Jurnal Matematika, Statistika dan Komputasi

The Comparative Analysis Of Hungarian, Pinalti And Alternate Mansi Methods

Yosina Arni Sihombing

This research discusses the problem of assigning a number of sources to a number of destinations, with a case study of a Multi Bangunan shop clerk. The saleswoman performs tasks randomly, this causes a lot of wasted time and a decrease in service levels. Therefore, it is necessary to solve the assignment problem using the Hungarian method, the Pinalti method and the Alternate Mansi method. The three methods are different methods to solve the assignment problem. The Hungarian method is a method used in solving balanced assignment problems while the Pinalti method is a method used to solve unbalanced assignment problems. Alternate Mansi method is a new alternative method used in solving the assignment problem. The purpose of this study is to determine the results of solving the assignment problem using the Hungarian method, the Pinalti method and the Alternate Mansi method. Then compare the results of the three methods, and find out the results of solving using POM-QM software. Based on the research results, the Hungarian method and Alternate Mansi method produce the same optimal total time of 197 minutes. However, the Alternate Mansi method is optimal in the second iteration while the Hungarian method is optimal in the fifth iteration. The Pinalti method produces an optimal total time of 289 minutes in the first iteration. So, from the three method comparisons, it can be concluded that.

ANALYSIS OF SHARIA STOCK VOLATILITY USING EXPONENTIAL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY

Salmaa Ariibah

Stocks are one form of investment that many people choose. This is because over the last 10 years stocks have tended to increase. On the other hand, Indonesia is a country with a Muslim majority, so sharia stocks are an alternative for Muslims who want to invest in stocks. Stocks move fluctuating, changing in a short time. If stocks experience high fluctuations, it will cause uncertainty in the stocks. Volatility is an important risk measure in finance because it canmeasure stock market uncertainty. Therefore, it is necessary to carry out stock volatility analysis research so that investors are able to estimate the right time when carrying out stock transactions. The Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model was chosen in this research because it is able to handle heteroscedasticity and asymmetric effects that are often found in stock data. The formation of the EGARCH model was carried out by estimating parameters and significance. The best model is selected based on the smallest AIC value and passes the model suitability test. The research results show that the best model for SMGR stocks is the ARCH (1) model and INDF stocks is the ARCH (2) model. Meanwhile, the best model for UNVR stocks is the EGARCH (1,1) model. All of these models have a high level of accuracy with the MAPE value of each stock, namely SMGR worth 1.27%, UNVR worth 1.22%, and INDF worth 0.95%. Or in other words the resulting model is very accurate.

LOG-RANK TEST AS A CONTINUATION OF THE KAPLAN-MEIER METHOD IN SURVIVAL ANALYSIS USING R LANGUAGE (Case Study: D-penicillamine Treatment on the Probabillity of Survival in Primary Biliary Cirrhosis (PBC) Patients at the Mayo Clinic)

Ferennia Putri

From 1974 to 1984, the Mayo Clinic collected 424 patients with Primary Biliary Cirrhosis (PBC) to test whether D-penicillamine could increase the probability of survival of patients with PBC. Mayo Clinic conducted a randomized, placebo-controlled trial as a comparison to D-penicillamine. In addition, some patients were not known about the treatment given but were still being observed as a comparison, so there were three groups of patients in the study, namely patients who were given D-penicillamine, placebo, and NA (patients who were not known about the treatment given). Mayo Clinic paid attention to other variables besides the time of death or censored time in its research, namely the Ascites, Hepatomegaly, Spider or the presence of esophageal varices, and other PBC symptom variables. The author conducted research in this thesis to find D-penicillamine as a drug treatment for PBC by only looking at the time of death or censored time variable and the drug treatment variables received by the patient during the ten-year data collection period and completing the research using statistics. Based on this, the authors used survival analysis with the Kaplan-Meier and Log-Rank methods to determine D-penicillamine as a drug treatment for PBC. This research shows how to analyze this solution using the R language. The results of the thesis research show that D-penicillamine treatment is not good enough to increase the probability of survival of patients with PBC.

Small Area Estimation for Percentage of Out-of-School Children Aged 7-17 Years in Sumatera Island, 2023

Wisly Ryan Eliezer

Ensuring the quality of education is a fundamental commitment towards achieving sustainable development goals (SDGs). One effective strategy to enhance education quality is addressing the high number of children out of school. More precise district/city-level data on the percentage of out-of-school children needs to be provided. Estimation results from Susenas data show that Sumatra Island has the highest proportion of districts/cities with a Relative Standard Error (RSE) of over 25% compared to other islands in Indonesia. Therefore, this study applies Hierarchical Bayes (HB) Beta method by utilizing accompanying variables. The research reveals that the HB Beta estimator is the most effective in estimating the percentage of out-of-school children aged 7—17 years at the district/city level on Sumatra Island. The Small Area Estimation (SAE) model offers a more precise estimate than the direct estimator. Furthermore, there are 25 districts/cities with a high percentage of children aged 7—17 years who are not in school, with the majority located in the southern region of Sumatra Island.

Kecepatan Deindustrialisasi di Indonesia Tahun 2014-2022

Michael Jonas.S Michael

Sektor manufaktur memiliki peran krusial dalam memacu pertumbuhan ekonomi di negara maju maupun
berkembang. Namun, pangsa sektor ini dalam PDB mencapai puncaknya di tahun 2000-an dan terus menurun,
sehingga mengindikasikan gejala deindustrialisasi di Indonesia. Laju pertumbuhan industri manufaktur secara
konsisten tertinggal dari laju pertumbuhan ekonomi secara keseluruhan, sehingga menggarisbawahi perlunya
mengatasi masalah ini. Penelitian ini bertujuan untuk mengidentifikasi faktor-faktor yang memengaruhi
terhadap deindustrialisasi dan mengukur laju deindustrialisasi di berbagai periode di Indonesia. Dengan
menggunakan regresi data panel, studi ini menemukan bahwa penanaman modal asing (PMA), keterbukaan
perdagangan dan penanaman modal dalam negeri (PMDN) berdampak positif terhadap sektor manufaktur,
karena peningkatan variabel-variabel ini mendukung perkembangannya. Selain itu, studi ini menunjukkan
penurunan yang lebih besar pada proporsi sektor manufaktur antara tahun 2017-2019 dibandingkan dengan
tahun 2020-2022, berdasarkan koefisien negatif dari masing-masing variabel dummy waktu. Oleh karena itu,
pemerintah perlu menerapkan strategi reindustrialisasi guna mengembalikan peran sektor manufaktur sebagai
penggerak utama pertumbuhan ekonomi.

Kecepatan Deindustrialisasi di Indonesia Tahun 2014-2022

Michael Jonas.S Michael

Sektor manufaktur memiliki peran krusial dalam memacu pertumbuhan ekonomi di negara maju maupun berkembang. Namun, pangsa sektor ini dalam PDB mencapai puncaknya di tahun 2000-an dan terus menurun, sehingga mengindikasikan gejala deindustrialisasi di Indonesia. Laju pertumbuhan industri manufaktur secara konsisten tertinggal dari laju pertumbuhan ekonomi secara keseluruhan, sehingga menggarisbawahi perlunya mengatasi masalah ini. Penelitian ini bertujuan untuk mengidentifikasi faktor-faktor yang memengaruhi terhadap deindustrialisasi dan mengukur laju deindustrialisasi di berbagai periode di Indonesia. Dengan menggunakan regresi data panel, studi ini menemukan bahwa penanaman modal asing (PMA), keterbukaan perdagangan dan penanaman modal dalam negeri (PMDN) berdampak positif terhadap sektor manufaktur, karena peningkatan variabel-variabel ini mendukung perkembangannya. Selain itu, studi ini menunjukkan penurunan yang lebih besar pada proporsi sektor manufaktur antara tahun 2017-2019 dibandingkan dengan tahun 2020-2022, berdasarkan koefisien negatif dari masing-masing variabel dummy waktu. Oleh karena itu, pemerintah perlu menerapkan strategi reindustrialisasi guna mengembalikan peran sektor manufaktur sebagai penggerak utama pertumbuhan ekonomi.

Optimization of Cost and Profit Functions as an Integer Linear Programming Problem Using Differential Evolution Algorithms

Werry Febrianti

Cost functions and profit functions are two functions that are often used in the production process as well as the sales process or marketing process. Many methods have been used in calculating the value of the cost function and profit function, such as the graph method and the simplex method. However, both methods have limitations if the function is more than two variables with many constraints. The differential evolution algorithm is therefore used in this study to overcome the shortcomings of both methods. This algorithm is a metaheuristic optimization algorithm that mimics nature's behavior in the process of finding the optimal solution. In this study, the cost function and profit function with the given constraints are used as integer linear program problems. Optimization results of the cost function and profit function of the three cases used in this study with differential evolution algorithm provide optimal values with a fairly fast time compared to the graph method and simplex method. Algorithm evolution can find solutions in the form of integers and provide results in a short time. This study shows that the differential evolution algorithm is effective in calculating the optimal solution of the cost function and profit function.

Solution of the Smallest Pair of Positive Numbers in Systems of Linear and Quadratic Diophantine Equations Having Positive Solutions Using Differential Evolution Algorithm

Werry Febrianti

Diophantine equations are equations that have solutions in the form of integers. The Diophantine equations studied are linear and quadratic Diophantine equations. This research uses algebraic method and differential evolution algorithm to solve linear and quadratic Diophantine equations. Based on the results obtained, the solution of the system of linear and quadratic Diophantine equations can be solved using the differential evolution algorithm and produces the smallest positive integer solution. As for the comparison of solving Diophantine equations using differential evolution algorithm and algebraically, there is a difference in the time obtained and the ease of solving the problem. Algebraic solutions to Diophantine equations can generally solve simple and limited problems to find solutions more broadly and take a long time for complex problems, while solving Diphantine equations using the differential evolution algorithm can find broader solutions and be able to solve complex problems that cannot be solved algebraically in a relatively short time, so the differential evolution algorithm becomes a reliable problem-solving method such as to solve linear and quadratic Diophantine equations

Estimating the Percentage of Children 0-17 Years Old whose Calorie Consumption is Less than 1400 kcal in Sulawesi Island in 2021: EBLUP with Additive Logistic Transformation

MILIE DIARTY

Indonesia is an agricultural country, but the problem in fulfilling calorie intake is still below 1400 kcal so that the second goal of the SDGs and the Indonesia Emas 2045 program are still not met. Children aged 0-17 years are the main target of government monitoring in preparing them to enter the workforce. The government needs data on children aged 0-17 years along with their calorie intake, but the availability of data in Indonesia is still not able to touch the level of district / city estimates available only at the provincial level. This is important as a consideration in making policies so that the second Sustainable Development Goals (SDGs) and the Golden Indonesia program in 2045 can run smoothly. So the research will use SAE modeling by comparing several estimation methods to get a fit modeling. The method used in this study is the univariate Small Area Estimation Fay-Herriot model with additive logistic transformation (SAE alr). The data used comes from BPS, to be precise in the 2021 Village Potential (PODES) data, the National Socio-Economic Survey (SUSENAS), and the 2020 Population Census (SP) long form. The result is that the RSE value of the estimation with the SAE alr method is below 25 percent for all districts/cities.

FORECASTING THE PRICE OF BIODIESEL AND PERTALITE FUEL OIL IN JAMBI USING FUZZY TIME SERIES MARKOV CHAIN

Rizkha Mardhatillah

Fuel oil is an important component in people's lives, especially in the fields of transportation, distribution, and production. The types of fuel oil that are quite widely used are biodiesel and pertalite. Unfortunately, the price of both types of fuel oil often changes. Fluctuating fuel prices can have an impact on the prices of other basic goods. This fluctuation problem forms something that is uncertain and difficult to predict. However, the price of fuel oil has historical data that can be collected in order of time so that efforts can be made to overcome it is prediction using the time series method. A method that can be used on uncertain fuel oil price data is the fuzzy time series markov chain method. The fuzzy time series markov chain method is a combination method between fuzzy time series and markov chain, this method has a connection to fuzzy logical relationships that pay attention to the relationship between the fuzzy value of one data and the next fuzzy value and in the markov chain there is a condition where the probability of the next event depends on the previous event. The forecasting results obtained are that the fuzzy time series Markov chain method is very good at predicting the price of biodiesel fuel oil which has a MAPE value of 0.052% and pertalite type with a MAPE of 0.063%. The fuel price predictions obtained tend to increase periodically.