Quantification Method for In-Vitro Tissue Culture Plants Morphology using Object Tracking and Digital Image Analysis

Manual measurement of morphology variables on in-vitro stored plants usually cause either physical damage or microorganism infection such that further monitoring of their in-vitro performance is precluded. This study adapted computer vision technology by which it is possible to conduct such measurement without physical contact or destructive test. Moreover, by applying object tracking and pattern recognition technique in the algorithm, the system could provide automatic and real time analysis. It was shown that this quantification method reach 80.2% and 87.9% in the measurement of leaf area and chlorophyll intensity. Intensity histogram and Fourier spectrum found to be the best feature for leaf recognition and interpolation usage to adjust pixel amount over the camera distance provide better estimation on leaf area

Pengenalan Chord pada Alat Musik Gitar Menggunakan CodeBook dengan Teknik Ekstraksi Ciri MFCC

Human auditory system is capable of extracting rich and meaningful data from complex audio signal. To recognize chord sequences that played in some kind of music is not an easy task. People need big effort to train their sense of hearing so they can recognize that kind sound of chords. This condition is also valid in a computer system. Finding the key and labeling the chords automatically from music are great use for those who want to do harmonic analysis of music. Hence automatic chord recognition has been a topic of interest in the context of Music Information Retrieval (MIR) for several years, and attempts have been made in implementing such systems using well understood signal processing and pattern recognition techniques. This research is about to recognize the sound of chord that played and recorded by guitar instrument. There are 24 major-minor chords that used in this research. MFCC is used as feature extraction and the number of coefficient cepstral that used are 13 and 26. Each chord signal that has been extracted then clustered using K-means algorithm with 8, 12, 16, 20, 24, 28, 32 k numbers to create codebook that use as a model of each chord. For the recognition process, there are two methods that used in this research, unstructured recognition and structured recognition. For the result, this research produces two kinds model of codebook that are codebook with 13 coefficients and codebook with 26 coefficients. Both types of codebook show a good result with accuracy level above 88%. The best result yielded from USAge of 26 coefficient cepstral with structured recognition. It's accuracy level reach 97%. Hence the USAge of 26 coefficient cepstral is better than the USAge of 13 coefficient cepstral with difference of accuration level is about 7%. This research also shows the affectation of the numbers k-means that used. An increasing accuration level shown by increasing the amount of k-cluster

Performance Comparison Between Support Vector Regression and Artificial Neural Network for Prediction of Oil Palm Production

The largest region that produces oil palm in Indonesia has an important role in improving the welfare of society and economy. Oil palm has increased significantly in Riau Province in every period, to determine the production development for the next few years with the functions and benefits of oil palm carried prediction production results that were seen from time series data last 8 years (2005-2013). In its prediction implementation, it was done by comparing the performance of Support Vector Regression (SVR) method and Artificial Neural Network (ANN). From the experiment, SVR produced the best model compared with ANN. It is indicated by the correlation coefficient of 95% and 6% for MSE in the kernel Radial Basis Function (RBF), whereas ANN produced only 74% for R2 and 9% for MSE on the 8th experiment with hiden neuron 20 and learning rate 0,1. SVR model generates predictions for next 3 years which increased between 3% - 6% from actual data and RBF model predictions

Analisa Laju Kegagalan Mesin Induk Pada Kapal-Kapal Penyeberangan Ujung - Kamal

Armada kapol penyeberang.an Ujung-Kamal sermg menp.alanu kep.uJ!.ulan operas1 ywrg. menyebebabkan ketidaknormalan operast pen_wberan?,an. Selam d11ebabkan karena w,w kapal yang .sudah hamptr mencapa1 30 ralum. penurunan unjuk kerya yang berlang.mng cepat dan komponen swem yang. ada dt kapal ju?,a mempengamht terjadmya kegap.a/an opera.\/ kapol. l fntuk menp.etahm potu kegagalan dan kondtsi dun kompanen ststem yang ada d1 kapol dtperlukan anallsa laju kegagalan (fatlure rate ana/J'!!ts). Dalcun tugas oklur mt akan dtcoba untuk menganaltsa /aju kegagalan kapal-kapa/ penyeberan?,an if;ung-Kamul. Penelitian akan difokuskan pada ana/i.w la;u kep.agalan mesin mduk pada kapal-kapal penyeberangcm UjungKamal. 'li1gas akhir ini hertujuan untuk mendapalkan dan membuktikan pol a la;u kegugalan dan mesm mduk kapal. l'ungst Juju kegagalan dari tiap mesin mduk yang dianahsa dipemleh berdasarkan distribusi probabilitas yang sesuai yang memm;ukkan kegap.alan. Metode maximum ltkefihood estimate {ivfU-.") digunakan untuk mendapatkan pendekatan parameter dan /tap distribust dengan meng?,unakan pmf!_ram aplikast Weibu/1 Hast! dan penehtwn mt akan mengetahui proses laju kegagalan dan kondt.w mesm mduk kapal penyeberangan Ujung-Kamal. /Jengan tuga.1 aklur ini btsa dl)adtkan pedoman pemihk kapal dalam mengambtl keputrL\011 untuk tmdakan perm•·atan ll/au pergantwn komponen mesin mduk kapal. Dari /rust/ wwft.,a dapot dl.\lmpulkan bahwa lll)ektor merupakan komponen krilts dun.wstem halran hakar merupokan \t.\lem mesm mduk yang memtliki IOJII kegaga/an yang tmggt. Dart luntl perhandmgan Ia] II kegagalan keempat mesin mduk yang ditelut dtketahut balrwa mesm mduk kapal penyeberangan tersebut mastlr berada dalam kondm usejulltfe It me karena menml)ukkan laju kegagalan yang konslan

Pemetaan Kapabilitas Kantor Wilayah Bank Syariah Indonesia Region Jakarta (Periode 2020 - 2022)

The banking industry is required to accelerate the implementation of digital banking in order to be able to compete in order to provide services that are easier and faster and in accordance with customer needs. banking is one of the important pillars as an intermediary for the community to build small medium and micro industries and the role of banking is very important in building the country's economy in the digital era. Small and Medium Industry players must start transforming to digitalization, by changing transactions that were previously traditional into digital financial transactions. The purpose of this research is in DKI Jakarta. This study aims to identify the resources and capabilities of the Bank Syariah Indonesia (BSI) Jakarta region which have the potential to become a sustainable competitive advantage. This study uses primary and secondary data. Primary data was obtained from distributing questionnaires to respondents and interviewing experts (upper and middle management of BSI Jakarta area) using in-depth interviews. Secondary data obtained from literature studies. Based on the results of the discussion that has been described, it can be concluded that there are as many as 30 variables in the resources and capabilities of the BSI Jakarta region that have the potential to become a competitive advantage. The results of the VRIO analysis show that there are 14 variables in the resources and capabilities of the BSI Jakarta region which have a sustainable competitive advantage. Apart from that, good human resource services should be maintained and their capabilities improved so that they can educate the Small and Medium Industries in using the digital banking system contained in BSI in the Jakarta region which is the backbone for growth both in terms of assets, third party funds and financing for BSI. BSI Jakarta region needs to have an operational strategy in order to be able to carry out the main strategy that is the focus of BSI, one of which is strengthening capacity and capability and developing Small and Medium Industries. The business strategy for the BSI Jakarta region in supporting BSI strategy can be formulated from the resources and capabilities of BSI

Optimization of Spaced K-mer Frequency Feature Extraction using Genetic Algorithms for Metagenome Fragment Classification

K-mer frequencies are commonly used in extracting features from metagenome fragments. In spite of this, researchers have found that their use is still inefficient. In this research, a genetic algorithm was employed to find optimally spaced k-mers. These were obtained by generating the possible combinations of match positions and don't care positions (written as *). This approach was adopted from the concept of spaced seeds in PatternHunter. The use of spaced k-mers could reduce the size of the k-mer frequency feature's dimension. To measure the accuracy of the proposed method we used the naïve Bayesian classifier (NBC). The result showed that the chromosome 111111110001, representing spaced k-mer model [111 1111 10001], was the best chromosome, with a higher fitness (85.42) than that of the k-mer frequency feature. Moreover, the proposed approach also reduced the feature extraction time.

A System Dynamics Simulation of Rice Agroindustry Development by Divestment Pattern for Increasing Rice Production and Farmer Income

About 60 percent of world populations and most of Indonesian people, rice is a staple food that must be available throughout the year. Therefore the existence of a rice mills for rice production become vital. The rice mills need to be developed continually in order to support an increasing of domestic rice production. To increase rice production,  postharvest loss can be reduced by replacing the  old rice mills with the  modern ones. By divestment pattern, a group of farmers can own and operate a rice mill, so they get an additional income from the operation of the rice mill. The main objective of this research was to findout the best scenario for a rice agroindustry development through divestment of ownership to the group of farmers. A system dynamics simulation methodology was applied in this research, with a case study in Cianjur District as a one of main area rice producer in West Java. The result from system dynamics simulation showed  that the investment of 10 units of rice mill per year along 10 years by divestment pattern will developed 212 units of rice mill that enough to mill the  paddy produced by Cianjur District farmers groups. Keywords: rice, rice agroindustry, divestment, system dynamics simulatio

SELEKSI FITUR YANG BERPENGARUH MENGGUNAKAN NILAI MEAN PADA KLASIFIKASI FRAGMEN METAGENOME

Pekuwali (2018) has conducted research into the classification of metagenome fragments using spaced k-mers. Optimize the arrangement of features using Genetic Algorithms. Pekuwali (2018) concluded that the best arrangement of features or called chromosomes is 111111110001 with a fitness value of 85.42. Chromosome 111111110001 produces 336 features of extracting DNA fragments. This research aims to find out which features influence classi fi cation and the resulting accuracy. The method used is the Mean value. The mean value method was chosen because the data distribution is normal or close to normal. This study concludes that the influential features in the classification are features 22 to 27 with an accuracy of 78.83% and features 38 to 43 with an accuracy of 79.67%.    Pekuwali (2018) telah melakukan penelitian klasifikasi fragmen metagenome menggunakan spaced k-mers. Optimasi susunan fitur menggunakan Algoritma Genetika. Pekuwali (2018) menyimpulkan bahwa susunan fitur terbaik atau disebut kromosom adalah 111111110001 dengan nilai fitness 85,42. Kromosom 111111110001 menghasilkan 336 fitur pengekstraksi fragmen DNA. Penelitian kali ini bertujuan untuk mengetahui fitur mana saja yang berpengaruh dalam pengklafikasian dan akurasi yang dihasilkan. Metode yang digunakan adalah nilai Mean. Metode nilai mean dipilih karena sebaran data normal atau mendekati normal. Penelitian ini menyimpulkan bahwa fitur yang berpengaruh dalam pengklasifikasian adalah fitur 22 sampai 27 dengan akurasi sebesar 78,83% dan fitur 38 sampai 43 dengan akurasi sebesar 79,67%. &nbsp

Modified Q-Learning Algorithm for Mobile Robot Path Planning Variation using Motivation Model

Path planning is an essential algorithm in autonomous mobile robots, including agricultural robots, to find the shortest path and to avoid collisions with obstacles. Q-Learning algorithm is one of the reinforcement learning methods used for path planning. However, for multi-robot system, this algorithm tends to produce the same path for each robot. This research modifies the Q-Learning algorithm in order to produce path variations by utilizing the motivation model, i.e. achievement motivation, in which different motivation parameters will result in different optimum paths. The Motivated Q-Learning (MQL) algorithm proposed in this study was simulated in an area with three scenarios, i.e. without obstacles, uniform obstacles, and random obstacles. The results showed that, in the determined scenario, the MQL can produce 2 to 4 variations of optimum path without any potential of collisions (Jaccard similarity = 0%), in contrast to the Q-Learning algorithm that can only produce one optimum path variation. This result indicates that MQL can solve multi-robots path planning problems, especially when the number of robots is large, by reducing the possibility of collisions as well as decreasing the problem of queues. However, the average computational time of the MQL is slightly longer than that of the Q-Learning