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Indonesia has the third highest number of motorcycles, which means the traffic flow in Indonesia is heterogeneous. Traffic flow can specify its condition, whether it is a free flow or very heavy traffic. Traffic condition is the most important criterion used to find the best route from an origin to a destination. This paper collects the traffic condition for several road segments which are calculated based on the degree of saturation by using two methods, namely, (1) by counting the number of vehicles using object detection in the public closed-circuit television (CCTV) stream, and (2) by requesting the traffic information (vehicle’s speed) using TomTom. Both methods deliver the saturation degree and calculate the traffic condition for each road segment. Based on the experiments, the average error rate obtained by counting the number of vehicles on Pramuka–Cihapit and Trunojoyo was 0–2 cars, 2–3 motorcycles, and 0–1 for others. Meanwhile, the average error on Merdeka-Aceh Intersection reached 6 cars, 11 motorcycles, and 1 for other vehicles. The average speed calculation for the left side of the road is more accurate than the right side, and the average speed on the left side is less than 3.3 km/h. Meanwhile, on the right side, the differences between actual and calculated vehicle speeds are between 11.088 and 22.222 km/h. This high error rate is caused by (1) the low resolution of the public CCTV, (2) some obstacles interfering with the view of CCTV, (3) the misdetection of the type of vehicles, and by (4) the vehicles moving too fast. The collected dataset can be used in further studies to solve the congestion problem, especially in Indonesia.

Congestion often hinders human mobility. This problem occurs due to the constant increase in vehicles every year. Reliable predictions of traffic conditions would allow drivers to choose their routes to avoid traffic jams while providing the police with traffic management strategies. Therefore, this paper tests the ability of various machine learning methods to predict traffic conditions. The study assesses Neural Networks, Bayes Classifier, Decision Trees, SVM, Deep Neural Network, and Deep Learning. Of these methods, the Decision Tree, Deep Neural Network, and Bayes Classifier show the highest performance in predicting traffic conditions using static data testing. However, in dynamic testing to assess the growth of knowledge, the performance of the Knowledge-Growing Decision Tree tends to decrease as the training data grows. Its performance decreased 3.89 points (88.24% to 84.35%) in accuracy, and 7.55 points (76.25% to 68.70%) for each precision, recall, and F1 Score. Conversely, the Knowledge-Growing Deep Neural Network and Bayes Classifier had a better performance than Decision Tree. The performances of Knowledge-Growing Deep Neural Network increased slightly by 0.35 points (93.38% to 93.73%) for accuracy and 0.69 points (86.77% to 87.64%) in other measurements. Although its performance increased, the processing time takes very long, namely 139452.76 seconds and 318832.80 seconds for sub-scheme (a) and (b), respectively. Meanwhile, the Knowledge-Growing Bayes Classifier offers a greater performance increase of 2.3 points (80.52% to 82.82%) for the accuracy and 4.6 points (65.63% to 61.03%) for the other performance measurements. In addition, it also scored better for processing time, as predictions only take 3 seconds using sub-scheme (a), and 7 seconds when using sub-scheme (b). Therefore, the paper proposes the Knowledge-Growing Bayes Classifier to predict rapidly changing traffic conditions. This method outperform the others. These can be attributed to its ability to 1) adjust to ever-changing the traffic conditions; 2) predict the result as soon as the data are acquired; and 3) make decentralized predictions.

In certain situations, a moving or stationary object can be a barrier for a vehicle. People and vehicles crossing could potentially get hit by a vehicle. Objects around roads as sidewalks, road separator, power poles, and railroad gates are also a potential source of danger when the driver is inattentive in driving the vehicle. A device that can help the driver to brake automatically is known as Automatic Braking System (ABS). ABS is a part of the Advanced Driver Assistance Systems (ADAS), which is a device designed to assist the driver in driving the process. This device was developed to reduce human error that is a major cause of traffic accidents. This paper presents the design of ABS based on fuzzy logic which is simulated in hardware by using a remote control car. The inputs of fuzzy logic are the speed and distance of the object in front of the vehicle, while the output of fuzzy logic is the intensity of braking. The test results on the three variations of speed: slow-speed, medium-speed, and high-speed shows that the design of ABS can work according to design.

A computing strategy called Double Track"“Most Significant Operation First (DT-MSOF) is proposed. The goal of this strategy is to reduce computation time by reducing the number of operations that need to be executed, while maintaining a correct final result. Executions are conducted on a sequence of computing operations that have previously been sorted based on significance. Computation will only run until the result meets the needs of the user. In this study, the DT-MSOF strategy was used to modify the Analytic Hierarchy Process (AHP) algorithm into MD-AHP in order to reduce the number of operations that need to be done. The conventional AHP uses a run-to-completion approach, in which decisions can only be obtained after all of the operations have been completed. On the other hand, the calculations in MD-AHP are carried out iteratively only until the conditions are reached where a decision can be made. The simulation results show that MD-AHP can reduce the number of operations that need to be done to obtain the same results (decisions) as obtained by conventional AHP. It was also found that the more uneven the distribution of priority values, the more the number of operations could be reduced.

Standard headlights will shine with a fixed intensity even though the environmental light changes. High-intensity lights sometimes make other drivers glare and potentially cause accidents. Adaptive Light Control (ALC) is a car headlights control system that can automatically adjust the intensity of the lamp according to the intensity of the light environment. In this research, we developed ALC which was able to regulate the intensity of the lamp according to the intensity of the environmental light including the light of the car in front. Light settings are performed using a fuzzy control system by utilising environmental light intensity and the distance of the car to the object as input. The developed ALC system was tested on remote control cars equipped with sensors and microcontrollers. The intensity of headlight begins to fade on the distance of the car to the object of 92 cm for objects in front of, 108 cm for objects on the left, and 22 cm for objects on the right.

This paper presents a different scheme of meeting scheduling negotiation among a large number of personnel in a heterogeneous community. This scheme, named Host-to-Host Negotiation, attempts to produce a stable schedule under uncertain personnel preferences. By collecting information from hosts’ inter organizational meeting, this study intends to guarantee personnel availability. As a consequence, personnel’s and meeting’s profile in this scheme are stored in a centralized manner. This study considers personnel preferences by adapting the Clarke Tax Mechanism, which is categorized as a non manipulated mechanism design. Finally, this paper introduces negotiation strategies based on the conflict handling mode. A host-to-host scheme can give notification if any conflict exist and lead to negotiation process with acceptable disclosed information. Nevertheless, a complete negotiation process will be more elaborated in the future works.

Implementing a secure cryptosystem requires operations involving hundreds of bits. One of the most recommended algorithm is Elliptic Curve Cryptography (ECC). The complexity of elliptic curve algorithms and parameters with hundreds of bits requires specific design and implementation strategy. The design architecture must be customized according to security requirement, available resources and parameter choices. In this work we propose the use of composite field to implement finite field multiplication for ECC implementation. We use 299-bit keylength represented in GF((213)23) instead of in GF(2299). Composite field multiplier can be implemented using different multiplier for ground-field and for extension field. In this paper, LUT is used for multiplication in the ground-field and classic multiplieris used for the extension field multiplication. A generic architecture for the multiplier is presented. Implementation is done with VHDL with the target device Altera DE2. The work in this paper uses the simplest algorithm to confirm the idea that by dividing field into composite, use different multiplier for base and extension field would give better trade-off for time and area. This work will be the beginning of our more advanced further research that implements composite-field using Mastrovito Hybrid, KOA and LUT.

About 10% of collisions resulting in death is due to frontal collision. Head-on Collision Avoidance System (HCAS) is a device that can prevent a frontal collision by means of braking or evasive. Two ultrasonic sensors are used to monitor the vehicles in front and to the right. Two distances are observed by sensors, then become input for decision making system based on fuzzy logic. This decision-making system output is the vehicle forward, stop, or evasive movement. Simulation result using a remote control car proves that the decision making system designed with fuzzy logic can work with a success rate of over 90%.

Pemodelan adalah salah satu proses awal dalam pengembangan suatu aplikasi atau produk. Tahap ini dilakukan untuk meminimalkan kesalahan pada produk akhir. Salah satu metode pemodelan berorientasi objek yang banyak digunakan adalah pemodelan UML (Unified Modeling Language). Dalam UML suatu sistem dipandang sebagai kumpulan objek yang memiliki atribut dan method. SystemC adalah bahasa perancangan perangkat keras yang berbasis C++. SystemC merupakan sebuah library yang mendefinisikan tipe-tipe komponen perangkat keras. Dalam pemodelan bersama perangkat keras dan perangkat lunak, UML dan SystemC memiliki kemampuan yang sama. Pada paper ini dilakukan analisis proses transformasi dari pemodelan berorientasi objek dengan UML dan implementasi dengan menggunakan SystemC. Hasil penelitian menunjukan bahwa proses transformasi UML-SystemC dapat dilakukan karena keduanya memiliki nature yang sama sebagai lingkungan yang dapat merancang bersama hardware dan software. Perangkat yang digunakan untuk penelitian ini adalah Rational Rose dan SystemC. Modeling is one of the first process in the development of an application or product. This phase is done to minimize errors in the final product. One method in object-oriented modeling that is widely used is UML (Unified Modeling Language). In UML a system is seen as a collection of objects that have attributes and methods. SystemC is a hardware design language based on C++. SystemC is a library that defines the types of hardware components. In a joint modeling of hardware and software, UML and SystemC have similar capabilities. In this paper, researchers analyzed the transformation of object-oriented modeling with UML and the implementation by using SystemC. The results shows that the transformation process of UML-SystemC can be done because both have the same nature as the environment that can design both hardware and software. The device used for this study is the Rational Rose and SystemC.

Pada embedded system terdapat dua bagian penting yaitu komponen komputasi (register) dan komponen komunikasi. Komponen komunikasi menjadi perhatian penting pada mekanisme pemodelan level transaksi (Transaction Level Modeling, TLM). Kanal komunikasi adalah komponen untuk transaksi antar register. Fokus pembahasan TLM adalah perancangan kanal yang dapat mengakomodasi untuk peningkatan level transaksi. Kanal (channel) adalah implementasi bus untuk komunikasi antar komponen pada embedded system. Hal ini adalah kunci penting untuk mencapai impelementasi TLM untuk meningkatkan efisiensi pemodelan. Pada paper ini diusulkan beberapa definisi rancangan kanal sebagai implementasi TLM untuk perancangan embedded system. Hasilnya menunjukan bahwa rancangan kanal dapat berjalan sebagai bus untuk transaksi pada TLM. Paper ini menggunakan SystemC sebagai bahasa pemodelan. On embedded systems, there are two important parts: computational components (registers) and communication components. Communication component becomes an important attention on the mechanism of transaction level modeling (TLM). Communication channel is a component for transactions between registers. The focus of TLM is the design of the channel that could accommodate for the increased level of transactions. Channel is the implementation of the bus for communication between components in embedded systems. This is an important key to achieve the implementation of TLM to improve the efficiency of modeling. This paper proposed a definition of the channel design as the implementation of TLM for embedded systems design. The result shows that the design of the channel can run as a bus for transactions on the TLM. This paper uses SystemC as modeling language.