One of the main causes of traffic congestion, especially at intersections, is because traffic lights have not been able to show the right time according to the existing traffic conditions. Time settings based on peak/off-peak traffic lights are not enough to handle unexpected situations. The fuzzy mamdani method makes decisions with several stages, the criteria used are the number of vehicles, the length of the queue and the width of the road to be able to optimize the time settings based on the real-time conditions required so that unwanted green signals (when there is no queue) can be avoided. The purpose of this research is to create a simulator to optimize traffic time management, so that the timers on each track have the intelligence to predict the right time, so that congestion at the intersection can be reduced by adding up to 15 seconds of green light from the previous time in the path of many vehicles.

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<p>This paper examines the integration of smart home and solar panel system that is controlled and monitored using IoT (internet ofthings). To enable the smart home system to monitor the activity within the house and act according to the current conditions, it is equipped with several sensors, actuators and smart appliances. All of these devices have to be connected to a communication network, so they can communicate and provide services forthe smart home’s in habitants. The smart home system was first introduced to provide comfort and convenience, but later it should also address many other things, e.g. the importance of the efficient use of energy or electricity and hybrid use of energy sources. A solar panel is added to the smart home prototype and its addition is studied. Adaptive linear neural network is implemented in the prototype as an algorithm for predicting decisions based on the current conditions. The construction of the proposed integrated systemis carried out through several procedures, i.e. the implementation of the adaptive linear neural network (ADALINE) as the neural network method, the design of the prototype and the testing process. This prototype integrates functionalities of several household appliances into one application controlled by an Android-based framework.</p>

This paper proposes a prototype of an improved smart home controller that implements a neural network-based algorithm for enabling the controller to make decisions and act based on the current condition. Unlike previous approaches, this design also utilizes the use of IoT (internet of thing) technology and neural network based-algorithm for developing the controller. Since a smart home is equipped with various sensors, actuators, smart appliances, and mobile terminals, all of these devices need to be connected to the Internet to be able to communicate and provide services for its occupants. The construction of the proposed controller is carried out through several procedures, i.e. the implementation of the ADALINE (adaptive liniear) as the neural network method, the design of the smart home controller prototype, and the validation process using mean average percentage error (MAPE) calculation. This prototype integrates functionalities of several household appliances into one application controlled by a smartphone. ADALINE is applied as an algorithm to predict output when the controller is in automatic mode. Although the obtained accuracy value is still not satisfactory, the value is bound to change when testing on more data. The work published in this paper may encourage the implementation of smart technology in more households in Indonesia.

VANET yang merupakan bagian dari MANET  adalah teknologi yang memungkinkan komunikasi nirkabel antar berbagai kendaraan yang bergerak atau antara kendaraan dengan unit komunikasi yang terdapat di sepanjang  jalan.  VANET dikembangkan untuk mendukung ITS  dalam meningkatkan keselamatandi  jalan raya dan efisiensi lalu lntas.  Jaringan ad-hoc ini juga dirancang untuk memungkinkan pertukaran pesan-pesan peringatan antar kendaraan untuk meningkatkan keselamatan penumpang dan informasi-informasi lain terkait dengan aplkasi dan layanan VANET. Untuk mewujudkan hal tersebut, ada beberapa hal yang perlu dipertimbangkan supaya VANET dapat beroperasi denganbaik. Hal-hal tersebut antara lain  masalah keterbatasan bandwidth, keamanan, skalabilitas, protokol kendali akses media (MAC) dan mekanisme routing. Ada beberapa karakteristik khusus dari  VANET yang perlu diperhatikan oleh protokol routing, antara lain :  mobilitas node yang tinggi, jumlah dan kepadatana node yang tidak tetap, dan topologi jaringan yang terus berubah.  Diperlukan protokol routing tertentu untuk  mewujudkan komunikasi yang handal, kontinyu dan tanpa batas antar node pada VANET.  Secara garis besar, protokol  routing pada VANET dikelompokkan menjadi dua: kelompok protokol yang berdasarkan posisi kendaraan dan kelompok yang berdasar pada topologi jaringan. Tulisan ini akan membahas mengenai protokol routing DSR, AODV, TORA dan menjelaskan bagaimana ketiga protokol reaktif ini menjalankan mekanisme routing pada VANET. Kelebihan dan kekurangan dari ketiga protokol tersebut juga akan dianalisis dengan didukung oleh beberapa hasil simulasi dari penelitian-penelitian yang telah dilakukan sebelumnya.

The development of Information Technology today, which continues to grow, can help overcome various problems because matters relating to the advancement of Information Technology have spread to almost all levels of Indonesian society. Along with the development of Information Technology, it is also marked by Artificial Intelligence which can simulate human intelligence and help handle tasks in the real world. By utilizing Information Technology, one of them can be used in terms of the classification of fruit freshness. Where this classification will be very useful and help farmers and fruit consumers. This study describes the use of the Convolutional Neural Network to classify the freshness of the following fruits: apples, oranges, and bananas. And also using six classes, namely fresh apples, fresh oranges, fresh bananas, unfresh apples, unfresh oranges, and unfresh bananas. The first thing to do is Convolutional Neuronal Network training using an image dataset as input using data sources from Kaggle.com, published by "Student at Stony Brook University, New York, United States". To determine the performance of the various models produced, the following Confusion Matrix is used: accuracy, precision, and recall. The best average obtained is 93%.

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The purpose of this research is to produce an optimal water distribution system for irrigation of rainfed land. The problem with conventional irrigation systems is that the water distribution process cannot be controlled and monitored automatically and in real time. The impact on water distribution becomes ineffective. The implementation of Ant Colony Optimization (ACO) is used in research as a method to determine the location or node based on the pheromone pattern of the soil dryness level at the sprinkler nodes to be distributed by the water flow, taking into account the criteria level on the soil as a trend of probability values ​​and determining the nodes according to the needs in the flow water. The results obtained from this study indicate that the data displayed is the level of dryness of each node, the volume of water in the reservoir, and the flow of water flowing. The ACO test shows the sequence of nodes that will be passed after the optimization process of water distribution in a rainfed irrigation system using the ACO method gets an error value calculated by the MAPE method of 43% so that it gets an accuracy value of 57%.