Human face recognition is one of the most challenging topics in the areas of image processing, computer vision, and pattern recognition. Before recognizing the human face, it is necessary to detect a face then extract the face features. Many methods have been created and developed in order to perform face detection and two of the most popular methods are Viola-Jones Haar Cascade Classifier (V-J) and Histogram of Oriented Gradients (HOG). This paper proposed a comparison between VJ and HOG for detecting the face. V-J method calculate Integral Image through Haar-like feature with AdaBoost process to make a robust cascade classifier, HOG compute the classifier for each image in and scale of the image, applied the sliding windows, extracted HOG descriptor at each window and applied the classifier, if the classifier detected an object with enough probability that resembles a face, the classifier recording the bounding box of the window and applied non-maximum suppression to make the accuracy increased. The experimental results show that the system successfully detected face based on the determined algorithm. That is mean the application using computer vision can detect face and compare the results.

Nowadays internet has been well known as an information source with many form including online news articles. People mostly search news in the internet. Online news articles are spreading on websites. Those articles' validity may both authentic and fake. Fake news article usually called as hoax news. Hoax news may lead the readers to feel burdened, provoked or even be in a loss. This research proposes to build an automatic hoax news detection. The research describe about hoax news article detection in Indonesian language. This research using own dataset on 250 pages of hoax and valid news articles. Three reviewers conduct manual classification for this purpose. Final tagging are obtained by voting of those three reviewers. Based on three times randomly on training and testing datasets using php-ml component library's obtained average highest on 70% training set and 30% testing set with accuracy is 78,6%, hoax precision is 67,1% valid precision is 91.6%, hoax recall is 89,4% and valid recall is 71,4. This dataset is openly so future research can replicate of dataset and comparison of the result and baseline testing.

Identifying the chicken meat freshness level is necessary since it involves the quality of the meat consumed. This research aims at identifying the freshness level of chicken meat based on the histogram color feature. The histogram color feature used is Red, Green, and Blue color (RGB) channel. RGB histogram value acquired from image sample dataset of chicken breast meat. First Order Statistical Method is used to reduce the color feature dimension such as Mean, Max, and Sum. The value is then classified using Naïve Bayes Classifier, Support Vector Machines (SVM) Classifier and C4.5 Decision Tree. The Classification method compared for analyzing their accuracy. The freshness of chicken meat level defined in three class, fresh, medium, and old. The chicken meat labeled as fresh from 0 to 4 hours after slaughtered, 4-6 hours labeled as medium, and more than 6 hours labeled as old. The result of color histogram feature by Naïve Bayes method shows 33.33%, Support Vector Machine (SVM) shows 58.33%, whereas C4.5 decision tree method shows 50% classification accuracy. The classification process of the chicken meat's freshness level based on the color histogram feature suggests using Support Vector Machine (SVM) method which indicates the highest classification accuracy of the experiments result.

Electricity as the primary source of power for gadgets, computers, machinery, and other electronic goods is used at the home-to-industrial level. Most of the world's Electric Grids operate on fossil fuels which are getting thinner and expected to run out soon, the best solution that is possible to do is Smart Grid. The Smart Grid realized extensive use of information sensing, transmission and processing. Today, IoT technology become a essential role in construction of power grid. This paper reviews the Integration of IoT in Smart Grids. IoT-integrated Smart Grid systems are already deployed, but the full capabilities of instant knowledge and sustainable large-scale data processing have not been exploited optimally. Two of the architectures of IoT integrated Smart Grids is four-layered architecture and Web Enabled Smart Grid. To process and manage the Big Data in Smart Grid, Supervisory Control and Data Acquisition (SCADA) system can be used and furthermore enhanced with cloud computing. The smart grid is the future of electric grid that solves the problems of information flows, rising energy consumption, energy waste, reliability and security in the conventional power grid.

Aplikasi Decision Support Systems (DSS) atau Sistem Penunjang Keputusan (SPK) penentuan Uang Kuliah Tunggal (UKT) Mahasiswa Politeknik Negeri Malang adalah aplikasi yang digunakan untuk menentukan kelompok biaya kuliah tunggal yang ditanggung oleh masing mahasiswa Politeknik Negeri Malang aplikasi ini dikembangkan menggunakan bahasa pemrograman PHP, database Mysql dan mengimplementasikan Metode Multi Objective Optimization on the Basis of Ratio Analysis (MOORA) dengan menggunakan metode tersebut dapat memberikan alternatif terbaik dalam penentuan uang kuliah tunggal berdasarkan kemampuan ekonomi mahasiswa.

Sistem Monitoring Budidaya Ikan Lele Berbasis Internet Of Things Menggunakan Raspberry Pi