Implementation of Bayesian inference MCMC algorithm in phylogenetic analysis of Dipterocarpaceae family

Dipterocarpaceae is one of the most prominent plant families, with more than 500 members of species. This family mostly used timber plants for housing, making ships, decking, and primary materials for making furniture. In Indonesia, many Dipterocarpaceae species have morphological similarities and are challenging to recognize in the field. As a result, the classification process becomes difficult and even results are inconsistent when viewed only from the morphology. This research will analyze the phylogenetic tree of Dipterocarpaceae based on the chloroplast matK gene. The aim of the research is to classify the phylogenetics tree of Dipterocarpaceae family using Bayesian inference algorithm. This research used the chloroplast gene instead of morphological characters which has more accurate. The analysis steps are collecting data, modifying the structure sequence name, sequence alignment, constructing tree by using Markov Chain Monte Carlo (MCMC) from Bayesian Inference, and evaluating and analyzing the phylogenetic tree. The results showed that the tree constructed based on the gene is different from the tree based on morphology. Based on the morphological, Dipterocarpus should be in the Dipterocarpeae tribe but based on the similarity of its genes, Dipterocarpus is more similar to the Shoreae tribe.  

Implementation of Bayesian inference MCMC algorithm in phylogenetic analysis of Dipterocarpaceae family

Dipterocarpaceae is one of the most prominent plant families, with more than 500 members of species. This family mostly used timber plants for housing, making ships, decking, and primary materials for making furniture. In Indonesia, many Dipterocarpaceae species have morphological similarities and are challenging to recognize in the field. As a result, the classification process becomes difficult and even results are inconsistent when viewed only from the morphology. This research will analyze the phylogenetic tree of Dipterocarpaceae based on the chloroplast matK gene. The aim of the research is to classify the phylogenetics tree of Dipterocarpaceae family using Bayesian inference algorithm. This research used the chloroplast gene instead of morphological characters which has more accurate. The analysis steps are collecting data, modifying the structure sequence name, sequence alignment, constructing tree by using Markov Chain Monte Carlo (MCMC) from Bayesian Inference, and evaluating and analyzing the phylogenetic tree. The results showed that the tree constructed based on the gene is different from the tree based on morphology. Based on the morphological, Dipterocarpus should be in the Dipterocarpeae tribe but based on the similarity of its genes, Dipterocarpus is more similar to the Shoreae tribe.  

Fiber Optic Attenuation Analysis Based on Mamdani Fuzzy Logic in Gambir Area, Central Jakarta

In this study, the authors conducted an analysis of the quality of fiber optic network maintenance based on attenuation value and maintenance time using fuzzy Mamdani logic and simulated using Matlab software, to improve accuracy in drawing conclusions on maintaining quality. This study uses a quantitative method, in which the author obtains a summary of customer data from PT. Telkom Indonesia in a period of 4 months of observation from August to November 2021. In August there were 776 customers, in September there were 362 customers, in October there were 359 customers, and in November 445 customers who underwent Indihome fiber optic cable maintenance. The test results with the centroid method with an input Handling Time of 1.5 hours and an Attenuation of 15 dB, then the output Repair Quality is 5.5 or categorized as Good. The greater the attenuation value generated, the more time it takes to maintain the IndiHome internet network disturbance. This is due to the many technical maintenance of fiber optic cables carried out by technicians to adjust for damage/trouble in the field. It is expected that maintenance can be carried out routinely in order to avoid fatal internet disturbances on the customer's side, and maximize maintenance time according to the dosage determined by the company, which is less than 3 hours, taking into account the work performance of technicians and also the quality of maintenance

Modification of Control Oil Feeding with PLC Using Simulation Visual Basic and Neural Network Analysis

The oil feeding system is an oil distribution system used in engine lubrication by flowing it directly to the engine parts to be lubricated through pipes. In addition, it is also a raw material for the production process by collecting the oil first in the storage tank, then weighing it on the oil scale before use in the production process. The current control is still using the conventional model. The operating system is still manual, and the absence of identity and damage information makes it difficult for the engineer to troubleshoot. The research method is to modify the oil feeding system control using PLC (Programmable Logic Controller) and Visual Basic to display process information. This process uses the Neural Network (NN) method. The simulation results show that the PLC program and visual basic software can be connected properly. The speed of the data transfer test connection that can be obtained is 32 ms. The prediction process of the oil feeding system using the backpropagation algorithm Neural Network and the activation function, which uses the binary sigmoid function (logsig) with the 17-10-1 architecture having very good performance getting the MSE value below the error value of 0.001 maximum epoch 961 and hidden layer 10 with an MSE value of 0.00099915

Fast Human Recognition System on Real-Time Camera

Technology development is very rapid, so all fields are required to develop technology to increase the effectiveness and efficiency of work. One of the focuses is related to image processing technology. We can get many benefits by implementing this system, so various fields have implemented image processing systems, such as security, health, and education. One of the current obstacles is in the area of safety, namely in the field of searching for people, which is still done manually. Often search teams find it challenging to find people because of the significant search area, low light conditions, and complex search fields. Therefore, we need a tool capable of detecting humans to assist in finding people. Therefore, to detect human objects, the authors try to research human object detection using a simple device for the human object detection system. The authors use the You only look once (YOLO) method with the YoloV4-Tiny type, where this algorithm has high detection speed and accuracy. Using the YOLOV4-Tiny simulation method for human object recognition, a detection rate of 100% is obtained with an FPS value of 5

Design Human Object Detection Yolov4-Tiny Algorithm on ARM Cortex-A72 and A53

Currently, many object detection systems still use devices with large sizes, such as using PCs, as supporting devices, for object detection. This makes these devices challenging to use as a security system in public facilities based on human object detection. In contrast, many Mini PCs currently use ARM processors with high specifications. In this research, to detect human objects will use the Mini PC Nanopi M4V2 device that has a speed in processing with the support of CPU Dual-Core Cortex-A72 (up to 2.0 GHz) + Cortex A53 (Up to 2.0 GHz) and 4 Gb DDR4 Ram. In addition, for the human object detection system, the author uses the You Only Look Once (YOLO) method with the YoloV4-Tiny type, With these specifications and methods, the detection rate and FPS score are seen which are the feasibility values for use in detecting human objects. The simulation for human object recognition was carried out using recorded video, simulation obtained a detection rate of 0,9845 or 98% with FPS score of 3.81-5.55.  These results are the best when compared with the YOLOV4 and YOLOV5 models. With these results, it can be applied in various human detection applications and of course robustness testing is needed

Fast Human Recognition System on Real-Time Camera

The rapid development of technology requires several technological fields to keep pace to increase work effectiveness and efficiency. One focus that is widely used is image processing technology. Many areas have implemented image processing systems due to various benefits, including security, health, and education. One of the current obstacles is safety, focusing on searching for missing people, which is still done manually. The vast search area, low light conditions, and complicated search fields made finding the person challenging for the search team. Therefore, it is necessary to have a tool that can detect people to assist in the search process. This paper proposes an object detector on a simple device capable of detecting human objects. The detection device was made using the You Only Look One (YOLO) method with the YoloV4-Tiny type, where this algorithm has high detection speed and accuracy. Using the YOLOV4-Tiny simulation method for human object recognition obtained satisfactory results with a detection rate of 100% with an FPS value of 5

Impact of Moving Sign (Running Text) Implementation at PKBM Wiyata Utama

The running information display board or Running Text is one of the information media or digital publications comprised of an ordered pattern of Light Emitting Diode (LED) lights, and each LED has a coordinate point that determines which LED position is on or off. This LED light is available in a range of colors, including red, yellow, green, blue, white, and blended hues. This running text is often used in Office Buildings, School Buildings, Shopping Buildings, and other locations where the general public must be informed. At this community service, running text has been installed in the PKBM Wiyata Utama school environment in Kembangan Utara, West Jakarta, which is suitable for school-related information media such as education level, school name, and school event

Kalman filter for tracking a noisy cosinusoidal signal with constant amplitude

This paper presents a Kalman filter based approach in order to solve the problem of tracking a noisy cosinusoidal signal with constant amplitude in the presence of noise. The objective is to estimate the state of the signal accurately, considering the inherent challenges posed by noise corruption. The Kalman filter is utilized as the core algorithm for state estimation, leveraging its ability to combine noisy measurements and a dynamic model to provide optimal estimates. The filter is initialized with zero states and covariance, and the state and covariance estimates are iteratively updated using time updates and measurement update equations. Through extensive simulations, the performance of the proposed Kalman filter-based algorithm is evaluated. The results demonstrate its effectiveness in accurately tracking cosinusoidal signals and mitigating the impact of noise. the Kalman Filter algorithm in this system produces low MSE at about 0.021 and MAE at about 0.111. The metrics results signify the algorithm’s ability to filter noise and estimate the actual state of the system, reflecting its robust tracking performance. The simulation results validate the effectiveness of the proposed approach and highlight its potential to enhance signal tracking accuracy in the presence of noise. Further research can explore the algorithm’s performance in various scenarios and investigate additional modifications to increase its robustness in challenging environments

Design Sistem Keamanan Pintu Menggunakan Face Detection

Perkembangan ilmu pengetahuan dan teknologi, terutama di bidang Elektronika dan  Informatika, diharapkan dapat mampu meningkatkan teknologi sistem keamanan ruangan atau rumah terutama sistem keamanan pintu rumah. Teknologi sistem keamanan pintu sudah banyak dikembangkan karena untuk mencegah dan membuat penghuni rumah merasa nyaman saat meninggalkan rumahnya. Beberapa sistem kemanan pintu yang banyak digunakan antara lain yaitu menggunakan kamera CCTV, finger print sensor, sensor suara dan RFID. Namun seiring perkembangan teknologi, sistem keamanan tersebut banyak di temukan celah kelemahan seperti terjadi banyak nya eror pada finger print dan voice sensor dan masih banyak lagi. Berdasarkan hal tersebut, untuk membuat sistem keamanan pada pintu rumah yang lebih aman dan invofatif maka penulis membuat sebuah sistem keamanan dengan face detection dan juga notifikasi peringatan sistem jarak jauh menggunakan telegram. Peneliti membuat sistem keamanan pintu dengan menggunakan EPS 32 – CAM sebagai alat untuk melakukan face detection dan juga sebagai controller untuk sistem internet of things. Selain itu juga dibuat sebuat sistem manual menggunakan keypad untuk input password. Hasil dari penelitian ini berupa tingkat akurasi dan kecepatan pengenalan wajah dan juga kecepatan pengiriman informasi peringatan sistem keamanan pada aplikasi telegram