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Roads are land transportation infrastructure that covers all parts of the road. Roads with bad conditions will interfere with the achievement of activities to a destination. The situation also includes damage to the road surface in the form of holes. To overcome this, in this Final Project a hole detector was detected in the road using the Gray Level Co-occurrence Matrix (GLCM) and Neural Network (NN). The tool detects holes in the surface of the road using a camera by walking along the road being examined. The camera is used instead of the eye to detect road surface damage. The method used to detect holes is the GLCM. The GLCM method produces several features, namely entropy, contrast, energy, homogeneity, and correlation which will then be processed using a NN to produce a decision whether there is a hole or not. In addition to knowing where the location of the damage is equipped with GPS (Global Positioning System). The results of image feature extraction using the GLCM and road classification using NN can be used in the hole detection process. Testing is done using a car prototype that is monitored through the computer. The percentage of successful hole detection is 86.6% using 10 hidden. When a hole is detected the device manages to take a picture, then sends the hole coordinates to the server.

ABSTRAKSTiga perempat wilayah Negara Kesatuan Republik Indonesia (NKRI) merupakan wilayah perairan.NKRI adalah negara kepulauan dengan jumlah pulau terbanyak di dunia yaitu 17.504 pulau serta mempunyai panjang garis pantai terpanjang kedua di dunia setelah Kanada. Kebiasaan nelayan Indonesia memasuki wilayah perairan negara lain membuat nelayan Indonesia ditangkap oleh penegak hukum negara lain. Kebiasaan nelayan Indonesia memasuki wilayah perikanan Australia kerap menimbulkan pasang surut hubungan kedua negara. Oleh karena itu, dalam penelitian ini dibuat prototipe perangkat yang dapat memberikan informasi kepada nelayan ketika berlayar bahwa posisi kapal melanggar batas perairan negara lain atau tidak. Sebagian besar kapal-kapal nelayan tradisional tersebut tidak dilengkapi dengan alat navigasi yang memadai. Sehingga perlu perangkat yang dapat memberikan informasi dini kepada jika telah mendekati batas zona perairan negara lain. Prototipe dibuat dengan menggunakan Arduino Mega 2560 / Arduino Uno dan GPS Neo-6M. Modul GPS Neo 6M digunakan sebagai penentuan lokasi posisi kapal kapal, posisi kapal latitude (x) dan longitude (y). Kemudian titik-titik pada garis perbatasan garis, data latitude (x i ) dan longitude (y i ) diinputkan terlebih dahulu dalam mikrokontroller. Mikrokontroller menghitung jarak posisi kapal dengan titik-titik pada garis perbatasan. Dari pengujian dan pengambilan data yang telah dilakukan, diketahui bahwa rata-rata error dengan Arduino Mega adalah 3,19 % dan dengan menggunakan Arduino Uno nilai error (rata-rata) adalah 5,32 %.

The development of installed capacity in power plants in 2018 was 41.696 MW or increased from the previous year in 2017 amounted to 39.652 MW (PLN Statistics, 2018). This has an impact on the reduced availability of fuel due to overexploitation. The highest energy sold per customer group in 2018 was the household customer group that was 41.7% higher than the industrial sector customer group by 32.8% (PLN Statistics, 2018). At present the use of electronic equipment for household needs is increasingly diverse. Many equipment that is often used in daily life is electronic equipment that is inductive load. Inductive load causes the value of the power factor to fall so that the power usage (Watt) becomes less than optimal. To overcome the problem caused by the large number of inductive loads a reactive power compensator is needed which is to use a capacitor. In this final project, a system designed to be able to measure and correct power factors automatically uses the Neural Network method and can monitor power online based on IoT. The results of testing the power factor improvement system were 97.8% successful in the trained electric load and 94.8% in the untrained electrical load.

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