Article Per Year (5 Year)
p-Index From 2021 - 2026
1.756
P-Index
This Author published in this journals
All Journal Journal of Information Systems and Informatics
Putri Taqwa Prasetyaningrum
Mercu Buana University of Yogyakarta
Computer Science & IT

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 10 Documents
Search

 1 
Sentiment Analysis of User Reviews for AI Applications: Evaluating SVM, Logistic Regression, and Random Forest Rivana Dwi Cahyani; Putri Taqwa Prasetyaningrum
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1366

Abstract

The rapid growth of AI applications such as CICI, GROK, and Gemini has resulted in a large volume of user reviews on platforms like the Google Play Store, making sentiment analysis a critical tool for understanding user perceptions. This study compares the performance of three machine learning models: Random Forest, Support Vector Machine (SVM), and Logistic Regression in classifying sentiments in 3,500 Indonesian-language reviews. A hybrid feature extraction approach, combining sentiment lexicons with TF-IDF, was applied to improve sentiment classification accuracy. The models were evaluated based on accuracy, precision, recall, and F1-score. Results indicated that all models achieved an accuracy greater than 96%, with Random Forest providing the most consistent and accurate results, achieving an overall accuracy of 99.62%. While SVM excelled in classifying positive and negative sentiments, it faced challenges with neutral reviews due to the ambiguity and overlap in sentiment expression. Logistic Regression also showed strong performance, especially on structured reviews. The findings suggest that Random Forest is the most robust and reliable model for sentiment analysis, particularly in handling diverse AI application reviews. These results offer practical insights for developers seeking to improve application performance by leveraging sentiment analysis on user feedback.
Sales Prediction on the Diamond Cell Counter Using Autoregresive Integrated Moving Average (ARIMA) Method Kris Rahayu; Putri Taqwa Prasetyaningrum
Journal of Information System and Informatics Vol 5 No 1 (2023): Journal of Information Systems and Informatics
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i1.450

Abstract

Diamond Cell is a specialized retailer that offers a diverse range of smartphone accessories, electronic credits, and internet vouchers from different providers, each with varying active periods. However, the uncertainty surrounding internet voucher sales transactions often leaves counter owners hesitant to increase their stock due to the short active period of the vouchers. This leads to frequent customer dissatisfaction as the internet vouchers run out, resulting in lost sales opportunities. To address this issue, this study aimed to predict voucher sales for the upcoming month to serve as a reference for calculating the stock of voucher supply. The Auto-regressive Integrated Moving Average (ARIMA) method was used based on voucher sales data from November 2022 to January 2023. Out of the three tentative models obtained, only one proved suitable for use. The best ARIMA model was the (2,1,0) model, with a MAD value of 29.65, an MSE value of 2409.95, and a MAPE value of 23.3%. Based on the February voucher sales, the stock level can remain the same as the previous period since the sales were stable. The findings of this study can help Diamond Cell counter owners make more informed decisions about stocking internet vouchers, resulting in better customer satisfaction and reduced likelihood of losses.
Enhancing Sales Determination for Coffee Shop Packages through Associated Data Mining: Leveraging the FP-Growth Algorithm Wahyuningsih Wahyuningsih; Putri Taqwa Prasetyaningrum
Journal of Information System and Informatics Vol 5 No 2 (2023): Journal of Information Systems and Informatics
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i2.500

Abstract

The coffee shop business offers a diverse range of coffee and food options. However, customers often experience delays during transactions due to the extensive selection of menu items and combinations. This inconvenience not only discomforts new customers but also hampers their likelihood of returning, potentially impacting the overall business turnover. To address this issue, this study aims to establish association rules by combining the least and most popular menu items for the upcoming month. These rules will serve as a guideline for creating shopping packages that streamline the decision-making process. The FP-Growth algorithm is employed to analyze sales transaction data from January to March 2023, comprising 2,336 transactions in .csv format. Among the generated association rules, two rules stand out with the highest support and confidence values. The first rule exhibits a support value of 0.3% and a confidence of 70.0%, while the second rule showcases a support value of 0.4% and a confidence of 69.2%. By considering these two rules alongside the existing menu options, coffee shop owners can effectively curate shopping packages that cater to customer preferences. It is anticipated that these packages will elevate the quality of service, attract a greater number of customers, and subsequently enhance the overall business turnover.
Designing Gamified Systems for Mental Health Support: An Exploratory Study Putri Taqwa Prasetyaningrum; Norshahila Ibrahim; Reny Yuniasanti; Putry Wahyu Setyaningsih; Ibnu Rivansyah Subagyo
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.760

Abstract

This exploratory study investigates the innovative design of gamified systems for mental health support, focusing on enhancing user engagement and well-being. By integrating user-centered design (UCD) principles with effective gamification elements, this research aims to develop engaging and therapeutically effective mental health interventions. Employing a mixed-methods approach, the study combines qualitative and quantitative data collection, including surveys, interviews, and user testing, to gather comprehensive insights from a diverse participant group. The findings reveal significant insights into user engagement, satisfaction, and the impact of gamification on mental health outcomes. While gamification enhances user engagement, balancing entertainment with therapeutic functionality is crucial. A comparative analysis between gamified and non-gamified app versions highlights the benefits and challenges of incorporating gamification in mental health contexts. The study concludes with practical recommendations for future research and design, emphasizing the need for continued innovation to optimize digital mental health interventions.
Development of a Student Depression Prediction Model Based on Machine Learning with Algorithm Performance Evaluation Penni Wintasari Simarmata; Putri Taqwa Prasetyaningrum
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1087

Abstract

This research explores the implementation of machine learning to predict depression among university students using a dataset of 2.028 responses containing PHQ-9 scores and academic-demographic attributes. The research implements a structured modeling process involving feature selection, normalization, the model’s efficacy was gauged through a suite of evaluate measures, encompassing accuracy, precision, recall, F1-score, The support vector machine (SVM) model’s accuracy improved from 58.8% to 99.5% after hyperparameter tuning. This investigation lends itself to the advancement of a proactive identification framework, which hold potential for incorporation within collegiate mental well-being surveillance infrastructures. Future implementations may consider real-time models and expand data sources through digital counseling systems and behavioral analytics
Analysis of Community Sentiment Towards Free Nutrition Meal Programs on Twitter Using Naïve Bayes, Support Vector Machine, K-Nearest Neighbors, and Ensemble Methods Gresensia Rosadelima Ati; Putri Taqwa Prasetyaningrum
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1098

Abstract

Meal program free nutritious food that was planned government reap diverse response from society, especially on social media like Twitter. Research This aiming for analyze sentiment public to the program with utilize text mining and machine learning techniques. Data of 1500 tweets was collected through the scraping process using Python. The sentiment in the tweets is classified into three categories: positive, negative, and neutral. In this study, four classification algorithms were used: Naïve Bayes, Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and ensemble, to compare their performance in sentiment analysis. Additionally, a text weighting method, TF-IDF, was tested to examine its impact on classification accuracy. The analysis results show that the Support Vector Machine (SVM) algorithm, when combined with the TF-IDF weighting method, provides the highest accuracy of 95.05%. Other algorithms also showed varied performance, with Ensemble achieving 86.57%, K-Nearest Neighbors 77.03%, and Naïve Bayes 60.42% accuracy. It is expected from results study This can give description general to perception public about the meal program free nutritious an
Sentiment Analysis and Classification of User Reviews of the 'Access by KAI' Application Using Machine Learning Methods to Improve Service Quality Hildegardis Kristina saka; Putri Taqwa Prasetyaningrum
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1099

Abstract

This research applies sentiment analysis to understand user perceptions of the Access by KAI application, especially specific aspects such as speed, payment process, and user interface (UI/UX). User reviews are collected and processed through preprocessing stages, balancing using the SMOTE method, and classified using three machine learning algorithms, namely Support Vector Machine (SVM), Decision Tree, and Logistic Regression. The SVM model achieved the highest accuracy of 89.33%, followed by Logistic Regression at 88%, and Decision Tree at 86.67%. Precision, recall, and F1-scores for each model were also evaluated, showing strong performance in detecting negative sentiments but lower performance for neutral and positive sentiments. In addition, keyword-based analysis revealed that negative sentiment was most commonly found in the aspects of the payment process and speed. WordCloud visualization also strengthens the results by showing the dominance of negative words in user reviews. The results of this study provide important suggestions and input for application developers to improve aspects of the service that are considered less satisfactory by users. Thus, this study can be used as a practical guide in making strategic decisions to improve the quality of service and user satisfaction of the Access by KAI application.
Comparative Analysis of Classification Algorithms for Predicting Membership Churn in Fitness Centers: Case Study and Predictive Modeling at EightGym Indonesia Dewi Lestari Mu'ti; Putri Taqwa Prasetyaningrum
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1120

Abstract

The fitness industry in Yogyakarta is experiencing rapid growth accompanied by intense competition among gym service providers. This has led to an increase in membership churn, negatively impacting business sustainability. This study aims to conduct a comparative analysis of three supervised classification algorithms Random Forest, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) to predict member churn at EightGym Indonesia. The dataset, consisting of 1,287 membership records collected between July 2024 and April 2025, includes features such as visit frequency, subscription duration, membership type, and churn status. The study focuses on predicting members at risk of subscription cancellation using historical data such as visit frequency, subscription duration, membership type, and churn status. The methodology follows the CRISP-DM framework, covering business understanding, data preparation, modeling, evaluation, and deployment stages. Evaluation results indicate that XGBoost delivers the best performance with 95% accuracy, high recall, and F1-score, making it the most effective algorithm for churn prediction in this context. Additionally, the model was implemented in a web-based prototype application to support gym management decision-making. The findings contribute significantly to the application of machine learning for customer retention strategies in the fitness industry and provide a foundation for the future development of predictive decision support systems.
Hierarchical Cognitive Structuring and Game Mechanics Alignment in Educational Game Systems: A Systematic Literature Review Umi Rosyidah; Purwanto; Putri Taqwa Prasetyaningrum
Journal of Information System and Informatics Vol 8 No 5 (2026): October
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i5.1739

Abstract

Educational games are increasingly used to support meaningful learning, yet the ways in which cognitive progression is translated into game mechanics and system-level design remain insufficiently explicit in the literature. This study followed the PRISMA 2020 guidelines to review empirical studies published between 2015 and 2025 and indexed in Scopus and IEEE Xplore. After screening and eligibility assessment, 68 studies were included. Constructivist approaches were predominant, while only 15 studies (22.1%) explicitly represented a hierarchical cognitive structure to organize gameplay progression; the remaining 53 studies (77.9%) represented cognitive progression implicitly. Recurring operational patterns included level-based progression, sequenced task organization, and adaptive scaffolding through feedback. The findings indicate that explicit cognitive-mechanics alignment is underreported in the reviewed corpus rather than absent from educational game design. For design practice, the results highlight the need for traceable frameworks that connect cognitive objectives with missions, rules, feedback, progression, and assessment tasks. Because the search was limited to Scopus and IEEE Xplore, the findings should not be interpreted as exhaustive coverage of all educational game research.
Evaluating Perceived Usability and Gamification Perception in a Lean UX Hotel Booking Prototype Putri Taqwa Prasetyaningrum; Norshahila Ibrahim
Journal of Information System and Informatics Vol 8 No 5 (2026): October
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i5.1843

Abstract

This single-group prototype evaluation examines the association between two self-reported post-use perceptions: perceived usability and gamification perception in an early-stage hotel-booking prototype. It does not test whether gamification improves booking performance.
 A high-fidelity prototype was developed through two Lean User Experience iterations. After the formative first iteration, 150 adults with prior hotel-booking-app experience completed the same ordered scenario remotely through Maze and then answered the System Usability Scale (SUS) and a study-specific ten-item Gamification Perception Questionnaire (GPQ). Objective task-success, completion-time, error, misclick, and abandonment metrics were not retained for analysis.
Mean SUS was 89.85 (SD = 11.37; 95% CI [88.02, 91.68]) and the GPQ descriptive mean was 4.67/5 (SD = 0.38; 95% CI [4.61, 4.73]). The two self-reported measures were positively associated (r = 0.66, 95% CI [0.56, 0.74], p < 0.001). Exploratory gender and age contrasts yielded small effects. The high SUS score represents perceived usability in this sample and does not establish task success or behavioral effectiveness.
 The contribution is empirical and design-oriented: it documents a Lean UX prototype evaluation in a transactional hospitality context. Interpretation is limited by the single-group design, possible novelty and ceiling effects, self-selection, remote testing, common-method bias, and the status of the GPQ as a study-specific descriptive index that still requires full psychometric validation.
Co-Authors Dewi Lestari Mu&#039;ti Gresensia Rosadelima Ati Hildegardis Kristina saka Ibnu Rivansyah Subagyo Kris Rahayu Norshahila Ibrahim Penni Wintasari Simarmata Purwanto Putry Wahyu Setyaningsih Reny Yuniasanti Rivana Dwi Cahyani Umi Rosyidah Wahyuningsih Wahyuningsih