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All Journal Building of Informatics, Technology and Science RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI
Lili Dwi Yulianto
Universitas Nusa Mandiri, Jakarta
Computer Science & IT

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Analisis Sentimen Dalam Pengkategorian Komentar Youtube Terhadap Layanan Akademik dan Non-Akademik Universitas Terbuka Untuk Prediksi Kepuasan Rhini Fatmasari; Virda Mega Ayu; Hari Anto; windu Gata; Lili Dwi Yulianto
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1738

Abstract

The key to the success of an educational organization in achieving its goals of course cannot be separated from the quality of service both in academic and non-academic forms. Where in achieving these goals of course by giving satisfaction to the academics. The case study was carried out to predict service satisfaction at the Open University by using comments on social media Youtube as data processing. The text mining approach is a good alternative in terms of interpreting the meaning in the comments given. This study aims to analyze the predictions of service satisfaction from several categories as a benchmark. The categories are: Module, Tutorial, Scholarship, Lecturer, Exam, Application, Non-Academic and Others. The research method used is comparative, by applying 4 algorithms, namely Decision Tree (DT), Support Vector Machine (SVM), Naïve Bayes (NB) and Random Forest (RF) for Prediction Accuracy. The total initial dataset is 7776 data and after cleansing and preprocessing is 6920 data. And then evaluated for the 7 categories after being accured to produce: Module category with the highest accuracy of 99.37% using the DT algorithm, Application Category with the highest accuracy of 100% using the DT algorithm, Teaching Category the highest accuracy of 99.42% using the algorithm DT. The tutorial category has the highest accuracy 92.4% using the SVM algorithm, the exam category has the highest accuracy 99.7% using the RF algorithm, the non-academic category has the highest accuracy 99.90% using the DT algorithm. And for the Others category the highest accuracy is 96.58% using the DT algorithm
Penerapan Machine Learning Dalam Analisis Stadium Penyakit Hati Untuk Proses Diagnosis dan Perawatan Jimmy; Lili Dwi Yulianto; Eni Heni Hermaliani; Laela Kurniawati
Resolusi : Rekayasa Teknik Informatika dan Informasi Vol. 3 No. 4 (2023): RESOLUSI Maret 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/resolusi.v3i4.709

Abstract

Liver disease is a disease that has existed for a long time and is quite common in society. This disease occurs because the liver cannot work optimally due to inflammation or viruses. Therefore, one of the ways used to determine liver disease is to do a blood test in the laboratory so as to obtain information in the form of enzyme levels, but blood tests in the laboratory require a fairly expensive so that predictions using machine learning is needed for this case, because the symptoms of liver disease need to be handled quickly. Medical record Data and laboratory results produce many features while too many features can reduce the value of accuracy in machine learning, so the features selection model is needed to determine the most influential features in machine learning. in this research that using three models of features selection, namely Random Forest Importance, Chi Square Test and Recursive Features Elimination and managed to get the two highest features, namely SGOT (Serum Glutamic Oxaloacetic Transaminase) and SGPT (Serum Glutamic Pyruvic Transaminase). Accuracy results will be compared between two features with eleven features using K-fold Cross Validation, and perform comparison using Features Extraction model using Principal Component Analysis (PCA). Accuracy calculation is done using Random Forest algorithm, Decision Tree, Naive Bayes, Logistic Regression, Support Vector Machine, KNN, Gradient Boosting and Artificial Neural Network, the calculation accuracy using Random Forest algorithm with PCA between Eleven and two features decreased by 0.6%, while using features selection increased by 0.7%, found the highest accuracy using Random Forest algorithm with 2 features of 72.2%.
Co-Authors Eni Heni Hermaliani Hari Anto Jimmy Laela Kurniawati Rhini Fatmasari Virda Mega Ayu windu Gata