Jurnal Matematika, Statistika dan Komputasi
Not a member yet
562 research outputs found
Sort by
Implementation of Robust Optimization Model to Controlling the Inventory Costs of Consumable Medical Equipment at Malahayati Islamic Hospital
Get PDFInventory management is critical to hospitals capacity to meet patient requests. Because of the fluctuating patient volume, hospitals frequently have trouble managing their inventory of consumable medical equipment. An optimization model must therefore be used to control inventories. One hospital that calculates inventory costs using traditional methods is Malahayati Islamic Hospital. This leads to high inventory expenses and storage costs when acquiring medical supplies like three-count syringes. anything meant to lower the cost of inventories. An optimization model that can use linear programming techniques to discover the optimal solution even in situations when the data is unclear is referred to as strong optimization. By applying a strong optimization model, it shows that the results of calculating the inventory costs of consumable medical equipment at the Malahayati Islamic Hospital can save costs of 24.71% of the total inventory costs of the Malahayati Islamic Hospital
Classification Of Country Status In 2022 Based On Social Indicators With Ordinal Logistic Regression
Get PDFThis research examines the classification of country status in 2022 by applying ordinal logistic regression on various social indicators including education, health and economic. The urgency of the research is to know the country determine factors with specific factors in the form of research variables that can be useful for policy makers, unlike the existing classification which is only divided based on GDP per capita or HDI score only. By dividing 3 country status classes, namely not developed, developing and developed countries using the world bank classification baseline, the accuracy results were obtained at 72,5% but there were several variables that were not significant. After re-modelling, the accuracy was found increased to 76.4% with the odds ratio results for the minimum wage variable being 42,32 in the high class compared to the middle class and 11,66 for the middle class compared to the lower class, which means that the higher the minimum wage tends to be classify countries as developed countries. Another variable that has significance level is the birth rate with an odds ratio of 0,71 in the high and middle classes and 0.89 in the middle and lower classes comparison, which shows that this variable has a negative effect because the odds ratio is <1, which means that the higher the birth rate tends to make the country will be classified as a non-developed country
Integer Linear Programming In Production Profit Optimization Problems Using Branch And Bound Methods & Gomory Cutting Plane
Get PDFInteger Linear Programming is a mathematical model that allows the results of solving cases in linear programming in the form of integers. Methods to solve Integer Programming problems include the Branch and Bound Method and the Gomory Cutting Plane Method. Both of these methods have certain rules for adding new constraint functions until an optimal solution to an integer is obtained. The purpose of this study is to optimize the profits of the production of UMKM Capal Classic Shoes Kab. Agam by using the Branch and Bound method and the Gomory Cutting Plane method and analyzing the comparison of optimal results resulting from the two methods. The data used in the study are data on raw materials for making classic sandals and profit data. The results obtained by these two methods produce the same maximum profit, namely RP. 664,000 with each producing 15 pairs of men\u27s sandals and 13 pairs of women\u27s sandals. But in its completion, the Branch and Bound method requires many iterations and a longer time compared to the Gumory Cutting plane method.Integer Linear Programming adalah sebuah model matematis yang memungkinkan hasil penyelesaian kasus pada pemrograman linier yang berupa bilangan bulat. Metode untuk menyelesaikan persoalan Integer Programming diantaranya adalah Metode Branch and Bound dan Metode Gomory Cutting Plane. Kedua metode ini memiliki aturan tertentu dalam menambahkan fungsi batasan baru hingga diperoleh solusi optimal bilangan bulat. Tujuan dari penelitian ini adalah mengoptimalkan keuntungan dari produksi UMKM Capal Classic Shoes Kabupaten Agam dengan menggunakan metode Branch and Bound dan metode Gomory Cutting Plane serta menganalisis perbandingan terhadap hasil optimal yang dihasilkan dari kedua metode tersebut. Hasil yang diperoleh kedua metode tersebut menghasilkan keuntungan optimal yang sama yaitu Rp. 664.00
The Robust Negative Binomial Regression Model on Under-five Mortality due to Pneumonia in the Province of East Java
Get PDFRobust Negative Binomial regression model (RNBR) is a modelling method to overcome a problem if there are outliers and overdispersion in the data. Outliers are data points that are significantly different from other data. Outliers have a significant effect on modelling to the resulting model. Furthermore, overdispersion is indicated by the presence of too large values of Pearson statistics. In this study, the RNBR model was used to determine the factors of the toddler immune variable at post neonatal age that significantly influenced the number of under-five deaths caused by pneumonia in East Java Province. Based on the modelling obtained, it shows that the RNBR model provides more robust results in handling outlier and overdispersion problems. This can be seen from the AIC value of the RNBR model is smaller than the AIC of the Poisson regression model. In addition, and which are measures of the influence of outliers on the model, decreased from 1 for the Poisson regression model to around 0.42 for the RNBR model
Covid-19 Vaccination Impact on Four Asean Countries’ Stock With Spatial Dependency: A Comparison of Panel and Geographically Weighted Regression
Get PDFResearch about various policies and responses toward COVID-19 cases and its impact on stocks has grown recently. It shows that spatial influence is one of the keys in this research. The pandemic is not free from spatial dependence regarding how it indirectly impacts a country’s economy. Each country has different policies to handle COVID-19, such as lockdowns and vaccination. WHO stated that all countries require vaccination to build human immunity against COVID-19 in the future. Naturally, ASEAN implemented this policy; thus, it is crucial to see the extent of the impact of vaccination on the ASEAN economy. However, the residuals have heterogeneity problems when using the panel regression model. One of the reasons is that there is spatial dependence, especially when modeling the COVID-19 pandemic. Therefore, comparing panel regression with a geographically weighted regression panel (GWR-Panel) is substantial when exploring the reaction of stock returns to vaccination and positive cases of COVID-19 in Indonesia, Malaysia, Singapore, and Thailan
Dimensi Metrik Graf Hasil Kali Graf Lengkap Orde Dua terhadap Graf Sarang Lebah
No full textMetric dimension is a concept in graph theory that has been developed in terms of the concept and its application. Let G be a connected graph and S be a vertex subset on connected graph G. The set S is called a resolving set for G if every vertex on graph G has a distinct representation of one to each other of S. A resolving set containing a minimum cardinality is called basis. The metric dimension on graph G is cardinality of basis on graph G, notated with dim (G). In this case, the cross-product graph will be used for the research. The aim of this research is to determine the metric dimension of the second order complete graph (K2) with honeycomb networks (HC(n)) cross-operation product. Utilizing mathematical induction, we generated dim(K2×HC(n)) = 3.Dimensi metrik merupakan konsep dalam teori graf yang terus dikembangkan dari segi konsep maupun penerapannya. Misalkan G adalah graf terhubung dan S adalah suatu subset titik pada graf terhubung G. Himpunan S disebut himpunan penentu pada G jika untuk setiap titik pada graf G memiliki representasi jarak yang berbeda terhadap S. Himpunan penentu dengan kardinalitas minimum disebut basis. Dimensi metrik graf G adalah kardinalitas basis dari graf G yang dinotasikan dengan dim (G). Pada penelitian ini disajikan hasil graf yang menggunakan operasi kali. Tujuan dari penelitian ini yaitu untuk menentukan dimensi metrik dari graf hasil operasi kali antara graf lengkap orde 2 (K2) terhadap graf sarang lebar (HC(n)). Menggunakan metode induksi matematika, diperoleh hasil pembahasan penelitian yaitu dim(K2×HC(n)) = 3
Estimasi Selang Dana Tabarru’ Pada Asuransi Jiwa Syariah dengan Menggunakan Perhitungan Cost of Insurance
Get PDFContributions is an amount of funds paid by the insured at the beginning of the period of a sharia life insurance contract. Contribution also constitutes the sum of net contributions with expenses. Net contributions are further categorized as Tabarru\u27 funds obtained based on the Cost of Insurance (COI) method. This research incorporates the influence of interest rate in estimating Tabarru\u27 funds. Assuming a Normal Distribution of interest rate and the Central Limit Theorem for a confidence level, a confidence interval is obtained from the interest rate mean. The research findings indicate that the larger the management costs and the older the insurance participants, the greater the COI value will be. Furthermore, the larger the interest rate value, the smaller the COI value. Consequently, as the interest rate value increases, the Tabarru\u27 funds will decrease, while the management costs increase and the age of the insurance participants rises, the Tabarru’ funds will increase.Kontribusi dalam asuransi syariah tanpa unsur tabungan hanya terdiri dari dana tabarru’. Dimana dana tabarru’ digunakan untuk membantu peserta asuransi lainnya ketika terjadi risiko. Dalam mengestimasi selang dana tabarru’ digunakan penaksir parameter dengan Metode Maximum Likelihood Estimation (MLE) dan penaksir selang dengan pendekatan Central Limit Theorem. Dalam menentukan besarnya nilai dana tabarru’ pada laki-laki dan perempuan digunakan metode Cost of Insurance (COI), dimana diasumsikan bahwa besarnya biaya pengelolaan bagi perusahaan asuransi syariah sebesar 10%, 30% dan 50%. Pada penelitian ini didapatkan bahwa penaksir selang kepercayaan rataan tingkat suku bunga yang digunakan yaitu rata-rata dari tingkat suku bunga sebesar 5,4912. Sehingga, didapatkan estimasi selang kepercayaan 95% dari tingkat suku bunga berada pada interval . Hasil penelitian yang didapatkan dengan menggunakan TMI IV 2019 dari perempuan dan laki-laki menyatakan bahwa besarnya dana tabarru’ untuk perempuan lebih kecil dibanding laki-laki dan semakin besar biaya pengelolaan semakin besar nilai dari dana tabarru’
Pengaruh Penggunaan Random Undersampling, Oversampling, dan SMOTE terhadap Kinerja Model Prediksi Penyakit Cardiovascular (CVD)
No full textCardiovascular Disease (CVD) or commonly known as Heart Disease is a leading cause of mortality globally, prompting extensive research into predictive models to assess individual risk and plan preventive measures. Machine learning approaches such as Random Forest, Support Vector Machine (SVM), and LASSO Logistic Regression have showed promise. Recent studies have indicated that traditional resampling methods like Random Oversampling, Random Undersampling, and SMOTE may not significantly improve model discrimination. This study aims to evaluate the impact of these techniques on the performance of Cardiovascular Disease (CVD) prediction models, utilizing data from the UCI Machine Learning Heart Disease database. By employing LASSO Logistic Regression, Random Forest, and Support Vector Machine (SVM) with resampling techniques, including Random Oversampling, Random Undersampling, and SMOTE. This research seeks to enhance understanding of model performance in addressing class imbalances within the dataset and contribute to refining cardiovascular disease (CVD) prediction strategies. This study demonstrates that the use of the SMOTE technique significantly enhances the performance of cardiovascular disease (CVD) prediction models. Specifically, when combined with the Random Forest algorithm, SMOTE achieves the best performance in terms of accuracy, sensitivity, and specificity. This highlights the importance of selecting appropriate resampling techniques to handle class imbalance in datasets. Consequently, this research contributes to refining CVD prediction strategies and provides new insights into improving prediction accuracy in imbalanced medical data.Penyakit Kardiovaskular (CVD) atau yang dikenal sebagai Penyakit Jantung merupakan penyebab kematian paling umum di seluruh dunia. Pendekatan pembelajaran mesin seperti Random Forest, Support Vector Machine (SVM), dan Regresi Logistik LASSO telah menunjukkan potensi untuk memodelkan prediktif guna menilai resiko individu dan merencanakan langkah-langkah pencegahan. Studi terbaru mengindikasikan bahwa metode resampling tradisional seperti Random Oversampling, Random Undersampling, dan SMOTE mungkin tidak secara signifikan meningkatkan diskriminasi model. Penelitian ini bertujuan untuk mengevaluasi dampak teknik-teknik ini terhadap kinerja model prediksi Kardiovaskular (CVD), dengan menggunakan data dari database UCI Machine Learning. Dengan menerapkan Regresi Logistik LASSO, Random Forest, dan Support Vector Machine (SVM) dengan teknik resampling, termasuk Random Oversampling, Random Undersampling, dan SMOTE, penelitian ini berupaya untuk meningkatkan pemahaman tentang kinerja model dalam menangani ketidakseimbangan kelas dalam dataset dan berkontribusi pada penyempurnaan strategi prediksi Kardiovaskular (CVD)
Pendugaan Area Kecil Tingkat Kemiskinan Anak di Pulau Maluku dan Papua Tahun 2023
Get PDFChild welfare issues such as child poverty pose a challenge for Indonesia. The provinces in Maluku and Papua have the highest rates of child poverty. Data on child poverty at regencies/municipalities level is needed to address this issue through targeted policies. The direct estimations have a Relative Standard Error (RSE) value of more than 25 percent, necessitating the use of an indirect method, Small Area Estimation (SAE). This study aims to compare the results of indirect estimates of the percentage of children aged 0-17 living in poverty at the regencies/municipalities level in Maluku and Papua using SAE Empirical Best Linear Unbiased Prediction (EBLUP) and Hierarchical Bayes Beta (HB Beta) methods. Susenas KOR March 2023 data is used to produce direct estimates, while Podes 2021 data is used to form auxiliary variables. The results indicate that the SAE HB Beta method provides estimates with better RSE compared to SAE EBLUP. All regencies/municipalities in the Maluku and Papua have a fairly good level of accuracy.Isu kesejahteraan anak seperti kemiskinan pada anak menjadi tantangan bagi Indonesia. Anak menjadi kelompok yang lebih berisiko untuk kehilangan akses terhadap kebutuhan dasarnya ketika hidup dalam rumah tangga miskin atau rentan miskin. Jika dilihat secara regional, provinsi-provinsi di Pulau Maluku dan Papua memiliki persentase kemiskinan anak tertinggi. Ketersediaan data kemiskinan anak sampai tingkat kabupaten/kota diperlukan untuk penyusunan kebijakan yang tepat sasaran. Estimasi langsung yang dihasilkan memiliki nilai Relative Standard Error (RSE) lebih dari 25 persen sehingga perlu digunakan metode estimasi tidak langsung yaitu Small Area Estimation (SAE). Penelitian ini bertujuan untuk membandingkan hasil estimasi tidak langsung persentase anak berumur 0-17 tahun yang hidup di bawah garis kemiskinan tingkat kabupaten/kota di Pulau Maluku dan Papua menggunakan metode SAE Empirical Best Linear Unbiased Prediction (EBLUP) dan Hierarchical Bayes Beta (HB Beta). Hasil yang diperoleh menunjukkan bahwa metode SAE HB Beta memberikan estimasi dengan RSE lebih baik dibandingkan estimasi langsung dan SAE EBLUP
Metode Machine Learning-Based Univariate Time Series Imputation Method untuk Estimasi Nilai Hilang pada Data Non-Stasioner
Get PDFHandling missing values in time series data is crucial because they can disrupt data analysis and interpretation. Sequentially missing values in time series often pose a more complex challenge compared to randomly missing values. One of the promising recent methods is Machine Learning-Based Univariate Time Series Imputation (MLBUI), although it is still not widely used and its accessibility is limited. MLBUI employs Random Forest Regression (RFR) and Support Vector Regression (SVR) algorithms. This study evaluates the performance of MLBUI in addressing missing data scenarios in non-stationary univariate time series data. The data used in this research is the average temperature data from Bogor Regency. The missing data scenarios considered include rates of 6%, 10%, and 14%. Besides MLBUI, five other comparison methods are used: Kalman StructTS, Kalman Auto-ARIMA, Spline Interpolation, Stine Interpolation, and Moving Average. The results show that MLBUI performs poorly for non-stationary data, although the obtained Mean Absolute Percentage Error (MAPE) is below 10%.Penanganan nilai hilang dalam data deret waktu adalah krusial karena dapat menyebabkan gangguan dalam analisis dan interpretasi data. Khususnya, nilai-nilai hilang yang terjadi secara berurutan dalam deret waktu seringkali menjadi tantangan yang lebih kompleks dibandingkan dengan nilai-nilai yang hilang secara acak. Salah satu metode terbaru yang menjanjikan adalah Machine Learning-Based Univariate Time Series Imputation (MLBUI), meskipun masih belum banyak digunakan dan aksesibilitasnya masih terbatas. MLBUI menggunakan algoritma adalah Random Forest Regression (RFR) dan Support Vector Regression (SVR). Dalam studi ini, evaluasi dilakukan terhadap kinerja MLBUI dalam mengatasi skenario data yang hilang pada deret waktu univariat yang non-stasioner. Data yang digunakan pada penelitian ini yaitu data suhu rata-rata dari Kabupaten Bogor. Skenario kehilangan data yang dipertimbangkan mencakup tingkat 6%, 10%, dan 14%. Selain MLBUI, lima metode perbandingan lainnya digunakan: Kalman StructTS, Kalman Auto-ARIMA, Interpolasi Spline, Interpolasi Stine, dan Moving Average. Hasil penelitian menunjukkan bahwa MLBUI memberikan hasil yang kurang baik untuk data yang non-stasioner walaupun nilai Mean Absolute Percentage Error (MAPE) yang diperoleh berada dibawah 10%