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All Journal Jurnal Simetris Jurnal technoscientia Telematika : Jurnal Informatika dan Teknologi Informasi Seminar Nasional Informatika (SEMNASIF) Jurnal Sistem Informasi dan Bisnis Cerdas Register: Jurnal Ilmiah Teknologi Sistem Informasi Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Indonesian Journal of Information System Jurnal Eksplora Informatika JMM (Jurnal Masyarakat Mandiri) JurTI (JURNAL TEKNOLOGI INFORMASI) Compiler CARADDE: Jurnal Pengabdian Kepada Masyarakat Voice Of Informatics Jurnal Teknologi Sistem Informasi dan Aplikasi Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat JMAI (Jurnal Multimedia & Artificial Intelligence) JUSIM (Jurnal Sistem Informasi Musirawas) Antivirus : Jurnal Ilmiah Teknik Informatika Building of Informatics, Technology and Science Technologia: Jurnal Ilmiah Journal of Information Systems and Informatics Jurnal Sistem informasi dan informatika (SIMIKA) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JURNAL TEKNOLOGI TECHNOSCIENTIA Journal of Information Technology Ampera Journal of Computer and Information Systems Ampera Journal of Software Engineering Ampera Charity : Jurnal Pengabdian Masyarakat Prosiding SNAST Jurnal Pengabdian Masyarakat Indonesia Jurnal Ilmiah Teknologi Informasi dan Robotika Jurnal Abdi Masyarakat Indonesia International Journal Of Computer, Network Security and Information System (IJCONSIST) Jurnal Sains dan Teknologi Jurnal Pengabdian Mitra Masyarakat (JPMM) Journal of Computer Science and Technology (JCS-TECH) Journal Of Information System And Artificial Intelligence Jurnal Sistem Informasi dan Bisnis Cerdas Journal Research of Social Science, Economics, and Management SisInfo : Jurnal Sistem Informasi dan Informatika Innovative: Journal Of Social Science Research SENAPAS SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan International Journal of Artificial Intelligence and Science
Prasetyaningrum, Putri Taqwa
Universitas Mercu Buana Yogyakarta
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Journal : Journal of Information Systems and Informatics

 1 
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 : Universitas Bina Darma

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 : Universitas Bina Darma

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 Prasetyaningrum, Putri Taqwa; Ibrahim, Norshahila; Yuniasanti, Reny; Setyaningsih, Putry Wahyu; Subagyo, Ibnu Rivansyah
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

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 Simarmata, Penni Wintasari; Prasetyaningrum, Putri Taqwa
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

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 Ati, Gresensia Rosadelima; Prasetyaningrum, Putri Taqwa
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

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 saka, Hildegardis Kristina; Prasetyaningrum, Putri Taqwa
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

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 Mu'ti, Dewi Lestari; Prasetyaningrum, Putri Taqwa
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

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.
Comparative Analysis of Machine Learning Algorithms for Sentiment Classification of Discord App Reviews Rosita, Rani; Prasetyaningrum, Putri Taqwa
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1367

Abstract

The increasing use of digital communication applications such as Discord has generated diverse user opinions expressed through reviews on the Google Play Store. This study aims to analyze user sentiment toward the Discord application using text mining and machine learning techniques. A total of 3,000 reviews were collected through web scraping, pre-processed, labeled using a lexicon-based approach with TextBlob, and balanced using the SMOTE-Tomek method. Sentiment classification was performed into positive, negative, and neutral categories using Decision Tree, Logistic Regression, Support Vector Machine (SVM), and an Ensemble method. The Ensemble model achieved the highest accuracy of 98.67%, followed by Decision Tree (96.50%), SVM (95.83%), and Logistic Regression (90.33%). Limitations of this study include the use of lexicon-based sentiment labeling, machine translation from Indonesian to English, and initial class imbalance. Despite this strong performance, the study has limitations related to lexicon-based labeling, translation of reviews into English, and the presence of a highly imbalanced class distribution in the original dataset. Overall, the findings demonstrate that the Ensemble approach effectively improves sentiment classification accuracy and can support data-driven decision-making in application development.
Co-Authors Adi Ronggo Wicaksono Affandi Putra Pradana Agung Supoyo Agustin, Isnaini Ahmad Iwan Fadli Ahmad Mukhlasin Ahsan, Moh Ajisari, Lanang Dian Albert Yakobus Chandra Albert Yakobus Chandra Alphi Mukti Anggie Kurniawati Anggo Luthfi Yunanto Ari Wibowo Arita Witanti Aritonang, Roselina Artika Sari Arwa Ulayya Haspriyanti Ati, Gresensia Rosadelima Azzahra, Bernica Bagus Nur Solayman Bambang Setio Purnomo Bambang Setio Purnomo Budianto, Alexius Endy Cindy Okta Melinda Dapit Virdaus Denny Jean Cross Sihombing Devi Febrianti dewi, Ine shinta Dhana Sudana Eka Aryani, Eka Erza, Muhammad Al-Ghifari Fransiskus Xaverius Pere GUNARTATIK ESTHININGTYAS Hamam Nurrofiq Hasnidar Hasnidar Heri Agus Prasetyo Herin, Sofia Ibnu Rivansyah Subagyo Ibrahim, Norshahila Irfan Pratama Irya Wisnubhadra Julius Bata Jumiyati Juwita Juwita Karlina, Leni Khalifah Samiih Sya'bani Sya'bani Khoirut Tamimi Kris Rahayu Kristina Andryani Larasaty, Raditha Latifah, Retno Leni Karlina Lewoema, Scholastica Larissa Zefira luky kurniawan, luky M. Anjas Leonardi M. Irfan Bahri Mita Oktafani Mu'ti, Dewi Lestari Mukti, Alphi Rinaldi Nalendra Mutaqin Akbar Nadeak, Puja Waldi Nanda, Tietan Geovanka Ningsih, Rully Ningsih, Ruly Norshahila Ibrahim Nuning Rusmilawati Nur Sholehah Dian Saputri Nuri Budi Hangesti Nurul Tiara Kadir Okta, Sri Oktafani, Mita Ozzi Suria Ozzi Suria Ozzi Suria Pipin Yuliyanto Pratama, Bagus Wahyu Ari Pratama, Harfin Ibna Pratama, Irfan Puja Waldi Nadeak Puja Putra, Rio Aji Hadyanta Putry Wahyu Setyaningsih Rani Dwi Lestari Reny Yuniasanti Resi Dwi Febrianti Rias Ilham Agung Nugroho Rosita, Rani Rustiawan, Muhammad Rizqi Akfani saka, Hildegardis Kristina Santoso Pamungkas Sari, Artika Scholastica Lewoema Setiyani, Santi Setyaningsih, Putry Wahyu Simarmata, Penni Wintasari Subagyo, Ibnu Rivansyah Suria, Ozzi Suyoto Suyoto Viony Julianti Sipayung Wahyuningsih Wahyuningsih