bluuebunny/crossref_metadata_2025 huggingface dataset:
APPLICATION OF NONPARAMETRIC REGRESSION SPLINE TRUNCATED FOR MODELING THE HEIGHT OF YEOP CHAGI KICKS OF TAEKWONDO ATHLETES IN SAMARINDA CITY
Authors: Sitohang Frans Karta Sayoga , Sifriyani Sifriyani , Mahmuda Siti
Published: May 2024

Nonparametric regression is a model approach method that is used when the shape of the regression curve between the response variable and the predictor variable is assumed to have an unknown shape or pattern. One of the estimators in the nonparametric regression approach is the truncated spline which has the ability to handle data whose behavior changes at certain sub intervals. The purpose of this study was to obtain the estimated value of the parameters of the nonparametric regression model with a truncated spline approach at one knot point, two knot points, and three knot points for kick height data of yeop chagi taekwondo athletes in Samarinda City. The results showed that the truncated spline nonparametric regression model was the best in modeling high kick height data for yeop chagi taekwondo athletes in Samarinda City with three knot points. This model has the minimum Generalized Cross Validation (GCV) value of 7.94 with an R2 value of 94.72% and a Mean Square Error (MSE) value of 2.62. Based on the results of the model parameter significance test, it was concluded that the factors that influence the kick height of the yeop chagi taekwondo athlete in Samarinda City are flexibility, leg power, leg length, and waist circumference.
MODELING STOCHASTIC ADVERSE EFFECTS OF CBN 2023 REDESIGNED NAIRA NOTES POLICY ON RURAL FARMERS IN NIGERIA
Authors: Osu Bright Okore , Chibuisi Chigozie , Akpanibah Edikan Edem
Published: May 2024

The recent Central Bank of Nigeria (CBN) 2023 redesigned naira notes is of good benefits to strengthen the economy of the country by checking counterfeiting and hoarding of large volume of banknotes by the public. Despite all the efforts made by the CBN for citizens to enjoy the benefits of this implementation, most rural farmers were faced with adverse effects of uncertainties in the production and marketing of their agricultural produce due to lack of redesigned new naira notes in circulation. The adverse effects of these uncertainties are modeled as Advanced Stochastic Time-Delay Differential Equation (ASTDDE). The modeled equation is solved using Extended Second Derivative Block Backward Differentiation Formulae Method (ESDBBDFM) without the use of interpolation techniques in the evaluations of the delay term and noise term. In comparing the numerical results of this method with other existing methods in literature, the newly developed mathematical expressions for the evaluations of the delay term and the noise term in solving ASDDEs with the discrete schemes of ESDBBDFM gives better results for step number than step numbers and 3 by producing Least Minimum Absolute Random Error (LMARE) in a Lower Computational Processing Unit Time (LCPUT) faster than other existing methods that applied interpolation techniques in evaluations of the delay term and the noise term.
VALUE AT RISK ESTIMATION USING EXTREME VALUE THEORY APPROACH IN INDONESIA STOCK EXCHANGE
Authors: Najamuddin Fadhila Febriyanti , Herdiani Erna Tri , Jaya Andi Kresna
Published: May 2024

Extreme Value Theory (EVT) is a method used to identify extreme cases in heavy tail data such as financial time series data. This research aimed to obtain an estimate of stock risk through the EVT approach and compare the accuracy of the two EVT approaches, Block Maxima (BM) and Peaks Over Threshold (POT). The method used to estimate stock risk is VaR with the BM and POT approaches, and the Z statistic is used to compare the accuracy. The data used, and the limitation in this research is daily closing price data for non-cyclical consumer stocks included in LQ45 for the period February 01, 2017, to January 31, 2023. Other research limitations are using weekly blocks or 5 working days in dividing BM blocks, using the percentage method in determining threshold values in the POT approach, and using Maximum Likelihood Estimation (MLE) to estimate EVT parameter estimates. The results of the VaR analysis show that the risk level generated by the POT method is greater than the risk level from BM. The results of backtesting between the two EVT approaches in estimating VaR values show that the POT approach is more accurate than the BM approach.
OPTIMAL CONTROL ANALYSIS OF HIV/AIDS DISEASE SPREAD MODEL IN INDONESIA
Authors: Utami Wika Dianita , Dzaky Ahmad Naufal , Fanani Aris
Published: May 2024

The Human Immunodeficiency Virus (HIV) is a contagious virus that weakens the immune system of infected individuals, making them more susceptible to various diseases. These individuals are referred to as those exposed to the AIDS disease, which unfortunately, cannot be cured. To effectively manage AIDS, prevention is crucial in slowing down the spread and growth of the HIV virus. Mathematical modeling can play a significant role in the optimal control of AIDS. In this study, the , , , ,  model with three different optimal controls were employed. Optimal control involves public health education campaigns, screening, and treatment. The goal is to minimize the number of individuals infected with HIV/AIDS using Pontryagin's Maximum Principle. This principle considers various factors, such as population class coefficients, cost weights, and control variables to determine the most effective approach. The simulation results indicate that counseling control in the exposed population class ( ) yields better outcomes compared to counseling control in the susceptible class and treatment control in the HIV-infected population class. This implies that focusing on educating and counseling individuals who are exposed to HIV can be more efficient in AIDS control than targeting those already infected or at risk. By applying these optimal control strategies, it may be possible to mitigate the impact of HIV/AIDS and improve public health outcomes.
REGRESSION OF SURVIVAL DAGUM 3 PARAMETER LINK FUNCTION IN HIV/AIDS PATIENTS IN EAST JAVA
Authors: Mahmudah Nur , Ningrum Ifa Khoiria
Published: May 2024

AIDS (Acquired Immuno Deficiency Syndrome) is is a symptom of blood cell disease which results in decreased immunity in humans. HIV/AIDS causes a decline in economic productivity, health, and environmental fields, so it needs treatment to reduce the spread of the disease in East Java. The spread of the HIV/AIDS virus is growing rapidly both in terms of region and pattern of spread. Therefore, it is necessary to have policies from the government to prevent the spread of HIV/AIDS through mathematical modeling to determine the actual and comprehensive factors that influence HIV/AIDS care. policies needed to handle and reduce HIV/AIDS in East Java. This study aims to determine the factors that influence the care of people with HIV/AIDS with Survival Regression modeling Dagum 3 Parameter link function. This model uses parameter estimation with a Bayesian analysis approach with the results that the factors that significantly influence the length of HIV/AIDS care in East Java are marital status (X5), absolute CD4 levels (X7), Suffering Stage (X8), Functional State (X9), and Therapy adherence (X10). These results indicate that marital status and condition characteristics of HIV/AIDS sufferers are important information in the socialization program for reducing HIV/AIDS in East Java.
TRACKING SYSTEM USING GPS AND SMART CARD AUTHENTIFICATION BASED ON ESP 32 MCU
Authors: Suryowinoto Andy , Herlambang Teguh , Widjanarko Angga Adhitya , Prabowo Yuliyanto Agung , Tomasouw Berny Pebo
Published: May 2024

Logistics distribution is a series of activities that are interconnected in each process. Therefore, an information system is necessary to record, report conditions, and display the current location of items directly. An Android-based application system is vulnerable to spoofing, such as fake GPS. The application is used to falsify the coordinates of digital map locations. This research aims to develop a common tracking system and prevent Fake GPS by employing the System Development Life Cycle method, consisting of several stages, i.e., planning, analysis, design, implementation, testing, and maintenance. In this research, a prototype of the tracker tool and its information system were made. It used an RFID Smart Card for courier identity authentication and a GPS module to track position information on a digital map. RFID and GPS were controlled using the ESP32. Furthermore, the Website served as an information system to read and create a QR Code to identify the goods carried. It received data from the RFID Smart Card and GPS, stored it in the Database, and displayed it online. The test results indicated that the tracker prototype succeeded in sending location data throughout the test and gained an average distance of 6.82 meters, which was different from a commercial GPS device. It had a delay of 268.5 seconds when the location was first read. The tracker prototype had an average power consumption of 1.5 watts, an average battery life of 2 hours, 28 minutes, and 40 seconds, as well as an average battery deficiency of 36.79%. In conclusion, the system test was successful.
SPATIAL MODELING OF POVERTY IN BENGKULU PROVINCE WITH MIXED GEOGRAPHICALLY WEIGHTED REGRESSION
Authors: Nugroho Sigit , Rini Dyah Setyo , Jomecho Tommy , Oktarina Cinta Rizky , Pratiwi Stevy Cahya , Karuna Elisabeth Evelin
Published: May 2024

The percentage of poor people in Bengkulu Province is high from year to year. The poverty rate in Bengkulu Province also tends to fluctuate. If there is a decrease in the poverty rate, the decrease is relatively small. Poverty in the regions of Bengkulu Province also varies from district to district, subdistrict, and village to village, because poverty data is spatial data that varies regionally. The diversity of poverty data in Bengkulu Province is influenced by spatial effects, namely spatial dependency and spatial heterogeneity. Spatial dependency occurs due to spatial error correlation in cross section data, while spatial heterogeneity occurs due to random area effects, which is the difference between one region and another. Therefore, classical methods are not qualified enough to analyze the resulting diversity. This research will model the poverty of each district/city in Bengkulu Province using Mixed Geographically Weighted Regression (MGWR), because this method is quite complex in modeling data that contains spatial heterogeneity and variations in geospatial data. This modeling aims to identify and analyze poverty indicators in Bengkulu Province spatially, namely based on poverty data in each district/city in Bengkulu Province. The results showed that by using the MGWR method, the variables that locally influence the percentage of extreme poor people in each district/city in Bengkulu Province are Female Household Head Gender  and not having a waterheater . Meanwhile, the variable that has a global effect on the percentage of the extreme poor in each district/city in Bengkulu Province is not having a flat screen television ().
DETERMINANTS OF POOR HOUSEHOLDS IN SOUTH SUMATRA USING A MULTILEVEL LOGISTIC MODEL
Authors: Putri Aida Devanty , Astuti Erni Tri
Published: May 2024

Poverty is still one of the problems experienced by all countries, including Indonesia. According to BPS, in March 2021, the poverty rate in Indonesia was 10.14 percent. South Sumatra is a province with a poverty rate, which is the tenth-highest nationwide and the third-highest on Sumatra Island. This poverty rate is accompanied by a contraction in economic growth in 2021 by 3.58 percent. This condition indicates a contradiction and suggests that poverty still needs to be resolved. Moreover, the disparity in social and economic aspects across regions could potentially make the poverty rate high. This research aims to see the individual and regional or contextual factors affecting poor households. To simultaneously capture the effects of individual and regional level, we perform a Multilevel Logistic model using hierarchical structured poverty data from SUSENAS. The result shows that 4 (four) variables at individual level, which are the number of household members, the status of residence building, health insurance, and saving ownership, had a significant effect on the poor household. The region with high unemployment rate tend to have a high percentage of poor households. This result indicates that local government need to have policies that can affect poor households directly, such as socializing more about health insurance program and family planning program, as well as supervision in social aid distribution. Moreover, they need to create a program that can employ more people in order to decrease the percentage of poor households in such regions.
BOUNDEDNESS AND EXISTENCE ANALYSIS SOLUTION OF AN OPTIMAL CONTROL PROBLEMS ON MATHEMATICAL COVID-19 MODEL
Authors: Hakim Lukman , Widayanti Lilis
Published: May 2024

The investigation given right here is part of a literature review on mathematical models that apply analytical mathematics. The present work focuses on the COVID-19 model, which incorporates optimum control variables previously investigated and interpreted by Hakim. Depending on the current model, we will further develop the analysis and demonstrate the non-negativity condition as well as the boundedness criteria for the solutions. Additionally, we conduct several supplementary analyses by applying the Lipschitz function to examine the uniqueness of the solutions and the existence of the solution are hold on the autonomous system. This work to supports the previously findings that incorporating an optimal control into the model can reduce COVID-19 treat on public. Finally, that research verifies that the control variables used in the research satisfy all of the existence criteria, as outlined in Theorem 5 of this work.
IMPLEMENTATION OF MEWMA CHART USING TIME SERIES MODEL FOR MONITORING THE WHITE CRISTAL SUGAR QUALITY
Authors: Pratama Donny Setya , Rozi Fachrur
Published: May 2024

The basic assumption of implementing a control chart is that the observed process values should be normally distributed and independent. However, the production process at the factory is carried out repeatedly with the same machine, so there is a possibility that the observation data is not independent and the resulting control chart becomes inaccurate. Therefore, a time series model approach is needed to create an accurate control chart. This study aims to conduct statistical quality control of white crystal sugar (WCS) production at Madukismo Sugar Factory (MSF) Yogyakarta to maintain product stability and quality according to the standards. MSF performs quality control on WCS products with several quality characteristic variables, including drying shrinkage (%), grain size (mm), polarization, and color of sugar solution (ICUMSA). This study is only limited to the four quality characteristics with research data in the form of secondary data from the MSF QC Laboratory. The data used were WCS quality characteristics from May 9 - July 16, 2022. The four characteristics influence each other, so a MEWMA control chart is used. This study found autocorrelation between observations so that time series modeling was carried out and resulted in VARIMA (1,1,2) as the best time series model. While the implementation of the MEWMA chart shows uncontrollable results with an optimal weighting value λ of 0.8. For process capability, the results show that the process is capable.
APPLICATION OF K-MEANS AND FUZZY C-MEANS ALGORITHMS TO DETERMINE FLOOD VULNERABILITY CLUSTERS (CASE STUDY: KUTAI KARTANEGARA REGENCY)
Authors: Nurjanah Desi , Anggriani Indira , Hasanah Primadina
Published: May 2024

Flooding show situation where areas that are not usually inundated, such as farmland and settlements, and city district areas, become inundated due to water. Floods can to occur when the flow of water on rivers or waste channels overrun its normal measurements. This study describes the K-Means and Fuzzy C-Means Algorithm methods for clustered flood-prone areas built on Districts in Kutai Kartanegara Regency. This research begins with data collection in the character of rainfall, land elevation, the number of victims affected, the quantity of damaged houses, the quantity of damage to facilities and the quantity of flood events. Before the data is processed using these two methods, data normalization will be carried out in a dataset which aims to shape the data into positional values from the same range. K-Means and Fuzzy C-Means are accustomed to identifying groups in each sub-district in Kutai Kartanegara Regency that have a level of vulnerability to floods. At this stage, 3 initial clusters were carried out, namely high, medium, and low vulnerability clusters. The validity test produces a Silhouette Index value of 0.574283589 and a Partition Coefficient Index of 0.78905. The outcome of the K-Means method with the standard deviation within and between clusters are 0.5131 and the Fuzzy C-Means method for the standard deviations within and between clusters is 0.3489. based uppon value of the silhouette index, partition coefficient index and standard deviation within and between clusters it results that Fuzzy C-Means is the best method of this study.
THE APPLICATION OF STANDARD GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (SGARCH) MODEL IN FORECASTING THE STOCK PRICE OF BARITO PACIFIC
Authors: Nugraha Edwin Setiawan , Alvina Celine
Published: May 2024

Stock potentially yields higher returns than other investments, but is riskier due to volatile prices. To minimize the risk of loss, investors can forecast the stock price to help in deciding whether to buy, sell, or hold the stock.  Several methods are available for forecasting the stock price such as ARIMA, ARCH, and SGARCH. ARIMA model works best for series with a constant variance of error. However, almost all stock price series have a non-constant variance of error, known as heteroscedasticity, as such ARIMA isn’t suited for modeling the stock price. In contrast, the SGARCH model can handle series with heteroscedasticity. This makes it better suited for modeling stock prices as they have similar characteristics. PT Barito Pacific (BRPT) is a publicly traded firm that works mainly in petrochemical and geothermal energy. BRPT’s net profit increased in 2023 by 243% and the demand for geothermal energy is expected to increase due to the government's renewable energy transition project. Therefore, this study forecasts the BRPT’s stock price using the SGARCH model with R Studio. The stock price used ranges from October 1st, 2018 to August 16th, 2023 gotten from the Yahoo Finance Website. Based on the least AIC, this study found that ARMA(6,2)-SGARCH(1,1) is the best model for forecasting the stock price. This model gives a very accurate prediction of the stock price from April 1st, 2023 – April 19th, 2023 with a mean absolute error of 78.11, root mean square error of 89.51, and mean absolute percentage error of 9.81%.
PROFITABILITY CALCULATION AND ANALYSIS FOR INTEREST RATE SWAP USING THE HULL WHITE MODEL
Authors: Hadiono Vania Rosalie , Kusnadi Felivia
Published: May 2024

The London Inter-Bank Offered Rate (LIBOR) volatility had resulted in higher interest rate risks faced by many big companies and financial institutions whose assets depend on the interest rate. Eventually, there was an appearance of the new financial product development that can be used for hedging, such as interest rate swap, one of the most popular methods, utilized by most financial institutions and big companies which use LIBOR as their benchmark. In fact, it is not uncommon for numerous companies to gain negative return from this swap transaction. Therefore, in this paper, we used the Hull-White model to predict the LIBOR rate, since this model has a decent level of accuracy, calculated using Root Mean Squared Error (RMSE) method. Furthermore, the estimation of LIBOR rate was used to calculate the net value of interest rate swap transaction, in three scenarios, using, respectively, the minimum value, the average value, and the maximum value of the LIBOR’s estimation results to provide an analysis of potential P/L (Profit & Loss) exposure due to the realization of interest rate swaps.
IMPLEMENTATION OF MAPPING-BASED MACHINE LEARNING ALGORITHM AS NON-STRUCTURAL DISASTER MITIGATION TO DETECT LANDSLIDE SUSCEPTIBILITY IN TAKARI DISTRICT
Authors: Fallo Sefri Imanuel , Nipu Lidia Paskalia
Published: May 2024

This research is primarily dedicated to providing a comprehensive exposition of the methodology applied in the deployment of a cartographic-based machine learning algorithm designed for the precise identification of areas susceptible to landslides within the geographical confines of the Takari District.This research delves into the application of mapping-based machine learning algorithms in the domain of non-structural disaster mitigation, with a specific emphasis on the detection of landslide susceptibility within the Takari District. A range of machine learning algorithms, including Support Vector Machine, Naive Bayes Classifier, Ordinal Logistic Regression, Random Forest, and Decision Tree, were harnessed to evaluate rainfall data within the context of landslide susceptibility. An evaluation of model performance, anchored in accuracy and Kappa metrics, unveiled that both the Ordinal Logistic Regression and Random Forest models exhibited noteworthy precision, reaching a commendable 74.36%. Nevertheless, a meticulous examination of Kappa values disclosed the ascendancy of the Random Forest model, which achieved a superior Kappa value of 0.5397. As portrayed in the visual representation provided, it becomes manifest that the Random Forest algorithm's prognostications yield 66 instances of cloudy atmospheric conditions, 48 occurrences of light precipitation, and 3 episodes of moderate rainfall. These predictions are influenced by several factors, including average temperature, humidity levels, wind speed, duration of sunlight, and wind direction at maximum speed. Consequently, this comprehensive analysis underscores the Random Forest algorithm as the most efficacious model for landslide susceptibility prediction. Furthermore, the study seamlessly integrated overlay maps, encompassing the Slope Inclination Map of the Takari District, Geological Map of the Takari District, and Soil Type Map of the Takari District, to contribute to the formulation of a definitive map delineating the susceptibility to landslides in the Takari District. Furthermore, further research could conduct spatial validation of the model predictions using additional datasets or remote sensing data to validate the accuracy of the landslide susceptibility map and ensure its applicability across different geographical regions.
LOSS MODEL OF CLIMATE INSURANCE BASED ON EFFECT OF GROWING DEGREE DAYS INDEX
Authors: Anggasari I Gusti Ayu Wulan , Zainuddin Ahmad Fuad , Indratno Sapto Wahyu , Yunus Muhammad Haekal
Published: May 2024

Climate change is a threat to agriculture, especially food crops such as rice. Climate index insurance is an alternative to cover the risk of agricultural losses due to crop failure due to climate change factors. The observed climate index is the effect of growing degree days which measures the impact of temperature on plant growth and development. The data used in this study is daily temperature data at Climatology Station Class 1 Darmaga, Bogor and Meteorological Station Class 3 Citeko, West Java, during the gadu (rice that is planted in the Gadu/Dry season) planting period. In determining the amount of loss, the average daily temperature on growing degree days is calculated using a combination of a time series model and a deterministic model. The deterministic model describes the trend and seasonality of the time series at each station. The parameters contained in the model will be estimated using least-square. To see the dependence of temperature at different stations using a normal bivariate distribution. The result show that the amount of loss based on the index of growing degree days per unit rupiah per degree Celsius (℃) for Meteorological Station Class 3 Citeko only occurs for certain percentages, namely 80%, 90%, and 95%, while for Climatology Station Class 1 Darmaga Bogor it can occur for each percentage. This indicates that the amount of losses obtained will depend on determining the strike level by using the mean and standard deviation of the growing degree days index distribution. Furthermore, these findings suggest that Climatology Station Class 1 Darmaga Bogor have higher risk of crop failure due to climate change than Meteorological Station Class 3 Citeko.
MODELING THE SPREAD OF COVID-19 DISEASE WITH TIME DELAY IN PONTIANAK CITY
Authors: Fatonah Fatma Arum , Noviani Evi , Pasaribu Meliana
Published: May 2024

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a coronavirus originating from the city of Wuhan in 2019. This disease affects the respiratory system. The city of Pontianak has the highest population density in West Kalimantan. This density results in a higher spread of Covid-19. In this article, the spread of COVID-19 is formulated into a mathematical model, equilibrium points are sought, stability is analyzed, and a delay time is introduced to reduce the spread of COVID-19. The magnitude of the delay time given during quarantine complies with health protocols, which is between 2 – 14 days. This article aims to analyze the influence of the delay time in modeling the spread of Covid-19. The problem of COVID-19 spread is constructed into an SIQR model, with a sub-population of recovered individuals returning to the susceptible sub-population. The population is divided into four sub-populations: susceptible (S), Infected (I), Quarantined (Q), and Recovered (R). The parameters used  include the natural birth rate ( ), the rate of susceptibility to infection ( ), the rate of infection under quarantine ( ), the recovery rate from infection ( ), the recovery rate from infection under quarantine ( ), the death rate from infection ( ), the death rate under quarantine ( ), the delay time from infection to quarantine process ( ), the natural death rate ( ), and the rate of recovered immunity returning to susceptibility ( ). The simulation results show that when the basic reproduction number is less than , the disease-free equilibrium is stable, and when the basic reproduction number is greater than , the endemic equilibrium point is stable. The addition of a time delay ( ) in the SIQR model affects the stability of the endemic equilibrium point but does not affect the stability of the disease-free equilibrium point.
DEVELOPMENT OF SEMIPARAMETRIC PATH ANALYSIS MODELING TRUNCATED SPLINE: DETERMINANTS OF INCREASED REGIONAL ECONOMIC GROWTH
Authors: Junianto Fachira Haneinanda , Fernandes Adji Achmad Rinaldo , Solimun Solimun , Hamdan Rosita Binti
Published: May 2024

This research aims to determine regional economic improvement to achieve a better Indonesian economy and accelerate the path to achieving a Golden Indonesia in 2045 so that it can be realized in a shorter time. This goal will be achieved with the help of statistical analysis methods, where the analysis used in this research is semiparametric truncated spline indirect effect and total effect analysis. The research becomes original in its approach with the utilization of this method and offers novel insights into the dynamics of regional economic development in Indonesia. These methods in this research serve as a tool for analyzing regional economic dynamics, identifying critical factors for improvement, informing policy decisions aimed at realizing Indonesia's economic aspirations for the future, and providing more flexible results to achieve the research objectives. The study was carried out on data with regional expenditure variables as exogenous variables, labor absorption variables as mediating endogenous variables, and regional economic growth variables as pure endogenous variables. The data used in the research are data published by the National/Provincial Central Bureau of Statistics in the form of the Indonesian Statistics Book, BPS publications in the form of Provinces, Provincial Government Financial Statistics, Directorate General of Financial Balance, Sumreg Bappenas, as well as from Ministries, Institutions or Agencies that related to providing data relating to the variables of this research in 2020. The results of this research are that the relationship between regional expenditure variables and labor absorption variables has a significant effect on regional economic growth variables.
MODELING THE INCIDENCE OF MALNUTRITION IN BOGOR REGENCY USING ZERO-INFLATED NEGATIVE BINOMIAL MIXED EFFECT MODEL
Authors: Sirodj Dwi Agustin Nuriani , Sadik Kusman , Kurnia Anang
Published: May 2024

Modeling response variables in the form of count data generally uses a model based on the Poisson distribution. However, some conditions, such as the presence of excess zero, can be found in the data that result in overdispersion, which will have an impact on the resulting variance in the model. In this paper, three approaches, namely the Poisson Mixed Model, the Negative Binomial (NB) Mixed Model, and the Zero-Inflated Negative Binomial (ZINB) Mixed Model, are used to model the incidence of malnutrition in Bogor Regency. The data used in this study are secondary data sourced from the West Java open data website. Based on the results of data analysis, it appears that the ZINB Mixed Model method is a method capable of accommodating random effects, overdispersion, and excess zero in modeling malnutrition in Bogor Regency. Variables that significantly affect the occurrence of malnutrition cases in villages in Bogor Regency include the Number of Children Weighed Routinely Every Month, Number of Children Measured for Length and Height Twice a Year, Number of Children under 12 Months Old Who Received Complete Basic Immunization, Number of Posyandu (Integrated Health Post), and Number of Parents/Caregivers Participating in Monthly Parenting (PAUD).
APPLICATION OF COX PROPORTIONAL HAZARD REGRESSION FOR ANALYZING FACTORS INFLUENCING THE RECOVERY RATE OF PULMONARY TUBERCULOSIS PATIENTS
Authors: Irfanullah Asrul , Damamain Ferina Lestari , Tuanaya Nur Amaliya
Published: May 2024

Pulmonary tuberculosis is a serious disease that requires special attention from the community and the Government of Indonesia, especially the Maluku Province. One commonly used analytical method in the health field is survival analysis. Survival analysis is a statistical method related to observing the period until the occurrence of an event or events. This study aims to model and identify factors that affect the recovery rate of patients with pulmonary tuberculosis in Ambon City using Cox Proportional Hazard regression. The results of the Hazard Ratio interpretation show that the variables that have a significant influence are chest pain and night sweats. Specifically, patients experiencing chest pain exhibit a recovery rate 0.487264 times faster than those devoid of such symptoms. Similarly, patients experiencing night sweats demonstrate a recovery rate of 0.619839 times faster than their counterparts not experiencing this symptom. This study highlights the imperative of recognizing and addressing symptoms like chest pain and night sweats in managing pulmonary tuberculosis in Ambon City.
NATIONAL HEALTH INSURANCE AND CATASTROPHIC HEALTH EXPENDITURE: A COMPLEMENTARY LOG-LOG APPROACH ON YOGYAKARTA’S INCIDENCE 2022
Authors: Fakhriandi Erdi , Sofa Wahyuni Andriana
Published: May 2024

National Health Insurance (JKN) aims to provide health care benefits to meet people's basic health needs. However, despite the high JKN coverage in D.I.Yogyakarta Province, catastrophic health expenditure events remain high. The study examines the implementation of National Health Insurance (JKN) in D.I.Yogyakarta Province and its effectiveness in reducing devastating health expenditures in 2022. Using the Complementary Log-log approach on the 2022 National Social and Economic Survey data, the research findings reveal that the implementation of JKN has been successful, and the ownership of JKN positively affects the reduction of catastrophic health expenditure. The study also shows that the education level of household members and household size have positive effects on reducing the incidence of catastrophic health expenditures. However, the presence of babies or the elderly, household economic status, and inpatient status could increase the risk of having catastrophic health expenditures. The study highlights the need for attention to be given to implementing JKN in D.I.Yogyakarta Province and other variables to achieve universal health coverage and reduce catastrophic health expenditure.
TWOFOLD SUBAREA MODEL FOR ESTIMATING COMMUTER PROPORTION IN 10 METROPOLITAN AREAS
Authors: Amin Yudi Fathul , Indahwati Indahwati , Kurnia Anang
Published: May 2024

The metropolitan area is a major contributor to national GDP. The metropolitan area is a center of attraction for many people who come to earn income as commuters. Commuters are people who carry out work activities in the center of the metropolitan area, which are carried out by residents who live in suburban areas around the center of the metropolitan area and commute regularly every day. The availability of commuter statistics from surveys for presentation level down to the smallest administrative level, such as regencies/municipalities, is unreliable. This happens because this level of presentation has poor precision due to insufficient samples due to the Statistics Indonesia survey design for making estimates at the national and provincial levels. It can be done using small area estimation (SAE) to meet increasing data needs, but existing SAE models can often estimate only at one level. To meet data requests more effectively, a model is needed that can estimate several small areas simultaneously. In SAE, one of the SAE models that can do this is the twofold subarea model. The twofold subarea model produces estimates of the proportion of commuters with good precision at the subarea level (regencies/municipalities) and area level (metropolitan area), with the RRMSE percentage value of the estimated proportion of commuters being below 25% for all regions. The results of this research can be used to present commuter data at the regencies/municipalities level and metropolitan area level where there is a lack of samples and become a new opportunity for Statistics Indonesia to increase statistical production in small areas, which is more effective compared to other SAE methods which have so far been used only to estimate one area level.
MATHEMATICAL MODEL OF REPELLENT EFFECT IN DENGUE TRANSMISSION
Authors: Handayani Dewi , Gunadi Audri Utami , Rachmawati Ria Nurlita
Published: May 2024

Dengue is a disease caused by the dengue virus, transmitted through the bite of an infected female Aedes aegypti. Dengue virus is a member of the genus Flavivirus, family Flaviviridae. Indonesia is one of the countries with the most dengue cases in Southeast Asia. Therefore, dengue transmission must be controlled to reduce the increase in dengue cases. One of the controls is by using repellents. Repellent is one of the human protection strategies to avoid mosquito bites used by spraying or smearing. This study models dengue transmission by reviewing the effect and control of repellent. A mathematical model of repellent effect and control in dengue transmission uses a SIR compartment model. The SIR model is modified by involving mosquitoes and the human population. Repellent is used in both susceptible humans, infected humans, and recovered humans. Numerical and analytical simulations are conducted to analyze the behavior of each compartment of the mosquito and human populations in dengue transmission. Analytical results show that the factors affecting the spread of infection are the transmission rate of the dengue virus and the loss of human-repellent protection. The transmission rate of dengue virus in the interval  increases the infected human by 2.73%, while the rate of loss of human repellent protection in the interval  increases the infected human by 0.03%. Optimal control is used to minimize the number of infected humans who do not use repellent. The results of numerical simulations on the optimal control problem show that an increase in the proportion of healthy humans who have campaign effect and use repellent regularly in the range of 14.67% can reduce infected individuals by 0.647%.
APPLICATION OF QUADRATIC PROGRAMMING ON PORTFOLIO OPTIMIZATION USING WOLFE’S METHOD AND PARTICLE SWARM OPTIMIZATION ALGORITHM
Authors: Syaripuddin Syaripuddin , Amijaya Fidia Deny Tisna , Wasono Wasono , Tulzahrah Shanaz , Suciati Rara
Published: May 2024

Stock portfolios can be modeled into quadratic programming problems using the Markowitz mean-variance model. Quadratic programming problems can be solved using two methods, namely classical and heuristic methods. In this research, the classical method uses Wolfe’s method, while the heuristic method uses the particle swarm optimization (PSO) algorithm. This research aims to determine optimal results in portfolio problems using two methods, namely Wolfe’s method and the PSO algorithm. The data used in this research is data from 10 stock companies that distribute the highest dividends in the IDX High Dividend 20 category for the 2022 period. The research results discuss the portfolios of PT Astra International Tbk and PT. Indo Tambangraya Megah Tbk. Based on the result, using Wolfe’s method, the ASII and ITMG stock portfolios are obtained, namely the optimal proportion of ASII shares = 0.76401 or 76.401% and ITMG shares = 0.23598 or 23.598%, while the PSO algorithm obtains a portfolio of ASII and ITMG shares, namely ASII shares = 75.02% and ITMG shares = 24.98%. Compared to Wolfe’s method, the PSO algorithm has a smaller Z value 5.7.
CLUSTERING ANALYSIS FOR GROUPING SUB-DISTRICTS IN BOJONEGORO DISTRICT WITH THE K-MEANS METHOD WITH A VARIETY OF APPROACHES
Authors: Nurdiansyah Denny , Ma'ady Mochamad Nizar Palefi , Sukmawaty Yuana , Utomo Muchammad Chandra Cahyo , Mutiani Tia
Published: May 2024

Population data is an important piece of information that is useful for regional planning and development. Insight into the state of an area is more straightforward to observe if there are grouped sub-districts. In this case, data mining techniques can identify patterns and relationships in population data. The K-Means algorithm is a clustering technique that divides data into groups or clusters based on similar characteristics. This research aims to apply the K-Means method with various approaches to clustering sub-districts in the Bojonegoro district according to population data. The research method used is a quantitative method with an exploratory study in the application of the K-Means method with a variety of approaches, namely the use of the Kernel K-Means method by utilizing the mapping function to map data to a higher dimension before the clustering process. In addition, the Fast K-Means method is used, which reduces the model training time to improve the cluster-centered recalibration problem as the amount of data increases. The data source used in this research is secondary population data in the form of birth, death, migrant, and moving variables obtained from the Satu Data Bojonegoro website developed by the Bojonegoro Regency Government. It is found that the best K-Means approach is the Kernel K-Means method with a number of clusters of 5. The performance of the cluster method is evaluated by measuring the average distance within the cluster. The data coordinate pattern in the Kernel K-means method clustering shows a smooth initial trend when the value of the number of clusters is 5 so that the clusters formed are obtained clearly. The conclusion from this study's results is that the K-Means method's best approach in grouping sub-districts in Bojonegoro district is the Kernel K-Means approach.
BAYESIAN NEURAL NETWORK RAINFALL MODELLING: A CASE STUDY IN EAST JAVA
Authors: Astutik Suci , Rahmi Nur Silviyah , Irsandy Diego , Saniyawati Fang You Dwi Ayu Shalu , Mashfia Fidia Raaihatul , Lusiana Evelin Dewi , Risda Intan Fadhila , Susanto Mohammad Hilmi
Published: May 2024

Rainfall is an important parameter in meteorology and hydrology, and it measures the amount of rain that falls from the atmosphere to the ground surface in liquid form. However, in the process of measuring rainfall, changes in the rainfall cycle sometimes occur due to climate change, global warming, and other factors. Therefore, this research aims to model daily rainfall using the Bayesian Neural Network (BNN) approach, combining the Bayesian Method and Artificial Neural Network (ANN). ANN is suitable for rainfall models that have intermittent characteristics. Meanwhile, the Bayesian method provides advantages in producing model parameter inferences that provide uncertainty measurements in predictions. BNN is expected to deliver better daily rainfall predictions than ANN. This research used daily rainfall data in East Jawa, and the results show that the Bayesian Neural Network produces better rainfall predictions when describing rainfall in East Java. These predictions will be very useful for the government and the people of East Java province to prevent flooding. Also, with rainfall predictions, people will know more about what crops should be planted during the rains.
FUZZY LOGIC APPLICATION FOR DETERMINING THE FEASIBILITY OF NICKEL MINING IN SOUTHEAST SULAWESI PROVINCE
Authors: Agustina Ni Luh Ika Tri , Abadi Agus Maman
Published: May 2024

This study uses the Mamdani method to assess the feasibility of nickel mining locations in Southeast Sulawesi Province. Despite the crucial role of mining in the Indonesian economy, research on the site feasibility decisions in mining using the Mamdani method still needs to be completed. Therefore, this study addresses this knowledge gap by providing new contributions and effective solutions. The Mamdani method is employed in the various stages of mining activities, particularly in feasibility studies, which are the main focus. Mining feasibility studies involve both technical and non-technical analyses, encompassing aspects such as nickel reserves and environmental impacts. This research seeks to expand the use of the Mamdani method in mining site feasibility decisions, offering sustainable and environmentally responsible solutions. The research results show that North Konawe Regency has very large estimated nickel reserves but has a relatively low environmental impact and is quite far from the port, thus achieving a high location suitability score for mining. On the other hand, Konawe Regency has lower nickel reserves but has quite a large environmental impact, and the distance to the port is quite far, so the location feasibility score is lower.  The outcomes of this research are expected to provide new insights, fill knowledge gaps, and serve as a valuable reference for future mining site feasibility decision-making. The translation is accurate, well-structured, and free from plagiarism.
COMBINATION OF ETHNOMATHEMATICS AND THE MOZART EFFECT TO IMPROVE PROBLEM-SOLVING SKILLS AND MATHEMATICAL DISPOSITION
Authors: Kusuma Dianne Amor , Ruchjana Budi Nurani , Widodo Sri Adi , Dwipriyoko Estiyan
Published: May 2024

The background of this research is that student learning outcomes in analytical geometry lecture during the transition from pandemic to Covid-19 endemic are still low, which is due to a lack of student interest in learning, and they are still accustomed to online learning, thus having an impact on their low problem-solving skills and mathematical disposition. This research aims to determine to what extent the implementation of ethnomathematics and the Mozart effect can improve students' problem-solving skills and mathematical disposition in analytical geometry lecture during the transition from pandemic to endemic COVID-19, so the research is important to do. The implementation of ethnomathematics and the Mozart effect in mathematics learning is unique because it is a combination of learning approaches that have never been used before in Indonesia and other countries. The research method used was a quasi-experimental non-equivalent control group design because this research was experimental and sample determination was not carried out randomly, but using purposive sampling technique on the second-semester students of the mathematics undergraduate program, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran. The instruments used in this study were problem-solving skills test, mathematical disposition scale, and students’ attitude questionnaire toward learning with the implementation of ethnomathematics and the Mozart effect. The results showed that: (1) problem-solving skills of students who received learning by implementing ethnomathematics and the Mozart effect are better than students who achieved direct instruction; (2) mathematical disposition of students who received learning by implementing ethnomathematics and the Mozart effect is better than students who achieved direct instruction; and (3) students are interested and motivated to learn mathematics by implementing ethnomathematics and the Mozart effect. This research concludes that the implementation of ethnomathematics and the Mozart effect can improve students' problem-solving skills and mathematical disposition in analytical geometry lecture during the transition period from the pandemic to endemic COVID-19. It can be seen from good average post test scores achieved by students.
ANALYSIS AND OPTIMAL CONTROL OF HIV GROWTH MODEL IN THE BODY WITH ANTIRETROVIRAL THERAPY
Authors: Damayanti Erlyana Trie , Imron Chairul
Published: May 2024

Human Immunodeficiency Virus (HIV) is a virus that affects the human immune system. HIV infection causes a decrease in the body's immunity because the virus attacks immune-building cells, especially T-CD4 cells. Currently, there is no treatment that can cure or eliminate HIV, but antiretroviral therapy can be done. This study discusses the growth model of HIV in the body that is given control in an effort to maximize healthy T-CD4 cells. In this model, the infection-free and infected equilibrium points are also discussed and their stability is analyzed. Then the optimal control is solved using the Pontryagin Maximum Principle method and solved numerically using the fourth-order Runge Kutta method. Based on the analysis and simulation results, the system is asymptotically stable around the infection-free equilibrium point and unstable around the infected equilibrium point. Simulation results show that with the control of antiretroviral therapy, the T-CD4 cell population grows significantly which can improve the quality of life of patients. And the growth of HIV in the body can be inhibited until it cannot reproduce itself.