PENGELOMPOKAN PROFIL PEKERJAAN ALUMNI MENGGUNAKAN ALGORITMA K-MEANS

Tracer Study adalah salah satu pelacakan jejak kepada alumni yang umum dilakukan program studi di perguruan tinggi sebagai upaya dalam memperbaiki kualitas penyelenggaraan pendidikan. Terdapat beberapa kuesioner yang ditujukan kepada alumni, namun tanggapan sebagai umpan balik yang diberikan alumni masih terbilang cukup rendah. Penelitian ini bertujuan mengoptimalkan program tracer study yang dilakukan dengan cara mengelompokkan profil pekerjaan alumni agar dapat disesuaikan dengan kebutuhan penyebaran kuesioner. Metode yang digunakan dalam pengelompokkan profil pekerjaan alumni adalah clustering yang dalam penelitian ini menggunakan algoritma K-Means. Hasil dari penelitian ini adalah cluster-cluster profil pekerjaan alumni yang setiap anggota dalam cluster yang sama memiliki kriteria pekerjaan yang mirip.------------- Tracer Study is one of methods used in university to track their alumnus’ traces as an approach to improve the quality of their education management. There exist a few questionnaires aimed at the alumnus, but responses the alumnus given are still quite lacking. This research focused on optimizing tracer study program by separating alumnus’ work profiles into parts so it could suit distribution of the questionnaire. Method used to group the alumnus work profiles is clustering with the help of K Means algorithm. The aforementioned research resulting in clusters of alumnus’ work profiles in which each member of the same cluster has similar work characteristics

Design Simulation and Perfomance Analysis of Efficient Low Energy Adaptive Clustering Hierarchy Protocol in Wireless Sensor Network

Network life has been defined by the use of nodes to store, process and distribute information, which have restricted energy usage. In other words, all aspects of the node must be designed for extremely energy-efficient applications from sensor module to hardware and protocol. Diminished energy consumption by a factor of two will increase the system's overall utility by doubling the device life. In addition, the protocols should be robust against node failures, tolerant of defects and scalable to optimise device life to minimise energy dissipation. LEACH is the first protocol for network networks that utilises hierarchical routing to enhance network life. All nodes in a network are grouped into local cluster groups, with the cluster head being one node. Although all non-cluster head nodes transmit their data to the cluster head, the cluster head node collects data from all the cluster members, conducts data signal processing (e.g. , data aggregation) functions and transmits data to the remote baseline. As a cluster-head node, it thus takes much more resources than a non-cluster-head node. So all nodes that belong to the cluster lose communication power if a cluster-head node dies. In this research, we introduced clustering as a means of overcoming this energy efficiency problem. Detailed description on the process of LEACH protocols is available. The information on the simulation and the findings have also been discussed

Design and Analysis of Enhanced LEACH based Energy Routing Protocol for Wireless Sensor Network

In recent times, wireless sensor networks, or WSNs, have attracted a lot of attention because of their extensive use in a variety of fields, such as industrial automation, healthcare, and environmental monitoring. Energy efficiency is a major problem for WSNs since sensor nodes frequently run on batteries and have little energy available. Effective routing techniques are essential for extending the life of the network and guaranteeing dependable data transfer. This work focuses on the performance analysis and numerical modeling of a new routing strategy that combines machine learning approaches to improve WSN energy efficiency. The suggested routing algorithm optimizes energy consumption and overall network performance by adjusting its recommendations in real-time in response to environmental and network variables. We assess this machine learning-based routing protocol's performance using large-scale numerical simulations, contrasting it with conventional routing protocols and emphasizing its possible advantages in terms of energy efficiency and dependable data delivery. We investigate a variety of situations in our simulations, taking into account different network topologies, traffic patterns, and environmental factors. We evaluate many measures, including energy consumption, network lifetime, packet delivery ratio, and end-to-end delay, in order to offer a thorough evaluation of the efficacy of the machine learning-based routing protocol. The outcomes show how energy-efficient the protocol is, guaranteeing long-lasting sensor nodes and reliable data transfer while adjusting to changing network conditions.The results of this study highlight how machine learning approaches can completely change how routing protocols are designed and optimized in wireless sensor networks with limited energy. This research helps to construct sustainable and dependable WSNs by enhancing energy efficiency and network performance, which makes it easier to deploy sensor networks in crucial applications

A modified Manhattan distance with application for localization algorithms in ad-hoc WSNs

International audienceBuilding an efficient node localization system in wireless sensor networks is facing several challenges. For example, calculating the square root consumes computational resources and utilizing flooding techniques to broadcast nodes location wastes bandwidth and energy. Reducing computational complexity and communication overhead is essential in order to reduce power consumption, extend the life time of the battery operated nodes, and improve the performance of the limited computational resources of these sensor nodes. To that end, we propose a novel modified Manhattan distance norm and employ it in a previous localization system so-called TALS (Trigonometric Ad-hoc Localization System), such that we optimize TALS. Furthermore, an analysis and an extensive simulation for the optimized TALS (OTALS) is presented showing its cost, accuracy, and efficiency, thus deducing the impact of its parameters on performance. Our novel similarity measure formula can be used in many other domains such as clustering and classification. However, we present its efficiency only for a particular problem in this work. Thus, the major contribution of this work can be summarized as follows: (1) Proposing and employing a novel modified Manhattan distance norm in the TALS localization process. (2) Analyzing and simulating of OTALS showing its computational cost and accuracy and comparing them with other related work. (3) Studying the impacts of different parameters like anchor density, node density, noisy measurements, transmission range, and non-convex network areas. (4) Extending our previous joint work, TALS, to consider base anchors to be located in positions other than the origin and analyzing this work to illustrate the possibility of selecting a wrong quadrant at the first iteration and how this problem is overcome. Through mathematical analysis and intensive simulation, OTALS proved to be iterative, distributed, and computationally simple. It presented superior performance compared to other localization techniques