UMMAT Scientific Journals (Universitas Muhammadiyah Mataram)
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Analisis Efektivitas Biaya Penggunaan Antibiotik Pada Pneumonia Anak di RSUD Klungkung
Full text linkCommunity-acquired pneumonia (CAP) merupakan salah satu penyebab kematian utama pada anak-anak diseluruh dunia. Antibiotik merupakan terapi utama pada pneumonia dan diperlukan studi farmakoekonomi dengan metode cost effectiveness analysis (CEA) untuk membandingkan biaya pengobatan dengan efektivitas terapi. Penelitian ini bertujuan untuk mengetahui efektivitas biaya antibiotik tunggal Seftriakson dan antibiotik kombinasi Ampisilin+Gentamisin pada pasien pneumonia anak di instalasi rawat inap RSUD Kabupaten Klungkung. Pengumpulan data secara retrospektif menggunakan rekam medis. Data biaya menggunakan biaya rumah sakit (healthcare perspective) dan biaya Badan Penyelenggara Jaminan Sosial (BPJS) (payer perspective). Hasil penelitian menunjukkan sebagian besar menerima terapi kombinasi (78,4%), dengan terbanyak kombinasi ampisilin dan gentamisin (52,8%). Nilai Average Cost-Effectiveness Ratio (ACER) berdasarkan payer perspective menunjukkan Kelompok tunggal adalah sebesar Rp 1.162.081/hari, sedikit lebih tinggi dibandingkan Kelompok kombinasi yang bernilai Rp 1.100.689/hari. Perspektif healthcare provider, Kelompok tunggal memiliki ACER lebih rendah yang bernilai Rp 908.449/hari dibandingkan Kelompok kombinasi yang bernilai Rp 906.053/hari. Nilai Incremental Cost-Effectiveness Ratio (ICER) dari perspektif payer adalah Rp 609.551 /hari dan perspektif healthcare nilai ICER Rp 886.880 /hari. Tidak ditemukan perbedaan yang signifikan dari efektivitas biaya dan efektivitas terapi. Kelompok terapi tunggal memiliki ACER payer sedikit lebih tinggi, tetapi memiliki nilai ICER yang lebih rendah dan LOS yang lebih cepat dibandingkan kelompok terapi kombinasi
Potensi Sinergis Emulgent Topikal Purifikasi Ekstrak Daun Sidr (Ziziphus spina-christi L.) dan Kemiri Bakar (Aleurites moluccanus L.) sebagai Terapi Alopecia
Full text linkAlopecia merupakan gangguan pertumbuhan rambut yang berkaitan dengan stres oksidatif dan inflamasi kronis pada folikel rambut. Terapi konvensional seperti minoxidil dan finasterid memiliki keterbatasan efektivitas serta potensi efek samping, sehingga diperlukan alternatif berbasis bahan alam yang lebih aman. Penelitian ini bertujuan mengevaluasi aktivitas biologis dan mutu fisik emulgent topikal yang mengandung purifikasi ekstrak daun sidr (Ziziphus spina-christi L.) dan biji kemiri bakar (Aleurites moluccanus L.) sebagai kandidat fitoterapeutik penumbuh rambut. Ekstraksi dilakukan menggunakan metode ultrasound-assisted extraction dan sokhletasi, dilanjutkan pemurnian berbasis air. Analisis HPLC menunjukkan kandungan utama berupa rutin (245 mg/L), quercetin (6,8 mg/L), dan asam galat (47 mg/L) pada ekstrak sidr, serta quercetin (27 mg/L) pada ekstrak kemiri bakar. Evaluasi fisik sediaan menunjukkan pH fisiologis (5,0–6,7), daya sebar optimal, viskositas stabil, serta stabilitas baik pada uji freeze–thaw. Uji in vivo selama 21 hari menunjukkan bahwa formula kombinasi secara signifikan meningkatkan pertumbuhan rambut dibandingkan basis dan minoxidil 2% (ANOVA, F = 9,28; p = 0,0003). Efek ini berkaitan dengan aktivitas antioksidan dan antiinflamasi flavonoid yang mendukung perpanjangan fase anagen
Hubungan Tingkat Pengetahuan Terhadap Penggunaan Kosmetik Pemutih Kulit Berlabel Halal di Kalangan Mahasiswa Daerah Istimewa Yogyakarta
Full text linkProduk kosmetik menjadi salah satu kebutuhan primer oleh masyarakat, khususnya remaja. Banyak remaja yang menginginkan kulit putih secara cepat untuk menunjang penampilan. Oleh karena itu, perlu diimbangi adanya pengetahuan dasar terkait penggunaan produk kosmetik yang aman dan berlabel halal agar terjamin keamanannya tanpa menimbulkan dampak yang tidak diinginkan. Penelitian ini bertujuan mengetahui tingkat pengetahuan, penggunaan, dan hubungan antara tingkat pengetahuan dengan penggunaan kosmetik pemutih kulit berlabel halal di kalangan mahasiswa Daerah Istimewa Yogyakarta. Penelitian ini menggunakan metode kuantitatif dengan desain deskriptif analitik dan pendekatan cross-sectional. Pengambilan sampel dilakukan menggunakan metode purposive sampling yang melibatkan sebanyak 400 responden. Analisis data dilakukan secara univariat dan bivariat menggunakan Spearman Rank. Hasil yang diperoleh dalam penelitian ini menunjukkan tingkat pengetahuan responden berada pada kategori baik (80%), cukup (18,8%), dan kurang (1,3%), sedangkan penggunaan kosmetik pemutih kulit berlabel halal pada kategori baik (91,3%), cukup (8,3%), kurang (0,5%). Uji korelasi Spearman Rank menghasilkan nilai signifikansi 0,001 (<0,05) dan koefisien korelasi 0,462 dengan kategori sedang dan arah korelasi positif sehingga terdapat hubungan signifikan yang searah antara tingkat pengetahuan dan penggunaan kosmetik pemutih kulit berlabel halal di kalangan mahasiswa Daerah Istimewa Yogyakarta
Modelling Consumer Price Index Effect on 10-year US Treasury Bond Yields using Least Square Spline Approach
Full text linkInflation measured by the Consumer Price Index (CPI) is a critical indicator in the government bond market that directly affects the yields of long-term securities such as the 10-year US Treasury Bond. This study is an explanatory quantitative study that aims to examine the complex dynamics of this relationship using the nonparametric least square spline method. The analysis uses monthly CPI data from FRED and 10-year US Treasury bond yield data from Investing.com for the period 2013-2025. This method divides the data into simple polynomial segments that are smoothly connected at transition points (knots), enabling the modelling of nonlinear patterns without assuming an initial curve shape. The analysis results indicate that a first-degree polynomial spline model (piecewise linear) with three knots successfully represents the bond yield response to inflation shocks with R^2 = 86.48%. Model segmentation identified four regimes: (1) Post-crisis recovery phase, with a negative relationship driven by Fed monetary stimulus suppresing yields despite initial inflation emergence; (2) Policy normalization phase, with a positive relationship aligned with monetary tightening in response to moderate inflation; (3) During the COVID-19 pandemic, a negative relationship due to a surge in demand for safe-haven bonds despite rising inflation; (4) Post-pandemic, the relationship turned positive again following the Fed’s aggressive monetary tightening in response to high global inflation. These findings highlight the urgency of regime-based monitoring for investors and policymakers, while contributing concretely to SDG 8 (decent work and economic growth) through the facilitation of appropriate interest rate policies for sustainable macroeconomic stability, and supporting SDG 9 (industry, innovation, and infrastructure) through the identification of inflation patterns that strengthen shock-resistant infrastructure investment planning and financial innovation during turbulent economic transitions
Optimization of Rice Production Forecasting using Hybrid ANN-PSO
Full text linkRice production is a critical component in sustaining national food security, especially Indonesia. The availability of sufficient, affordable, and equitable food is a major challenge for Indonesia. One approach to addressing this challenge is by developing reliable and accurate models for predicting food production. In this study, a hybrid approach that combines Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) algorithms is used to optimize the performance of modeling and prediction of rice production in Central Java, Indonesia. This study uses secondary data in the form of monthly time series data from the Central Java Provincial Statistics Agency (BPS), Meteorology, Climatology, and Geophysics Agency (BMKG), and satellite imagery data with an observation period from January 2019 to December 2024. The input variables in this study include harvested area, precipitation, number of rainy days, atmospheric pressure, wind speed, NDWI, and NDVI while the output variable is rice production in Central Java. The test results using the ANN model provided an RMSE value of 0.1312 and a MSE of 0.0172, while the ANN-PSO model provided an RMSE value of 0.0259 and a MSE of 0.00067. These results indicate that the PSO algorithm is able to optimize the performance of the ANN model in predicting rice production in Central Java.
Analysis Comparison of BiLSTM and BiGRU Models for Aircraft Visibility Prediction
Full text linkSevere weather conditions such as fog and heavy precipitation pose significant threats to aviation safety. Accurate prediction of aircraft visibility is therefore essential to support operational decision-making and reduce the likelihood of accidents. This study aims to compare and evaluate the performance of two bidirectional deep learning models, BiLSTM and BiGRU, in predicting aircraft visibility using historical meteorological data from BMKG Juanda Sidoarjo. The novelty of this research lies in applying and comparing bidirectional recurrent architectures for visibility prediction, an approach rarely explored in aviation meteorology, to assess their capability in capturing temporal dependencies within time-series visibility patterns. Both models were trained using hyperparameter tuning, with the best configuration obtained from a 24-hour input window, batch size of 32, 64 neurons, a dropout rate of 0.1, and 100–200 epochs. The dataset was divided into training and testing sets (80:20), and model performance was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) to assess both predictive accuracy and computational efficiency. The results indicate that while BiLSTM achieved slightly higher accuracy, BiGRU demonstrated superior overall efficiency, obtaining competitive error metrics (MSE = 1.50 × 10⁶, RMSE = 1,223.5, MAPE = 19.35%) compared to BiLSTM (MSE = 1.58 × 10⁶, RMSE = 1,258.1, MAPE = 19.50%). BiGRU’s advantage lies in its simpler structure and faster computation, which reduce training complexity without sacrificing forecast accuracy. Overall, this research contributes to the development of efficient bidirectional time-series models for aviation meteorology, offering a practical framework for real-time visibility forecasting in computationally limited environments. The balance between accuracy, speed, and model simplicity makes BiGRU a more scalable and applicable choice for enhancing flight safety operations
Analisis Drug Related Problem (DRPs) Pada Pasien Congestive Heart Failure (CHF) di Instalasi Rawat Inap Rumah Sakit X Kota Bandar Lampung
No full textCongestive Heart Failure (CHF) is a condition in which the heart is unable to maintain sufficient cardiac output to meet the oxygen and metabolic demands of body tissues despite adequate venous return. Drug-Related Problems (DRPs) are events or circumstances involving drug therapy that have the potential to interfere with the desired health outcomes. This study aimed to identify the occurrence of DRPs in patients with CHF and to determine the category with the highest percentage of DRP events among CHF patients hospitalized at Hospital X in Bandar Lampung City in 2024. This study employed an observational design with a descriptive cross-sectional approach and retrospective data collection, using medical records from January to December 2024. A total of 51 samples were selected using purposive sampling. The study population consisted of 51 CHF patients, comprising 38 (74.51%) male patients and 13 (25.49%) female patients. The results showed that the incidence of DRPs among CHF patients was relatively high, at 87.93%. The category with the highest proportion of DRP events was drug selection, with 165 events (59.36%). Other categories included treatment effectiveness with 54 events (19.43%), therapy safety with 51 events (18.35%), and dose selection with 8 events (2.88%). In conclusion, the subcategory with the highest percentage of DRP events was the use of medications that were not in accordance with clinical guidelines or the hospital formulary, accounting for 50 events (17.99%)
SOSIALISASI DAN BIMTEK ADMINISTRASI PENDIRIAN CABANG DAN RANTING MUHAMMADIYAH
Full text linkAbstrak: Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kemampuan administratif dan pemahaman kelembagaan kader Muhammadiyah di tingkat cabang dan ranting melalui kegiatan sosialisasi dan bimbingan teknis (Bimtek) administrasi pendirian lembaga. Kegiatan dilaksanakan sebagai bentuk sinergi antara Universitas Muhammadiyah Mataram (UMMAT) dan Lembaga Pengembangan Cabang, Ranting, dan Pengembangan Masjid (LPCR-PM) PWM NTB. Metode pelaksanaan kegiatan meliputi tiga tahap, yaitu sosialisasi kebijakan kelembagaan, bimbingan teknis penyusunan dokumen administratif, serta pendampingan dan validasi hasil. Pelaksanaan dilakukan pada tanggal 11 Oktober 2025 di Aula Fakultas Kedokteran UMMAT dengan peserta dari PDM Kota Mataram dan PDM Lombok Barat. Hasil kegiatan menunjukkan adanya peningkatan signifikan terhadap kemampuan peserta dalam memahami prosedur pendirian dan pengesahan cabang serta ranting Muhammadiyah. Peserta mampu menyusun dokumen administrasi sesuai standar LPCR-PM PWM NTB dan menunjukkan antusiasme tinggi terhadap kegiatan ini. Luaran kegiatan berupa draf dokumen pendirian cabang, artikel ilmiah siap terbit, serta publikasi di media daring.Abstract: This community service activity aims to enhance the administrative competence and institutional understanding of Muhammadiyah cadres at the branch and sub-branch levels through socialization and technical guidance (Bimtek) on institutional establishment administration. The program was a collaborative effort between the University of Muhammadiyah Mataram (UMMAT) and the Branch, Sub-Branch, and Mosque Development Institute (LPCR-PM) of Muhammadiyah Regional Board of West Nusa Tenggara (PWM NTB). The implementation consisted of three stages: institutional policy socialization, administrative document preparation training, and document mentoring and validation. The activity took place on October 11, 2025, at the Faculty of Medicine Auditorium, UMMAT, involving participants from the Muhammadiyah Regional Boards of Mataram City and West Lombok. The results showed a significant improvement in participants’ understanding of the procedures for establishing and legitimizing Muhammadiyah branches and sub-branches. Participants successfully produced administrative documents that met LPCR-PM PWM NTB standards and demonstrated high enthusiasm throughout the activity. The outcomes included validated draft establishment documents, a scientific article ready for publication, and online media coverage
Dimensionality Reduction Evaluation of Multivariate Time Series of Consumer Price Index in Indonesia
Full text linkMultivariate time series (MTS) analysis of the Consumer Price Index (CPI) in Indonesia often encounters challenges such as outliers, missing data, and inter-variable correlations. Principal Component Analysis (PCA) is a practical approach for dimensionality reduction; however, its performance may vary depending on the data characteristics. This study is a quantitative comparative study that integrates empirical analysis and Monte Carlo simulation based on a first-order Vector Autoregressive (VAR(1)) model to evaluate three PCA approaches: Classical PCA, Robust PCA (RPCA), and PCA of MTS. These methods were applied to weekly price data of eight strategic food commodities across 70 districts and cities in Indonesia. The evaluation employed three criteria: (1) dimensionality reduction efficiency (empirical and simulation), (2) reconstruction accuracy measured using Root Mean Square Error (RMSE) (empirical), and (3) robustness to outliers and inter-variable correlations (simulation). Empirical results indicate that Classical PCA (lag 1) and RPCA (lag 1) are both efficient and effective in reducing dimensionality with minimal information loss. Using the first three principal components, all three methods were able to explain at least 85% of the total variance, with lag 1 identified as optimal. Simulation results reveal that RPCA (lag 1) provides the most stable and consistent performance in the presence of outliers, while Classical PCA (lag 2) performs better under conditions of high inter-variable correlation and a low proportion of outliers. These findings suggest that robust covariance estimation can improve the accuracy of dimensionality reduction and enhance the stability of multivariate time-series analysis for food price data in Indonesia
Innovative Mathematics Learning: The Impact of Augmented Reality and Ethnomathematics on Communication Skills
Full text linkThe integration of digital technology with a cultural approach has become an important innovation in mathematics education to enhance meaningful learning. However, there is still limited research combining Augmented Reality (AR) with ethnomathematics to strengthen students' mathematical communication skills. This study aims to analyze the impact of Augmented Reality and ethnomathematics-based learning on students' mathematical communication skills. The study employed an experimental design involving 60 seventh-grade students selected randomly from eight classes at SMP Negeri 6 Langke Rembong, Ruteng, Indonesia, during the 2024/2025 academic year. The research instrument consisted of a five-item mathematical communication test, which was validated through expert judgment and empirical testing, and demonstrated satisfactory reliability based on internal consistency analysis. SPSS and CMA software were used to support data analysis. A t-test was conducted to examine differences in mathematical communication ability between the experimental and control groups after fulfilling prerequisite assumptions. The findings indicate that the integration of AR and ethnomathematics significantly improved students’ ability to express mathematical ideas clearly, both orally and in written form. Additionally, students showed higher levels of cultural engagement and appreciation, which positively contributed to the development of their communication skills. This study recommends the integration of AR and ethnomathematics as a sustainable innovation in mathematics learning and suggests further research to explore its application across diverse mathematical topics and broader educational contexts