Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
windu Gata
Universitas Nusa Mandiri, Jakarta
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
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
Perbandingan Kinerja Algoritma Machine Learning dalam Klasifikasi Sentimen Komentar Publik terhadap Pelayanan Perpajakan Dadang Kurniawan; Windu Gata; Taufik Asra
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.9164

Abstract

Early identification of malignant transformation in oral leukoplakia is crucial to prevent progression to Oral Squamous Cell Carcinoma (OSCC). However, conventional clinical assessment still faces limitations in terms of accuracy and interpretability, highlighting the need for reliable and transparent predictive approaches. This study aims to evaluate the performance of interpretable machine learning models in predicting malignant transformation of oral leukoplakia and OSCC based on clinical and histopathological data. A retrospective dataset consisting of 237 patient medical records was analyzed using several interpretable models, including Explainable Boosting Machine (EBM), Generalized Additive Models (GAM), and Symbolic Regression, and compared with black-box models such as Random Forest and Deep Neural Network. Model performance was evaluated using accuracy, sensitivity, specificity, and area under the curve (AUC). The results demonstrate that interpretable models achieve competitive predictive performance compared to black-box models while offering superior transparency and interpretability. Feature contribution analysis indicates that histopathological characteristics are the most influential factors in malignancy prediction. These findings suggest that interpretable machine learning models have strong potential as clinical decision support systems for early oral cancer detection.
Co-Authors Dadang Kurniawan Hari Anto Lili Dwi Yulianto Rhini Fatmasari Taufik Asra Virda Mega Ayu