A public complaint is a reciprocal of the population against the government to convey opinions or problems encountered in certain areas. The complaint process using a suggestion box or counter complaint is less effective and efficient so that the complaint handling process is slow. The geographic information system of public complaints is an information system built as an intermediary for the public to make complaints against the government. This public complaint geographic information system is built by utilizing location-based services. Geographic information systems of public complaints that have been built require a test to ensure all functions contained on the system can run properly. This study discusses the testing of the geographic information system of public complaints that have been built by blackbox testing and test by involving respondents from the general public. The results of testing system usage by the user based on aspect of system interface display and conformity aspects of processes and features involving respondents from the general public. Tests conducted to get the average results of respondents gave very good value 28%, good 59.8, enough 10.2% and less by 2%. Comparison of systems conducted on two similar systems taken through a literature study showed that a mobile web-based public complaint geographic information system (Public Complaint) has more features in tracking the location of complaints.

Watershed management becomes very important because the more maintained its watershed, the risk for disasters caused by the overflowing river became smaller. Watershed management could be done if the information on that watershed could be complete, but untill this day, the available information was lacking. This condition caused the difficulty of data collected, so required a system that could be used to perform watershed data processing. A system that to be used is Geographic Information Systems Watershed Mapping. This system  is a Web-based system that can be used for collected data and mapping the watershed using a map from Google Maps. Features polyline which was owned by Google Maps can be used to describe a network of rivers and long inundation, library geometry was used to calculate the length of polylines, feature marker was used to describe the location of the dam and the point was prone to flooding of a river and features a polygon used to describe the watershed. This system can collected data watershed in two ways, namely digitization and input the coordinates that can be done by the admin. Results from watershed data can provide information to the user about the location of the dam along with its description, the river network in the watershed, a point prone to flooding, inundation and limit the length of the watershed and its description.

Pesatnya pertambahan penduduk berdampak pada sektor pertanian sebagai akibat dari pengalihan lahan pertanian. Pengalihan ini berdampak juga pada persediaan bahan pangan dan kerusakan ekosistem. Untuk menghadapi permasalahan tersebut, perlu diupayakan penggunaan teknologi sebagai sebuah solusi di era digitalisasi. Penelitian ini memadukan tanaman hidroponik dengan bantuan teknologi Internet of Things (IoT) menggunakan teknik penanaman Aeroponik. Aeroponik merupakan teknik dalam penanaman hidroponik yang memberikan larutan nutrisi dalam bentuk kabut langsung menuju ke akar, sehingga tanaman lebih mudah menyerap nutrisi. Perancangan perangkat IoT menggunakan mikrokontroler raspberry pi yang diinetgrasikan dengan mikrokontroler Arduino Mega sebagai pusat dalam menjalankan sensor pendukung seperti sensor water level, ultrasonik, ph, tds, dan dht22. Aplikasi mobile android digunakan sebagai interface dalam kontrol dan monitoring perangkat oleh pengguna. Dari rancangan dan serangkaian uji coba pada sistem disimpulkan bahwa, rancangan sistem tanaman hidroponik aeroponik berbasis IoT mampu melakukan monitoring dan controlling tanaman, serta otomatisasi dalam pencampuran nutrisi sesuai dengan kebutuhan tanaman.

Kebutuhan rumah tinggal sementara seperti rumah kost yang semakin meningkat berbanding lurus dengan peningkatan pertumbuhan urbanisasi oleh pekerja di Indonesia. Pekerja yang mendapatkan pekerjaan di luar daerah harus mencari rumah kost pada area lokasi bekerja. Pencarian rumah kost yang ada pada masyarakat masih melakukan pencarian sederhana seperti datang ke lokasi untuk melihat langsung rumah kost. Pencarian dengan cara sederhada tersebut menyulitkan pencari melakukan pembandingan harga sewa, dan fasilitas karena harus datang ke lokasi rumah kost secara langsung. Permasalahan dalam mencari rumah kost tersenbut mendorong pembuatan aplikasi pencarian dan penyewaan rumah kost berbasis Web dan mobile Android dengan menggunakan metode penelitian terapan dengan diagram fishbone untuk menganalisis kebutuhan pencari dan pemilik rumah kost. Pencari terbantu dalam melakukan pencarian dan penyewaan rumah kost yang diinginkan dan pemilik terbantu dalam melakukan promosi rumah kost. Kata kunci: Pencarian, Penyewaan, Rumah Kost, Website, Android

The use of a face as a biometric to identify a person in order to keep the system safe from an unauthorized person has advantages over other biometric characteristics. The face as a biometric has more structure and a wider area than other biometrics, while can be retrieved in a non-invasive manner. We proposed a cloud-based architecture for face identification with deep learning using convolutional neural network. Face identification in this study used a cloud-based engine with four stages, namely face detection with histogram of oriented gradients (HOG), image enhancement, feature extraction using convolutional neural network, and classification using k-nearest neighbor (KNN), SVM, as well as random forest algorithm. This study conducted a classification experiment with cloud-based architecture using three different datasets, namely Faces94, Faces96 and University of Manchester Institute of Science and Technology (UMIST) face dataset. The results from this study are with the proposed cloud-based architecture, the best accuracy is obtained by KNN algorithm with an accuracy of 99% on Faces94 dataset, 99% accuracy on Faces96 dataset, 97% on UMIST face dataset, and performance of the three algorithms decreased in UMIST face dataset with facial variations from various angles from left to right profile.