Article Per Year (5 Year)
p-Index From 2021 - 2026
1.066
P-Index
This Author published in this journals
All Journal Natural Science: Journal of Science and Technology Media Statistika Jurnal Gaussian JURNAL ILMIAH MATEMATIKA DAN TERAPAN BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Aplikasi Statistika & Komputasi Statistik Enthusiastic : International Journal of Applied Statistics and Data Science Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA)
Iut Tri Utami
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Jurnal Gaussian

 1 
METODE ENSEMBLE ROBUST CLUSTERING USING LINKS (ROCK) UNTUK PENGELOMPOKAN PERGURUAN TINGGI SWASTA (PTS) DI KOTA SEMARANG Jannah, Berliana; Utami, Iut Tri; Hakim, Arief Rachman
Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.3.445-452

Abstract

The purpose of this research is to group PTS that have performance achievements in five years, through the quality of Human Resources and Students (Input), the quality of Institutional Management (process), the quality of Short-Term Performance Achievements (Output) and the quality of Long-Term Performance Achievements (Outcome). In addition, it can also be seen from the form of PTS, PTS Accreditation and PTS Research Performance. This PTS grouping uses mixed data, namely numerical data and categorical data. The method used for grouping mixed data is the ROCK ensemble method (Robust Clustering Using Links). The results of clustering numerical data obtained the optimum number of groups 3, on categorical data obtained the optimum group 4. After clustering each type of data and merging and clustering obtained the optimum group 3 with a threshold (θ) is 0.2. The results of each group are: low quality consist of 29 PTS, medium quality consist of 7 PTS, and high quality there is 1 PTS. The results of this research can be used to cluster private universities in Semarang City, so that it can be used as a reference for prospective students in choosing private universities in Semarang, and can be referenced to the Central Java LLDIKTI in determining the quality of private universities in Semarang City.
ANALISIS SENTIMEN KEBIJAKAN PENYELENGGARA SISTEM ELEKTRONIK LINGKUP PRIVAT MENGGUNAKAN PENALIZED LOGISTIC REGRESSION DAN SUPPORT VECTOR MACHINE Amalia, Nur Afnita; Utami, Iut Tri; Wilandari, Yuciana
Jurnal Gaussian Vol 12, No 4 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.4.560-569

Abstract

The implementation of the Electronic System Operator (ESO) regulation, which imposes blocking sanctions on several ESOs that do not register, has caused a variety of opinions from the public, especially on social media Twitter to raise the hashtag #BlokirKominfo. In this research, sentiment analysis was carry outed to determine the response of Twitter users to the implementation of ESO regulations by MoCI. Sentiment analysis is a textual information extraction process that classifies sentiment into positive and negative categories. The steps that are used including crawling data, text preprocessing, labeling, feature selection, term weighting with TF-IDF and classification using the Penalized Logistic Regression (PLR) with the L1 regularization and Support Vector Machine (SVM) with the RBF kernel. Sentiment classification in PLR is basically finding the optimal weight parameter. The idea of SVM sentiment classification is to find the best hyperplane to separate the data points. Evaluation of classification performance uses the accuracy value calculated through the confusion matrix. The highest percentage of accuracy in sentiment classification results using the PLR is 84,12% and SVM is 83,53%. It means that the PLR algorithm works better than the SVM algorithm in classifying public sentiment towards the implementation of ESO regulations on Twitter.
Co-Authors Amalia, Nur Afnita Arief Rachman Hakim Diah Daniaty Dwi Ispriyanti Fadjryani, Fadjryani Indria, Nabila Dwi Jannah, Berliana Junaidi Junaidi Junaidi Junaidi Masithoh Yessi Rochayani Nureni, Nureni Puspita Kartikasari Rahman, Syair Dafiq Faizur Rais Rais Rancendo, H M Suparti Suparti Suryaningrum, Fahlevi Susiana, Luluk Tulak, Dewi Yuliastuti Yuciana Wilandari ZK Abdurahman Baizal