk diperhatikan mengingat paru-paru merupakan salah satu organ vital manusia yang menjadi penyebab kematian terbesar di dunia. Selain dari itu, keterbatasan informasi serta mahalnya biaya pengobatanmerupakan salah satu penyebab munculnya permasalahan yang lebih luas pada penanganan penyakit paruparu. Pada penelitian ini dibangun suatu aplikasi sistem pakar berbasis java guna membantu konsultasi bagi pasien penderita penyakit paru-paru. Metode yang diterapkan adalah Naive Bayes Classifier. Pada penelitianini telah dihasilkan sebuah aplikasi yang bertujuan untuk mendiagnosa penyakit paru-paru secara offline, pasien cukup mendaftar pada petugas klinik (bagian administrasi) serta menyebutkan gejala-gejala yang dialaminya tanpa perlu berkonsultasi secara langsung dengan dokter spesialis paru-paru sehingga proses pelayananmenjadi lebih efektif dan efisien. Berdasarkan hasil percobaan kepada 12 orang pasien penyakit paru-paru menggunakan sistem, prosentase kesesuaian diagnosa penyakit paru-paru jika dibandingkan dengan hasil diagnosa dari pakar sebenarnya sebesar 83%. Kata Kunci: Sistem Pakar, Penyakit Paru-paru, Naive Bayes Classifier, Java

One factor affecting the quality of tea is the selection of plant material that would be planted on the field. Clonal selection is a common way to produce tea with better quality. However, as a natural cross pollination species, tea often consists of various clones or progenies of cross-pollinated process. This commonly occurs on plantations owned by smallholder farmers. To produce a consistent quality tea, the clones or progenies need to be identified. Usually, human experts distinguish the plants from leaves by visual inspection on the physical attributes of the leaves, such as the textures, the bone structures, and the colors. It is very difficult for non-experts or common farmers to do such identifications. In this, we propose a deep learning-based identification of tea clones. We apply deep convolutional neural network (CNN) to identify 3 types of tea clones of Gambung series, a series of tea clones developed at Research Institute of Tea and Cinchona. Our study indicates that the performance of the CNN systems are affected by the depth of the convolutional layers. VGGNet, a popular CNN architectures with 16 layers, achieves better accuracy compared to AlexNet, a CNN with 6 layers.

No abstract

In this paper, we propose a novel deep learning-based feature learning architecture for object classification. Conventionally, deep learning methods are trained with supervised learning for object classification. But, this would require large amount of training data. Currently there are increasing trends to employ unsupervised learning for deep learning. By doing so, dependency on the availability of large training data could be reduced. One implementation of unsupervised deep learning is for feature learning where the network is designed to “learn” features automatically from data to obtain good representation that then could be used for classification. Autoencoder and generative adversarial networks (GAN) are examples of unsupervised deep learning methods. For GAN however, the trajectories of feature learning may go to unpredicted directions due to random initialization, making it unsuitable for feature learning. To overcome this, a hybrid of encoder and deep convolutional generative adversarial network (DCGAN) architectures, a variant of GAN, are proposed. Encoder is put on top of the Generator networks of GAN to avoid random initialisation. We called our method as EGAN. The output of EGAN is used as features for two deep convolutional neural networks (DCNNs): AlexNet and DenseNet. We evaluate the proposed methods on three types of dataset and the results indicate that better performances are achieved by our proposed method compared to using autoencoder and GAN.