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All Journal Unnes Journal of Public Health Heca Journal of Applied Sciences Journal of Educational Management and Learning Infolitika Journal of Data Science Grimsa Journal of Science Engineering and Technology Grimsa Journal of Business and Economics Studies
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 Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

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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
Evaluation of Gradient Boosted Classifier in Atopic Dermatitis Severity Score Classification Rivansyah Suhendra; Suryadi Suryadi; Noviana Husdayanti; Aga Maulana; Teuku Rizky Noviandy; Novi Reandy Sasmita; Muhammad Subianto; Nanda Earlia; Nurdjannah Jane Niode; Rinaldi Idroes
Heca Journal of Applied Sciences Vol. 1 No. 2 (2023): October 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v1i2.85

Abstract

This study investigates the application of the Gradient Boosting machine learning technique to enhance the classification of Atopic Dermatitis (AD) skin disease images, reducing the potential for manual classification errors. AD, also known as eczema, is a common and chronic inflammatory skin condition characterized by pruritus (itching), erythema (redness), and often lichenification (thickening of the skin). AD affects individuals of all ages and significantly impacts their quality of life. Accurate and efficient diagnostic tools are crucial for the timely management of AD. To address this need, our research encompasses a multi-step approach involving data preprocessing, feature extraction using various color spaces and evaluating classification outcomes through Gradient Boosting. The results demonstrate an accuracy of 93.14%. This study contributes to the field of dermatology by providing a robust and reliable tool to support dermatologists in identifying AD skin disease, facilitating timely intervention and improved patient care.
Leveraging Artificial Intelligence to Predict Student Performance: A Comparative Machine Learning Approach Aga Maulana; Ghazi Mauer Idroes; Pati Kemala; Nur Balqis Maulydia; Novi Reandy Sasmita; Trina Ekawati Tallei; Hizir Sofyan; Asep Rusyana
Journal of Educational Management and Learning Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i2.132

Abstract

This study explores the application of artificial intelligence (AI) and machine learning (ML) in predicting high school student performance during the transition to university. Recognizing the pivotal role of academic readiness, the study emphasizes the need for tailored interventions to enhance student success. Leveraging a dataset from Portuguese high schools, the research employs a comparative analysis of six ML algorithms—linear regression, decision tree, support vector regression, k-nearest neighbors, random forest, and XGBoost—to identify the most effective predictors. The dataset encompasses diverse attributes, including demographic details, social factors, and school-related features, providing a comprehensive view of student profiles. The predictive models are evaluated using R-squared, Root Mean Square Error, and Mean Absolute Error metrics. Results indicate that the Random Forest algorithm outperforms others, displaying high accuracy in predicting student performance. Visualization and residual analysis further reveal the model's strengths and potential areas for improvement, particularly for students with lower grades. The implications of this research extend to educational management systems, where the integration of ML models could enable real-time monitoring and proactive interventions. Despite promising outcomes, the study acknowledges limitations, suggesting the need for more diverse datasets and advanced ML techniques in future research. Ultimately, this work contributes to the evolving field of educational AI, offering practical insights for educators and institutions seeking to enhance student success through predictive analytics.
ANFIS-Based QSRR Modelling for Kovats Retention Index Prediction in Gas Chromatography Rinaldi Idroes; Teuku Rizky Noviandy; Aga Maulana; Rivansyah Suhendra; Novi Reandy Sasmita; Muslem Muslem; Ghazi Mauer Idroes; Raudhatul Jannah; Razief Perucha Fauzie Afidh; Irvanizam Irvanizam
Infolitika Journal of Data Science Vol. 1 No. 1 (2023): September 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i1.73

Abstract

This study aims to evaluate the implementation and effectiveness of the Adaptive Neuro-Fuzzy Inference System (ANFIS) based Quantitative Structure Retention Relationship (QSRR) to predict the Kovats retention index of compounds in gas chromatography. The model was trained using 340 essential oil compounds and their molecular descriptors. The evaluation of the ANFIS models revealed promising results, achieving an R2 of 0.974, an RMSE of 48.12, and an MAPE of 3.3% on the testing set. These findings highlight the ANFIS approach as remarkably accurate in its predictive capacity for determining the Kovats retention index in the context of gas chromatography. This study provides valuable perspectives on the efficiency of retention index prediction through ANFIS-based QSRR methods and the potential practicality in compound analysis and chromatographic optimization.
Maternal and Child Healthcare Services in Aceh Province, Indonesia: A Correlation and Clustering Analysis in Statistics Novi Reandy Sasmita; Siti Ramadeska; Reksi Utami; Zuhra Adha; Ulayya Putri; Risky Haezah Syarafina; La Ode Reskiaddin; Saiful Kamal; Yarmaliza Yarmaliza; Muliadi Muliadi; Arif Saputra
Infolitika Journal of Data Science Vol. 1 No. 1 (2023): September 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i1.88

Abstract

Infant mortality remains a public health problem in Aceh Province, Indonesia. Health services during pregnancy are an essential factor in reducing infant mortality. Studies examining factors such as maternal and child health services that have implications for infant mortality in Aceh province are still scarce. Therefore, this study aims to examine the correlation between maternal and child health services variables such as Blood-Supplementing Tablets (TTD), Coverage of the First Visit of Pregnant Women (K1), Coverage of the First Visit of Pregnant Women (K4), and management of Obstetric Complications to live births and to map the maternal and child health services obtained during pregnancy. A cross-sectional study was used as the research study. This study used descriptive statistics, such as measures of data centering and data dispersion. In this work, inferential statistical analysis was conducted using the Shapiro-Wilk test, Spearman test, and fuzzy c-means. The result of the Shapiro Wilk test stated that the live birth rate variable and all Maternal and Child Healthcare Services variables were not normally distributed (p-value < 0.05), all Maternal and Child Healthcare Services variables were positively correlated to live birth rate based on the Spearman test (p-value < 0.05). Based on the Silhouette Index with 0.555, the formation of 3 clusters is the optimal cluster. The clustering is based on the Maternal and Child Healthcare Services that have been provided, where the first, second, and third clusters consist of five districts/city, eight districts/city, and ten districts/city, respectively, as a result of Fuzzy C-Means Clustering.
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.
Statistical Assessment of Human Development Index Variations and Their Correlates: A Case Study of Aceh Province, Indonesia Novi Reandy Sasmita; Rahmatil Adha Phonna; Mumtaz Kemal Fikri; Mhd Khairul; Feby Apriliansyah; Ghalieb Mutig Idroes; Ayu Puspitasari; Fachri Eka Saputra
Grimsa Journal of Business and Economics Studies Vol. 1 No. 1 (2024): January 2024
Publisher : Graha Primera Saintifika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61975/gjbes.v1i1.14

Abstract

The Human Development Index (HDI) provides a holistic measure of human development in a country or locality. This study aims to identify factors correlated with changes in the Human Development Index and analyze changes in the distribution of the Human Development Index in Aceh Province from 2012 to 2022. Apart from the Human Development Index as the variable used in this study, five variables are used in this study as indicators: Life Expectancy, Gross Regional Domestic Product (GRDP), Per Capita Expenditure, Average Years of Schooling, and Expected Years of Schooling as socioeconomic factors. This research uses an ecological study design. Data was sourced from the "Aceh in Figures" report by the Central Bureau of Statistics of Aceh Province. The statistical methods used were descriptive statistics, the Shapiro-Wilk test for normality, the Spearman test for correlation analysis, the Wilcoxon one-sample test for data distribution, and the Kruskal-Wallis test to compare distributions. Based on the correlation analysis, the study revealed that the five socioeconomic variables tested showed a significant positive correlation with changes in the HDI in Aceh Province (p-value < 0.05). In addition, the difference analysis showed a significantly different distribution of HDI across the years studied (p-value < 0.05), with a pattern of increasing HDI observed from the beginning to the end of the study period. The recommended based on finding of the study is policymakers and stakeholders focus on strategies that enhance the positive correlates identified Finally, these results provide important and structured insights into the role of factors in HDI change.
Exploring Indonesia's CO2 Emissions: The Impact of Agriculture, Economic Growth, Capital and Labor Putri Maulidar; Fitriyani Fitriyani; Novi Reandy Sasmita; Irsan Hardi; Ghalieb Mutig Idroes
Grimsa Journal of Business and Economics Studies Vol. 1 No. 1 (2024): January 2024
Publisher : Graha Primera Saintifika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61975/gjbes.v1i1.22

Abstract

This study examines the dynamic impact of agriculture, economic growth, capital, and labor on carbon dioxide (CO2) emissions in Indonesia from 1990-2022. Employing the Autoregressive Distributed Lag (ARDL) method, the findings indicate that agriculture plays a substantial role in decreasing CO2 emissions in the short and long run. Additionally, a consistent positive correlation exists between economic growth and CO2 emissions, underscoring the difficulty in decoupling economic progress from its environmental repercussions. Capital formation, on the other hand, exerts a noteworthy negative influence on CO2 emissions, particularly in the long run, implying that increased investment in capital formation, potentially in environmentally friendly technologies, could contribute to a gradual reduction in emissions. However, the expanding labor is identified as a significant driver of CO2 emissions, particularly in the long run. Highlighting the challenges associated with mitigating the environmental impact of workforce growth. Furthermore, the Granger causality results indicate unidirectional causality from CO2 emissions and labor to agriculture, from agriculture to economic growth and capital formation, and from economic growth to capital formation. Therefore, promoting sustainable agriculture, aligning economic growth with green technologies, incentivizing eco-friendly investment, integrating comprehensive planning, and maintaining flexible policies are crucial for Indonesia's effective environmental and economic management.
Co-Authors Adinda Salsabila Adis Aufa Rafiqhi Aga Maulana Arif Saputra Asep Rusyana Athiya Iffaty Ayu Puspitasari Azharuddin Azharuddin Fachri Eka Saputra Farnida Farnida Feby Apriliansyah Fitriyani Fitriyani Ghalieb Mutig Idroes Ghalieb Mutig Idroes Ghazi Mauer Idroes Hizir Sofyan Ira Maya Irsan Hardi Irvanizam Zamanhuri La Ode Reskiaddin Mhd Khairul Muhammad Subianto Muhammad Yusuf Muliadi Muliadi Mumtaz Kemal Fikri Muslem Muslem Nanda Earlia Nanda Earlia Noviana Husdayanti Nur Balqis Maulydia Nurdjannah Jane Niode Pati Kemala Putri Maulidar Rahmatil Adha Phonna Raihan Raihan Raudhatul Jannah Razief Perucha Fauzie Afidh Reksi Utami Rinaldi Idroes Risky Haezah Syarafina Rivansyah Suhendra Rusdi Andid Saiful Kamal Siti Ramadeska Suryadi Suryadi Taufik Ridwan Teuku Rizky Noviandy Trina Ekawati Tallei Tuti Fadlilah Ulayya Putri Yarmaliza Zuhra Adha