Detection of the Use of Mask to Prevent the Spread of COVID-19 Using SVM, Haar Cascade Classifier, and Robot Arm

In the effort to hold up the case spread of COVID-19’s growth rate by implementing health protocols such as the use of masks, supervision is needed especially for the people who have not or still have problems to wearing masks. In this research, the system utilizes the robotic power to identify visitors whether they are wearing masks or not, and automatically distribute masks if the user is detected as not wearing a mask. The user face detection process uses the Haar Cascade Classifier algorithm and SVM (Support Vector Machine) to classify users who wear masks or not. For the user who is detected as not wearing masks, myCobot-Pi with the support of suction pump will distribute masks to users. The use of myCobot-Pi as a raspberry pi based robotic arm allows the application of the system on devices that are minimal in terms of specifications and size. Through trials by taking 41 examples of detection cases, 29 cases were found that managed to detect the correct use of masks. In addition, in this study we use PP sheet plastic protector to replace the packaging of the mask because it can be carried by the suction pump properly

PENINGKATAN SDM PROMOSI DINAS PARIWISATA SAMOSIR MELALUI PELATIHAN WEBSITE MENGGUNAKAN CMS WORDPRESS

Abstrak: Pertambahan kunjungan wisatawan pariwisata samosir adalah tolak ukur usaha tim promosi Dinas Pariwisata Samosir yaitu melalui media website visitsamosir. Permasalahan (1) mitra kapasitas website semakin hari semakin besar sehingga tidak lagi dapat menampung data yang nantinya dimasukan kedalam website; dan (2) website visitsamosir.com mengalami serangan malware sehingga ketika pengunjung mengakses website mendapatkan pesan-pesan dari malware. Metode yang dilakukan tim PKM (1) melakukan upgrade server visitsamosir dengan penerapan server yang lebih besar dalam penyimpanan data; (2) pembersihan website dari malware dan redesign ulang tampilan website; dan (3) melakukan pelatihan dalam management dan keamanan website visitsamosir.com. Tujuan PKM adalah peningkatan keterampilan sdm devisi promosi dinas pariwisata samosir dalam memanajemen konten website dengan memperhatikan aspek keamamannya, publikasi website dan publikasi kegiatan di media sosial, media elektronik dan jurnal. Hasil PKM staf SDM Promosi dinas pariwisata dapat mempraktikkan pengelolaan website. Proses transfer knowledge tim pkm dapat berjalan baik, peningkatan keterampilan 90% dan adanya keberlanjutan dari kegiatan PKM dengan diskusi melalui group whatsapp jika terdapat kendala dalam mengelola website dinas Pariwisata.Abstract: The increase in tourist visits to Samosir tourism is a benchmark for the efforts of the promotion team of the Samosir Tourism Office, namely through the visitsamosir website media. Problems (1) the partner's website capacity is getting bigger day by day so that it can no longer accommodate data that will later be entered into the website; dan (2) the visitsamosir.com website experiences malware attacks so that when visitors access the website they get messages from malware. The method used by the PKM team (1) is to upgrade the visitsamosir server by implementing a larger server in data storage; (2) cleaning the website from malware and redesigning the website appearance; dan (3) conducting training in the management and security of the visitsamosir.com website. The purpose of PKM is to improve the skills of the promotion division of the Samosir tourism department in managing website content by paying attention to security aspects, website publications and publication of activities on social media, electronic media and journals. The results of PKM HR staff Promotion of the tourism office can practice website management. The PKM team's knowledge transfer process can run well, 90 percent skill improvement and the continuity of PKM activities with discussions via WhatsApp groups if there are obstacles in managing the Tourism Office website

Identifikasi Penyakit Diabetic Retinopathy menggunakan Learning Vector Quantization (LVQ)

Diabetic retinopathy (retinopati diabetik) merupakan sejenis penyakit mata yang terjadi pada pengidap diabetes. Untuk mendeteksi jenis penyakit ini, dokter mata biasanya akan melakukan pemeriksaan dengan cara memeriksa mata dengan pupil lebar dan komprehensif. Adapun hambatan dalam mendeteksi retinopati diabetik adalah alat pemeriksaan yang belum masif dan belum memadai serta masih memakan waktu dalam mengidentifikasi tahap demi tahap pada retina manual. Berdasarkan masalah tersebut dibutuhkanlah suatu sistem untuk membantu dokter dalam mengidentifikasi retina yaitu dengan menerapkan pattern recognition menggunakan algoritma Learning Vector Quantization (LVQ). Sistem yang dijalankan dengan memasukkan citra tetina kemudian akan melaui proses preprocessing citra dan ekstraksi fitur statistik untuk mendapatkan hasil yang sesuai untuk dilakukan identifikasi menggunakan LVQ. Data retina yang digunakan terbagi menjadi 3 yaitu data training, data validation dan data testing. Pada data validation diuji dan mendapatkan hyperparameter untuk membentuk model jaringan terbaik yaitu pada epoch 50 dan learning rate 0,001. Kemudian dilakukan pelatihan hingga menghasilkan bobot akhir dengan algoritma pelatihan LVQ. Bobot akhir tersebut akan digunakan pada proses pengujian dengan data uji dan menghasilkan accuracy 82% sensitivity 80% dan precision 83,33

IMPROVED SUPPORT VECTOR MACHINE PERFORMANCE USING PARTICLE SWARM OPTIMIZATION IN CREDIT RISK CLASSIFICATION

In Classification using Support Vector Machine (SVM), each kernel has parameters that affect the classification accuracy results. This study examines the improvement of SVM performance by selecting parameters using Particle Swarm Optimization (PSO) on credit risk classification, the results of which are compared with SVM with random parameter selection. The classification performance is evaluated by applying the SVM classification to the Credit German benchmark credit data set and the private credit data set which is a credit data set issued from a local bank in North Sumatra. Although it requires a longer execution time to achieve optimal accuracy values, the SVM+PSO combination is quite effective and more systematic than trial and error techniques in finding SVM parameter values, so as to produce better accuracy. In general, the test results show that the RBF kernel is able to produce higher accuracy and f1-scores than linear and polynomial kernels. SVM classification with optimization using PSO can produce better accuracy than classification using SVM without optimization, namely the determination of parameters randomly. Credit data classification accuracy increased to 92.31%

Combination of TOPSIS Method with Attribute Weighting of Information Gain in Decision-Making

In this research, a combination of the Technique for Order Preference by Similarity lto Ideal Solution (TOPSIS) lalgorithm was carried out with the attribute weighting of the Information Gain method to obtain better decision support results. The data processed in this study is the Indian Liver Patient Dataset (ILPD) dataset obtained lfrom UCI Machine Learning Repository which has 583 instances, 11 attributes and 1 class label. The class label is a text type that consists of two values, namely a liver patient and a non-liver patient. The experimental results show that TOPSIS’ running time and information gain combination algorithm is 1.13 seconds. The result of the accuracy value obtained with a final threshold value greater than 0.5 is 91.25%.In this research, a combination of lthe lTechnique lfor lOrder lPreference lby Similarity lto lIdeal lSolution (TOPSIS) lalgorithm was carried out with the attribute weighting of the Information Gain method to obtain better decision support results. The data processed in this study is the Indian lLiver lPatient lDataset (ILPD) dataset obtained lfrom UCI lMachine Learning lRepository which has 583 instances, 11 attributes and 1 class label. The class label is a text type that consists of two values, namely a liver patient and a non-liver patient. The experimental results showthat TOPSIS’ running time and information gain combination algorithm is 1.13 seconds. The result of the accuracy value obtained with a final threshold value greater than 0.5 is 91.25

Genetic Algorithms Dynamic Population Size with Cloning in Solving Traveling Salesman Problem

Population size of classical genetic algorithm is determined constantly. Its size remains constant over the run. For more complex problems, larger population sizes need to be avoided from early convergence to produce local optimum. Objective of this research is to evaluate population resizing i.e. dynamic population sizing for Genetic Algorithm (GA) using cloning strategy. We compare performance of proposed method and traditional GA employed to Travelling Salesman Problem (TSP) of A280.tsp taken from TSPLIB. Result shown that GA with dynamic population size exceed computational time of traditional GA

Pengamanan Citra Menggunakan Kombinasi Algoritma Kriptografi Hill Cipher dan Teknik Transposisi Segitiga

Cara untuk mengamankan citra dapat dilakukan dengan kriptografi. Penelitian pada algoritma kriptografi sudah cukup banyak berkembang. Beberapa penelitian menyebutkan bahwa menggabungkan dua algoritma kriptografi dapat lebih meningkatkan keamanan dari citra dibandingkan dengan hanya satu algoritma. Penelitian ini melakukan enkripsi menggunakan kombinasi dua algoritma yaitu teknik transposisi segitiga dan Hill Cipher. Proses penggabungan dua algoritma dilakukan dengan terlebih dahulu mengenkripsi menggunakan teknik transposisi segitiga dan kemudian dilanjutkan dengan Hill Cipher. Begitu juga dengan proses dekripsi yang dilakukan secara kebalikannya. Pada penelitian ini menghasilkan performa yang lebih baik dibandingkan dengan menggunakan satu metode yang dapat dilihat pada nilai rata-rata MSE yang besar yaitu 10878,992 dan rata-rata PSNR yang kecil yaitu 0,781. Hal tersebut menandakan dengan menggabungkan dua algoritma dapat membuat pesan menjadi lebih aman. Metode dalam penelitian ini juga berhasil mengembalikan citra dangan baik tanpa adanya penambahan maupun pengurangan yang dapat dilihat dari hasil MSE dan PSNR yaitu 0 dan ∞

Analysis Of Variation In The Number Of MFCC Features In Contrast To LSTM In The Classification Of English Accent Sounds

Various studies have been carried out to classify English accents using traditional classifiers and modern classifiers. In general, research on voice classification and voice recognition that has been done previously uses the MFCC method as voice feature extraction. The stages in this study began with importing datasets, data preprocessing of datasets, then performing MFCC feature extraction, conducting model training, testing model accuracy and displaying a confusion matrix on model accuracy. After that, an analysis of the classification has been carried out. The overall results of the 10 tests on the test set show the highest accuracy value for feature 17 value of 64.96% in the test results obtained some important information, including; The test results on the MFCC coefficient values of twelve to twenty show overfitting. This is shown in the model training process which repeatedly produces high accuracy but produces low accuracy in the classification testing process. The feature assignment on MFCC shows that the higher the feature value assignment on MFCC causes a very large sound feature dimension. With the large number of features obtained, the MFCC method has a weakness in determining the number of features

Sistem Pendeteksian Manusia untuk Keamanan Ruangan menggunakan Viola – Jones

Aspek keamanan sangat dibutuhkan dalam berbagai kehidupan saat ini seperti keamanan rumah, gedung, atau ruangan yang memiliki nilai penting bagi pemilik. Keamanan dapat dikerjakan oleh tenaga manusia tetapi cara ini kurang efisien karena menghabiskan banyak resources seperti uang, waktu, tenaga dan juga sangat rentan terhadap kelalaian manusia (human error). Oleh karena itu diperlukan suatu pendetekatan untuk dapat melakukan keamanan tersebut.Salah satu pendekatan yang dapat dilakukan adalah dengan melakukan pendeteksian objek manusia melalui kamera yang terhubung dengan komputer.Dalam penelitian ini digunakan Viola-Jones untuk mendeteksi objek manusia dalam citra berdasarkan fitur. Citra yang diinput dari webcam dengan fungsi capture dalam library OpenCV diubah menjadi citra abu-abu setelah mengalami proses scaling, dilanjutkan ekualisasi histogram, perhitungan fitur dengan citra integral, dan pendeteksian objek dengan cascade of classifier. Pada penelitian ini ditunjukkan bahwa metode yang diajukan mampu melakukan pendeteksian objek dengan hasil akurasi mencapai 86,88% . Kata Kunci : viola-jones, pendeteksian manusia, keamanan ruangan, cascade of classifier, opencv

Implementation Of Face-To-Face Online Learning System Based On Audio Video, Presentation And Chat Using The Moodle E-Learning Platform

Currently, the implementation of teaching and learning at SMP Negeri 1 Binjai Kwala Begumit was done in the classroom alternately. However, with the current condition of pandemic covid-19, the learning process no longer carried out fully in schools. The school has not been using information technology in the form of e-learning applications in the teaching and learning process. The school has difficulty in recording the existing teaching and learning process: assignments, exams, assessments, and other activities. Therefore the use of e-learning applications is now very much needed. With existing school facilities, such as internet facilities and the ICT teachers, training in developing and implementing e-learning for teaching staff become the best alternative so that learning process can be done properly