Article Per Year (5 Year)
p-Index From 2020 - 2025
1.399
P-Index
This Author published in this journals
All Journal Journal of Informatics and Computer Science (JINACS) Modem : Jurnal Informatika dan Sains Teknologi Journal of Emerging Information Systems and Business Intelligence (JEISBI) Journal of Education Technology and Information System Inovate : Jurnal Ilmiah Inovasi Teknologi Informasi
I Kadek Dwi Nuryana
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Library & Information Science

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

Found 12 Documents
Search

 1   2 
Penerapan Klasifikasi Pelanggan Berdasarkan Segmentasi Pelanggan pada UMKM Monex Toys Bekasi Eugenea Chiquita Zahrani Assyarif; I Kadek Dwi Nuryana
Modem : Jurnal Informatika dan Sains Teknologi. Vol. 3 No. 3 (2025): Juli : Modem : Jurnal Informatika dan Sains Teknologi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/modem.v3i3.533

Abstract

This study aims to conduct customer segmentation and develop a classification model to predict the clusters of new customers at Monex Toys Abadi Bekasi, a micro, small, and medium enterprise (MSME). Segmentation was performed using the K-Means Clustering algorithm, incorporating parameters such as Recency, Frequency, Monetary (RFM), purchased products, payment methods, shipping cost discounts, and the total number of products purchased by customers. The segmentation results revealed two clusters: (1) Discount Hunters and (2) Loyal Customers. Subsequently, a classification process was conducted to predict customer clusters using the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms. Evaluation results indicated that all models achieved high accuracy exceeding 98%. The best-performing model was obtained with SVM using a 70:30 data split, achieving an accuracy of 98.81%. This classification model was then implemented into a Streamlit-based cluster prediction application, enabling users to identify customer segments in real-time. The findings of this research are expected to assist MSMEs in understanding customer behavior, enhancing service quality, and supporting more effective marketing strategies.
Prediction and Analysis of Customer Churn at Telkomsel Using Machine Learning Approach Achmad Mauludi Asror; I Kadek Dwi Nuryana
Journal of Emerging Information Systems and Business Intelligence Vol. 6 No. 2 (2025): Vol. 06 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i2.65825

Abstract

Customer churn is one of the main problems in the telecommunications industry, including Telkomsel, the largest cellular operator in Indonesia. This study aims to build a classification model to predict customer churn and analyze the factors influencing churn using the CRISP-DM approach. Data was obtained through an online questionnaire from 100 respondents who are active students of Universitas Negeri Surabaya. The research process includes stages of data preparation (normalization, encoding, and removal of irrelevant attributes) and the application of classification algorithms such as Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine, and Naïve Bayes. Evaluation was carried out using metrics such as accuracy, precision, recall, and F1-Score. The results show that Random Forest is the best algorithm with an F1-Score of 87.50% on an 80:20 data ratio. Feature analysis indicates that the attribute of previous churn status has the greatest influence on churn prediction
TOPIC MODELING OF UNESA LAKE REVIEW ON GOOGLE MAPS USING LATENT DIRICHLET ALLOCATION (LDA) METHOD Kurrotul Uyun; I Kadek Dwi Nuryana
Journal of Emerging Information Systems and Business Intelligence Vol. 6 No. 2 (2025): Vol. 06 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i2.69653

Abstract

User reviews on digital platforms hold valuable information that can be used to improve service quality. This study aims to explore the topics that appear in visitor reviews of Lake UNESA based on rating categories with a topic modeling approach using the Latent Dirichlet Allocation (LDA) method. The analysis process follows the stages in the Knowledge Discovery in Databases (KDD) framework, starting from the selection of Google Maps review data, text preprocessing (cleaning, letter normalization, tokenization, word normalization, and stopword removal), and data transformation into bag-of-words representation through bigram-trigram formation and dictionary-corpus creation. Topic modeling is performed using LDA, and the results are evaluated and interpreted through pyLDAvis and wordcloud visualization. Model validation is carried out through Word Intrusion Task and Topic Intrusion Task testing, with accuracy levels of 0.91 and 0.88, respectively. The results show that LDA is able to identify topics optimally. Each rating category produces different topics that represent visitor perceptions of aspects that are not yet available, still k-aspects such as atmosphere, cleanliness, culinary, and facilities. These findings are expected to provide data-based insights to support the development and management of Lake UNESA more effectively.
USER SATISFACTION ANALYSIS OF SIDIA UNESA BASED ON PERCEIVED USEFULNESS, SYSTEM, INFORMATION, AND SERVICE QUALITY Mukhtarul Fata An Nadwi; I Kadek Dwi Nuryana
Journal of Emerging Information Systems and Business Intelligence Vol. 6 No. 2 (2025): Vol. 06 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i2.70637

Abstract

SIDIA UNESA (Sinau Digital UNESA) is an information system that supports lectures and administration at Surabaya State University. The purpose of this study was to determine the effect of perceived usefulness, system quality, information quality, and service quality variables on user satisfaction of the SIDIA UNESA system. A preliminary survey of several Surabaya State University students revealed that they were not fully satisfied with the services provided by SIDIA UNESA because several obstacles were often experienced by students. The research used quantitative methods with an associative descriptive approach. The analysis method used is Structural Equation Modeling (SEM) with the help of the SmartPLS application. The population in this study were UNESA students who were actively studying, with a sample size of 115 respondents. The findings revealed that the variables of perceived usefulness, system quality, information quality, and service quality positively impact user satisfaction with the UNESA SIDIA system. Additionally, the variables of perceived usefulness, system quality, information quality, and service quality simultaneously impact user satisfaction with the UNESA SIDIA system.
Public Complaint Text Classification in the Wargaku Application Using Natural Language Processing Alfina Dian Febyani; I Kadek Dwi Nuryana
Journal of Education Technology and Information System Vol. 2 No. 02 (2026): Journal of Education Technology and Information System (JETIS)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The Wargaku application is utilized by Surabaya residents to submit complaints concerning population administration services. With the increasing number of complaints, manual categorization becomes inefficient and susceptible to errors. This research aims to create an automatic classification system utilizing Natural Language Processing (NLP) and machine learning techniques. The dataset comprises 2,303 complaints divided into 18 categories. During preprocessing, text data was converted into numerical form using the Term Frequency–Inverse Document Frequency (TF-IDF) approach. Three machine learning models were tested: Support Vector Machine (SVM), Random Forest (RF), and Neural Network (NN), with evaluations based on accuracy and F1-score. Hyperparameter tuning was applied to enhance model performance. The SVM model yielded the best outcome with a training-to-testing data ratio of 85:15, resulting in a training accuracy of 93.96%, an F1-score of 96.08%, and a testing F1-score of 94.15%. This model was deployed in a web-based application via Streamlit to automatically categorize public complaints. The findings confirm the effectiveness of combining NLP and SVM in improving the efficiency of digital public service systems.
Implementation of SVM and GLDA for Gap Analysis on Mobile JKN Balqis Nur Aura Shaviradilla; I Kadek Dwi Nuryana
Journal of Education Technology and Information System Vol. 2 No. 02 (2026): Journal of Education Technology and Information System (JETIS)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study aims to analyze user perceptions of the Mobile JKN application developed by BPJS Kesehatan through sentiment analysis, topic modeling, and service quality gap analysis (SERVQUAL). Sentiment analysis was conducted using the Support Vector Machine (SVM) algorithm with linear and RBF kernels. The classification results, based on an 80:20 data split, indicate that the majority of user reviews express negative sentiment—2,784 reviews using the linear kernel and 2,847 using the RBF kernel—followed by positive and neutral sentiments. These findings suggest a general dissatisfaction among users regarding the application’s performance. Topic modeling was performed using the Guided Latent Dirichlet Allocation (GLDA) method, successfully grouping reviews into five main topics aligned with the SERVQUAL dimensions: tangibles, assurance, empathy, reliability, and responsiveness. The empathy topic appeared most frequently, while responsiveness was the least represented. The GLDA model achieved a coherence score of 0.64 and a UMass score of -1.99, indicating the model’s interpretability and consistency. Finally, the dimension-by-dimension gap analysis revealed that the assurance dimension had the smallest gap (-0.06), while the reliability dimension had the largest gap (-0.74). The overall SERVQUAL gap score was 0.11, highlighting a notable disparity between user expectations and their actual experiences. These results underline the need for targeted improvements in several service aspects of the Mobile JKN application.
Recommendations For Improving Application Services Using Root Cause Analysis Based On User Review Sentiment Analysis (Case Study: Digital Korlantas Polri) Mawarni, Lintang Iqhtiar Dwi; I Kadek Dwi Nuryana
Journal of Education Technology and Information System Vol. 2 No. 02 (2026): Journal of Education Technology and Information System (JETIS)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abstract. Amid the wave of digital transformation, the Digital Korlantas Polri application emerged as a solution and breakthrough in the digital-based SIM (driver's license) issuance process. However, concerns regarding the application’s performance quality remain, one of which can be assessed through user comments on the Google Play Store. A disparity was found between the app’s rating and the content of user reviews, raising questions about the actual quality of the application. This study aims to identify the root causes of user issues with the Digital Korlantas Polri application and generate improvement recommendations based on the identified problems. The research utilizes the pre-trained IndoBERT model for sentiment classification, followed by semi-supervised topic modeling using Guided LDA to uncover hidden patterns in the review data. Furthermore, the pre-trained GPT-2 model is employed as a text generator to produce application improvement recommendations based on the identified issues. Evaluation results show that the sentiment model achieved a confidence score of 0.99, the Guided LDA model reached a coherence score of 0.51, and the GPT-2 model yielded a perplexity value of 1.3. Overall, the models successfully fulfilled their respective roles, enabling more effective and efficient analysis, and generating realistic and timely recommendations for addressing the identified issues
Article Reviewer Recommendation System Using Euclidean Distance Similarity with Content-Based Collaborative Filtering (Case Study: ICVEE) wilda; I Kadek Dwi Nuryana
Journal of Education Technology and Information System Vol. 3 No. 01 (2027): Journal of Education Technology and Information System (JETIS)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The growth of research publications in academic environments has resulted in large volumes of unstructured data, particularly in the form of article titles and abstracts. However, the majority of educational institutions still manage these resources manually, without optimizing them for academic decision-making. This study proposes an article reviewer recommendation system using a content-based filtering method with TF-IDF for text representation and Euclidean Distance as the similarity measure. Reviewer profiles are constructed based on previously reviewed articles. A new article is represented as a vector and compared against reviewer profiles to determine relevance. The system was evaluated using 20 articles as ground truth. Results show that the Euclidean Distance approach outperformed Cosine Similarity, achieving an accuracy of 55%, precision of 0.2333, recall of 0.2121, and F1-score of 0.222. This study demonstrates the potential of content-based filtering in enhancing reviewer assignment efficiency for academic conferences such as ICVEE.
SISTEM PENDUKUNG KEPUTUSAN PERKEMBANGAN BELAJAR ANAK TK EL-YAMIEN 1 TUBAN MENGGUNAKAN METODE SMART BERBASIS WEB Alfi Azqia Uzzulfa Haq; Tanhella Zein Vitadiar; I Kadek Dwi Nuryana; Muhammad Fatkhur Rizal
Inovate Vol 9 No 1 (2024): September
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v9i1.7270

Abstract

El-Yamien 1 Tuban Kindergarten is an Islamic-based kindergarten and is located in Tuban District. Thisresearch focuses on developing a Decision Support System for Assessment of Children's Learning Progressat El-Yamien 1 Tuban Kindergarten using the web-based SMART Method. This research problemaddresses the inefficiency of the existing manual assessment process in schools, which hinders teachers inevaluating children's learning development effectively. The goal was to develop a web-based system thatwould automate the assessment process and provide ratings of children's learning progress. Using theSimple Multi Attribute Rating Technique (SMART) method for decision making, focusing on five criteriafor assessing children's learning progress. The results show that the web-based system simplifies the processof inputting teacher data and automatically produces assessment results, making it easier to evaluatechildren's learning progress. The implications of this research indicate the importance of incorporatingdigital technology in education to improve assessment processes and improve decision making.Keywords: Decision Support System, SMART, Children's Learning Development.
PENERAPAN SISTEM INFORMASI PENGGAJIAN KARYAWAN BERBASIS WEB PADA CAFE TEATIME MENGGUNAKAN METODE GROSS M. Renaldi Wildan F; I Kadek Dwi Nuryana; Ahmad Heru Mujianto; Kistofer, Terdy
Inovate Vol 9 No 1 (2024): September
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v9i1.7280

Abstract

Employees usually receive a fixed salary from the company and can also be interpreted as a motivation toemployees that can be done periodically. The remuneration is made up of basic salary, overtime, and so on.The administration calculates the salaries of employees and the data necessary for the salary calculationprocess such as employee data, employee time accuracy reports, and such reports are obtained through theprocessing of the percentage of employees received when recapitulating the presence and working hours datafrom HRD. In the calculation of salaries, three methods that can be applied by the agencies of the cafe areavailable Net, Gross method, and Gross Up method. The processes that have not been integrated into thesystem including data processing and the process of employee wage calculation are still manual.A summary of the results of this study is a web-based employee remuneration application that can helpinternal cafes such as HRD, admin, general manager, and general manager.Keywords: wage system, gross, information system.
Co-Authors Achmad Mauludi Asror Alfi Azqia Uzzulfa Haq Alfina Dian Febyani Aries Dwi Indriyanti Balqis Nur Aura Shaviradilla Bonda Sisephaputra Eugenea Chiquita Zahrani Assyarif Ghea Sekar Palupi Kistofer, Terdy Kurrotul Uyun M Wildan Kafafi M. Renaldi Wildan F Mawarni, Lintang Iqhtiar Dwi Muhammad Fatkhur Rizal Mujianto, Ahmad Heru Mukhtarul Fata An Nadwi Tanhella Zein Vitadiar Wilda Zakaria Nur Abidin