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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) Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang 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 Jurnal INFOTEL 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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Pengembangan E-Business pada Industri Rumahan Briket Arang Batok Kelapa di Bantul: Pendekatan SWOT, PIECES, dan PESTEL Putri Taqwa Prasetyaningrum; Affandi Putra Pradana; Bagus Nur Solayman; Viony Julianti Sipayung
SisInfo Vol 7 No 1 (2025): SisInfo
Publisher : Universitas Informatika dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37278/sisinfo.v7i1.1067

Abstract

Usaha Mikro, Kecil, dan Menengah (UMKM) di Indonesia, khususnya di sektor briket arang batok kelapa, memiliki peran strategis dalam mendukung perekonomian nasional. Namun, UMKM ini menghadapi berbagai tantangan, seperti kapasitas produksi yang terbatas, pemasaran konvensional, dan pengelolaan stok manual, yang menghambat daya saing mereka. Penelitian ini bertujuan untuk merancang konsep E-Business berbasis web dengan pendekatan analisis SWOT, PIECES, dan PESTEL untuk mengidentifikasi faktor internal dan eksternal yang memengaruhi pengembangan UMKM. Hasil penelitian menunjukkan bahwa implementasi E-Business mampu mengoptimalkan operasional UMKM, memperluas jangkauan pasar hingga tingkat internasional, dan meningkatkan daya saing produk ramah lingkungan. Website yang dikembangkan mencakup fitur manajemen produk, transaksi online, analitik bisnis, dan integrasi pembayaran digital. Solusi ini berkontribusi pada efisiensi operasional dan peningkatan akses pasar, memungkinkan UMKM untuk beradaptasi di era digital dan bersaing secara global.
Implementasi Business Intelligence Untuk Menganalisis Jumlah Mahasiswa Baru Tahun 2024 di Universitas Mercu Buana Yogyakarta Putry Wahyu Setyaningsih; Putri Taqwa Prasetyaningrum
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 1 (2025): January
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.14756830

Abstract

Visualisasi data adalah representasi data dalam bentuk grafik, diagram, atau elemen visual lainnya yang memudahkan pemahaman pola, tren, dan informasi dari data tersebut. Visualisasi data membantu menyampaikan informasi kompleks secara lebih sederhana dan intuitif. Jumlah mahasiswa baru merupakan indikator penting dalam mengevaluasi kinerja dan daya saing suatu institusi pendidikan tinggi. Namun, analisis data jumlah mahasiswa baru seringkali menghadapi kendala, seperti kurangnya visualisasi data yang disajikan. Implementasi Business Intelligence (BI) menjadi solusi strategis untuk mengatasi masalah tersebut dengan menyediakan platform yang memungkinkan menyajikan visualisasi data secara efektif. Penelitian ini bertujuan untuk menerapkan BI dalam menganalisis jumlah mahasiswa baru. Alat yang digunakan dalam pengembangan BI meliputi perangkat lunak pengolahan data dan platform dashboard seperti Looker Studio. Hasil implementasi menunjukkan bahwa BI mampu memberikan wawasan yang lebih mendalam mengenai tren penerimaan mahasiswa baru. Dengan demikian, penerapan BI dapat meningkatkan efisiensi analisis data dan mendukung pengambilan keputusan strategis di institusi pendidikan tinggi.
Digital transformation of population administration: Enhancing data accessibility in local communities Suria, Ozzi; Prasetyaningrum, Putri Taqwa; Pratama, Irfan
Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Vol. 10 No. 1 (2025): February 2025
Publisher : University of Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/abdimas.v10i1.14983

Abstract

This community service initiative addresses the need for an efficient population administration system in RT3/RW3, Rejowinangun Utara Village, Magelang, where several key issues hinder effective data management. These problems include manual record-keeping with logbooks, which may increase the risk of errors and data loss, limited accessibility to population information, and challenges in adopting digital technology due to limited technical knowledge and infrastructure. To resolve these problems, this activity is conducted in four phases: data gathering, system development, evaluation, and user training. During the data-gathering phase, discussions with the community leader and the collection of family record data were conducted to identify specific needs and challenges. The system development phase focused on creating a user-friendly web-based Population Administration System (PAS) tailored to these requirements. In the evaluation phase, the system was tested and refined based on feedback from the community leader to ensure functionality and usability. The user training phase provided hands-on experience to the community leader, enabling independent use of the system for managing data and generating demographic summaries. The implementation of PAS successfully transformed administrative processes into digital and improved data accessibility for the local community.
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.
Multiclass Classification with Imbalanced Class and Missing Data Pratama, Irfan; Putri Taqwa Prasetyaningrum
IJCONSIST JOURNALS Vol 2 No 1 (2020): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (481.493 KB) | DOI: 10.33005/ijconsist.v2i1.25

Abstract

In any data mining field, the presence of a good shaped data is needed. Yet in the reality, the data condition is far from the expectation as there are possible to have missing values, redundant data, and inconsistent data. There are problems with the dataset to begin with before we overcome the problem of data mining process interpretation. In the raw data level, possible problem such as missing values and data redundancy or inconsistency can be solved by some certain process called preprocessing. On the preprocessing step, the raw dataset is adjusted to the needs of the whole process, one of the adjustments is to handle missing values. Missing values is a certain condition where the expected values of the data are not recorded. The other problems that happen in the real-world dataset especially in categorical data with label or class is the imbalance distribution of the instance for each class. The imbalanced class is a condition where the distribution of the class is skewed or biased. This study emphasizing on the problem solving of missing values and imbalanced class on the dataset. K-NN imputation is a missing value handling method of this study. As for the imbalanced class problem, this study utilizes SMOTE and ADASYN for the comparison. While the dataset will further be tested by various classification methods such as Decision tree, Random Forest, and Stacking. The original dataset produced bad score from the classification process due to the imbalanced data. Then the data undergoing an oversampling process using SMOTE and ADASYN methods in hope that the accuracy will be hugely better. Yet the reality is the accuracy score do not move to the expected number at all with only averaging in 32%-37% of accuracy score in any scheme of process.
COMPARISON OF SUPPORT VECTOR MACHINE RADIAL BASE AND LINEAR KERNEL FUNCTIONS FOR MOBILE BANKING CUSTOMER SATISFACTION ANALYSIS Putri Taqwa Prasetyaningrum; Nurul Tiara Kadir; Albert Yakobus Chandra; Irfan Pratama
IJCONSIST JOURNALS Vol 4 No 1 (2022): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v4i1.75

Abstract

Banking services using mobile banking applications, including Indonesian state bank (called BRI). A study on feedback regarding BRI services based on mobile applications was done. In order to compete with other banks, that is used to enhance and modernize the quality of BRI services provided to clients. Based on phenomena that occur in these situations. This study aims to classify comments from users of the BRI Mobile Banking Application on Google Play services into positive and negative comment sentiments. In this study, the Support Vector Machine (SVM) technique is utilized to determine between positive or negative reviews. The sentiment analysis of BRI google play data was carried out by comparing the Radial Basis Function (RBF) kernel function and the Linear kernel. As well as the experiment of adding feature selection, parameters, and n-grams for a period of two years, from January 1st,, 2017 to December 31st, 2018. The results of the study using the k-fold cross-validation test, the precision value of the SVM kernel linear is 90.80 percent and the SVM kernel RBF is 90.15 percent. In the RBF kernel, there are 1,816 positive classes and 1,455 negative classes. While the Linear kernel obtained a positive class of 1,734 and a negative class of 1,637.
Application of Gray Level Co-Occurrence Matrix (GLCM) for Abdominal Wave Image Classification: A Comparative Study of LVQ, KNN, and SVM Putri Taqwa Prasetyaningrum; Ibnu Rivansyah Subagyo
IJCONSIST JOURNALS Vol 6 No 2 (2025): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v6i2.126

Abstract

Medical image classification is a crucial research area in medical imaging analysis to support clinical diagnosis. In this study, we implemented the Gray Level Co-Occurrence Matrix (GLCM) method to extract texture features from abdominal wave images and enhance classification accuracy. Three machine learning classification methods—Learning Vector Quantization (LVQ), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)—were employed and compared based on their classification performance. The experimental results show that the KNN method achieved the highest accuracy of 96.83%, followed by SVM with 95.24%, and LVQ with 84.13%. These findings indicate that KNN is the most effective classification method for abdominal wave images among those tested. This study highlights the significance of texture feature extraction using GLCM in improving medical image classification accuracy. The results of this study can contribute to the advancement of digital healthcare technologies, particularly in gastrointestinal disorder detection and digestive health monitoring. Future research should explore hybrid deep learning approaches and larger datasets to further enhance classification accuracy and model robustness.
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 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