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Irigasi adalah usaha untuk menyalurkan air dengan membuat bangunan dan saluran untuk mengalirkan air guna keperluan pertanian, membagikan air ke sawah dan ladang dengan cara yang teratur dan membuang air yang tidak diperlukan lagi. Proses irigasi yang dilakukan masih secara konvensional, karenanya perlu ditambahkan teknologi untuk mempermudah kinerja dan pemantau dengan mengembangkan sistem irigasi yang dikontrol secara otomatis oleh mikrokontroler dengan menambahkan sensor water flow sebagai pengukur debit air dan sensor ultrasonik untuk mengetahui tinggi air. Pada prototipe irigasi air menggunakan mikrokontroller Arduino Uno dan Wemos sebagai pemroses pada bagian perangkat dan terhubung ke Aplikasi Smartphone Android melalui koneksi internet atau biasa disebut dengan IOT (Internet Of Things), IOT memungkinkan aplikasi dapat terhubung pada semua perangkat untuk melakukan pengiriman data informasi ke petugas irigasi, sesuai dengan jadwal permintaan masyarakat kapan air itu dialirkan. perangkat bekerja berdasarkan jadwal yang telah ditentukan pada aplikasi android yang selanjutnya disimpan pada server untuk dibaca pada mikrokontroller, setelah perangkat selesai melakukan irigasi sesuai dengan jadwal, perangkat mengirimkan notifikasi pada Aplikasi dan mengirimkan data debit air serta tinggi air. Pada pengujian pengiriman data diperoleh rata-rata waktu untuk pengiriman data selama 1287 ms.

The Numerical Weather Prediction (NWP) was developed by the Meteorological, Climatological, and Geophysical Agency in Indonesia for the purpose of weather forecasting, however, it comes with a high level of bias. This purpose of this study therefore was to improve this model with the use of Geostatistical Output Perturbation (GOP), implemented in the conformal-cubic atmospheric model (CCAM) on NWP data from the eight meteorological stations in Indonesia, i.e. Kemayoran, Priok, Cengkareng, Pondok Betung, Curug, Dermaga, Tangerang and Citeko stations. The findings indicated exponential as the best distribution model for analyzing temperature in Indonesia using GOP. Also, locations which are considerably far away from other locations could have significant impact on the accuracy of the weather forecasts. In this case, Citeko station has quite different characteristics location considering the fact that it is located on higher elevation compared with other stations. Therefore, the exclusion of Citeko station produced better forecasting in terms of accuracy and precision, increasing to about twice the result when the station was included in the analysis.

Indonesia is one of the countries in this world that has the most outstanding fishery potential. There are more than 3000 fish species under Indonesia's sea, yet the people are still not able to relish them completely. Illegal fishing by foreign ships in Indonesia's territorial sea is one of the reasons why this happens. In order to minimize this kind of loss, those ships should be detected automatically by implementing image processing and artificial intelligence techniques. The study proposed techniques for automatic detection of ships at sea on digital images. These techniques are global image thresholding and artificial neural network backpropagation. The result of this research is proposed of technique able to detect ship with 85% accuracy level. This method may be improved by adding more training data varieties.

Culinary is a part of life that is closely related to the daily food consumption. Some people have difficulty in determining places to be visited for eating, it is affected by several considerations, such as the budget and the facilities at the eating place.The purpose of this paper is to build an application on the culinary recommendations in Semarang used Mamdani Fuzzy Model with android-based. This application had a function to give recommendation based on budget and facilities selected by the user used Mamdani Fuzzy Model. This application is Android based with Javascript programming language and Ionic framework. Applications can be used on Android smartphone connected to the Internet, then give recommendations based on budget and the facilities selected by the user.

The Karangbolong karst is situated in the southern zone of Java where Miocene limestone has been uplifted and has experienced karstification since the late Pliocene. The research documented here aims at exploring morphological characteristic of the area. Special interest is attributed to differentiation of valley or depression morphology, conical karst morphology, and the roles of jointing system and uplift history in their development. The morphology investigation was mostly undertaken using aerial photograph visual interpretation of panchromatic aerial photograph and analytically shaded DEM, as well as field observations. The results show that the general morphological features of Karangbolong karst are characterized by aligned valleys and aligned enclosed depression with three different patterns. The orientation of the valleys and enclosed depressions coincide with the structural pattern of the area, indicating that the formation of aligned valleys and aligned enclosed depressions is preferential dissolution through jointing. The residual hills are typified by conical karst morphology with sharp peaks. It is found that tight joint spacing appears to be the main reason for the sharp peak of the conical hills. Asides from jointing system, morphology of the area is likely governed also by topographical position and gravity sliding of the limestone bed during the uplift. Uplift history has important control on the differentiation of morphology between plateau part and sloping part. Limitation of this research is that the aerial photograph was not rectified well, because the analyses in this research were more on pattern identification. More accurate rectification is needed for better morphometric analyses. Key words: Southern Java, Karangbolong karst, polygonal karst, tropical geomorphology.

Diabetes mellitus is one of the urgent health problems in the world. Diabetes is a condition primarily defined by the level of hyperglycemia giving rise to risk of micro vascular damage. Those who suffer from this disease generally do not realize and tend to overlook the early symptoms. Late recognition of these early symptoms may drive the disease to a more concerning level. One solution to solve this problem is to create an application that may perform early detection of diabetes mellitus so that it does not grow larger. In this article, a new method in performing early detection of diabetes mellitus is suggested. This method is backpropagation with three optimization namely early initialization with Nguyen-Widrow algorithm, learning rate adaptive determination, and determination of weight change by applying momentum coefficient. The observation is conducted by collecting 150 data consisting of 79 diabetes mellitus patient and 71 non diabetes mellitus patient data. The result of this study is the suggested algorithm succeeds in detecting diabetes mellitus with accuracy rate of 99.33%. Optimized backpropagation algorithm may allow the training process goes 12.4 times faster than standard backpropagation.

Technical challenges in using diatoms for paleolimnological work are the identification and enumeration of diatom valves. Variations exist in the minimum number of valves to identify, ranging from 100 to 700 valves of the dominant species. This task can be very time consuming, particularly when the diatom valves are not abundant. This research was conducted to determine the minimum number of valves to be identified in the diatom assemblages from Rawapening Lake, Central Java, Indonesia. Based on the 314 samples obtained from Rawapening Lake, the diatom efficiency rose above 0.85 upon the minimum count of 300 valves. The number of diatom species identified remained stable after the minimum of 300 valves. Therefore, the minimum number of diatom’s valves identified to represent the assemblage for paleolimnological analysis was 300.

One of the world’s leading causes of death is traffic accidents. Data from World Health Organization (WHO) that there are 1.25 million people in the world die each year. Meanwhile, based on data obtained from Statistics Indonesia, traffic accidents from 2006 to 2013 continue to increase. Of all these accidents, the largest accident occurred at motorcyclists, especially motorcyclists who not wearing standard helmet. In controlling the motorcyclists, police view directly at the highway, so that there are weaknesses which there are still a possibility of motorcyclist offenders who are undetectable especially for motorcyclists who are not wear helmet. This paper explains research on image classification of human head wearing a helmet and not wearing a helmet with backpropagation neural network algorithm. The test results of this analysis is the application can performs classification with 86.67% accuracy rate. This research can be developed into a larger system and integrated that can be used to detect motorcyclists who are not wearing helmet.

Supervision of television and radio broadcast programs by the “Komisi Penyiaran Indonesia (KPI)” Central Java was still performed manually i.e. direct supervision by humans. It certainly had some weaknesses related to the human error such as tiredness and weary eyes. Therefore, we needed intelligent software that could automatically detect broadcast infringement. Currently, research in this area had not been studied. This research was to design an integrated system to detect broadcast infringement including data design, architecture design and main module interface design. Two main stages in this system are the Indonesian language speech recognition and detection of infringements of the broadcast program. With the method of Mel Frequency cepstral Coefficients (MFCC) and Hidden Markov Model (HMM) speech recognition application that used the 1050 sample data produces about 70% accuracy rate. This research would continue to implement the plan that had been created using speech recognition applications that had been built.