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Prasetyaningrum, Putri Taqwa
Universitas Mercu Buana Yogyakarta
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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.
IMPLEMENTASI TERAPI VIRTUAL REALITY SEBAGAI INOVASI LAYANAN BIMBINGAN DAN KONSELING DI SEKOLAH MENENGAH Prasetyaningrum, Putri Taqwa; Aryani, Eka; Ningsih, Ruly
JMM (Jurnal Masyarakat Mandiri) Vol 8, No 5 (2024): Oktober
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v8i5.26542

Abstract

Abstrak: Layanan bimbingan dan konseling di sekolah menengah di SMP Negeri 2 Godean menghadapi kendala seperti rasio guru konselor yang tidak ideal dan metode layanan konvensional yang kurang inovatif. Tujuan pengabdian ini adalah untuk meningkatkan aksesibilitas dan kualitas layanan bimbingan dan konseling melalui implementasi terapi Virtual Reality (VR). Metode yang digunakan meliputi sosialisasi, pelatihan, workshop, dan pendampingan bagi guru dan siswa, dengan evaluasi yang dilakukan melalui observasi dan wawancara terstruktur. Program ini melibatkan 10 guru bimbingan dan konseling serta 384 siswa. Hasil menunjukkan peningkatan 100% dalam kemampuan guru menggunakan teknologi VR dan peningkatan 85% aksesibilitas layanan bimbingan dan konseling bagi siswa. Selain itu, terjadi peningkatan keterlibatan siswa dalam layanan bimbingan sebesar 70%, menunjukkan dampak positif terhadap kualitas layanan yang diberikan.Abstract: The guidance and counseling services at SMP Negeri 2 Godean face challenges such as an inadequate counselor-to-student ratio and conventional, less innovative service methods. The goal of this community service is to enhance the accessibility and quality of guidance and counseling services through the implementation of Virtual Reality (VR) therapy. Methods include socialization, training, workshops, and mentoring for teachers and students, with evaluation conducted through structured interviews and observations. The program involves 10 guidance counselors and 384 students. Results show a 100% increase in teachers' ability to use VR technology and an 85% improvement in service accessibility for students. Additionally, there is a 70% increase in student engagement in counseling services, indicating a positive impact on the quality of services provided.
Implementasi Data Mining Pada Klasifikasi Status Gizi Bayi Dengan Metode Decision Tree CHAID (Studi Kasus: Puskesmas Godean 1 Yogyakarta) Lewoema, Scholastica Larissa Zefira; Prasetyaningrum, Putri Taqwa
Journal of Information Technology Ampera Vol. 5 No. 1 (2024): Journal of Information Technology Ampera
Publisher : APTIKOM SUMSEL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalita.v5i1.538

Abstract

Penelitian ini mengukur akurasi metode Decision Tree CHAID dalam mengklasifikasikan status gizi bayi dengan menambahkan atribut jenis kelamin dan lokasi desa posyandu. Hasil penelitian ini adalah situs web berbasis server lokal untuk menguji sistem klasifikasi tersebut. Prosesnya meliputi impor data, pembagian data latih dan uji, pelatihan model, pemilihan algoritma, dan pengujian matriks. Dari 3106 data antara Januari hingga Februari 2024, akurasi pada data uji mencapai 0,90, pada data latih 0,99, dan akurasi algoritma CHAID 0,84. Variabel yang digunakan meliputi usia, desa, posyandu, tinggi badan, berat badan, dan jenis kelamin. Kelas status gizi meliputi gizi baik, gizi buruk, gizi kurang, gizi berlebih, obesitas, dan risiko gizi berlebih. This research aims to measure the accuracy of the Decision Tree CHAID method in classifying the nutritional status of infants by adding new attributes such as gender and village posyandu location. The outcome of this research is a locally hosted website for testing the classification system using the CHAID-based Decision Tree method. The process includes data import, splitting data into training and testing sets, training the machine learning model, selecting the appropriate algorithm, and performing a confusion matrix test. From 3106 data entries collected between January and February 2024, the accuracy on the test data reached 0.90, on the training data 0.99, and the CHAID algorithm accuracy was 0.84. The variables used include age, village, posyandu, height, weight, and gender. The nutritional status classes used as labels in this study are good nutrition, malnutrition, undernutrition, overnutrition, obesity, and risk of overnutrition.
Klasifikasi Daun Teh Klon Seri GMB Menggunakan Convolutional Neural Network dengan Arsitektur VGG16 dan Xception Mukti, Alphi Rinaldi Nalendra; Prasetyaningrum, Putri Taqwa
Journal of Information Technology Ampera Vol. 5 No. 1 (2024): Journal of Information Technology Ampera
Publisher : APTIKOM SUMSEL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalita.v5i1.540

Abstract

Indonesia memiliki tingkat konsumsi teh tertinggi di dunia, di mana kualitas daun teh sangat bergantung pada lokasi tumbuhnya. Untuk mengidentifikasi jenis teh, sistem otomatisasi dengan pengolahan citra digital digunakan. Penelitian ini membandingkan dua arsitektur model yaitu dengan augmentasi data dan tanpa augmentasi dalam mengklasifikasikan daun teh klon seri GMB 1-5. Hasil penelitian menunjukkan bahwa model CNN tanpa augmentasi memberikan akurasi yang lebih tinggi dibandingkan dengan yang menerapkan augmentasi. Secara spesifik, model Xception tanpa augmentasi mencapai akurasi 98%, sedangkan VGG16 tanpa augmentasi mencapai 95%. Sebaliknya, model dengan augmentasi memperoleh akurasi 92% untuk Xception dan 94% untuk VGG16. Temuan ini menunjukkan bahwa, dalam konteks dataset terbatas, model tanpa augmentasi cenderung lebih akurat karena menghindari overfitting yang sering terjadi pada dataset kecil. Indonesia has the highest tea consumption rate in the world, where the quality of tea leaves is heavily dependent on their growing location. To identify tea types, an automation system using digital image processing is employed. This study compares two model architectures: one with data augmentation and one without, in classifying GMB 1-5 series tea leaves. The results indicate that the CNN model without augmentation achieved higher accuracy compared to the one with augmentation. Specifically, the Xception model without augmentation reached an accuracy of 98%, while VGG16 without augmentation achieved 95%. In contrast, the model with augmentation achieved 92% accuracy for Xception and 94% for VGG16. These findings suggest that, in the context of a limited dataset, models without augmentation tend to be more accurate as they avoid overfitting commonly encountered with small datasets.
Analisis Sentimen Terhadap Klinik Natasha Skincare di Yogyakarta Dengan Metode Google Review Rustiawan, Muhammad Rizqi Akfani; Prasetyaningrum, Putri Taqwa
Journal of Information Technology Ampera Vol. 5 No. 1 (2024): Journal of Information Technology Ampera
Publisher : APTIKOM SUMSEL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalita.v5i1.556

Abstract

Penelitian ini bertujuan menganalisis sentimen terhadap Klinik Natasha Skincare di Yogyakarta melalui ulasan Google Review. Data dikumpulkan dari ulasan pengguna yang mengunjungi klinik, dan analisis sentimen digunakan untuk mengevaluasi opini serta perasaan positif atau negatif dalam ulasan tersebut. Hasil analisis diharapkan membantu manajemen klinik memahami persepsi dan pengalaman pengguna, serta meningkatkan kualitas layanan. Penelitian ini juga menggunakan algoritma Support Vector Machine (SVM) untuk mengklasifikasikan sentimen ulasan, dengan tujuan memberikan wawasan mendalam tentang reputasi Klinik Natasha Skincare di Yogyakarta. This study aims to analyze sentiments towards Natasha Skincare Clinic in Yogyakarta through Google Reviews. Data was collected from reviews by users who visited the clinic, and sentiment analysis was used to evaluate the positive or negative opinions and feelings contained in these reviews. The results of this analysis are expected to help the clinic management understand user perceptions and experiences, and to improve the quality of services provided. This study also employs the Support Vector Machine (SVM) algorithm to classify the sentiments of the collected reviews, aiming to provide deeper insights into the reputation of Natasha Skincare Clinic in Yogyakarta.
Measuring Resampling Methods on Imbalanced Educational Dataset’s Classification Performance Pratama, Irfan; Prasetyaningrum, Putri Taqwa; Chandra, Albert Yakobus; Suria, Ozzi
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 10 No 1 (2024): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v10i1.3397

Abstract

Imbalanced data refers to a condition that there is a different size of samples between one class with another class(es). It made the term “majority” class that represents the class with more instances number on the dataset and “minority” classes that represent the class with fewer instances number on the dataset. Under the target of educational data mining which demands accurate measurement of the student’s performance analysis, data mining requires an appropriate dataset to produce good accuracy. This study aims to measure the resampling method’s performance through the classification process on the student’s performance dataset, which is also a multi-class dataset. Thus, this study also measures how the method performs on a multi-class classification problem. Utilizing four public educational datasets, which consist of the result of an educational process, this study aims to get a better picture of which resampling methods are suitable for that kind of dataset. This research uses more than twenty resampling methods from the SMOTE variants library. as a comparison; this study implements nine classification methods to measure the performance of the resampled data with the non-resampled data. According to the results, SMOTE-ENN is generally the better resampling method since it produces a 0,97 F1 score under the Stacking classification method and the highest among others. However, the resampling method performs relatively low on the dataset with wider label variations. The future work of this study is to dig deeper into why the resampling method cannot handle the enormous class variation since the F1 score on the student dataset is lower than the other dataset.
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 Alfin Ainin Ramdhani Alphi Mukti Anggie Kurniawati Anggo Luthfi Yunanto Ari Wibowo Arita Witanti Aritonang, Roselina Artika Sari Arwa Ulayya Haspriyanti Aryani, Eka Ati, Gresensia Rosadelima Azzahra, Bernica Bagus Nur Solayman Bambang Robiin 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 Fendi Pradana Saputra Fithriatus Shalihah Fransiskus Xaverius Pere GUNARTATIK ESTHININGTYAS Hamam Nurrofiq Hasnidar Hasnidar Heri Agus Prasetyo Herin, Sofia Herlina Angraini Susanti Ibnu Rivansyah Subagyo Ibnu Rivansyah Subagyo Ibnu Rivansyah Subagyo Ibrahim, Norshahila Imam Riadi 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 Nabil Fauzan Nanda, Tietan Geovanka Ningsih, Ruly Norshahila Ibrahim Nuning Rusmilawati Nur Sholehah Dian Saputri Nuri Budi Hangesti Nursila Latambaga Nurul Tiara Kadir Nurul Tiara Kadir Okta, Sri Oktafani, Mita Ozzi Suria Ozzi Suria Pipin Yuliyanto Pratama, Bagus Wahyu Ari Pratama, Irfan Puja Waldi Nadeak Puja Waldi Nadeak Puja Purwanto Purwanto Putra, Rio Aji Hadyanta raden roro christawani herawati Raditha Larasaty Rani Dwi Lestari Reny Yuniasanti Resi Dwi Febrianti Rias Ilham Agung Nugroho Rosita, Rani Rully Ningsih Rustiawan, Muhammad Rizqi Akfani saka, Hildegardis Kristina Santoso Pamungkas Sari, Artika Scholastica Larissa Zefira Lewoema Scholastica Lewoema Setiyani, Santi Setyaningsih, Putry Wahyu Sidiq Purnomo, Agus SILA MILDAWATI Simarmata, Penni Wintasari Subagyo, Ibnu Rivansyah Suharjo, Imam Suria, Ozzi Suyoto Suyoto Syalom Kristian Manurung Umi Rosyidah Viony Julianti Sipayung Wahyuningsih Wahyuningsih