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All Journal JSI: Jurnal Sistem Informasi (E-Journal) Global Medical and Health Communication SISFOTENIKA JMM (Jurnal Masyarakat Mandiri) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Media Keperawatan Indonesia JURNAL TEKNOLOGI INFORMASI Applied Technology and Computing Science Journal International Journal of Informatics and Computation Prosiding Seminar Nasional Multidisiplin Ilmu Respati Jurnal Teknik Informatika (JUTIF) Journal of Computer, Electronic, and Telecommunication (COMPLETE) Prosiding Seminar Nasional Pengabdian Kepada Masyarakat International Journal of Informatics Engineering and Computing
Sri Hasta Mulyani
Universitas Respati Yogyakarta
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Upaya Peningkatan Pemantauan Tumbuh Kembang Balita Selama Masa Pandemi Melalui Pengembangan Aplikasi SiKIA Di Desa Triharjo, Pandak, Bantul Venny Vidayanti; Sri Hasta Mulyani; Rizky Erwanto
PROSIDING SEMINAR NASIONAL PENGABDIAN KEPADA MASYARAKAT UNIVERSITAS NAHDLATUL ULAMA SURABAYA Vol. 1 No. 1 (2022): Prosiding Seminar Nasional Pengabdian Kepada Masyarakat : Perguruan Tinggi Meng
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (773.031 KB) | DOI: 10.33086/snpm.v1i1.872

Abstract

Desa Triharjo merupakan daerah intervensi lokus stunting di Bantul dimana terdapat balita stunting dengan kategori pendek sebanyak 87 balita. Pemantauan dan layanan kesehatan balita di Desa Triharjo tidak dapat berjalan dengan optimal pada masa pandemi karena terbatasnya peran kader dalam pemantauan perkembangan dan pertumbuhan balita. Metode. Kegiatan pengabdian masyarakat ini memberikan solusi permasalahan melalui pengembangan aplikasi berbasis-website yang dapat memudahkan kader dalam pendokumentasian kegiatan posyandu dan pemantauan kesehatan balita. Pada tahap implementasi kegiatan telah dilakukan proses pengujian sistem oleh perwakilan kader balita sebanyak 22 orang dan petugas kesehatan untuk diketahui kekurangan yang harus disesuaikan sehingga sistem maupun tampilan antar muka menjadi user-friendly. Kemudian mitra yang terdiri dari Petugas kesehatan perwakilan Puskesmas Pandak II, Koordinator Kader Kesehatan, Kader Balita dan Ibu Hamil telah mendapatkan pelatihan sebanyak 3 kali dijadwalkan secara bertahap sesuai protokol kesehatan dalam pencegahan penyebaran Covid-19 (dilaksanakan secara daring dan luring). Seluruh mitra juga telah diberikan pendampingan dan monitoring oleh tim pengusul dalam pemanfaatan aplikasi. Kader ibu dan anak juga berperan dalam mensosialisasikan pemanfaatan aplikasi secara aktif di wilayah Desa Triharjo sehingga dapat mewujudkan inovasi sebagai Desa Cerdas dan Desa Pilot penerapan aplikasi berbasis teknologi menggunakan Smartphone. Hasil uji Wilcoxon didapatkan nilai p-value sebesar 0,000< 0,05 artinya ada pengaruh pelatihan terhadap peningkatan ketrampilan kader balita dalam pemantauan kesehatan dan tumbuh kembang balita. Pengembangan Aplikasi SiKIA yang dikembangkan oleh tim pengabdi menjadi solusi untuk menyelesaikan permasalahan yang dihadapi oleh kader balita di Desa Triharjo untuk membuat proses dokumentasi data tumbuh kembang balita, pelayanan posyandu, dan kesehatan ibu hamil menjadi lebih efektif dan efisien.
Promosi ASI Eksklusif Melalui Konsultasi Laktasi Secara Real-Time-Online Menggunakan Ruang Sehati Mobile Application di Kawasan Wisata Kota Yogyakarta Giyawati Yulilania Okinarum; Sri Hasta Mulyani
PROSIDING SEMINAR NASIONAL PENGABDIAN KEPADA MASYARAKAT UNIVERSITAS NAHDLATUL ULAMA SURABAYA Vol. 2 No. 1 (2022): Prosiding Seminar Nasional Pengabdian Kepada Masyarakat : BERKARYA DAN MENGABDI
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (619.362 KB) | DOI: 10.33086/snpm.v2i1.997

Abstract

Pemerintah memberikan dukungan positif terhadap aktivitas menyusui melalui promosi ASI eksklusif, namun model yang dirancang oleh pemerintah tidak sepenuhnya berhasil dilaksanakan di kawasan pedagang kaki lima (PKL) Teras Malioboro 2, Kota Yogyakarta. Hal tersebut dibuktikan dengan adanya potret ibu menyusui PKL yang tidak secara eksklusif memberikan ASI pada anaknya. Metode: Pengabdian masyarakat ini dilakukan selama bulan Juli–September 2022. Prioritas masalah mitra disusun terlebih dahulu dalam menentukan ruang lingkup dan justifikasi masalah. Berdasarkan wawancara dengan 30 PKL, aspek kesehatan anak dan ibu menyusui menjadi masalah prioritas mitra, yang perlu diselesaikan dengan solusi berupa mobile application dengan fitur konseling laktasi yang user friendly. Selanjutnya pengabdi menggunakan aplikasi Ruang Sehati ini dalam melakukan pengabdian masyarakat berupa promosi ASI eksklusif pada PKL ibu menyusui. Aplikasi ini telah dikembangkan menggunakan design thinking, yang merupakan lanjutan dari penelitian yang telah dilakukan sebelumnya. Hasil dan pembahasan: Tim pengabdi melaksanakan kegiatan pendampingan ASI eksklusif dengan menggunakan Aplikasi Ruang Sehati x SapaBidan pada pemberian informasinya, serta melalui fitur konseling menyusui bersama konselor laktasi di Aplikasi Ruang Sehati. Persen peningkatan pengetahuan pada 30 PKL ibu menyusui sebelum dengan setelah dilakukan pendampingan, sebesar 86,6% melebihi rencana capaian awal 80%. Pengguna Aplikasi Ruang Sehati pun mengalami persen peningkatan sebesar 71,4% yang juga mencapai lebih dari 70% rencana yang diharapkan. Seluruh partisipan pengabdian masyarakat (100%) telah menggunakan fitur konsultasi laktasi dan menyatakan kepuasannya. Kesimpulan: Promosi ASI pada PKL ibu menyusui lebih efektif saat dilakukan menggunakan aplikasi, yang memungkinkan dapat berkonsultasi secara langsung dengan konselor laktasi. Pemerintah diharapkan dapat mengintegrasikan aplikasi ini ke dalam aplikasi JSS Kota Yogyakarta supaya masyarakat dapat menikmati layanan ini dengan lebih leluasa.
Enhancing Weather Prediction Using Stacked Long Short-Term Memory Networks MOHAMMAD DIQI; HAMZAH HAMZAH; SRI HASTA MULYANI
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 10 No 3 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i3.5376

Abstract

Weather prediction is crucial in various domains, such as agriculture, transportation, and disaster management. This research investigates the Stacked Long-Short Term Memory (LSTM) for weather prediction using the Denpasar Weather Data spanning 20 years from January 1, 1990, to January 7, 2020. The dataset contains hourly weather data, including temperature, pressure, humidity, and wind speed. Our Stacked LSTM model consists of multiple LSTM layers that capture temporal dependencies and patterns in the data. Evaluating the model's performance using metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared (R2), we obtain an average RMSE of 0.03471, an average MAE of 0.02718, an average MAPE of 0.05572, and an average R2 of 0.87087. These results demonstrate the effectiveness of the Stacked LSTM model in accurately predicting weather conditions. The findings have practical implications for weather forecasting applications and suggest avenues for future research, such as exploring different deep learning architectures and incorporating additional features to improve weather prediction accuracy further.
Why is the lactation pod essential? a "Ruang Sehati" innovation pilot project for wellness tourism in Yogyakarta, Indonesia Okinarum, Giyawati Yulilania; Vidayanti, Venny; Mulyani, Sri Hasta
Media Keperawatan Indonesia Vol 7, No 1 (2024)
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/mki.7.1.2024.22-30

Abstract

Yogyakarta is a popular tourist destination in Indonesia, yet lactation rooms at public facilities are difficult to access and substandard. This will certainly have an impact on breastfeeding practices for mothers who travel through their daily activities in public areas. Researchers propose using "Ruang Sehati" lactation pods as a kind of wellness tourism in Yogyakarta. This study aims to determine participants' perceptions of the lactation pod. Descriptive analytical methods were used in this study. Twenty participants were involved with this purposive sampling technique. The study showed that lactation pod innovation is essential, including maintaining privacy, sufficient breastfeeding facilities, high accessibility, and place efficiency. This innovation is also a model that can be demonstrated nationally, thereby showing a portrait of wellness tourism in the city of Yogyakarta as an ideal tourist destination for mothers and children.
Ruang Sehati: Innovating Portable Lactation Pods for Wellness Tourism Using Design Thinking Method in Yogyakarta Okinarum, Giyawati Yulilania; Vidayanti, Venny; Mulyani, Sri Hasta
Global Medical & Health Communication (GMHC) Vol 12, No 1 (2024)
Publisher : Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/gmhc.v12i1.12691

Abstract

Yogyakarta, a renowned tourist city in Indonesia, currently needs more lactation rooms and public facilities within tourist areas. To address this, inventors propose a portable lactation pod. This study utilized the design thinking method, emphasizing user needs. Interviews were conducted with thirty breastfeeding mothers on Malioboro Street in Yogyakarta to assess the necessity of lactation rooms in this popular tourist spot. From February to June 2022, the stages of "empathize," "define," "ideate," "prototype," and "test" were completed. The findings indicate that the "SEHATI" portable lactation room innovation meets user requirements, with feature satisfaction scores ranging from 4.2 to 4.9 out of 5.0. However, improvements are needed in the ventilation, exhaust fan, and fan sections, which received lower satisfaction scores during the "testing" stage. This innovation could serve as a pilot project, showcasing wellness tourism in Yogyakarta nationally.
Optimizing Breast Cancer Detection: A Comparative Study of SVM and Naive Bayes Performance Diqi, Mohammad; Hiswati, Marselina Endah; Hamzah, Hamzah; Ordiyasa, I Wayan; Mulyani, Sri Hasta; Wijaya, Nurhadi; Wanda, Putra
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 7 No 1 (2024): June
Publisher : Universitas Nahdlatul Ulama Surabaya

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

Abstract

This study evaluates the performance of Support Vector Machine (SVM) and Naive Bayes algorithms in classifying breast cancer using the Breast Cancer Wisconsin dataset. Both models exhibited high accuracy, with Naive Bayes achieving a slightly higher overall accuracy of 97% and demonstrating a balanced performance between precision and recall. The SVM model showed strong proficiency in detecting positive cases, with an overall accuracy of 95%, though it faced minor challenges in recall for negative cases. These results highlight the effectiveness of both algorithms in breast cancer detection, emphasizing the significance of model selection based on specific diagnostic requirements. Although there are limitations, such as the small sample size and assumptions made in the model, the findings provide useful insights into the use of machine learning in medical diagnostics. This supports the idea that these models have the potential to enhance early detection and treatment results. Future research should focus on utilizing larger, more diverse datasets, exploring advanced feature processing techniques, and integrating additional algorithms to enhance further the accuracy and reliability of breast cancer detection systems.
SIMANTUL: Model of Internal Quality Audit Management System in Higher Education Sri Hasta Mulyani; Ariyanto Nugroho; Maisarah Nurain
International Journal of Informatics and Computation Vol. 4 No. 2 (2022): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v4i2.52

Abstract

Many organizations carry out the quality assurance system through the Internal Quality Assurance System (SPMI) and the External Quality Assurance System (SPME). The SPMI framework uses the stages of a continuous quality assurance cycle with the PPEPP method (Application, Implementation, Evaluation, Control, and Improvement), which is carried out periodically to achieve University's Vision, Mission, Goals, and Targets. This paper discusses the implementation stages of the internal quality audit management system at Universitas Respati Yogyakarta, Indonesia. Using an information system, the university audit body, BPM, regularly and consistently carries out an Internal Quality Audit (AMI) every year to audit the implementation of academic and non-academic activities at the University. In this research, we construct an audit system, namely the E-Audit application, with the Waterfall software development method. This study can produce an efficient system called SIMANTUL, which refers to the Higher Education Accreditation assessment instrument version 3.0 and can store documents digitally.
Enhancing Mental Health Disorders Classification using Convolutional Variational Autoencoder Sri Hasta Mulyani
International Journal of Informatics and Computation Vol. 6 No. 1 (2024): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v6i1.65

Abstract

This research investigates the application of Convolutional Variational Autoencoder (CVAE) for multi-class classification of mental health disorders. The study utilizes a diverse dataset comprising five classes: Normal, Anxiety, Depression, Loneliness, and Stress. The CVAE model effectively captures spatial dependencies and learns latent representations from the mental health disorder data. The classification results demonstrate high precision, recall, and F1 scores for all classes, indicating the model's robustness in distinguishing between different disorders accurately. The research contributes by leveraging the unique capabilities of CVAE, combining convolutional neural networks and variational autoencoders to enhance the accuracy and interpretability of the classification process. The findings highlight the potential of CVAE as a powerful tool for accurate and efficient mental health disorder classification. This research paves the way for further advancements in deep learning techniques, supporting improved diagnosis and personalized healthcare in mental health.
Fake News Detection in Health Domain Using Transformer Models Sri Hasta Mulyani; Suwarto; Hamzah; R.Nurhadi Wijaya; Rodiyah; Wita Adelia
International Journal of Informatics and Computation Vol. 6 No. 2 (2024): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v6i2.89

Abstract

The rise of fake news in the health sector poses a serious threat to public well-being and accurate health communication. This study investigates the effectiveness of transformer models, particularly BERT (Bidirectional Encoder Representations from Transformers), in detecting fake news related to health. By leveraging the advanced contextual understanding of BERT, we aim to enhance the accuracy of fake news detection in this critical domain. Our approach involves training the BERT model on a curated dataset of health news articles, followed by rigorous evaluation on its ability to differentiate between genuine and misleading content. The results reveal that the transformer-based model significantly outperforms traditional methods, achieving high accuracy and robust performance metrics. This research underscores the potential of transformer models in combating health misinformation and provides a foundation for future improvements in automated fake news detection systems.
Enhancing Heart Disease Detection Using Convolutional Neural Networks and Classic Machine Learning Methods Mulyani, Sri Hasta; Wijaya, Nurhadi; Trinidya, Fike
Journal of Computer, Electronic, and Telecommunication (COMPLETE) Vol. 4 No. 2 (2023): December
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/complete.v4i2.394

Abstract

This study addresses the problem of heart disease detection, a critical concern in public health. The research aims to compare the performance of Convolutional Neural Networks (CNN) with conventional machine learning algorithms in diagnosing heart disease using a dataset comprising 14 features. The primary objective is to determine whether CNNs can provide more accurate and reliable results than traditional techniques. The research employs rigorous preprocessing, normalizing relevant features, and splits the dataset into an 80-20 training-testing split. The model is trained for 300 epochs with a batch size of 64, and performance evaluation is conducted using confusion matrices and classification reports. The results reveal that the CNN model achieved a remarkable accuracy of 100%, demonstrating its potential to outperform conventional machine learning algorithms. These findings emphasize the significance of deep learning techniques in improving heart disease diagnostics, although further research is needed to optimize CNN models and address interpretability concerns for practical implementation in healthcare settings.
Co-Authors Ariyanto Nugroho Catur Paryadi Fajar Y Zebua Giyawati Yulilania Okinarum Hamzah Hamzah Hamzah Husna Arwa Salsabil Izzurohman, Moh. Jerry Hardianto Maisarah Nurain Marselina Endah Hiswati Mohammad Diqi Okinarum, Giyawati Yulilania Ordiyasa, I Wayan Putra Wanda, Putra R.Nurhadi Wijaya Rizky Erwanto Rizky Erwanto Rodiyah Suwarto Trinidya, Fike Venny Vidayanti Vidayanti, Venny Wijaya, Nurhadi Wita Adelia