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The aim of this research in this paper is to develop smart building information application using augmented reality in android mobile gadget. A case study is implemented in STMIK Handayani Makassar. The system is able to show the room condition, temperature, and humidity with marker based tracking. Performance evaluation is done with marker testing, with distance, angle, and blocked marker surface area as parameters. The result shows that the best distance between the gadget and marker is 10 cm-50 cm with mobile angle 0 0-30 0 and coveraged surface area 10%-70%. Specifications of the device are 1 GB RAM, 5 MP Camera, Android 4.4 OS, and Quad core 1 GHz Processor. Intisari-Makalah ini membahas tentang pengembangan aplikasi sistem informasi smart building dengan teknologi augmented reality (AR) pada perangkat mobile android. Studi kasus dilaksanakan pada kampus STMIK Handayani Makassar. Aplikasi AR ini memperlihatkan tampilan bangunan secara bertingkat yaitu keseluruhan gedung hingga ke setiap lokasi ruangan. Sistem juga dapat memperlihatkan kondisi ruangan, kondisi suhu, dan kelembaban dalam ruangan dengan marker based tracking. Evaluasi unjuk kerja sistem dilakukan pada pengujian marker dengan beberapa parameter uji, yaitu jarak, sudut, dan luas permukaan marker yang tertutupi. Hasil pengujian menunjukkan bahwa jarak terbaik antara perangkat mobile ke marker adalah 40 cm-50 cm dan kemiringan perangkat mobile 0 0-30 0 dengan permukaan marker yang tertutupi 10%-70%. Spesifikasi perangkat mobile yang digunakan yaitu RAM 1 GB, Kamera 5 MP, Android 4.4, dan Prosesor Quad core 1 GHz.

Classification of Strawberry Maturity Based on Color Segmentation using HSV Method. Manual fruit maturity classification has many limitations because it is influenced by human subjectivity. Hence, the application of digital image processing and artificial intelligence becomes more effective and efficient. This study aims to create a classification system that automatically divides strawberry maturity into three categories, namely not ripe, half-ripe, and ripe. The process of identifying the level of fruit maturity is based on the color characteristics Red, Green, Blue (RGB) value of the image. The method used for color segmentation is Hue, Saturation, Value (HSV) and for the classification of strawberry maturity using the Multi-Class Support Vector Machine (SVM) algorithm with a Radial Basic Function (RBF) kernel. Strawberry image data was retrieved using the Logitech C920 camera. The dataset consisted of 158 images of strawberries. The results showed that the classification of strawberry maturity using the multi-class SVM algorithm with kernel parameters RBF cost (C) = 10 and gamma (γ) = 10-3 produced the highest accuracy of 97%.