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All Journal Unnes Journal of Public Health Infolitika Journal of Data Science Grimsa Journal of Science Engineering and Technology
Novi Reandy Sasmita
Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Engineering Medicine & Pharmacology Physics Public Health

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Patient Satisfaction and its Socio-Demographic Correlates in Zainoel Abidin Hospital, Indonesia: A Cross-Sectional Study Azharuddin Azharuddin; Novi Reandy Sasmita; Ghalieb Mutig Idroes; Rusdi Andid; Raihan Raihan; Tuti Fadlilah; Nanda Earlia; Taufik Ridwan; Ira Maya; Farnida Farnida; Rinaldi Idroes
Unnes Journal of Public Health Vol 12 No 2 (2023): Unnes Journal of Public Health
Publisher : Universitas Negeri Semarang (UNNES) in cooperation with Association of Indonesian Public Health Experts (Ikatan Ahli Kesehatan Masyarakat Indonesia (IAKMI))

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujph.v12i2.69233

Abstract

Patient satisfaction based on a patient’s opinion is the main criterion and can be one of the best ways to measure the quality of healthcare services. In this study, we determined patient satisfaction in hospital healthcare services at Zainal Abidin Hospital (RSUZA) and the socio-demographic factors associated with patient satisfaction. We conducted a cross sectional study at RSUZA. To recruit participants, purposive sampling and accidental sampling are used in this study. The binomial proportion is used to determine the total number of respondents. The questionnaire was tested for was performed. The chi-square test of independence was used to analyze the difference in each answer on each variable. The Kendall tau correlation was used to explore the correlation between social demographic variables and the total patient satisfaction value. The questionnaire used in this study is valid (p < 0.000) and reliable (Cronbach’s Alpha = 0.785). Of 904 respondents, 809 respondents (89.5%) were satisfied with the services provided by RSUZA. There was no difference on respondent in the gender category (p = 0.309), the age category (p = 0.095), and the respondent type (p = 0.377). Sociodemographic factors such as age (p < 0.000), education (p < 0.000), and occupation (p < 0.000) were significantly related to the level of patient satisfaction. We concluded that the majority of respondents are satisfied with the healthcare services provided by RSUZA. Healthcare providers need to focus on the patient’s age, educational level, and occupation in order to increase patient satisfaction, which is a benchmark for the quality of a healthcare service
Enhancing Water Quality Assessment in Indonesia Through Digital Image Processing and Machine Learning Athiya Iffaty; Adinda Salsabila; Adis Aufa Rafiqhi; Rivansyah Suhendra; Muhammad Yusuf; Novi Reandy Sasmita
Grimsa Journal of Science Engineering and Technology Vol. 1 No. 1 (2023): October 2023
Publisher : Graha Primera Saintifika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61975/gjset.v1i1.3

Abstract

Indonesia's diverse climate types, influenced by its unique geographical features, pose significant environmental challenges, including water quality issues related to turbidity and Total Dissolved Solids (TDS). Many Indonesians lack awareness of water quality, particularly turbidity, which can harbor harmful microorganisms. To address these challenges, this study employs digital image processing and machine learning, specifically Support Vector Machine (SVM) algorithms, for water quality assessment. A dataset of 80 water images, categorized into seven turbidity classes, is used to train and test the model. Results show a clear correlation between turbidity levels and TDS concentrations and pH values. The system accurately assesses water suitability for different sources, offering a user-friendly and cost-effective solution for water quality monitoring in dynamic environmental conditions. However, limitations include the dataset size and the narrow focus on turbidity. Future research could expand to encompass a broader range of water quality factors. This approach holds promise for enhancing water quality management in Indonesia and similar regions.
Time Series Analysis of UV Radiation and Temperature Using Seasonal ARIMA Rahmatul Fauzi; Tasyaul Husna; Izzul Akrami; Novi Reandy Sasmita
Infolitika Journal of Data Science Vol. 4 No. 1 (2026): May 2026
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v4i1.401

Abstract

High exposure to ultraviolet (UV) radiation in Banda Aceh poses significant risks to public health and the environment. Daily forecasts of UVA, UVB, and maximum temperature are important for climate planning. But current models often overlook daily changes in tropical regions or fail to incorporate them into their forecasts. This study develops a climatologically informed SARIMA framework incorporating a semiannual seasonal structure (s = 180) to model ultraviolet radiation and temperature dynamics in an equatorial tropical region. The variables used were UVA (W/m²), UVB (W/m²), and maximum temperature (°C) in Banda Aceh during the period December 2018-July 2024. The SARIMA method was applied after data pre-processing, such as Box-Cox transformation that stabilizes the variance (λ ≈ 1) and seasonal differencing (s = 180 days) to overcome non-stationarity. Model identification using ACF/PACF plots, with diagnostic tests (Ljung-Box white noise test, Shapiro-Wilk normality test) and accuracy metrics (MAPE, MASE, BIC, and AIC) for optimization. SARIMA(1,0,2)(1,1,0)¹⁸⁰ was selected as the optimal model for all variables. The selected SARIMA models yielded MAPE values of 18.93% (UVA), 0.48% (UVB), and 13.03% (temperature), indicating that the selected SARIMA specifications were able to capture the dominant temporal patterns observed in the analyzed dataset. The peak values for March-April 2025 were predicted to be 17.69 W/m² (UVA), 0.69 W/m² (UVB), and 31.85°C.
Co-Authors Adinda Salsabila Adis Aufa Rafiqhi Athiya Iffaty Azharuddin Azharuddin Farnida Farnida Ghalieb Mutig Idroes Ira Maya Izzul Akrami Muhammad Yusuf Nanda Earlia Rahmatul Fauzi Raihan Raihan Rinaldi Idroes Rivansyah Suhendra Rusdi Andid Tasyaul Husna Taufik Ridwan Tuti Fadlilah