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Tati Mardiana, Tati

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Author-ID : 644681

Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Social Sciences Other

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CLASSIFICATION OF STUDENT SATISFACTION WITH ONLINE LECTURE Ruhyana, Nanang; Mardiana, Tati; Amsury, Fachri; Sulistyowati, Daning Nur
Jurnal Riset Informatika Vol. 4 No. 1 (2021): December 2021
Publisher : Kresnamedia Publisher

Abstra Covid-19 has had a significant impact on people's lives, resulting in the paralysis of almost the entire economy and education, especially in the education sector, resulting in many students being unable to carry out teaching and learning activities at schools or universities. Based on this, the Ministry of Education and Culture has issued an appeal to stop face-to-face teaching and learning activities at schools and universities and replace them with distance or online learning. Resulting in teaching and learning activities to be less than optimal for students or students, there is dissatisfaction with the distance or online learning system, the purpose of this study is to measure the level of student satisfaction with online lectures by applying data mining techniques, classifying the level of online learning satisfaction using an online learning approach. k-NN algorithm and Decision Tree with 100 questionnaire data that has been collected from active students who carry out online lectures with an accuracy rate of 96.00% from the k-NN algorithm and a satisfied precision value of 95.51%, a satisfied recall value of 98.84% on a precision value the dissatisfied class is 90.91%, the recall value of the dissatisfied class is 71.43%. While the accuracy results using the Decision Tree algorithm approach is lower with an accuracy of 95.00%. based on research results that the level of student satisfaction with distance learning or online is quite high.

THE EFFECTIVENESS ANALYSIS OF RANDOM FOREST ALGORITHMS WITH SMOTE TECHNIQUE IN PREDICTING LUNG CANCER RISK Yulianti, Ita; Rahmawati, Ami; Mardiana, Tati
Jurnal Riset Informatika Vol. 4 No. 2 (2022): March 2022
Publisher : Kresnamedia Publisher

Abstract When compared with other types of cancer, most of the population with cancer die from lung cancer.A person needs to do a screening test through X-rays, CT scans, and MRI to detect the disease. However, before carrying out the process, the doctor will ordinarily investigate a medical history and physical examination first to study the symptoms and possible risk factors for lung cancer. The lung cancer data set has a class imbalance that affects the performance of the random forest algorithm in predicting the risk of lung cancer. This study aims to employ the SMOTE technique to the random forest algorithm to increase accuracy in predicting lung cancer risk. In this research, data processing and analysis use the Python programming language. The test results show an accuracy value of 88% with an AUC value of 0.93. When employing the random forest method to forecast lung cancer risk, the SMOTE technique is useful in dealing with class imbalances in the data set.

COMPARISON OF CLASSIFICATION ALGORITHMS FOR ANALYSIS SENTIMENT OF FORMULA E IMPLEMENTATION IN INDONESIA Amsury, Fachri; Ruhyana, Nanang; Mardiana, Tati
Jurnal Riset Informatika Vol. 4 No. 3 (2022): June 2022
Publisher : Kresnamedia Publisher

The Formula E racing series has become one of the world's most prestigious competitions. In 2022, Indonesia hosted the famous Formula E race. The event possesses the potential for economic benefits for Indonesia worth 78 million euros through the arrival of 35,000 spectators. Indonesians are enthusiastic about Formula E since it allows their nation to encourage tourists and gain international prominence. However, some people do not support this event. Since they regard that amid the COVID-19 pandemic, it is preferable for the government to focus on people affected by the pandemic rather than support a Formula E event. This study compares the Support Vector Machine and Naive Bayes algorithms in classifying public opinion in the Formula E race. This study gets its information from user comments on social media platforms, especially Twitter. The stages start with text preprocessing and include cleaning, case folding, tokenization, filtering, and stemming. Proceed with weighting using the TF-IDF approach. Data testing uses a confusion matrix to evaluate the classification results by testing accuracy, precision, and recall. Categorizing public opinion using the SVM algorithm has an accuracy of 82 percent, a precision of 97.86 percent, and a recall of 77.90 percent. On the other hand, the accuracy of the Naive Bayes technique is more limited, at 87.54 percent. Society's opinion on Twitter shows positive sentiment towards implementing Formula E.

DECISION SUPPORT SYSTEM FOR DETERMINING APPROPRIATE FRANCHISE LOCATIONS USING THE PROFILE MATCHING METHOD Mardiana, Tati; Malau, Yesni
Jurnal Riset Informatika Vol. 3 No. 1 (2020): December 2020 Edition
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v3i1.48

Finding the appropriate location is crucial when starting a franchise. The appropriate location will affect the overall business risk and profitability of the franchise. Nevertheless, some franchises have a bankruptcy in running their business. One of the factors that contribute to the bankruptcy of a franchise business is a location that does not meet several criteria that support business success. Therefore, this study aims to propose a decision support system model to determine the location of the franchise based on matching profiles between the actual data value of a location and the value of the location profile expected by the franchisor. The profile matching method has a better level of objectivity because it measures the value of each indicator variable. The criteria for determining the location of a franchise are potential customers, access to location, competition, and costs. The test results show that the decision support system to determine the location of the franchise using the profile matching method meets the functional requirements. This decision support system helps franchises to determine the appropriate when starting a franchise.

An Expert System for Detection of Diabetes Mellitus with the Forward Chaining Method Mardiana, Tati; Ditama, Ega Maulana; Tuslaela, Tuslaela
Jurnal Riset Informatika Vol. 2 No. 2 (2020): March 2020 Edition
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v2i2.49

In recent years, diabetes mellitus in Indonesia has become a health problem in the community because its population has increased 2-3 times faster than in other countries. Diabetes prevalence in Indonesia ranks 4th highest globally after China, India, and the United States. People can prevent complications and premature death if they detect early symptoms of diabetes. However, people do not know that they are at risk of diabetes and do not have knowledge about the symptoms of diabetes, the complexity of the process of diagnosis, and the high cost of examinations. Therefore, we need an application that can provide the results of the type of diabetes and its management solutions as practiced by experts. This research aims to develop an expert system for detecting types of diabetes such as type one diabetes, type two diabetes, neuropathy diabetes, diabetes retinopathy, and diabetes nephropathy. The object of this research is diabetes, which was carried out from March to April 2019 in the Klinik Pratama Desa Putera. This study uses primary data from patients with a history of diabetes at Klinik Pratama Desa Putra and secondary data from literature, research journals, and data documents needed to compile this study. In addition, we generated a knowledge base using forward chaining. The test results show that the expert system meets the functional requirements, and the system performance reaches an accuracy of 100%. This expert system helps people in Indonesia to detect diabetes early so that it can prevent complications.

Co-Authors Ami Rahmawati Andi Saryoko ANI OKTARINI SARI, ANI OKTARINI Anton Bayhaqy, Achmad Ditama, Ega Maulana Dwiza Riana Eka Wajar Wati, Eka Wajar Ernawan, Ferda Ernawan Fachri Amsury Hasanah, Riyan Latifahul Instianti Elyana, Instianti Ita Yulianti Julianti, Yesi Leony Kanaya Salsabila Setiawan Lia Mazia, Lia Na'am, Jufriadif Nakdin Satria Firmansya nanang ruhyana Nur Khofifah Nur Sulistyowati, Daning Nurmalasari Pribadi, Denny Rahmawati, Ami Riyadi , Andri Agung ruhyana, nanang Setiaji Setiaji, Setiaji Siswahyudianto Sri Hadianti Sulistyowati, Daning Nur Syahreva, Hafiz Tuslaela Verry Riyanto Yesni Malau

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