Sosialisasi Strategi Business Continuity Plan Memasuki Era Baru (New Normal)

Pandemi COVID_19 yang terjadi lebih dari satu tahun mempengaruhi aktivitas dan bisnis proses, jika terhenti ataupun tidak normal menyebabkan kerugian. Perlu strategi yang komprehensif, agar aktivitas dan bisnis proses yang dijalankan tidak menimbulkan dampak kerugian yang besar terhadap kesehatan karena terpapar COVID_19. Menjalankan kembali aktivitas dan bisnis proses secara normal pasca atau saat pandemi COVID_19 memerlukan perencanaan dan pengetahuan tentang mengelola resiko. Hal ini penting dilakukan agar resiko yang dihadapi dapat dikelola, sehingga aktivitas dan proses bisnis dapat berkelanjutan. Pengetahuan mengelola resiko juga selaras seperti yang dibutuhkan mitra yang tergabung di kanal youtube RTIK Bali. Untuk itu mitra dipandang perlu untuk mengetahui dan memahami konsep business continuity plan (BCP) dan disaster recovery plan (DRP) sebagai dasar menjalankan aktivitas dan bisnis proses yang aman dan berkelanjutan. Mitra perlu diberikan sharing pengetahuan dalam bentuk sosialisasi tentang BCP dan DRP. Pemahaman yang komprehensif tentang BCP dan DRP merupakan kunci memasuki era baru. Kolaborasi BCP dan DRP yang diperkuat adanya evaluasi adaptif merupakan hal utama berada di era baru yang berkesinambungan. Keberlangsungan aktivitas dan bisnis proses di era baru tidak terpisahkan adanya pemahaman mengalihdayakan sumber daya. Sosialisasi dilaksanakan secara langsung (live streaming), menggunakan kanal YouTube RTIK Bali. Kegiatan tersebut sebagai bentuk peran aktif Institut Teknologi dan Bisnis STIKOM Bali saat pandemi COVID_19. Sosialisasi dilaksanakan tanggal 23 Juni 2020, diikuti 619 peserta. Kegiatan sosialisasi terlaksana dengan baik dan interaktif, serta setelah mengikuti sosialisasi lebih memahami pentingnya DRP dan BCP serta perencanaan mengalidayakan sumber daya sebagai dasar untuk dapat beradaptasi di era baru

Object Classification Model Using Ensemble Learning with Gray-Level Co-Occurrence Matrix and Histogram Extraction

In the field of object classification, identification based on object variations is a challenge in itself. Variations include shape, size, color, and texture, these can cause problems in recognizing and distinguishing objects accurately. The purpose of this research is to develop a classification method so that objects can be accurately identified. The proposed classification model uses Voting and Combined Classifier, with Random Forest, K-NN, Decision Tree, SVM, and Naive Bayes classification methods. The test results show that the voting method and Combined Classifier obtain quite good results with each of them, ensemble voting with an accuracy value of 92.4%, 78.6% precision, 95.2% recall, and 86.1% F1-score. While the combined classifier with an accuracy value of 99.3%, a precision of 97.6%, a recall of 100%, and a 98.8% F1-score. Based on the test results, it can be concluded that the use of the Combined Classifier and voting methods is proven to increase the accuracy value. The contribution of this research increases the effectiveness of the Ensemble Learning method, especially the voting ensemble method and the Combined Classifier in increasing the accuracy of object classification in image processing

Object Classification Model Using Ensemble Learning with Gray-Level Co-Occurrence Matrix and Histogram Extraction

In the field of object classification, identification based on object variations is a challenge in itself. Variations include shape, size, color, and texture, these can cause problems in recognizing and distinguishing objects accurately. The purpose of this research is to develop a classification method so that objects can be accurately identified. The proposed classification model uses Voting and Combined Classifier, with Random Forest, K-NN, Decision Tree, SVM, and Naive Bayes classification methods. The test results show that the voting method and Combined Classifier obtain quite good results with each of them, ensemble voting with an accuracy value of 92.4%, 78.6% precision, 95.2% recall, and 86.1% F1-score. While the combined classifier with an accuracy value of 99.3%, a precision of 97.6%, a recall of 100%, and a 98.8% F1-score. Based on the test results, it can be concluded that the use of the Combined Classifier and voting methods is proven to increase the accuracy value. The contribution of this research increases the effectiveness of the Ensemble Learning method, especially the voting ensemble method and the Combined Classifier in increasing the accuracy of object classification in image processing

GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning

In the era of modern technology, object detection using the Gray Level Co-occurrence Matrix (GLCM) extraction method plays a crucial role in object recognition processes. It finds applications in real-time scenarios such as security surveillance and autonomous vehicle navigation, among others. Computational efficiency becomes a critical factor in achieving real-time object detection. High computing time delays can cause overall system failure. Hence, there is a need for a detection model with low complexity and satisfactory accuracy. This research aims to enhance computational efficiency by selecting appropriate features within the GLCM framework. Two classification models, namely K-Nearest Neighbours (K-NN) and Support Vector Machine (SVM), were employed, with the results indicating that K-NN outperforms SVM in terms of computational complexity. Specifically, K-NN, when utilizing a combination of Correlation, Energy, and Homogeneity features, achieves a 100% accuracy rate with low complexity. Moreover, when using a combination of Energy and Homogeneity features, K-NN attains an almost perfect accuracy level of 99.9889%, while maintaining low complexity. On the other hand, despite SVM achieving 100% accuracy in certain feature combinations, its high or very high complexity can pose challenges, particularly in real-time applications. Research contribution to improving computational efficiency in object detection using the GLCM method and KNN and SVM classification models to achieve high accuracy with low complexity. Therefore, based on the trade-off between accuracy and complexity, the K-NN model with a combination of Correlation, Energy, and Homogeneity features emerges as a more suitable choice for real-time applications that demand high accuracy and low complexity. This research provides valuable insights for optimizing object detection in various applications requiring both high accuracy and rapid responsivenes

PENGEMBANGAN PRINTER FORENSIK UNTUK IDENTIFIKASI DATA DOKUMEN CETAK

In the process of identification documents that are evidence of the particular case, by analyzing the characteristics contained in the document. The process of getting characterize the evidence carried out by extracting documents, previously digitally converted to forms through the process of scanning. Characteristics contained in the documents of evidence when compared with the characteristics of the comparator (unpredictable), and both expressed the same then the results of the analysis is the printer used to print the document came from the same printer. However, the identification process is also constrained when unexpected printer has indicated the type and the same type. For that we need for the development of forensic printer to identify the problems. In this study developed a printer forensic identification process, especially compared with the comparative evidence documents originating from the printer that has the same type and type. Tests carried out using five types of printer, one of which is a printer used to print the proof. The data used for comparison are each printer will print the data taken as much as 6 sheets with the contents of the document that has been determined, and the data is duplicated into digital through the scan with the quality of 600 dpi, 600 dpi aims to make use of the duplication process as much as possible approached document original. From the documents of each sheet comparators used traits as much as 9 different characters as well as the evidence used to document as much as 9 different characters with 6 sampeli. So the whole character is used as a benchmark of 270 characters, and evidence of as many as 54 data. The next process the document is processed using Canny edge detection method to highlight the characteristics before extracted using GLCM., This extraction results obtained characterize as many as 162 proof characteristics and thus also to the printer from one to five. Based on testing performed for the results obtained with FCM centroid values between evidence and comparative approach the same value as compared to other comparable printers. Additionally seen that kind of character "H" and "o" can not recognize. As for the character "k", "b" and "image" can be used for comparison with the good. EER value of 0.4 on test results obtained with average accuracy rate 80.0707%

Digital Image Object Detection with GLCM Multi-Degrees and Ensemble Learning

Object detection in digital images has been implemented in various fields. Object detection faces challenges, one of which is rotation problems, causing objects to become unknown. We need a method that can extract features that do not affect rotation and reliable ensemble-based classification. The proposal uses the GLCM-MD (Gray-Level Co-occurrence Matrix Multi-Degrees) extraction method with classification using K-Nearest Neighbours (K-NN) and Random Forest (RF) learning as well as Voting Ensemble (VE) from two single classifications. The main goal is to overcome the difficulty of detecting objects when the object experiences rotation which results in significant visualization variations. In this research, the GLCM method is used to produce features that are stable against rotation. Furthermore, classification methods such as K-Nearest Neighbours (KNN), Random Forest (RF), and KNN-RF fusion using the Voting ensemble method are evaluated to improve detection accuracy. The experimental results show that the use of multi-degrees and the use of ensemble voting at all degrees can increase the accuracy value, and the highest accuracy for extraction using multi-degrees is 95.95%. Based on test results which show that the use of features of various degrees and the ensemble voting method can increase accuracy for detecting objects experiencing rotation &nbsp

GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning

In the era of modern technology, object detection using the Gray Level Co-occurrence Matrix (GLCM) extraction method plays a crucial role in object recognition processes. It finds applications in real-time scenarios such as security surveillance and autonomous vehicle navigation, among others. Computational efficiency becomes a critical factor in achieving real-time object detection. Hence, there is a need for a detection model with low complexity and satisfactory accuracy. This research aims to enhance computational efficiency by selecting appropriate features within the GLCM framework. Two classification models, namely K-Nearest Neighbours (K-NN) and Support Vector Machine (SVM), were employed, with the results indicating that K-Nearest Neighbours (K-NN) outperforms SVM in terms of computational complexity. Specifically, K-NN, when utilizing a combination of Correlation, Energy, and Homogeneity features, achieves a 100% accuracy rate with low complexity. Moreover, when using a combination of Energy and Homogeneity features, K-NN attains an almost perfect accuracy level of 99.9889%, while maintaining low complexity. On the other hand, despite SVM achieving 100% accuracy in certain feature combinations, its high or very high complexity can pose challenges, particularly in real-time applications. Therefore, based on the trade-off between accuracy and complexity, the K-NN model with a combination of Correlation, Energy, and Homogeneity features emerges as a more suitable choice for real-time applications that demand high accuracy and low complexity. This research provides valuable insights for optimizing object detection in various applications requiring both high accuracy and rapid responsiveness