KLASIFIKASI FAKTOR PENYEBAB RENDAHNYA MINAT MAHASISWA DALAM MENULIS ARTIKEL ILMIAH

Scientific articles are scientific articles in the form of reviews and research to be published in reputable scientific articles. This is one of the main things that becomes a reference for students in higher education during their studies and at the end of their studies. then the problem that must be faced is how to classify the factor of low student interest and being able to increase student interest in writing scientific articles. The factors of low student interest in writing scientific articles are the level of understanding, motivation, difficulty level, time, and infrastructure. The data used comes from questionnaire data from students of the 2016 batch of information systems study program. It has been obtained that the factor classification of the low interest of students in writing scientific articles is the factor that has top priority on the factor of low student interest is the infrastructure. The results showed that the C4.5 Decision Tree Algorithm was accurate for classifying the factors of low student interest in writing scientific articles with an accuracy rate of 100%.

Automatic Decision Algorithm of Interpretation Power in Criminal Justice Based on Data Activity Consultant

In order to solve the problems existing in the current decision algorithm, such as poor data processing performance, low decision accuracy, and long decision time, an automatic decision algorithm of criminal justice interpretation right based on data activity consultant was designed. According to the requirements of reasonable design consultant system data activity and demanded data activity consultants provide data processing requirements and scope. Use the Scrapy web crawler framework to crawl data related to criminal justice interpretation and criminal law provisions from related websites, and clear and extract the collected data to realize data query. Based on the obtained data, the feature array of criminal law is designed, and the decision of criminal judicial interpretation right is made. The C4.5 decision tree algorithm is used to predict the correct rate of decision. The decision of criminal judicial interpretation right is adjusted constantly according to the prediction results to achieve the goal of the automatic decision of criminal judicial interpretation right. Experimental results show that the algorithm has superior data processing performance, high decision accuracy, and short decision time, which verifies the effectiveness of the algorithm.

RESEARCH ON STRATEGIES OF HOME CARE AND INTERVENTION FOR STROKE BASED ON KNOWLEDGE RULES

For patients with stroke at home, strategies have been formulated for emotional nursing, sports rehabilitation nursing, and interventions for poor lifestyle habits such as smoking, drinking, and picky eating. Data were obtained through tracking investigation, effect evaluation indexes were developed according to Hamilton depression scale (HAMD), activities of daily living (ADL) and other rating scale; C4.5 decision tree algorithm was used to analyze the effect of nursing intervention strategy, then we derived the corresponding knowledge rules. We come to conclusions: ① Effective emotional care and bad living habits interventions are contributed to reduce the risk of stroke. ② Smoking, drinking, picky eating, exercising and other factors are associated, so we should combine and intervene them as to better perfect the risk of stroke to provide decision-making reference for home nursing and rehabilitation intervention of stroke patients.

KLASIFIKASI SISWA BERDASARKAN MATA PELAJARAN LINTAS MINAT MENGGUNAKAN METODE DECISION TREE C4.5

Abstrak - Kurikulum 2013 dirancang untuk memberikan kesempatan kepada siswa belajar berdasarkan minat siswa. Selain memilih mata pelajaran dalam suatu peminatan tertentu, siswa juga diberi kesempatan untuk mengambil mata pelajaran lintas minat. SMA Negeri 1 Anyer salah satu sekolah yang telah menerapkan program lintas minat. Dalam proses penentuan kelas lintas minat disekolah tersebut masih mengalami kendala yaitu tidak terspesifikasinya siswa yang memiliki minat pada mata pelajaran tertentu, dan pada proses pemilihan lintas minat ditentukan oleh pihak sekolah. Penelitian ini bertujuan untuk mengklasifikasi siswa berdasarkan minat dan bakat siswa pada mata pelajaran tertentu. Metode yang digunakan yaitu Decision Tree dan algoritma C4.5. Pada penelitian ini didapat nilai akurasi sebesar 82,82%. Penelitian menghasilkan sebuah sistem penentuan kelas lintas Minat. Model klasifikasi ini dapat membantu siswa dalam menentukan lintas minat dan dapat digunakan sebagai alternatif referensi bagi guru BK untuk dapat mengelompokkan siswa berdasarkan minat dan bakat siswa. Kata Kunci : Algoritma C4.5, Decision Tree, Klasfikasi, Lintas Minat

Implementasi Algoritma Ant Tree Miner Untuk Klasifikasi Jenis Fauna

Classification is a field of data mining that has many methods, one of them is decision tree. Decision tree is proven to be able to classify many kinds of data such as image data and time series data. However, there are several obstacles that are often encountered in the decision tree method. Running time required for the execution of this algorithm is quite long, so this study proposed to use the ant tree miner algorithm which is a development algorithm from the C4.5 decision tree. Ant tree miner works by utilizing ant colony optimization in the process of building its tree structure. Use ant colony optimization expected can optimize the tree that will be formed. From the testing that have been carried out, an accuracy of about 95% is obtained in the process of classifying Zoo dataset with the number of ants between 60 - 90.

Stability Analysis of Semi-active Suspension Systems Using a Data-driven Approach

The modern vehicles are getting equipped with more and more sensors, which allows the engineers to collect more information about the states of the vehicle and its environment during its operation. This information can be used to increase the capacity and the performances of the control systems. In this paper, a novel data-driven approach is presented to compute the reachability sets of the vehicles, which are equipped with a semi-active suspension system. The dataset, which is used in this paper, is provided by the high fidelity vehicle simulation software, CarSim. Firstly, the dataset is categorized using a stability criterion. Then, a machine-learning algorithm (C4.5 decision tree) is trained, which can categorize a given instance using only the onboard signals of the vehicle. Finally, a possible application of the reachability sets is presented to show the use of the computed sets.

Modeling on Disruption Risk Prediction of Manufacturing Supply Chain Based on C4.5 Algorithm

Under the impact of covid-19, the global and domestic manufacturing supply chains, almost suffered from the serious interruption crisis of manpower flow, logistics, information flow and capital flow. The risk of supply chain disruption has become the primary risk of the supply chain. However, some risk inducement of supply chain interruption is complex and diverse, so it is very difficult to grasp and screen the risk data needed for research from the supply chain operation data. To improve the robustness of supply chain for boosting the domestic and international circulation of China's manufacturing, in this paper, according to the characteristics of China's manufacturing supply chain and its risk incentives, the data needed for risk prediction modeling has been sorted out through questionnaire survey, and a regression model of risk prediction for manufacturing supply chain by using empirical method would be put forward. Then, C4.5 decision tree method is used to train and evaluation the risk prediction model. The conclusion shows that the customer satisfaction has great diagnostic value for risk, and the model has a strong sensitivity to market information risk and market order risk. The conclusion is more consistent with general cognition, and the model fits well, indicating that the model proposed in this paper has a certain theoretical significance, and its practical application value is worthy of further testing.

Classification of Toddler Nutrition Using C4.5 Decision Tree Method

Nutrition is very much needed in the growth of toddlers. It is very important to give babies a balanced nutritional intake at the right stage so that the baby grows healthy and is accustomed to a healthy lifestyle in the future. Children under five years of age are a group that is vulnerable to health and nutrition problems. In determining the nutritional status, it can be done in a system manner using the C4.5 decision tree classification method and entering several variables or attributes. The dataset tested was 853 toddlers. Classification is carried out to determine the nutritional status based on the weight/age (BB/U), height/age (TB/U) and weight/height (BB/TB) categories. The attributes used for the classification of BB/U are gender, weight and age. The attributes used for TB/U are gender, body length or height, and age. The attributes used for BB/TB are gender, weight, body length or height, and age. The average accuracy of the BB/U category is 90.16%, the average accuracy of the TB/U category is 76.64%, and the average accuracy of the BB/TB category is 83.83%.

Model Prediksi Kelulusan Mahasiswa Menggunakan Decision Tree C4.5 dan Software Weka

In this research, we build a model to predict graduation status of students in Institut Bisnis dan Informatika Kesatuan using C4.5 decision tree algorithm. The prediction model is built using students’ GPA from semester 1 to semester 4, for students with admission year of 2013 to 2016. The prediction model obtained is a decision tree with 26 rules, with the attribute IPS_4 being the attribute that determines the graduation label of students. This prediction model yields an accuracy of 73%, a result that is not good enough. This result is probably due to unbalanced proportion of the data used.