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All Journal JTAM (Jurnal Teori dan Aplikasi Matematika) Journal of Applied Data Sciences Heca Journal of Applied Sciences Leuser Journal of Environmental Studies Ekonomikalia Journal of Economics 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 Unnes Journal of Public Health
Sasmita, Novi Reandy
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering 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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Decision Tree versus k-NN: A Performance Comparison for Air Quality Classification in Indonesia Sasmita, Novi Reandy; Ramadeska, Siti; Kesuma, Zurnila Marli; Noviandy, Teuku Rizky; Maulana, Aga; Khairul, Mhd; Suhendra, Rivansyah
Infolitika Journal of Data Science Vol. 2 No. 1 (2024): May 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v2i1.179

Abstract

Air quality can affect human health, the environment, and the sustainability of ecosystems, so efforts are needed to monitor and control air quality. The Plume Air Quality Index (PAQI) is one of the indices to measure and determine the level of air quality. In measuring the accuracy of the air quality level, it is necessary to do the right classification. Some previous studies have conducted classification analysis using the decision tree and K-Nearest Neighbor (k-NN) methods, but only evaluated using accuracy values. Therefore, this study uses both methods to evaluate the results of air quality level classification not only with accuracy but also with precision, recall, and F1-score. Secondary data of pollutant concentration values and PAQI categories based on particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), and ozone (O3) derived from Plume Labs for 33 provincial capitals in Indonesia in the time period from July 1 to December 31, 2022, were used in this study. From the results of comparing the performance of the two methods, it is found that the decision tree has a greater performance value than the performance value of k-NN. The decision tree performance values for accuracy, precision, recall and F1-score are 90.67%, 90.61%, 90.67%, and 90.63%, respectively. So, it can be concluded that the decision tree performs better than k-NN in classifying PAQI categories with better overall evaluation metric values.
Spatial Estimation for Tuberculosis Relative Risk in Aceh Province, Indonesia: A Bayesian Conditional Autoregressive Approach with the Besag-York-Mollie (BYM) Model Sasmita, Novi Reandy; Arifin, Mauzatul; Kesuma, Zurnila Marli; Rahayu, Latifah; Mardalena, Selvi; Kruba, Rumaisa
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.185

Abstract

Tuberculosis (TB) remains a significant public health challenge globally, with Indonesia being the second-highest country in TB cases worldwide. Aceh Province has one of the highest TB incidence rates in Indonesia. This study aims to estimate and map the spatial distribution patterns of TB relative risk across districts in Aceh Province, Indonesia, to reveal significant variations. The study employed an ecological time-series study design, utilizing the Bayesian Conditional Autoregressive (CAR) approach with the Besag-York-Mollie (BYM) model for spatial estimation and mapping of TB relative risk. TB case data and population data for 23 districts/cities in Aceh Province from 2016 to 2022 were analyzed. Spatial analysis was used to estimate and map TB's relative risk, aiding in identifying areas with higher transmission risks. The results showed that the relative risk of TB varied across districts/cities in Aceh Province over the study period. However, Lhokseumawe and Banda Aceh consistently exhibited high to very high relative risks over the years. In 2022, Lhokseumawe City and Banda Aceh City had the highest relative risks by 2.26 and 2.17, respectively, while Sabang City and Bener Meriah District had the lowest by 0.43 and 0.32, respectively. This study provides valuable insights into the heterogeneous landscape of TB risk in Aceh Province, which can inform targeted interventions and planning strategies for effective TB control. Using the Bayesian CAR BYM model proved effective in estimating and mapping TB's relative risk, highlighting areas requiring prioritized attention in TB prevention and control efforts.
Spatial Estimation of Relative Risk for Dengue Fever in Aceh Province using Conditional Autoregressive Method Rahayu, Latifah; Sasmita, Novi Reandy; Adila, Wulan Farisa; Kesuma, Zurnila Marli; Kruba, Rumaisa
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.141

Abstract

Dengue Fever (DHF) is a dangerous infectious disease that can cause death in an infected person. DHF is a disease transmitted by the Aedes Aegypti mosquito. Dengue cases have been reported in 449 districts/cities spread across 34 provinces with deaths spread across 162 districts/cities in 31 provinces, one of which is in Aceh Province. However, there are districts and cities in Aceh Province with a large number of cases and population at risk, and there are also districts and cities with fewer cases and population at risk. As a result, the number of cases and population at risk of DHF varies. Therefore, it is important to do planning to see which districts and cities have a high chance of DHF. In this study, the type of data used is secondary data sourced from the Aceh Provincial Health Profile from 2016 to 2022. The approach used is the Bayesian Conditional Autoregressive (CAR) prior model Besag-York-Mollie (BYM). The results of this study showed that mortality in dengue cases in Aceh Province from 2016 to 2022 had the highest mortality values in 2016 and 2022. The results of estimating the relative risk of DHF cases using the Bayesian Conditional Autoregressive (CAR) approach of the Besag-York-Mollie (BYM) Model in Aceh Province fulfill all categories with their relative risk values. Some districts/cities have relative risk values. Some districts/cities have high relative risk values of DHF cases and low relative risk values of DHF cases. Sabang city had the highest relative risk value of 3.54 and Bener Meriah district had the lowest relative risk of 0.2.
Forecasting Upwelling Phenomena in Lake Laut Tawar: A Semi-Supervised Learning Approach Ulhaq, Muhammad Zia; Farid, Muhammad; Aziza, Zahra Ifma; Nuzullah, Teuku Muhammad Faiz; Syakir, Fakhrus; Sasmita, Novi Reandy
Infolitika Journal of Data Science Vol. 2 No. 2 (2024): November 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v2i2.211

Abstract

The current climate change is causing the upwelling phenomenon to occur frequently in lakes and reservoirs. As a result of this phenomenon, thousands of fish die, causing floating net cage fish farmers to suffer losses. From existing studies, temperature sensors are used to determine the current condition of a body of water experiencing upwelling or not. Therefore, this study applies clustering to historical climate data from 2017-2023 using a semi-supervised learning approach that produces two labels: "potential for upwelling" and "no potential for upwelling." In the clustering process, the data is divided into two clusters using K-Means Clustering, and Support Vector Machine (SVM) is chosen to classify them. The performance of the proposed algorithm is expressed with accuracy, precision, recall, and F1-score values of 0.99, 0.995, 0.970, and 0.985, respectively. The analysis results show that this model has excellent performance in identifying upwelling potential. By using this method, information about upwelling potential can be obtained more quickly and accurately, allowing fish farmers to take appropriate preventive measures. This study also shows that the combination of K-Means Clustering and Support Vector Machine (SVM) can be effectively used to analyze historical climate data and generate useful predictions.
Optimizing Long-Term Meteorological Data Completeness in North Aceh, Indonesia: A Comparative Analysis of Interpolation Methods Sasmita, Novi Reandy; Saragih, Novita Sari; Rahayu, Latifah; Malfirah, Malfirah
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 1 (2025): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i1.27929

Abstract

More data in meteorological records is needed to ensure the accuracy of meteorological modeling, particularly in long-term datasets. This study aims to identify the most effective interpolation method for addressing missing data in North Aceh's meteorological dataset from 2010 to 2023, with a focus on the accuracy of methods applied across various meteorological variables. The study analyzed data from North Aceh Regency, Indonesia, comprising 25,565 daily observations of temperature, humidity, rainfall, sunshine duration, and wind speed. Missing values were interpolated using three methods: spline, stineman, and moving average interpolation. Performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Squared Logarithmic Error (MSLE) across 10%, 20%, and 30% levels of simulated missing data. All analysis in this study were carried out using R-4.4.2 software. While spline interpolation performed reasonably well, it showed increased variability, especially for high-variance variables like rainfall. Moving average interpolation was less reliable, with error rates increasing alongside higher levels of missing data. In contrast, stineman interpolation consistently achieved the lowest error metrics across all levels of missing data, with MAE ranging from 0.219 to 0.6691, MSLE from 0.035 to 0.109, and RMSE from 1.247 to 2.245, demonstrating superior robustness. Stineman interpolation offers a highly effective approach for managing missing meteorological data in North Aceh’s long-term dataset, enhancing data reliability for meteorological modeling and decision-making in meteorological-sensitive sectors. This study provides practical recommendations for selecting optimal interpolation techniques, especially in regions with variable meteorological data quality.
Optimizing Energy Consumption Prediction Across the IMT-GT Region Through PCA-Based Modeling Farid, Muhammad; Nuzullah, Teuku Muhammad Faiz; Aklya, Zatul; Nazila, Syifa; Ulhaq , Muhammad Zia; Apriliansyah, Feby; Sasmita, Novi Reandy
Infolitika Journal of Data Science Vol. 3 No. 1 (2025): May 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v3i1.286

Abstract

This study aims to improve the accuracy of energy consumption prediction in the Indonesia-Malaysia-Thailand Growth Triangle (IMT-GT) region by addressing multicollinearity among independent variables such as energy production (Mtoe), lignite coal production (million tons), crude oil production (million tons), refined oil production (million tons), natural gas production (billion cubic meters), and electricity production (terawatt-hours). By integrating Principal Component Analysis (PCA) with Random Forest (RF), six correlated variables were reduced into two uncorrelated principal components (PC1 and PC2), explaining 80.77% of the data variance. The PCA-RF hybrid model outperformed the standalone Random Forest (RF) model, with an increase in the coefficient of determination (R2) from 0.976 to 0.993. Additionally, it achieved significant reductions in error metrics, with the mean absolute error (MAE) decreasing from 5.811 to 4.169 and the root mean square error (RMSE) dropping from 9.278 to 4.786. These results demonstrate PCA’s effectiveness in isolating dominant drivers such as energy and lignite coal production while improving model stability. The framework provides policymakers with a reliable tool to forecast energy demand and align economic growth with sustainability in fossil fuel-dependent economies.
Spatial-Temporal Epidemiology of COVID-19 in Aceh, Indonesia: A Statistical Perspective Sasmita, Novi Reandy; Phonna, Rahmatil Adha; Kesuma, Zurnila Marli; Kamal, Saiful; Yusya, Nudzran
Unnes Journal of Public Health Vol. 13 No. 2 (2024)
Publisher : Universitas Negeri Semarang (UNNES) in cooperation with the 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.v13i2.3428

Abstract

 The development of COVID-19 cases in Aceh for each region based on spatio-temporal is vital information to know. Spatio-temporal mapping is carried out to knowthe distribution of cases in diversity based on regional and time conditions. The timeseries design study was used as the research design in this study. This study aims toobtain factors that influence the incidence of COVID-19 cases in Aceh using paneldata regression analysis and the GTWR model for more accurate results. There arenine variables from 23 districts/cities in Aceh Province in 2020 and 2021. Based onpartial panel data regression analysis, of the eight independent variables that arefactors for analysis, it shows that only the variable number of doctors (p < 0.000),number of Tuberculosis Cases (p < 0.000), Number of Villages with Puskesmas (p< 0.026), and Number of Poor population (p < 0.035) have a significant effect onthe increase in COVID-19 cases in Aceh. The number of Tuberculosis Cases is avery dominant variable. Then, the results of the GTWR analysis using the AdaptiveKernel Exponential weighting function show that regional and time diversity affectthe factors that cause an increase in COVID-19 cases in Aceh. These factors need tobe a concern in controlling COVID-19 cases in Aceh in the future. 
Statistical Assessment of Human Development Index Variations and Their Correlates: A Case Study of Aceh Province, Indonesia Sasmita, Novi Reandy; Phonna, Rahmatil Adha; Fikri, Mumtaz Kemal; Khairul, Mhd; Apriliansyah, Feby; Idroes, Ghalieb Mutig; Puspitasari, Ayu; Saputra, Fachri Eka
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 Maulidar, Putri; Fitriyani, Fitriyani; Sasmita, Novi Reandy; Hardi, Irsan; Idroes, Ghalieb Mutig
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.
Comparison of Spatial Interpolation Methods: Inverse Distance Weighted and Kriging for Earthquake Intensity Mapping in Aceh, Indonesia Rahayu, Latifah; Utami, Cut Chairilla Yolanda; Fauzi, Rahmatul; Sasmita, Novi Reandy
Infolitika Journal of Data Science Vol. 3 No. 2 (2025): November 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v3i2.347

Abstract

Aceh Province, located in the Sumatra megathrust zone of Indonesia, is one of the most seismically active regions in Southeast Asia. Understanding the spatial distribution of earthquake magnitudes is essential for disaster mitigation and risk management. This study compares two spatial interpolation methods Inverse Distance Weighted (IDW) and Kriging to determine the most accurate approach for mapping earthquake intensity in Aceh Province. A total of 2,255 earthquake events with magnitudes of 2.5 M and above, recorded between 1990 and 2024 by the United States Geological Survey (USGS), were analyzed. IDW was tested using five power parameters (p = 1–5), while Kriging applied three semivariogram models (spherical, exponential, and Gaussian). The interpolation accuracy was assessed through Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE). Results indicated that Kriging with the exponential semivariogram achieved the highest accuracy, with RMSE = 0.0848, MSE = 0.0072, and MAPE = 1.14%, outperforming IDW (RMSE = 0.2288, MSE = 0.0523, MAPE = 1.24%). The Kriging model effectively represented the gradual spatial decay of seismic energy, identifying Aceh Singkil and northern Simeulue as the most earthquake-prone zones, consistent with regional tectonic patterns. These findings confirm that incorporating spatial autocorrelation enhances interpolation accuracy and geophysical interpretation. The study establishes Kriging as a reliable tool for seismic hazard mapping and provides valuable insights for disaster preparedness, infrastructure planning, and future geostatistical applications in earthquake risk assessment.
Co-Authors Adha, Zuhra Adila, Wulan Farisa Ahmad Watsiq Maula Aklya, Zatul Apriliansyah, Feby Arif Saputra, Arif Arifin, Mauzatul Asep Rusyana Ayu Puspitasari, Ayu Aziza, Zahra Ifma Dahlawy, Arriz Dimas Chaerul Ekty Saputra Earlia, Nanda Erkata Yandri Fauzi, Rahmatul Fikri, Mumtaz Kemal Fitriyani Fitriyani Ghazi Mauer Idroes Hizir Sofyan Husdayanti, Noviana Idroes, Ghalieb Mutig Iffaty, Athiya Iin Shabrina Hilal Irsan Hardi Irvanizam, Irvanizam Kamal, Saiful Kemala, Pati Khairul, Mhd Khairul, Moh Kruba, Rumaisa Kusumo, Fitranto La Ode Reskiaddin Malfirah, Malfirah Mardalena, Selvi Maria Paristiowati Maulana, Aga Maulidar, Putri Maulydia, Nur Balqis Muhammad Farid Muhammad Subianto Muhammad Yusuf Muliadi Muslem Muslem Nazila, Syifa Nurdjannah J. Niode Nuzullah, Teuku Muhammad Faiz Phonna, Rahmatil Adha Putri, Ulayya Rafiqhi, Adis Aufa RAHAYU, LATIFAH Ramadeska, Siti Raudhatul Jannah Razief Perucha Fauzie Afidh Rinaldi Idroes Salsabila, Adinda Saputra, Fachri Eka Saragih, Novita Sari Satrio, Justinus Sofyan, Rahmi Suhendra, Rivansyah Suryadi Suryadi Syakir, Fakhrus Syarafina, Risky Haezah Teuku Rizky Noviandy TRINA EKAWATI TALLEI Ulfa, Elvitra Mutia Ulhaq , Muhammad Zia Ulhaq, Muhammad Zia Utami, Cut Chairilla Yolanda Utami, Reksi Yarmaliza Yusya, Nudzran Zahriah, Zahriah Zurnila Marli Kesuma