Forecasting the efficiency of weft knitting production: a decision tree method

Purpose This paper aims to develop a decision support system for predicting the knitting production’s efficiency based on the input parameters of an order. This tool supports the operations managers to make reliable decisions of estimated delivery time, which will result in reducing waste arising from late delivery, overtime and increased labor. Design/methodology/approach The decision tree method with a set of logical IF-THEN rules is used to determine the knitting production’s efficiency. Each path of the decision tree represents a rule of the following form: “IF <Condition> THEN <Efficiency label>.” Starting with identifying and categorizing input specifications, the model is then applied to the observed data to regenerate the results of efficiency into classification instances. Findings The production’s efficiency is the result of the interaction between input specifications such as yarn’s component, knitting fabric specifications and machine speed. The rule base is generated through a decision tree built to classify the efficiency into five levels, including very low, low, medium, high and very high. Based on this, production managers can determine the delivery time and schedule the manufacturing planning more accurately. In this research, the correct classification instances, which is simply a ratio of the correctly predicted observations to the total ones, reach 80.17%. Originality/Values This research proposes a new methodology for estimating the efficiency of weft knitting production based on a decision tree method with an application of real data. This model supports the decision-making process of the estimated delivery time.

Socio–demographic Impacts on the Personal Savings Portfolio Choice - a Decision Tree Approach

Insufficiently developed financial system, poor standard of living and inappropriate education of citizens on the saving products, lead to low level of investment in the financial market of developing countries. In this paper special attention is paid to examining the socio-demographic profile of Montenegrin citizens that invest their funds in some of the offered form of savings, as well as examining main factors that restrict their investment. For this purpose, data collected through the survey of Montenegrin citizens were processed using Decision Tree method. Survey results have shown that there is a low level of savings, as well as that citizens prefer deposits and life insurance products rather than pension plans and debt securities. Also, the results indicate that the main causes of the current state of savings in Montenegro are low standard of living, citizens´ poor awareness and the financial system which causes the insufficiently attractive supply of savings.

Perbandingan Metode KNN, Decision Tree, dan Naïve Bayes Terhadap Analisis Sentimen Pengguna Layanan BPJS

BPJS is really helpful because one of its goal is to provide good service for the member in terms of healthiness. But, when there’s many people using the service, then it will cause more pros and contras. Therefore, researcher will be doing sentiment analysis in the field of data mining towards bpjs users on social media Twitter as much as 1000 data that later will be filtered to be 903 data because there are some data that has been duplicated. Researchers used the KNN, Decision Tree, and Naïve Bayes methods to compare the accuracy of the three methods. Researchers used the RapidMiner version 9.7.2 tools. The results showed that the sentiment analysis of Twitter data on BPJS services using the KNN method reached an accuracy level of 95.58% with class precision for pred. negative is 45.00%, pred. positive is 0.00%, and pred. neutral is 96.83%. Then the Decision Tree method the accuracy rate reaches 96.13% with the precision class for pred. negative is 55.00%, pred. positive is 0.00%, and pred. neutral is 97.28%. And the last one is the Naïve Bayes method which achieves 89.14% accuracy with precision class for pred. negative is 16.67%, pred. positive was 1.64%, and pred. neutral is 98.40%.

Use of the Human Walking Gait Cycle for Assistive Torque Generation for the Hip Joint Exoskeleton

The development of an assistive robot to assist human beings in walking normally is a difficult task. One of the main challenges lies in understanding the intention to walk, as an initial phase before walking commences. In this work, we classify the human gait cycle based on data from an inertial moment unit sensor and information on the angle of the hip joint and use the results as initial signals to produce a suitable assistive torque for a lower limb exoskeleton. A neural network module is used as a prediction module to identify the intention to walk based on the gait cycle. A decision tree method is implemented in our system to generate the assistive torque, and a prediction of the human gait cycle is used as a reference signal. Real-time experiments are carried out to verify the performance of the proposed method, which can differentiate between various types of walking. The results show that the proposed method is able to predict the intention to walk as an initial phase and is also able to provide an assistive torque based on the information predicted for this phase.

Instance Selection dengan Naïve Bayes pada Klasifikasi Kanker Serviks

There are several deadly disease for woman, one of which is servical cancer. The growth and development of the disease is very slow, so that treatment if know form the beginning will facilitate the healing process, but conversely unknown cancers from the beginning will become dangereous and deadly disease due to relatively difficult healing. Biopsy action is one way to detect the presence of cancer. In the previous study, classification of cervical cancer had the bighest accuracy value of 97,515% using the decision tree method of several feature selection technique. for this reason, this research will use the decision tree or tree C4.5 classification method, logistic function and zeroR which were previously carried out processing with instance selection with Naïve Bayes by reducing the elimination of missing values with the aim of increasing the level of accuracy better than previous studies. C4.5 classification in this study has the most maximum results compared to other classification methods with an accuracy value of 99,69%.

Fathers’ Predominance in Transport Arrangements for Family Tourism: E-Chaid-Based Profiling in East Asia

In decisions about transportation for family vacations, the distribution of the decision-making role between fathers and other family members is subject to characteristics of the society, the travel and the household. Therefore, the purpose of this study is to present a data-mining model that identifies the relative importance of those determining characteristics in predicting the probability of the father’s predominance in transportation decisions for family vacations. By investigating cases across four East Asian societies and using exhaustive chi-square automatic identification detector analysis, it was found that the primary source of the family’s income was the strongest predictor of the father-determined likelihood of decisions about vacation transportation. The results also suggested that the decision tree method is appropriate for targeting the father-predominant market of transportation in cross-societal contexts.

An Investigation of Brain Signal Characteristics between Hafiz/Hafizah Subjects and Non-Hafiz/Hafizah Subjects

Tahfiz education has gain its popularity among Malaysians thus expand the circle of hafiz and hafizah all over the country. This study has been done to investigate effect of memorizing Al-Quran by determining the difference between hafiz/hafizah subjects and non-hafiz/hafizah subjects in terms of their focus using brain signal characteristics. 10 subjects (5 hafiz/hafizah and 5 non-hafiz/hafizah) have been participated in this study. Database of EEG was recorded by using EegoSport (ANT Neuro, ES-230, The Netherlands) while listening no music, rock music, instrumental music and Al-Quran audio simultaneously with Continuous Performance Task (CPT). The classification has been done by using machine learning method. Decision Tree method have obtained the highest accuracy (96.63%) for PSD Burg by using beta wave. The finding shows that hafiz/hafizah group were more focus in all given tasks compared to non-hafiz/hafizah group. Statistical analysis by using Wilcoxon Signed-Ranks Test found that the designed methodology was significant with 95% confidence interval.