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.

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Face recognition plays an important role in the identity recognition system, the color and geometry feature has been claimed able to be used as parameter for face recognition. This study aims to analize the performance of geometric features, color features, and both of them on the human face using Gaussian Naïve Bayes (GNB) and the other Machine Learning method. This study using various geometric features: the distance between the eyes, nose, mouth by using Euclidean distance, and classified using GNB, K-Nearest Neighbour (KNN), and Support Vector Machine (SVM). The result compared with color feature: normalized RGB values, mean of normalized RGB, and RGB Variant as color features. The feature values obtained are assembled and processed using GNB and the other ML method to classified and recognized the faces. The dataset obtained from Aberdeen faces the dataset, which has 687 color faces from Ian Craw at Aberdeen. Between 1 and 18 images of 90 individuals. Some variations in lighting, varied viewpoints, and the resolution have varied between 336x480 to 624x544. The experimental results show that the system successfully recognized the face based on the determined algorithm and based on three models, SVM reached nearly 74.83%, GNB reached nearly 74.67%, and KNN with K = 5 reached nearly 72.17%.

A pandemic situation such as Covid-19 which is still ongoing has given significant impacts to various sectors such as education, economy, tourism, and social which is in turn impacting the community at a national scale. On the other hand, the pandemic situation has also brought a positive impact on companies engaged in finance that utilizes information technology, namely digital wallets, a company that runs a market place in the digital world. In an effort to anticipate a dynamic market place, the company needs to predict the movement of transactions from time to time by building a model and performain the simulation to such model. Based on this problem, this paper presents simulations on the prediction models based on methods namely, naïve, Single Moving Average (SMA), Exponential Moving Average (EMA), combined SMA-naive methods, combined EMA-naive methods, as well as did the comparison of the best performance of every model by using Mean Absolute Percentage Error (MAPE) measurement. From the results of comparison, it is concluded that exponential moving average method delivers the best performance as prediction tool with MAPE of 23,4%.

Educators have problems conducting online learning, such as monitoring student attendance while presenting the material. This paper aims to predict student names who attend zoom video conferences with various lighting conditions and face angles by comparing two detection and two recognition methods. This paper proposes an intelligent system based on the use of a bot that will analyse a combination of face detection and recognition method for attendance systems using video conferencing applications to carry out online learning. The proposed system will use the best combination of two methods to recapitulate student attendance. The face detection system uses Haar Cascade and MTCNN, and the face recognition system uses ResNet and FaceNet. The tests were conducted on video zoom footage taken during online lectures. The results show that MTCNN and FaceNet get the highest accuracy, 93.23%.

Object recognition has been a challenge for an intelligent system. There have been various approaches to develop such a system by utilizing machine learning especially which are based on neuron that is, neural network and deep learning. Common problems when using those approaches are the first one is dataset availability and the second one is the number of data. Lack of data causes neural-based approaches cannot be well operated, while a small number of data causes low accuracy results on the system. From another point of view, a considered-new technology from Cognitive Artificial Intelligence (CAI) perspective called as Knowledge Growing System (KGS) which may cope with such problems. With the capability to build its own knowledge from nothing, KGS is able to carry out recognition while developing its knowledge regarding the phenomenon it is trying to recognize. In this research, we showed KGS capability to perform object recognition as it is developing knowledge when interacting with such object directly. We did a benchmark on face recognition use-case with some common machine learning methods to show their performance on a small number of data, and KGS showed good results. With 100 feature-set from 5 persons’ face images, KGS achieves Degree of Certainty (DoC) as much as 80% which is the system’s prediction accuracy that enables it recognizing the person based on that-moment data. Even though it is still lower compared to machine learning methods, but KGS shows advantages that it does not require high computational cost because it requires no training and no model development. In this research, we also showed that KGS enables the fast-deployment light-operated object recognition system.

<p class="Abstract"><em>Abstract</em>—This experiment aims to analyze the forecasting of the Indonesian Democracy Index (IDI) in 2019, which uses each province data by the Moving Average method. The parameters used in this experiment refer to data obtained from the Central Statistics Agency (BPS) in 2009-2018. The level of achievement of IDI is measured based on the development and implementation of 3 aspects, 11 variables, and 28 indicators. Experiment purposes to find the average percentage of absolute error MAPE (Mean Absolute Percentage Error) for each province and looks for correlations between the three main aspects of forming IDI namely civil liberties, political rights, and democratic institutions. IDI Indonesia's forecasting results in 2019 the IDI has an average value of 68.28 with a MAPE of 4.78%. The results of the correlation between the three aspects of forming the IDI using the Pearson correlation coefficient resulted in the aspect of civil liberties having no correlation with aspects of political rights or aspects of democratic institutions with Pearson values of -0.05 and -0.19. Whereas aspects of political rights correlate with democratic institutions with Pearson's value of 0.48.<em></em></p>Keywords—Forecasting, Indonesian Democracy Index, Moving Average. Pearson Correlation Coefficient

Pelaksanaan sistem presensi pada Jurusan Teknologi Informasi di Politeknik Negeri Malang perlu dikembangkan agar lebih efektif dalam implementasinya. Efektifitas yang dimaksud adalah mahasiswa tidak perlu lagi untuk mengambil lembar presensi saat akan melakukan presensi perkuliahan dan mengembalikan lembar tersebut setelah selesai melakukan presensi. Penelitian ini bertujuan untuk membangun suatu sistem presensi untuk menjawab permasalahan yang telah dipaparkan. Dalam pembangunan sistem presensi, digunakan 2 algoritma yaitu DS RSA dan Template Matching. Algoritma DS RSA yang disisipkan pada pembangkitan QR Code digunakan demi keamanan dan reliabilitas pada pengacakan kunci yang telah diinisialisasi oleh algoritma DS RSA. Pengacakan itu bermaksud untuk memberikan batasan pada mahasiswa agar tidak bisa melakukan presensi jika kelasnya tidak sesuai. Sedangkan untuk Template Matching digunakan untuk pengecekan wajah mahasiswa. Hal ini dilakukan untuk memastikan bahwa mahasiswa tersebut hadir pada kelas dan dosen dapat mengantisipasi adanya kecurangan mahasiswa dalam melakukan absensi. Penerapan sistem presensi ini membutuhkan setiap mahasiswa untuk memfoto wajah mereka lalu membaca QR Code yang telah dibangkitkan dengan smartphone sesuai dengan kelas dan ditampilkan pada layar proyektor kelas. Penelitian ini berhasil membangun sistem presensi berbasis QR Code dengan mengimplementasikan algoritma DS RSA dan template matching. Sistem pembangkitan QR Code ini memiliki reliabilitas sangat baik dan akurasi tertinggi dari algoritma template matching adalah 93.33% dengan jumlah 80 template.