Pada bidang pertanian, Internet of Things (IoT) digunakan untuk mengendalikan sensor pertanian dan menghubungkannya dengan infrastruktur cloud dengan tujuan untuk menunjang pertanian presisi. Salah satu tantangan dalam penerapan IoT dalam pertanian presisi adalah keberagaman aplikasi dan protokol komunikasi di tengah keterbatasan sumber daya perangkat sensor yang digunakan. Ketergantungan library dan versi program yang saling kontradiktif mendorong diperlukannya terobosan baru dalam implementasi aplikasi sensor atau gateway pertanian presisi. Pada penelitian ini penulis mengimplementasikan virtualisasi berbasis kontainer untuk perangkat IoT pada pertanian presisi yang memudahkan pengimplementasian program pada satu perangkat tanpa mengurangi kinerja dari perangkat IoT. Program virtualisasi yang kami gunakan adalah Docker yang diimplementasikan ke dalam perangkat gateway IoT berupa Raspberry Pi. Hasil penelitian ini membuktikan bahwa penggunaan Docker, tidak menurunkan kinerja Raspberry Pi. Sehingga penggunaan Docker pada infrastruktur IoT pertanian presisi sangat mungkin untuk dilakukan karena memberikan keuntungan berupa kemudahan dalam implementasi, update, dan pengisolasian proses.

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The weather has a substantial impact on the ability to live organisms to carry out everyday activities, particularly outside activities. Weather data is helpful in various fields, including marine, aviation, and agriculture. The maritime domain is beneficial for establishing the optimal navigation time for a fisherman, the aviation domain helps reduce climate-related mishaps, and the agriculture sector uses weather information to develop harvest season models for agricultural products. Indonesia is a tropical nation with heavy precipitation. Utilized for various objectives, rainfall forecasting models seek the utmost precision, particularly in specialized areas such as flood control. This study is based on two techniques: the Radial Basis Function Neural Network (RBFNN) and Backpropagation Neural Network (BPNN) techniques using multiple training functions. The RBFNN approach yields less accurate results for predicting precipitation, but the multi-practice BPNN method yields more accurate results.

This research aims to provide the influence of non-debt variable tax shield and cost of financial distress affect the capital structure of the company's sub-sector metals and the like listed on the Indonesia Stock Exchange in 2013-2017. The method on this research is a quantitative approach with the type of correlation study. The data collection techniques in this study use secondary data with saturated sampling techniques. The population of this research is a metal sub-sector company and the like listed on the Indonesia Stock Exchange (IDX). The samples in this study were as many as 16 metal sub-sector companies and the like listed on the Indonesia Stock Exchange (IDX). The results showed that both the partial and simultaneous variables of the non-debt tax shield and cost of financial distress had no effect on the capital structure of the metal sub-sector companies and the like listed on the Indonesia Stock Exchange ( IDX). It shows that the T-Test in a non-debt tax shield variable is obtained by the T-calculate result of 1.401 and the value of Sig. T. Acquired by 0, 165 > 0.05, then Ho accepted and H1 rejected which means there is no positive influence on the capital structure and in variable cost of financial distress obtained with the result of T-Calculate of 1.756 and the value of Sig. T. Acquired by 0, 083 > 0.05, then Ho is accepted and H1 is rejected which means there is no positive influence on the capital structure. Then simultaneously F test result in can with a fcalculate value of 2.295 with a level of Sig. 0, 108, because of the value of Sig. F > 0.05, then Ho accepted and H1 rejected. This means that there is no variable influence of non debt tax shield (X1) and cost of financial distress (X2) to the capital structure (Y).

Research on simulating the design of a solar power plant in the village of Wantilan Antosari aims to promote the use of new renewable energy sources. The method of PLTS is completed by paying attention to and accounting for the tilt angle in the Helioscope Software, designing the positioning of solar panel modules, designing inverters, configuring circuits, accounting for the number of batteries used, choosing Battery Control Units, and calculating investment. According to the simulation run by the helioscope program, nine solar modules at an angle of 58.7 can power a battery with a 500Ah capacity. The simulation also establishes an inverter with a 24.06kW inverter that generates 4.0 kWp power.