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All Journal EMITTER International Journal of Engineering Technology KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JAST : Jurnal Aplikasi Sains dan Teknologi Jurnal Penelitian Rumpun Ilmu Teknik Journal of Community Research and Engagement RAGAM: Journal of Statistics and Its Application
Rabiatul Adawiyah
Universitas PGRI Adi Buana Surabaya
Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Mathematics Public Health Social Sciences Other

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HOSPITAL LENGTH OF STAY PREDICTION BASED ON PATIENT EXAMINATION USING NEURAL NETWORK Rabiatul Adawiyah
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 8, No 1 (2021)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v8i1.368

Abstract

Length of stay (LOS) or length of stay is the main indicator in improving health services, which is expected to continue to increase along with population growth. The population of Indonesia is included in the largest category in the world. This was followed by an increase in the number of inpatient and emergency unit visits which had a high burden of care costs. This study aims to provide a solution by predicting LOS using Neural Network (NN). Predictions can be used as a consideration in improving effective and efficient health services. To improve the performance of the NN algorithm, we implement parameter optimization using the Grid Search to find the combination of the number of epochs, learning rate and momentum that can produce the best accuracy value. The results showed that NN could predict LOS with an accuracy rate of 89.22% when using the default parameter. Meanwhile, by performing parameter optimization using the Grid Search to find the ideal combination of parameters for learning rate, momentum and epoch, the accuracy rate is increased to 92.20%.Keywords: length of stay, neural network, patient examination, machine learningLength of stay (LOS) atau lama rawat inap merupakan indikator utama dalam peningkatan pelayanan kesehatan yang diperkirakan akan terus meningkat bersamaan dengan jumlah pertumbuhan penduduk. Jumlah penduduk Indonesia termasuk dalam kategori terbanyak di dunia. Hal ini diikuti dengan pertambahan jumlah kunjungan pasien rawat inap dan unit gawat darurat yang memiliki beban biaya perawatan yang tinggi. Penelitian ini bertujuan untuk memberikan solusi dengan melakukan prediksi LOS menggunakan Neural Network (NN). Prediksi dapat digunakan sebagai pertimbangan dalam peningkatan pelayanan kesehatan yang efektif dan efisien. Untuk meningkatkan performansi dari algoritma NN, kami menerapkan optimasi parameter menggunakan Grid Search untuk menemukan kombinasi jumlah epoch, learning rate dan momentum yang dapat menghasilkan nilai akurasi terbaik. Hasil penelitian menunjukkan bahwa NN dapat memprediksi LOS dengan tingkat akurasi 89,22% jika menggunakan default parameter. Sedangkan dengan melakukan optimasi parameter menggunakan Grid Search untuk menemukan kombinasi ideal parameter learning rate, momentum dan epoch, maka tingkat akurasi meningkat menjadi 92,20%.Kata kunci: length of stay, neural network, patient examination, machine learning
Cluster Text Random Opinion Tweet In Yogyakarta Using Automatic Clustering Rabiatul Adawiyah
Jurnal Penelitian Rumpun Ilmu Teknik Vol. 2 No. 1 (2023): Februari : Jurnal Penelitian Rumpun Ilmu Teknik
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (528.714 KB) | DOI: 10.55606/juprit.v2i1.1194

Abstract

Tweet Besides making computations difficult, the data obtained is also inefficient and complicated to interpret. Therefore, it is necessary to explore how to overcome these problems. This study proposes an approach to find the global optimum and make automatic grouping by analyzing moving averages, namely K-Means Automatic Clustering. So the purpose of this study was to explore and evaluate high-dimensional data from a collection of tweets, namely random opinion text tweets in Yogyakarta. The K-means Automatic Clustering algorithm is used for clusters based on the data attributes that have been obtained. Pre-processing experiments were carried out among others. Cleansing, Case folding, Tokenizing, Filtering, Stemming. Then look for the variance cluster to find the global optimum as an ideal cluster by identifying the moving variance by placing λ as the threshold (Global Optimum). So that the ideal cluster value is 0.332975. That is, the closer the cluster value obtained to number 1, the more the cluster search finds the optimum point. This research can be utilized in exploring and evaluating high-dimensional data, so that it becomes a consideration in providing approximate patterns from unstructured data sets with Visualization.
Peningkatan Literasi Bioinformatika bagi Siswa Sekolah Menengah melalui Pelatihan Implementasi Sains Data Anuraga, Gangga; Fitriani, Fenny; Adawiyah, Rabiatul; Utami, Diva Aprilia Trisha; Faramaysty, Laura Sekar
JAST : Jurnal Aplikasi Sains dan Teknologi Vol 9, No 1 (2025): EDISI JUNI 2025
Publisher : Universitas Tribhuwana Tunggadewi Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33366/jast.v9i1.7081

Abstract

Bioinformatics is an interdisciplinary field that integrates biology, statistics, and computer science to analyze large-scale biological data. In the context of secondary education, students' understanding of this concept is still minimal. This study aims to evaluate the effectiveness of a training on the Implementation of Data Science in Bioinformatics, organized by the Statistics Study Program at Universitas PGRI Adi Buana Surabaya as part of a community service activity. The training methodology used a hybrid approach combining offline and online sessions. Twelfth-grade science students from five partner high schools participated. The training materials covered the basics of statistics, an introduction to bioinformatics, and biological data analysis case studies. The training showed increased participants' conceptual understanding and interest in data science. Furthermore, active interaction between students and speakers demonstrated the success of the participatory approach in learning activities. This activity also created collaborative relationships between partner universities and schools, extending the educational impact to secondary education environments. This training demonstrates the importance of integrating bioinformatics in secondary education to prepare young people to face the challenges of data-driven science.ABSTRAK Bioinformatika merupakan bidang interdisipliner yang mengintegrasikan biologi, statistika, dan ilmu komputer untuk menganalisis data biologis dalam skala besar. Dalam konteks pendidikan menengah, pemahaman siswa terhadap konsep ini masih sangat terbatas. Penelitian ini bertujuan untuk mengevaluasi efektivitas pelatihan bertema Implementasi Sains Data pada Bidang Bioinformatika yang diselenggarakan oleh Program Studi Statistika Universitas PGRI Adi Buana Surabaya sebagai bagian dari kegiatan pengabdian kepada masyarakat. Metodologi pelatihan menggunakan pendekatan hybrid yang menggabungkan sesi luring dan daring. Siswa kelas XII jurusan IPA dari lima SMA mitra dilibatkan sebagai peserta. Materi pelatihan mencakup dasar-dasar statistika, pengenalan bioinformatika, serta studi kasus analisis data biologis. Hasil pelatihan menunjukkan adanya peningkatan pemahaman konseptual dan minat peserta terhadap bidang sains data. Selain itu, terjadi interaksi aktif antara siswa dan narasumber yang mencerminkan keberhasilan pendekatan partisipatif dalam kegiatan pembelajaran. Kegiatan ini juga menciptakan hubungan kolaboratif antara universitas dan sekolah mitra, memperluas dampak edukatif ke lingkungan pendidikan menengah. Pelatihan ini membuktikan pentingnya integrasi bioinformatika dalam pendidikan menengah untuk mempersiapkan generasi muda menghadapi tantangan ilmu pengetahuan berbasis data.
FOSTERING CRITICAL THINKING IN BIVARIATE DATA ANALYSIS INSTRUCTION FOR SENIOR HIGH SCHOOL TEACHERS IN NGANJUK REGENCY Adawiyah, Rabiatul; Anuraga, Gangga; Sadewa, Arief Triatmaja Permana
Journal of Community Research and Engagement Vol. 2 No. 1 (2025): MAY
Publisher : Universitas Muhammadiyah Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38040/jcore.v2i1.1227

Abstract

Statistics education is a vital component of the learning process, particularly in fostering logical, analytical, and quantitative thinking skills. Within this context, critical thinking plays a crucial role. Critical thinking is defined as the ability to objectively analyze information, evaluate arguments, identify assumptions, and draw logical conclusions. The development of critical thinking skills in statistics education aligns closely with the demands of the 21st century. This seminar aims to positively impact educators and students, specifically high school teachers in Nganjuk Regency, through the subtopic "Critical Thinking in Bivariate Data Analysis Learning." This theme was selected due to its high relevance to contemporary needs and its potential to provide extensive insights for teachers regarding the importance of critical thinking in statistics education, especially bivariate data analysis. The seminar's objectives extend beyond providing technical knowledge, aiming also to cultivate a critical mindset among teachers. The seminar activities include preparatory stages, theoretical and practical approaches, case studies, and interactive sessions such as question-and-answer and feedback discussions designed to achieve the seminar’s primary goals. Throughout these phases, the seminar enhances teachers' understanding of the importance of raising awareness and developing students’ critical thinking skills in statistics education. Consequently, teachers can more effectively foster students' critical thinking abilities through statistics learning. Keywords: Critical Thinking; Bivariate; Learning; Community Service; High School
Hospital Length of Stay Prediction based on Patient Examination Using General features Rabiatul Adawiyah; Badriyah, Tessy; Syarif, Iwan
EMITTER International Journal of Engineering Technology Vol 9 No 1 (2021)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v9i1.609

Abstract

As of the year 2020, Indonesia has the fourth most populous country in the world. With Indonesia’s population expected to continuously grow, the increase in provision of healthcare needs to match its steady population growth. Hospitals are central in providing healthcare to the general masses, especially for patients requiring medical attention for an extended period of time. Length of Stay (LOS), or inpatient treatment, covers various treatments that are offered by hospitals, such as medical examination, diagnosis, treatment, and rehabilitation. Generally, hospitals determine the LOS by calculating the difference between the number of admissions and the number of discharges. However, this procedure is shown to be unproductive for some hospitals. A cost-effective way to improve the productivity of hospital is to utilize Information Technology (IT). In this paper, we create a system for predicting LOS using Neural Network (NN) using a sample of 3055 subjects, consisting of 30 input attributes and 1 output attribute. The NN default parameter experiment and parameter optimization with grid search as well as random search were carried out. Our results show that parameter optimization using the grid search technique give the highest performance results with an accuracy of 94.7403% on parameters with a value of Epoch 50, hidden unit 52, batch size 4000, Adam optimizer, and linear activation. Our designated system can be utilised by hospitals in improving their effectiveness and efficiency, owing to better prediction of LOS and better visualization of LOS done by web visualization.
ANALISIS INFLASI DI INDONESIA: PEMODELAN ARIMA DAN IMPLIKASI KEBIJAKAN EKONOMI Artanti Indrasetianingsih; Alfisyahrina Hapsery; Rabiatul Adawiyah
RAGAM: Journal of Statistics & Its Application Vol 4, No 1 (2025): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v4i1.14570

Abstract

One of the important indicators on a country's economy is inflation. Inflation is an increase in the price of goods and services in general and continuously over a certain period of time. Various studies have been conducted to predict inflation, both using conventional methods and those using artificial intelligence. This research uses the ARIMA method specifically to help the government in monitoring fiscal and monetary policies so that they are more responsive to the threat of inflation, such as interest rate adjustments or basic commodity price policies. The main objective of this study is to obtain a model that can be used to predict inflation in Indonesia with a high degree of accuracy. The results of the descriptive analysis show that the highest inflation in Indonesia occurred during the monetary crisis, namely in February 1998, which was 12.76, while the highest average inflation occurred in 1998 at 4.818. ARIMA modeling for inflation results in an ARIMA([1,3,5,8,48],0,0) model with an outlier , the model satisfies the residual white noise assumption, but does not meet the normally distributed residual assumption. Based on the RMSE and MAPE values, the results show that the RMSE data out sample has a smaller RMSE value when compared to the in sample data, while the MAPE value is smaller in sample data when compared to the out sample data.
Co-Authors Alfisyahrina Hapsery Arief Triatmaja Permana Sadewa Faramaysty, Laura Sekar Fitriani, Fenny Gangga Anuraga Indrasetianingsih, Artanti iwan Syarif Tessy Badriyah, Tessy Utami, Diva Aprilia Trisha