ANALISA FILOGENETIK SUKU DIPTEROCARPACEAE BERDASARKAN GEN KLOROPLAS MATK MENGGUNAKAN ALGORITMA BAYESIAN INFERENCE

Suku Dipterocarpaceae memiliki jenis yang cukup banyak dan masing-masing dari jenis tersebut mempunyai kemiripan morfologi yang sangat mirip. Hal ini mengakibatkan adanya kesulitan pengelompokan jika hanya dilihat berdasarkan morfologi. Oleh karena itu, penelitian ini akan melakukan analisa filogenetik berdasarkan gen kloroplas matK. Penelitian ini bertujuan untuk menganalisa hubungan kekerabatan dari beberapa jenis suku Dipterocarpaceae menggunakan algoritma Bayesian Inference dan membandingkan hasil analisa berupa pohon filogenetik menggunakan algoritma Bayesian Inference, Neighbour-joining, Maximum likelihood dan Maximum parsimony. Tahapan analisa yang dilakukan terdiri dari pengumpulan data, perubahan struktur nama sekuen, penjajaran sekuen, konstruksi pohon menggunakan metode MCMC, evaluasi dan analisa pohon filogenetik. Hasil analisa menunjukkan bahwa marga Dipterocarpus tidak membentuk monophyletic group dengan marga lain dari puak Dipterocarpeae. Hasil perbandingan menggunakan tiga algoritma lain (Neighbour-joining, Maximum likelihood dan Maximum parsimony) menunjukkan bahwa algoritma Bayesian memiliki waktu konstruksi pohon paling lama diantara algoritma lainnya. Kata Kunci : Dipterocarpaceae, Filogenetik, bayesian inference, mcm

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.  

STRATEGI PENGEMBANGAN PARIWISATA KABUPATEN LAHAT SUMATERA SELATAN (Study Objek Wisata TWA Bukit Serelo Kecamatan Merapi Selatan)

Abstrak Dilatar belakangi kurang optimalnya pengelolaan pariwisata yang ada di Kabupaten Lahat khususnya Objek Wisata Taman Wisata Alam Bukit Serelo sehingga berdampak belum adanya sumbangsih PAD dari kegiatan Pariwisata. Tujuan penelitian ini adalah untuk merumuskan alternatif-alternatif strategi dan menyusun arah kebijakan Pengembangan Objek Wisata Taman Wisata Alam Bukit Serelo Kabupaten Lahat. Jenis penelitian ini adalah Mixed Methods yang dilakukan pada Dinas Priwisata dan Kebudayaan Kabupaten Lahat, Kecamatan Merapi Selatan, Masyarakat, dan wisatawan. Penentuan informan dilakukan secara Purposive. Pengumpulan data dilakukan melalui observasi, wawancara, dokumentasi, FGD dan pengisian angket analisis hirarki proses. Pengolahan Data dilakukan dengan analisis SWOT dan AHP. Hasil penelitian dapat dirumuskan sebelas strategi dan kebijakan pengembangan yang harus dilakukan pemerintah, yang menjadi prioritas utama kebijakan pengembangan objek wisata TWA Bukit Serelo adalah peningkatan sumber daya manusia birokrasi di lingkungan dinas Pariwisata dan Kebudayaan Kabupaten Lahat

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.  

Local Wisdom Values for Managing the Conservation Forestarea in Mountain Kaba Selupu Rejang Sub-District

This study aims to determine the local wisdom values for managing the conservation forest area in mountain Kaba Selupu Rejang Sub-District. This research is classified as a descriptive study with a qualitative approach. The selection of informants is based on snowball throwing techniques. Informants in this study consisted of village heads, traditional leaders, and supporting community members. Data collection is done through observation, documentation, and interviews with several speakers. The data analysis technique used is the analysis of Strengths, Weaknesses, Opportunities and Threats (SWOT). The results showed that the local wisdom of the community could improve the management of the mountain Kaba conservation area, which was divided into strengths, weaknesses, opportunities, and threats to the values ​​of that local wisdom. &nbsp

Prestasi Belajar Mahasiswa Penerima Bidik Misi Selama Masa Pandemi Covid-19

This study aims to see the achievements of Bidik Misi Scholarships of students during the Covid-19 outbreak at Prof. Dr. Hazairin University. This research was conducted using descriptive research methods. The population and samples used in this study are all students who received Bidik Misi Scholarships in the academic year Odd 2019/2020 and Even 2019/2020 who were studying as many as 219 students. Data collection technique is a documentary technique, a tool used in the form of a stuffing format designed in such a way that it can record all the necessary data. This research data is quantitative data that is analyzed using descriptive analysis and categorized based on Academic Guidelines. The results of the study, the achievement of students who received Bidik Misi Scholarships in the academic year 2019/2020 judging from the average cumulative achievement index of students in the Odd semester is 3.31 and the average cumulative achievement index of students in the Even semester is 3.24. The average academic achievement score of students seen from 219 students who received mission objectives decreased by 0.07 during the Covid-19 pandemic. The decrease in the index of learning achievement of mission-aiming students has been seen in the Aquaculture program wherein the odd semester before the pandemic the average academic achievement was 3.8 and the average academic achievement during the Covid-19 pandemic was 2.7. In conclusion, the percentage of cumulative achievement index of the average recipient of Bidik Misi Scholarships during the Covid-19 outbreak at Prof. Dr. Hazairin University in the category is very satisfactory or above 2.75.   Keywords: Bidik Misi Scholarships, Covid-19, Learning Achievement

L2T-DLN: Learning to Teach with Dynamic Loss Network

With the concept of teaching being introduced to the machine learning community, a teacher model start using dynamic loss functions to teach the training of a student model. The dynamic intends to set adaptive loss functions to different phases of student model learning. In existing works, the teacher model 1) merely determines the loss function based on the present states of the student model, i.e., disregards the experience of the teacher; 2) only utilizes the states of the student model, e.g., training iteration number and loss/accuracy from training/validation sets, while ignoring the states of the loss function. In this paper, we first formulate the loss adjustment as a temporal task by designing a teacher model with memory units, and, therefore, enables the student learning to be guided by the experience of the teacher model. Then, with a dynamic loss network, we can additionally use the states of the loss to assist the teacher learning in enhancing the interactions between the teacher and the student model. Extensive experiments demonstrate our approach can enhance student learning and improve the performance of various deep models on real-world tasks, including classification, objective detection, and semantic segmentation scenarios

Multilabel image analysis on Polyethylene Terephthalate bottle images using PETNet Convolution Architecture

Packaging is one of the important aspects of the product. Good packaging can increase the competitiveness of a product. Therefore, to maintain the quality of the packaging of a product, it is necessary to have a visual inspection. Furthermore, an automatic visual inspection can reduce the occurrence of human errors in the manual inspection process. This research will use the convolution network to detect and classify PET (Polyethylene Terephthalate) bottles. The Convolutional Neural Network (CNN) method is one approach that can be used to detect and classify PET bottle packaging. This research was conducted by comparing seven network architecture models, namely VGG-16, Inception V3, MobileNet V2, Xception, Inception ResNet V2, Depthwise Separable Convolution (DSC), and PETNet, which is the architectural model proposed in this study. The results of this study indicate that the PETNet model gives the best results compared to other models, with a test score of 96.04%, by detecting and classifying 461 of 480 images with an average test time of 0.0016 seconds

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