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Prasetyaningrum, Putri Taqwa
Universitas Mercu Buana Yogyakarta
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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.
Enhancing Accessibility, Engagement, and Motivation in Counseling Services for Secondary Schools through Gamified Blended Mobile and Virtual Reality Therapy Prasetyaningrum, Putri Taqwa; Ibrahim, Norshahila; Aryani, Eka; Ningsih, Rully; Subagyo, Ibnu Rivansyah
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 2 (2025): August 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i2.24814

Abstract

Background: Secondary school counseling services often face challenges such as limited counselor availability and low student participation. Traditional counseling methods frequently fail to engage students, thus reducing both accessibility and impact. Integrating Virtual Reality (VR) and mobile-based interventions presents a promising solution to address these issues. Objective: This study aims to evaluate the effectiveness of a gamified blended mobile and VR therapy in enhancing accessibility, cognitive-emotional-behavioral engagement, and motivation within secondary school counseling services. Methods: A mixed-methods research design was employed, combining quantitative methods (pre- and post-intervention surveys, along with behavioral tracking) and qualitative methods (semi-structured interviews and thematic analysis of focus group discussions). These methods were chosen to capture both measurable impacts and participants’ perceptions of the intervention. A total of 384 students and 10 counselors participated in an 8-week intervention. Results: The intervention led to a significant improvement in the Accessibility Index (from 3.2 to 4.6). Additionally, engagement across cognitive, emotional, and behavioral dimensions showed marked improvements. Thematic analysis revealed that students appreciated the safety and realism provided by the digital counseling environment. Conclusion: The gamified blended therapy approach effectively enhanced counseling accessibility and multidimensional engagement, offering a scalable, student-centered solution for secondary school counseling services.
Optimizing Sentiment Analysis of Hotel Reviews Using PCA and Machine Learning for Tourism Business Decision Support PRASETYANINGRUM, PUTRI TAQWA; Norshahila Ibrahim; Ozzi Suria
Indonesian Journal of Information Systems Vol. 8 No. 1 (2025): August 2025
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

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

Abstract

Sentiment analysis of hotel reviews provides valuable insights for improving customer satisfaction and service quality in the tourism industry. However, the high dimensionality and unstructured nature of review data pose challenges in extracting meaningful insights. This study optimizes sentiment analysis by applying Principal Component Analysis (PCA) for dimensionality reduction and utilizing machine learning models for classification. The proposed approach involves data preprocessing, feature selection using PCA, model training, and performance evaluation. Experimental results show that PCA enhances classification accuracy and computational efficiency by eliminating redundant features, improving sentiment prediction. The comparative analysis demonstrates that the Voting classifier achieves the highest accuracy (95.29%) and F-score (97.50%), while the BiLSTM-FNN model attains the highest recall (99.95%). These findings highlight the potential of PCA-based sentiment analysis in supporting data-driven decision-making for hotel management, enabling enhanced service quality, improved customer experience, and effective marketing strategies.
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.
Sistem Pendukung Keputusan Pemilihan Supplier Bahan Baku Daging Menggunakan Metode Weighted Aggregated Sum Product Assesment (Waspas) (Studi Kasus : Kawasan Kuliner Sekar Mataram) Agustin, Isnaini; Prasetyaningrum, Putri Taqwa
Jurnal Sains dan Teknologi (JSIT) Vol. 2 No. 2 (2022): Mei - Agustus
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v2i3.203

Abstract

Individuals or businesses that supply or sell raw materials to other parties, whether individuals or businesses, so that they can be transformed into finished goods or services are referred to as suppliers. Suppliers must also make raw materials available to those who require them. Furthermore, the supplier must ensure that the raw materials are delivered to the buyer in good condition. The Sekar Mataram Culinary Area is located in Bantul City, Yogyakarta, specifically in Bangunjiwo Village. It is one of the business units owned by Village Owned Enterprises in Mbangun Kamulyan Bangunjiwo Village, which is growing and has received many offers from suppliers to supply raw meat materials to the Sekar Mataram Culinary Area. So far, the Sekar Mataram Culinary Area management has chosen raw meat suppliers through manual selection. Using the Weighted Aggregated Sum Product Assessment (WASPS) method, a Decision Support System will be built to determine the best raw material supplier and meet the restaurant's needs. This study requires criteria and alternatives to obtain a solution for making decisions on raw material supplier selection. This Decision Support System is web-based, making it more effective and efficient.
Sistem Pakar Pengidentifikasian Jenis Kulis Wajah Dalam Pemilihan Msglow Series Menggunakan Naïve Bayes Hasnidar, Hasnidar; Prasetyaningrum, Putri Taqwa
Jurnal Sains dan Teknologi (JSIT) Vol. 2 No. 2 (2022): Mei - Agustus
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v2i3.204

Abstract

There are still many problems with choosing treatment products that do not match the needs of their skin and skin type. Some people follow what their friends do or what is popular now, and numerous people still try to figure out their skin problems and types without first talking to an expert. Using MS GLOW Makassar as a case study, this research was done based on a needs history. The Naive Bayes method was used to make this system. The goal of designing a skincare selection expert system was to apply and use an expert system so that experts and non-experts could use it to choose skincare based on skin type. Based on 30 costumer data that the system and experts have tested by applying Naive Bayes, the accuracy rate was 100%
Deteksi Leukemia Limfoblastik Akut menggunakan Convolutional Neural Network Akbar, Mutaqin; Prasetyaningrum, Putri Taqwa; Setyaningsih, Putry Wahyu; Ahsan, Moh; Budianto, Alexius Endy
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 1 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i1.34168

Abstract

Acute lymphoblastic leukemia is the most important type of childhood leukemia, and accounts for 25% of childhood cancers. Accurately differentiating normal cell precursors from cancer cells is key to the diagnosis of acute lymphoblastic leukemia (ALL). However, under a microscope, cancer cells are so similar to normal cells that it is difficult to classify them. This article presents a detection of acute lymphoblastic leukemia using Convolutional Neural Network (CNN). The dataset which is obtained from ALL_IDB is 582 color image data which is divided into 482 training image data and 100 testing image data. The image data will be resized to 128x128x3 before being input to the CNN model. The CNN model used in this study is a multi-scale CNN which consists of 3 convolution layers (filter size of 3x3, number of filters for each convolution layer is 32, 64, and 128 respectively, and ReLU activation function), 3 subsampling layers using maxpool with filter size of 2x2 , 1 concatenate layer is used to combine the output of each subsampling layer, 1 fully-connected layer with a softmax activation function and a cross-entropy error function, and finally an output layer with 2 classes, namely normal cells and cancer cells. The CNN model will be trained using the Adam optimizer training algorithm with a training rate of 0.0002 and iterated 20 times. Based on the training results after iterating 20 times, the smallest error value was obtained, namely 0.0001 and the largest accuracy value, namely 100% in the 20th epoch. The CNN model was then tested with 100 testing image data and obtained an accuracy rate of 98% and an error value of 0.0482.
Co-Authors Abdul Hadi 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 Aziza, Fadilla Maharani Azzahra, Bernica Bagus Nur Solayman Bambang Setio Purnomo Bambang Setio Purnomo Budianto, Alexius Endy Cahyani, Rivana Dwi Cindy Okta Melinda Dapit Virdaus Denny Jean Cross Sihombing Devi Febrianti Dewi, Amelia Kristiana dewi, Ine shinta Dhana Sudana Eka Aryani, Eka Erza, Muhammad Al-Ghifari Fithriatus Shalihah Fransiskus Xaverius Pere GUNARTATIK ESTHININGTYAS Hamam Nurrofiq Hasnidar Hasnidar Heri Agus Prasetyo Herin, Sofia 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 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 Raharjo, Fajar Sujud Rani Dwi Lestari Reny Yuniasanti Resi Dwi Febrianti Rias Ilham Agung Nugroho Robiin, Bambang Rosita, Rani Rustiawan, Muhammad Rizqi Akfani Sabilla, Annisa Calza Sasa 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