Multivariate Adaptive Regression Splines (MARS) for Modeling Student Status at Universitas Terbuka

Multivariate Adaptive Regression Splines (MARS) used to model the active student’s status in the Department of Statistics at Universitas Terbuka and determine the factors that influence the response variable. This study consists of 9 variables, namely gender, age, education, marital status, job, initial registration year, number of registrations, credits, and GPA, but after modeling using the MARS method, the explanatory variable can affect the response variable is the initial registration year. Several registrations, GPA, and credits. Based on the results of the R output and using a 95% confidence interval, each base 1 to 10 function is partially significant with the p-value of the base 1-10 function being smaller than 0.05 and simultaneously with a smaller p-value. of 0.05, so that the above model has a significant effect partially or simultaneously on the response variable. From these results, it is concluded that the MARS model is suitable for determining the factors that affect the active status of students

Application of Machine Learning for Heart Disease Classification Using Naive Bayes

The Naive Bayes classifier uses an approximation of a Bayes theorem by combining previous knowledge with new ones. The purpose of this research is to develop machine learning using Naive Bayes classification techniques and as a decision system in producing fast and accurate classification accuracy in diagnosing cardiovascular diseases such as heart disease. Cardiovascular disease is the leading cause of death, 32% of all global deaths, of which 85% are caused by stroke and heart disease. Based on the results of the analysis, it was found that the accuracy of classification accuracy in the training data on patient data was classified as having and not having heart disease, respectively 83,21% and 83,1%. In data testing, the percentage of patient data classified as having and not having heart disease was 83,78% and 87,50%, respectively. Based on the AUC values ​​in the training data and testing data, they are 83,15% and 85,24%, respectively. So, from these results, it can be concluded that the Naive Bayes method is good for classifying heart disease patient data

Analysis of CART and Random Forest on Statistics Student Status at Universitas Terbuka

CART and Random Forest are part of machine learning which is an essential part of the purpose of this research. CART is used to determine student status indicators, and Random Forest improves classification accuracy results. Based on the results of CART, three parameters can affect student status, namely the year of initial registration, number of rolls, and credits. Meanwhile, based on the classification accuracy results, RF can improve the accuracy performance on student status data with a difference in the percentage of CART by 1.44% in training data and testing data by 2.24%.CART and Random Forest are part of machine learning which is an essential part of the purpose of this research. CART is used to determine student status indicators, and Random Forest improves classification accuracy results. Based on the results of CART, three parameters can affect student status, namely the year of initial registration, number of rolls, and credits. Meanwhile, based on the classification accuracy results, RF can improve the accuracy performance on student status data with a difference in the percentage of CART by 1.44% in training data and testing data by 2.24%

Model Hidrologi Das Di Sebagian Daerah Indonesia Berdasarkan Kajian Ilmu Statistika

Beberapa model hidrologi telah dikembangkan untuk menggambarkan dengan jelas proses mengubah input (curah hujan) menjadi output (debit aliran sungai) dengan mempertimbangkan berbagai karakteristik fisik Daerah Aliran Sungai (DAS). Model hidrologi dirancang untuk menyederhanakan suatu sistem hidrologi, sehingga perilaku dari beberapa komponen dalam sistem dapat diketahui. Tulisan ini membahas model dalam studi hidrologi berbasis pendekatan pembentukan model dan beberapa model hidrologi yang sudah diterapkan di Indonesia (Harsoyo, 2010). Kekurangan dan kelebihan beberapa model hidrologi yang sudah diterapkan tersebut ditinjau dari sudut pandang ilmu Statistika. Menjadi tantangan bagi para statistisi untuk mencari model dengan data terbatas, tetapi menghasilkan model yang dapat diterapkan, akurat dan sesuai dengan hasil pengukuran dilapangan. Selain itu dari keseluruhan model yang dikaji, belum ada yang mempertimbangkan perubahan iklim (climate change)

Analisis Multidimensional Scaling pada Bencana Alam di Provinsi Banten Tahun 2010-2019

Negara Indonesia berlokasi di cincin Api Pasifik yang merupakan wilayah dengan banyak aktivitas tektonik, maka Indonesia memiliki resiko yang cukup besar dalam menghadapi bencana alam. Provinsi Banten merupakan salah satu wilayah rawan bencana alam selama 10 tahun terakhir. Analisis statistik yang digunakan untuk memetakan wilayah berdasarkan jumlah bencana alam yang terjadi khususnya di provinsi Banten yaitu metode Multidimensional Scaling (MDS). MDS merupakan metode analisis multivariat yang digunakan untuk menganalisis suatu data berdasarkan kemiripan dan ketidakmiripan dalam ruang multidimensi menggunakan konsep jarak, konsep jarak yang umum digunakan pada MDS adalah jarak euclidian. Berdasarkan hasil analisis, jenis bencana alam yang paling banyak terjadi di provinsi Banten adalah banjir, puting beliung, dan tanah longsor sedangkan pada tingkat wilayah rawan bencana alam provinsi Banten dikelompokkan menjadi 3 wilayah yaitu wilayah 1 (Serang), wilayah 2 (Lebak), dan wilayah 3 (Pandeglang, Tangerang dan Cilegon). Wilayah 1 dan 2 merupakan wilayah yang paling rawan bencana alam dibandingkan dengan wilayah 3. Nilai STRESS model diperoleh sebesar 2.09% dan RSQ sebesar 99.93% yang artinya model MDS yang terpilih sempurna untuk memodelkan wilayah provinsi Banten berdasarkan jenis bencana alam yang terjadi dalam 10 tahun terakhir

Development of Website-Based Statistics Learning Videos

One source of learning in the learning process is learning materials. In distance education, learning resources prepared for students must be able to be studied independently. The separation of lecturers and students needs to be bridged by learning materials that can be understood by students. A video is a form of learning materials that can be used to help students understand the material. Through videos, students can learn anywhere and anytime according to the time available. In the video, the duration of the material delivery is very significant. Likewise, components such as intro, greeting, and outro greatly support student motivation. The effective video duration in learning website-based statistical data analysis is 10-15 minutes. The component of presenting material that is in great demand by students in the discussion of sample questions and their application using R software

Analysis of User Satisfication with Graduates in Statistical Study Program Universitas Terbuka

Revolution 4.0 requires the Universitas Terbuka Statistics study program to change the educational curriculum that aims to produce quality graduate competencies. Therefore, to collect informationand evaluate the competence of graduates, it is necessary to conduct tracer study research on each graduate. This study aims to measure user satisfaction with graduate competencies using Gap analysis, Importance-Performance Analysis (IPA), Customer Satisfaction Index (CSI), and a multi-attribute Fishbein model. Based on the value of Gap and Science, the main priority that must be improved by graduates to meet user expectations is the ability to solve problems, generate ideas, and be able to present the results of these ideas in the form of reports/journals. The value of the level of suitability between user satisfaction and the importance of the ability of graduates is very good at 92.87% and a CSI value of 78.25%, which means that overall user satisfaction with graduates is good, besides thatbased on the results of the multi-attribute Fishbein model, an Ao value of 158.20 which means that graduate users have a positive attitude towards the abilities of UT Statistics program graduates

COUNSELING ON HEALTHY FOOD MANAGEMENT FOR UNDERNOURISHED TODDLERS AT TUNAS HARAPAN PAMULANG TANGERANG SELATAN

Abstrak: Penyuluhan Pengolahan Makanan Sehat yang dilakukan di Pos Gizi Tunas Harapan Pamulang Timur merupakan penyuluhan dalam peningkatan kemampuan para kader Pos Gizi dan ibu-ibu yang memiliki balita berstatus gizi kurang. Kegiatan ini memberikan penyuluhan tentang cara pengolahan makanan sehat, perilaku hidup sehat, dan pemberian makanan tambahan (PMT). PMT ini menggunakan bahan baku pangan lokal dengan menu khas daerah yang disesuaikan dengan kondisi setempat. Data bayi/balita BBR berasal dari Kader Pos Gizi yang berjumlah 14 anak. Kegiatan ini berlangsung bersamaan dengan terjadi pandemi, sehingga strategi pelaksanaan menjadi berubah, walaupun demikian protokol Covid 19 dilaksanakan dengan ketat. Penyuluhan disertai dengan pembagian flyer untuk kader Pos Gizi dan para ibu bayi/balita, serta diharapkan dapat membantu relawan/kader Pos Gizi melaksanakan tugas meningkatkan gizi balita. Hasil penyuluhan dan PMT secara statistik tidak ada perbedaan yang signifikan antara rata-rata BB sebelum dan sesudah PMT dengan taraf nyata 5%. Penyebabnya adalah jadwal PMT berubah, yang semula 5 kali dalam seminggu di awal bulan, menjadi 5 kali selama 2 bulan.Abstract: Counseling on Healthy Food Processing conducted at the Tunas Harapan Pamulang East Heart Center is an extension to increase the capacity of Hearth cadres and mothers who have under-fives with undernourished status. This activity provides counseling on how to process healthy food, healthy living behavior, and providing additional food/PMT. This PMT uses local food raw materials with regional specialties adapted to local conditions. Data for BBR infants/toddlers came from the Hearth Cadre, totaling 14 children. This activity took place simultaneously with the pandemic, so the implementation strategy changed, even though the Covid 19 protocol was implemented strictly. The counseling is accompanied by the distribution of flyers for Hearth cadres and mothers of babies/toddlers, and it is hoped that they can help Hearth volunteers/cadres carry out the task of improving toddler nutrition. The results of counseling and PMT statistically there was no significant difference between the average weight before and after PMT with a significance level of 5%. The reason is that the PMT schedule has changed, which was originally 5 times a week at the beginning of the month, to 5 times for 2 months

METODE KLASIFIKASI JARINGAN SYARAF TIRUAN BACKPROPAGATION PADA MAHASISWA STATISTIKA UNIVERSITAS TERBUKA

Backpropagation Artificial Neural Network (ANN) is an ANN that uses a supervised learning algorithm. The purpose of this study is to determine the parameters and measure the accuracy of the classification accuracy of the student status of the Open University Statistics Study Program. Based on the results,                  the simulation obtained 15 parameters that can affect student status, including gender, age, education (Senior High School, Diploma, Bachelor, and Magister), marital status, employment status (not working, private employees, entrepreneurs, and civil servants), initial registration year, registration number, semester credit system, and GPA). Meanwhile, for the classification accuracy, the activation function and the learning rate are used minimum mean square of error (MST) on training data. The simulation results are also applied to the testing data with a cut-off point value of 0.3481, so the accuracy of the ROC curve is obtained in the training data for not active students is 99.43% and 99.14% active, while the testing data for not active students is 94.00%. and active 93.94%. So from this research, it can be concluded that ANN Backpropagation is a very good method in applying the classification method

PENERAPAN METODE HUNGARIAN DAN APLIKASI QM UNTUK MEMINIMALISASI KOMPLAIN KEBERSIHAN DARI KLIEN

An outsourcing company with a work contract period can run well if the work agreement made by the client is carried out properly. One of the things that affect the contract period is client complaints. By minimizing client complaints, the sustainability of work contracts between outsourcing companies and client companies is getting higher. For this reason, outsourcing companies assign workers based on the abilities of each worker so that the work results are optimal. One of the methods in solving assignment problems is the Hungarian method and the QM application. The purpose of this research is to minimize the value of hygiene complaints from clients. The results showed a reduction in the value of 15 client complaints from 43 complaints to 28 complaints related to the completion of the assignment of employees of outsourcing company using the Hungarian method and the QM application