bluuebunny/crossref_metadata_2025 huggingface dataset:
HYPERCALCEMIA IN NEWBORN SECONDARY TO MALIGNANT HYPERCALCEMIA IN PREGNANT WOMAN WITH METASTATIC BREAST CANCER: A CASE REPORT
Authors: Marinho Matheus Lavigne , Evaristo Jéssica de Jesus Simões , Soares Yuri Vieira Campos , Oliveira Gabriela de , Lima Luis Henrique Dias , Lopes Clara Sobreira Dias , Vieira Carolina Martins , Fagundes Theara Cendi
Published: April 2023

A multipara pregnant woman, 38 years old, presented with an initial suspicion of antibiotic-refractory mastitis. A breast biopsy revealed luminal type B invasive carcinoma and the image showed bone and liver metastases. She developed oligosymptomatic hypercalcemia requiring hydration and underwent cesarean at 35 weeks and 6 days; 2 days later, she received an osteolysis inhibitor, and a week later started chemotherapy. A baby boy was born with Apgar 9/10; hypercalcemia was observed after 12 h; then, he developed lethargy, hypotonia, and bradypnea after 21 h, requiring continuous positive airway pressure and monitoring. He received volume expansion with an improvement in his condition. Signs of breast cancer are confused with physiological breast changes in pregnancy such as engorgement, hypertrophy, and nipple discharge, implying a delay in diagnosis. Hypercalcemia during pregnancy occurs in about 0.03% of women; maternal complications include acute kidney injury, severe hypertension, and pre-eclampsia. Fetal adverse outcomes include intrauterine growth restriction, fetal demise in utero, neonatal hypocalcemia, tetany, and permanent hypoparathyroidism. Primary hyperparathyroidism is the main cause; hypercalcemia from metastatic breast cancer can be due to osteolytic metastases and humoral hypercalcemia of malignancy caused by peptide release related to parathyroid hormone, which promotes bone resorption and renal calcium reabsorption. In cases of malignant hypercalcemia, pamidronate is effective in inhibiting osteolysis, but it reduces uterine contractions and presents a teratogenic risk. Calcitonin is well tolerated during pregnancy but has a limited effect; the use of glucocorticoids can also be considered. In case of maternal hypercalcemia, the newborn’s calcium levels must be monitored. Hypercalcemia in pregnancy is a rare condition when cancer-related hypercalcemia may cause great maternal morbidity and fetal and neonatal morbidity and mortality. The manifestations presented by the neonate were compatible with the suspicion of neonatal hypercalcemia. Diagnostic confirmation was made based on the metabolic condition; the basis of emergency treatment is intravenous hydration.
CONSERVATIVE SURGERY IN ADENOID CYSTIC CARCINOMA: A CASE REPORT
Authors: Santos Mayra de Oliveira , Lauria Patrícia Bittencourt Marques , Santos Isadora Maria de Oliveira , Borges Maxlânio Azevedo , Nunes Cristiana Buzelin , Silva Jane Braga da , Lucena Clécio Ênio Murta de
Published: April 2023

Objectives: Adenoid cystic carcinoma of the breast (CACM) is uncommon, comprising less than 0.1% of breast carcinomas. In the literature, many authors argue that local control is achieved through mastectomy, especially in high-grade histological tumors. We report a case of CACM treated with conservative surgery at the Mastology Service of Hospital das Clínicas da UFMG. Methods: ASG, a 62-year-old, female patient presented with a 2-cm palpable nodule on the left breast, with the following mammographic representation: isodense nodule in the upper lateral quadrant, obscured contours, measuring 20 mm. She underwent a diagnostic sectorectomy and was diagnosed with basaloid solid-type CACM, grade 3, triple-negative phenotype, measuring 1.5 cm, with compromised lateral and anterior margins. She underwent a new sectorectomy and sentinel lymph node biopsy, with no invasive neoplasm in the sample and four sentinel lymph nodes, all free of metastasis. Subsequently, she underwent radiotherapy. On biopsy, CACM, grade 2, triple-negative phenotype was diagnosed. She was treated with conservative surgery and radiotherapy. Results: Follow-up with no signs of local recurrence or distant metastasis at one-year follow-up. Conclusion: Although most adenoid cystic carcinomas present with the basal-like, triple-negative phenotype, these tumors are generally of low histological grade and present an indolent biological behavior. Based on these clinical characteristics, the most recent studies have demonstrated the effectiveness of treatment with a complete excision of the tumor with wide margins and the addition of radiotherapy.
PHOTOBIOMODULATION AND MANUAL LYMPH DRAINAGE FOR THE TREATMENT OF NIPPLE NECROSIS IN BREAST CANCER PATIENT: TWO CASE REPORTS
Authors: Sgrott Francine Fischer , Baiocchi Jaqueline Munaretto Tim , Catão Francilma
Published: April 2023

Introduction: Breast cancer is the most common malignant neoplasm among women in the world and in Brazil, accounting for approximately 28% new cases each year. Recently, breast reconstruction after mastectomy with preservation of the nipple has been an alternative breast cancer treatment. However, despite its efficacy and esthetic superiority, the preservation of the nipple has been associated with several complications in the postoperative period. The therapy of photobiomodulation, formerly known as low-level laser therapy, has been demonstrated to promote repair of tissues by cellular repair biostimulation, angiogenesis, and anti-inflammatory effects. These characteristics suggest a potential role for the repair of chronic wounds and may be applicable in the treatment of necrosis. Objective: This study aimed to observe the effects of physiotherapeutic intervention through photobiomodulation therapy in two patients with nipple necrosis after mastectomy. Methods: Two Brazilian females with necrosis of more than 40% of the nipple on the right breast after breast mastectomy and reconstruction were referred to physical therapy (PT). PT sessions were composed of manual lymph drainage, manual therapy, and exercises of strength and flexibility, followed by photobiomodulation. Patient 1 received laser therapy with a laser at 660 nm, 3 joules per point every 1 cm. The device used was the Laser Therapy DMC, with a power output of 100 mw. The therapy was implemented 12 times in total, and a reevaluation was performed monthly until 12 months of follow-up. Patient 2 received led therapy at 660 and 850 nm, 3 joules per point every 2 cm. The device used was the Ledtherapy Cosmedical, with a power output of 5 mW, for a total of 24 treatments, and follow-up was performed until 6 months post-therapy. Conclusion: Photobiomodulation was helpful for wound healing. Confirmation of the sustained effects of photobiomodulation was shown in a 6-month follow-up.
THE IMPORTANCE OF MANAGING B3 LESIONS: A CASE REPORT
Authors: Chagas Carlos Ricardo , Chagas Natascha Carneiro , Silva Nathália Alves , Magalhães Gabriela Del Prete , Bedran Sálua Saud
Published: April 2023

The lesions of uncertain malignant potential of the breast, classified as B3, besides increasing the relative risk for breast cancer, have very heterogeneous abnormalities and raise a big question when defining conduct. A good multidisciplinary evaluation is necessary, comparing biopsy and imaging test results. This study reports the case of a 54-year-old patient, without other risk factors for breast cancer, who was referred to MAMARJ, a mastology clinic, from a gynecology service, in November 2019 for evaluation of category 4 mammography, due to alterations in the right breast: linear and heterogeneous calcifications in the upper outer quadrant (UOQ) and punctiform and grouped calcifications in the lower inner quadrant (LIQ). Mammotomies were indicated, and histopathological reports were compatible with columnar cell hyperplasia with a focus on planar atypia — in the UOQ — and adenomyoepithelioma and columnar cell hyperplasia without atypia in the LIQ. She was taken to surgery to remove the lesion from the UOQ (histopathology without malignancy). In July 2020, she underwent mammography with a category 2 (BIRADS) report due to parenchymal distortion from previous surgery, and ultrasonography with sparse cysts and bilateral ductal ectasia (category 3). One year later, in July 2021, she presented mammography — amorphous calcifications in the upper quadrants and punctate calcifications in the LIQ, near the clip from the previous mammotomy. Mammotomy of the calcifications in the upper quadrants was performed. The diagnosis of the vacuum-guided biopsy was columnar cell changes with minimal architectural atypia in the upper quadrants. Removal of the lesion from the upper quadrants and the LIQ (target of the previous mammotomy) was indicated. The histopathological diagnosis was ductal carcinoma in situ (LIQ), associated with atypical ductal hyperplasia, microcalcifications, and flat epithelial atypia. Immunohistochemical panels include estrogen receptor positive, progesterone receptor positive, and human epidermal growth factor receptor-type 2 negative. The upper quadrant lesion was compatible with a focus of intraductal proliferation with discrete atypia. A simple mastectomy was performed with immediate reconstruction of the right breast. Mastectomy was indicated mainly because it was the patient’s choice. As suggested, since the first diagnosis of B3 lesion and after that of ductal carcinoma in situ, the patient did not accept chemoprevention. It should be noted that risk-reducing mastectomy is cited only rarely for prevention in cases of recurrent and multicentric premalignant lesions, as in this case.
PRIMARY INVASIVE DUCTAL CARCINOMA OF AXILLARY ACCESSORY BREAST
Authors: El-Helou Etienne , Pop Catalin-Florin , Shall Ammar , Zaiter Manar , Naccour Jessica , Hoang Huu , Nguyen Thi Hoa , Ho Xuan Dung
Published: April 2023

Primary accessory breast cancer is an extremely rare pathology, representing less than 1% of all breast cancers, and it is found in more than 90% of cases in the axilla. The diagnosis of accessory axillary breast cancer (AABC) is often late and at an advanced stage, with an average delay of 40.5 months. Histological sampling and immunohistochemical results confirm the diagnosis. Most patients are diagnosed with stage II disease or higher, so it is considered to have a poor prognosis. There is no proper management for AABC; it follows the guidelines for orthotopic pectoral breast cancer. We therefore report the case of a 50-year-old woman diagnosed with grade II invasive ductal carcinoma found in accessory axillary breast, treated with neoadjuvant chemotherapy followed by a wide local resection and axillary lymph node dissection.
DUCTAL IN SITU ARISING IN FIBROADENOMA OF THE BREAST
Authors: Carvalho Fernando Silva de , Chagas Carlos Ricardo , Carneiro Sandra Mendes , Bedran Sálua S. , Chagas Natascha Carneiro
Published: April 2023

Fibroadenomas are common benign tumors of the female breast. In the appropriate clinical treatment, they are managed without excision, rarely a carcinoma arising within a fibroadenoma. We presented the case of a ductal carcinoma in situ (DCIS) arising in a fibroadenoma. We present a case of a 62-year-old woman with an isolated lump, no palpable, in her right breast that underwent a core biopsy, and histological findings of the lesion revealed a DCIS within a fibroadenoma. After 4 years (in 2021) without realizing mammography, by screening, she underwent mammography and ultrasound and was characterized as ACR BI-RADS category 4. The matologist should be aware of the possibility, particularly in older women, to inform the rationale for prompt surgical evaluation and follow-up of all breast masses. Mammography revealed a 1.4 cm mass on the right upper outer without microcalcifications. An ultrasound revealed a solid mass, hypoechoic, 1.4×0.7×1.1 cm, micronucleated. A core biopsy showed a biphasic neoplasia (fibroepithelial): a diagnostic suggestive of fibroadenoma with colonization of DCIS, cribriform, high grade, measuring 2 mm. Immunohistochemistry: ER: POSITIVE: 90–100%. PR: 90–100%. CerbB-2/Her2-neu score 0: negative. Lumpectomy: all margins greater than 2.0 mm: HYALINIZED FIBROADENOMA WITH USUAL DUCTAL HYPERPLASIA (06×04 mm)
QUANTILE REGRESSION MODEL ON RAINFALL IN MAKASSAR 2019
Authors: Sanusi Wahidah , Sukarna Sukarna , Harisahani Nur
Published: April 2023

Makassar is an area that has a monsoon rainfall pattern. This study aims to find a quantile regression model and to determine the factors that significantly influence rainfall in the city of Makassar. This applied research applies a quantile regression model to rainfall data which is seasonal data. The advantage of this quantile regression model is that it is able to detect extreme conditions of rainfall, such as heavy rain. The data used is daily data in 2019. The estimation results obtained 9 (nine) models from each quantile used. The best model is obtained based on the largest coefficient of determination ( ), namely the 0,8th quantile ( ) of 0.28%. Furthermore, based on the model, it is found that the factor that significantly influences rainfall in the city of Makassar is humidity. At the same time, the air temperature and wind speed have no significant effect on rainfall in the city of Makassar.
EOQ MODEL FOR DETERIORATING AND AMELIORATING ITEMS UNDER CUBIC DEMAND AND PARTIAL BACKLOGGING
Authors: Laily Hafizha Nur , Abusini Sobri , Sa’adah Umu
Published: April 2023

The inventory model aims to determine policies in inventory control. Therefore, the availability needs to be managed as well as possible to obtain optimal performance. This study aimed to produce EOQ models for deteriorating and ameliorating products with shortage and partial backlogging policies. The traditional Economic Order Quantity (EOQ) inventory model was used to develop the model. The search algorithm of the model solution was made to get a solution from the model. In the end, a case study of the model implementation at Minimarket SATUMART, Sidoarjo, is given
ON SUPER (3n+5,2)- EDGE ANTIMAGIC TOTAL LABELING AND IT’S APPLICATION TO CONSTRUCT HILL CHIPER ALGORITHM
Authors: Prihandini Rafiantika Megahnia , Adawiyah Robiatul
Published: April 2023

Graph labeling can be implemented in solving problems for various fields of life.  One of the application of graph labelling is in security system. Information security is needed to reduce risk, data manipulation, and unauthorized destruction or destruction of information. Cryptographic algorithms that can be used to build security systems, one of the cryptographic algorithms is Hill Cipher. Hill chipper is a cryptographic algorithm that uses a matrix as a key to perform encryption, decryption, and modulo arithmetic. This study discusses the use of Super (3n+5,2)- edge antimagic total labeling to construct the Hill Chiper algorithm. The variation of the edge weight function and the corresponding edge label on the  graph, will make the constructed lock more complicated to hack
THE NEXUS BETWEEN URBANIZATION, LIVESTOCK, AND DEFORESTATION IN SOUTHEAST ASIA: EVIDENCE FROM PMG AND PANEL-CAUSALITY
Authors: Destiartono Mohamad Egi
Published: April 2023

Southeast Asia is one of the regions that has received a red report card related to forest management. This article intends to investigate the relationship between urbanization, livestock, and deforestation in Southeast Asia, as well as elaborate on STRIPAT's concept. This study uses panel data of 9 countries and 28 periods, obtained from the statistical publication of WDI, World Bank. This article applies the Pooled Mean Group (PMG) estimation and performs unit root, co-integration, and causality tests. The estimation results show that urbanization, GDP per capita, and livestock positively and significantly impact deforestation. The increase in the level of urbanization, GDP per capita, and livestock production will be followed by a decrease in forest cover area. In contrast, population density is not the driver of deforestation. In addition, this paper confirms bidirectional causality between urbanization and deforestation rates, as well as a unidirectional causality from income per capita to deforestation and from population density to deforestation. National development plans, urban development, and livestock expansion must be integrated with forest management to reduce deforestation rates
PRE-EMPTIVE GOAL PROGRAMMING METHOD FOR OPTIMIZING PRODUCTION PLANNING
Authors: Hasbiyati Ihda , Desri Rama , Gamal Moh Danil Hendry
Published: April 2023

In this paper the mathematical modelling of production planning is put forward using the pre-emptive goal programming method for priority based decision making. The objective of this problem is to design production planning satisfying the speciﬁed constraints while minimizing deviations from each goal to be achieved. The goals consist of ﬁve types of best seller bread, production proﬁts, production costs, production quantities, and availability of bread raw materials. LINGO 11.0 package is implemented for computational purposes. Computational results show that the pre-emptive goal programming is an efficient method for optimizing production planning of multiple goals based on the order of priority.
BIFURCATION ANALYSIS MATHEMATICAL MODEL FOR THE SPREAD OF EXOGENOUS REINFECTION TUBERCULOSIS
Authors: Winanda Rara Sandhy , Ahmad Defri , Putri Sovia Helmi , Putri Ariana
Published: April 2023

The spread of tuberculosis can occur in two ways, namely exogenous and endogenous. The spread of tuberculosis exogenously or Exogenous Reinfection of tuberculosis can be observed using a mathematical model. Then an analysis of the mathematical model with a bifurcation approach was carried out. Based on the result, it was found that there was a change in stability properties and the type of equilibrium point in the distribution equation system of exogenous reinfection tuberculosis, where the parameter that occurred bifurcation was , with . When value of  is smaller than zero, the system of differential equations of exogenous reinfection tuberculosis shows an unstable with a saddle point type, when the value of     is equal to zero the system of differential equations cannot be determined its stability, and when  system of differential equations shows asymptotic stability, where there is a change in species. The points are nodes, star nodes, and spirals.
THE IMPLEMENTATION OF FINITE-STATES CONTINUOUS TIME MARKOV CHAIN ON DAILY CASES OF COVID-19 IN BANDUNG
Authors: Monika Putri , Soetikno Christophorus , Abdullah Atje Setiawan , Ruchjana Budi Nurani
Published: April 2023

Markov chain is a stochastic process to describe a phenomenon in the future based on a previous state. In practice, Markov chains are distinguished by time into two, namely discrete-time Markov chain and continuous-time Markov Chain. This research will discuss the continuous-time Markov chain with finite-state. COVID-19 phenomena can describe and predict using the continuous-time Markov chain. Authors use the data daily cases of COVID-19 in Greater Bandung including Bandung City, Bandung District, West Bandung District, Cimahi City and Sumedang District. Used data came from simulated data of daily cases of COVID-19 in Greater Bandung from August, 2020 until November 14, 2021 that recorded through the website COVID-19 of West Java. In terms of described and predicted the COVID-19 phenomenon in Greater Bandung for long-term probability, authors use stationary distribution and limit distribution. COVID-19 phenomenon is described into two states: state 0 (lower than average of data) and state 1 (higher than average of data). The result of continuous-time Markov chain with finite-state shows that the probability of the daily cases of COVID-19 for five locations in Greater Bandung is state 0 have a larger probability than state 1. It means that COVID-19 in Greater Bandung over the long-term will decrease.
APPLICATION OF TWO GROUPS ANALYSIS AND CLUSTER ANALYSIS ON COMPARISON OF CHARACTERISTICS OF PAGARALAM COFFEE FARMERS CATEGORIES
Authors: Irmeilyana Irmeilyana , Ngudiantoro Ngudiantoro , Maiyanti Sri Indra
Published: April 2023

Land care is one of the factors that must be considered in the sustainability of coffee production. A small number of Pagaralam coffee farmers know and apply reductant herbicide in weed control. This study aims to compare the characteristics of Pagaralam coffee farmers based on the use of reductant herbicide by using two groups analysis and cluster analysis. The characteristics of the farmers studied included 17 variables on 165 respondents who were selected by purposive sampling. The variables studied include land productivity and farmers' income. Respondents were divided into 2 categories, namely users and non-users of reductant herbicide. The initial stages of data processing in the form of descriptive statistics, hypothesis testing by mean difference test, correlation between variables, Principal Component Analysis, and biplot analysis. According to the results of the two groups analysis and cluster analysis, the variables dominantly tend to characterize the similarity in comparison between the categories of non-users and users are the variables that join into one cluster, namely land area, number of trees, coffee bean production, estimated yield, total harvest, Gross income, and Net income. While the variables that dominantly characterize the dissimilarity between the two categories are the variables that form 5 separate clusters, namely the clusters of plantation productivity, respondent identity, education, tree age, and length of harvest period.
TVECM TO ANALYZE THE RELATIONSHIP BETWEEN NET FOREIGN ASSETS AND CURRENCY CIRCULATION
Authors: Ana Chantika Putri , Lestari Trianingsih Eni , Permadi Hendro
Published: April 2023

In the analytical balance sheet of the base money monetary authority, Bank Indonesia explained that net foreign assets (NFA) affected the circulation of base money, including currency. This gives an assumption regarding the indication of a causal relationship between currency circulation and NFA. In addition to the causality test, the primary purpose of this study is to identify long-term equilibrium relationships between NFA and Currency Circulation. The cointegration test obtained r = 1, indicating cointegration (long-term equilibrium). Time series data plots of these two variables tend to have a trend and are not stationary. The Vector Error Correction Model (VECM) is applied as an analytical method used to correct long-term relationships between variables that are not stationary. However, in the concept of VECM deviation and short-term dynamics, the association is assumed to be linear. At the same time, in applying economics, the relationship between economic variables is not necessarily linear. The significance test for the presence of a threshold using a fixed regressor bootstrap shows a threshold effect or nonlinear VECM, so it is necessary to use an analytical method that can combine nonlinearity and cointegration through the Threshold Vector Error Correction Model (TVECM). In this study, modeling of TVECM 2 regimes and three regimes were carried out. TVECM 3 regimes obtain the best model with two threshold values ​​of -310850 and -260156.
IMPLEMENTATION OF THE FUZZY GUSTAFSON-KESSEL METHOD ON GROUPING DISTRICTS/CITIES IN KALIMANTAN ISLAND BASED ON POVERTY ISSUES FACTORS
Authors: Paradilla Yunda Sasha , Hayati Memi Nor , Sifriyani Sifriyani
Published: April 2023

Cluster analysis is an analysis that is useful in summarizing data by grouping objects based on certain similarity characteristics. One of the group analysis is Fuzzy Gustafson-Kessel (FGK) which is the development of the Fuzzy C-Means (FCM) method. The FGK method has a good way in adjusting the form of cluster membership function correctly for a data. This study aims to determine the results of the optimal number of groups based on the Partition Coefficient (PC) and Classification Entropy (CE) validity indexes and to find out the results of grouping 56 districts/cities on the island of Kalimantan based on poverty issue factors in 2021. The optimal number of groups using the FGK method based on the validity indexes of PC and CE are two groups. The first group and the second group each consist of 28 districts/cities in Kalimantan Island.
BAYESIAN ADDITIVE REGRESSION TREE APPLICATION FOR PREDICTING MATERNITY RECOVERY RATE OF GROUP LONG-TERM DISABILITY INSURANCE
Authors: Budiana Stevanny , Kusnadi Felivia , Irawan Robyn
Published: April 2023

Bayesian Additive Regression Tree (BART) is a sum-of-trees model used to approximate classification or regression cases. The main idea of this method is to use a prior distribution to keep the tree size small and a likelihood from data to get the posterior. By fixing the tree size as small as possible, the approximation of each tree would have a little effect on the posterior, which is the sum of all output from all the trees used. Bayesian additive regression tree method will be used for predicting the maternity recovery rate of group long-term disability insurance data from the Society of Actuaries (SOA). The decision tree-based models such as Gradient Boosting Machine, Random Forest, Decision Tree, and Bayesian Additive Regression Tree model are compared to find the best model by comparing mean squared error and program runtime. After comparing some models, the Bayesian Additive Regression Tree model gives the best prediction based on smaller root mean squared error values and relatively short runtime.
REGRESSION NONPARAMETRIC SPLINE ESTIMATION ON BLOOD GLUCOSE OF INPATIENTS DIABETES MELLITUS AT SAMARINDA HOSPITAL
Authors: Sari Ar Ruum Mia , Sifriyani Sifriyani , Huda Mohammad Nurul
Published: April 2023

This study used a biresponse nonparametric regression method with truncated spline estimation that used two response variables. Nonparametric regression method is used when the regression curve is not known for its shape and pattern.One of the nonparametric regression model approaches that is often used is the spline. The truncated spline approach has a segmented polynomial function that provides flexibility. The data used in the study were blood glucose levels in patients with diabetes mellitus, cholesterol levels, and triglyceride levels in 2020. From the results of the study, the best nonparametric biresponse spline truncated regression model with three knot points has been obtained where the minimum GCV value is  and has the an  value of  .
RADIO LABELING OF BANANA GRAPHS
Authors: Sarbaini Sarbaini , Nazaruddin Nazaruddin , Rizki Muhammad , Umam Muhammad Isnaini Hadiyul , Hamzah Muhammad Luthfi , Prasetyo Tegar Arifin
Published: April 2023

Let G=(V, E) be a graph. An L(3,2,1) labeling of G is a function  such that for every , , and  if . Let , a  labeling is a labeling  where all labels are not greater than . An ) number of , denoted by , is the smallest non-negative integer  such that  has a  labeling. In this paper, we determine   of banana graphs.
APPLICATION OF RANDOM FOREST ALGORITHM ON WATCH PRICE PREDICTION SYSTEM USING FRAMEWORK FLASK
Authors: Dalimunthe Dzakiyyatul Kirom , Hakim Raden Bagus Fajriya
Published: April 2023

In the modern era like today, watches not only function as timepieces, but have become a fashion trend for the community, especially teenagers. The increasing market demand for watches opens up opportunities for counterfeit watch sellers to sell their products by claiming that the watches they sell are genuine watches by offering relatively cheaper prices compared to genuine watches. This is very detrimental to consumers and also the watch industry. To minimize fraud committed by fake watch sellers, it is necessary to know the price of the original watch in advance, before buying the desired watch. Therefore, the purpose of this study is to predict the price of watches using the Random Forest method and will be developed into a web system using the Framework Flask. The results of the study using 3337 trees obtained an accuracy rate of 84,98% with a MAPE of 15,02%. The most influential variable on the price of watches is the material variable with the level of importance obtained at 0,359. After getting the best model, the model is then developed into a web system using the help of the Framework Flask and Heroku which can later be accessed online.
ASSOCIATION RULES IN RANDOM FOREST FOR THE MOST INTERPRETABLE MODEL
Authors: Ilma Hafizah , Notodiputro Khairil Anwar , Sartono Bagus
Published: April 2023

Random forest is one of the most popular ensemble methods and has many advantages. However, random forest is a "black-box" model, so the model is difficult to interpret. This study discusses the interpretation of random forest with association rules technique using rules extracted from each decision tree in the random forest model. This analysis involves simulation and empirical data, to determine the factors that affect the poverty status of households in Tasikmalaya. The empirical data was sourced from Badan Pusat Statistik (BPS), the National Socio-Economic Survey (SUSENAS) data for West Java Province in 2019.  The results obtained are based on simulation data, the association rules technique can extract the set of rules that characterize the target variable. The application of interpretable random forest to empirical data shows that the rules that most distinguish the poverty status of households in Tasikmalaya are house wall materials and the main source of drinking water, house wall materials and cooking fuel, as well as house wall materials and motorcycle ownership.
INTRODUCTION OF PAPUAN AND PAPUA NEW GUINEAN FACE PAINTING USING A CONVOLUTIONAL NEURAL NETWORK
Authors: Haay Happy Alyzhya , Trihandaru Suryasatriya , Susanto Bambang
Published: April 2023

In this research, the face painting recognition of Papua and Papua New Guinea was identified using the Convolutional Neural Network (CNN). This CNN method is one of the deep learning that is very well known and widely used in face recognition. The best training process model is obtained using the CNN architecture, namely ResNet-50, VGG-16, and VGG-19. The results obtained from the training model obtained an accuracy of 80.57% for the ResNet-50 model, 100% for the VGG-16 model, and 99.57% for the VGG-19 model. After the training process, predictions were continued using architectural models with test data. The prediction results obtained show that the accuracy of the ResNet-50 model is 0.70, the VGG-16 model is 0.82, and the VGG-19 model is 0.83. It means that the CNN architectural model that has the best performance in making predictions in identifying the recognition of Papua and Papua New Guinea's face painting is the VGG-19 model because the accuracy value obtained is 0.83.
ANALYSIS OF ROBUST CHAIN LADDER METHOD IN ESTIMATING AUSTRALIAN MOTOR INSURANCE RESERVES WITH OUTLYING DATASET
Authors: Johan Jonathan Prasetyo , Kusnadi Felivia , Yong Benny
Published: April 2023

Reserves are one of the most crucial components for an insurance company to make sure it has enough money to pay off all the incurred claims. The presence of outliers in the incurred claims data harbors risk on inaccurately predicting reserves to cover claim amounts, usually achieved by the standard chain ladder reserving method. To remedy the effect of the outliers, the robust chain ladder reserving method is used by setting the median value to predict estimated reserve. On this research, we utilized both methods on various datasets. The purpose of this paper is to determine the best method that can be utilized by insurance company in various scenario to obtain the most optimized reserved estimate that can minimize the risk of being unable to pay the insurance claim or even the risk of over allocating reserves that could pose profitability issue. The primary data used are the Australian domestic motor insurance claims from 2012 to 2017, obtained from Australian Prudential Regulation Authority (APRA). The dataset is then manipulated to have outliers. After calculating the estimation, the result is compared to assess the strength of the methods using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) calculation. In conclusion, we found that the robust chain ladder reserving method works better in an outlying dataset. We also identify cases in which robust chain ladder are not appropriately used.
SIMULATION OF THE SARIMA MODEL WITH THREE-WAY ANOVA AND ITS APPLICATION IN FORECASTING LARGE CHILLIES PRICES IN FIVE PROVINCES ON JAVA ISLAND
Authors: Sanusi Ratna Nur Mustika , Susetyo Budi , Syafitri Utami Dyah
Published: April 2023

Commodities that become potential in the Horticulture Sub-sector are large chilies, so supply and prices must be controlled. One of the efforts that can be made is to predict the price of large chili in the future. However, forecasting is sometimes constrained by several things, such as small sample sizes and outliers. The effect of several factors on the parameter estimation bias can be determined by experimental design by simulating the data obtained from the generation results with several scenarios. The results of the analysis show that all factors have a significant effect on the magnitude of the parameter bias, so that all factors can affect forecasting results. When applying forecasting methods to actual data, paying attention to these three factors is necessary. The application of actual data using the SARIMA method gives good results. It can be seen from the RMSE and MAPE values ​​, which tend to be small. Based on the forecast results for the following 12 periods, it is estimated that the price of big chili in 2022 in five provinces will still fluctuate. The high price of chili in five provinces is predicted to reach its highest in the first three months of 2022. The highest price is predicted to occur in DIY Province in February, which is Rp. 74.230.00/kg. However, from the middle to the end of the year, prices will tend to fall and stabilize. The price will be the lowest in Middle Java Province in December, which is Rp. 20,689.00/Kg.
MODIFIED WEIGHT MATRIX USING PRIM’S ALGORITHM IN MINIMUM SPANNING TREE (MST) APROACH FOR GSTAR(1;1) MODEL
Authors: Huda Nur'ainul Miftahul , Fran Fransiskus , Yundari Yundari , Fikadila Lisa , Safitri Fauziah
Published: April 2023

The Generalized Space-Time Autoregressive (GSTAR) model is able to utilize modeling of both space and time simultaneously. The existence of a weight matrix is one of the aspects that established this model. The matrix illustrates the spatial impact that occurs between locations. In this research, a modified weight matrix is presented using the Minimum Spanning Tree approach of graph theory. Prim's algorithm is utilized for calculation here. Not only does the modified weight matrix depend distance, but also highlights the correlation. It makes the modified weight matrix unique. Before starting Prim's algorithm, the correlation is first utilized as an input in forming the initial graph. Following that, find the graph with the least of MST weight. Afterwards, the graph is described utilizing weight matrix, which is applied to the normalization process. Following this, the GSTAR(1;1) modelling process is carried out, beginning with estimating the parameters and then forecasting. The case study is Covid-19 cases that occurred on Java Island between July 2020 (when early Covid-19 entered Indonesia) and the beginning of January 2021. The aim of the research is to model the Covid-19 cases using modified weights and to predict the following five times. The outcome is a GSTAR(1;1) model with modified weights can captures both temporal and spatial patterns. The accuracy of the model is achieved for both the training data and the testing data by the MAPE computations, which yielded of 11.40% and 21.57%, respectively. Predictions are also obtained for each province in the next five times.
REGIONS GROUPING IN CENTRAL SULAWESI PROVINCE BY TRANSMITTED DISEASE USING FUZZY GUSTAFSON KESSEL
Authors: Fajri Mohammad , Rais Rais , Handayani Lilies
Published: April 2023

Health is one of the main indicators in determining the human development index. This is in contradiction with the situation in several areas in Indonesia where infectious diseases are the cause of death and have become extraordinary events. It was recorded in Central Sulawesi that in 2020 there were 8 extraordinary events due to infectious diseases which made this province become relatively high infectious diseases. One of the efforts that can be made to identify infectious diseases in an area is to form a grouping of locations into a group that has similarities and same characteristics. This is intended to provide information related to health in each region. Cluster analysis is one of method that can be used to grouping the data. Cluster analysis is the process of dividing data into a group based on the degree of similarity. Data with similar characteristics will be gathered in one group. One of the algorithms in cluster analysis is Fuzzy Gustafson Kessel which can produce relatively better groupings compared to the basic algorithms in cluster analysis. This study will use data on infectious diseases in Central Sulawesi Province with several recorded infectious diseases. From 13 regions, 5 clusters were formed. Clusters 1, 2 and 3 each consist of 3 regions, while clusters 4 and 5 each consist of 2 regions.
FORECASTING TOURISM DEMAND DURING THE COVID-19 PANDEMIC: ARIMAX AND INTERVENTION MODELLING APPROACHES
Authors: Rianda Fahriza , Usman Hardius
Published: April 2023

The tourism sector in Indonesia is one of the economic sectors severely impacted by the COVID-19 epidemic and its variants. The government is attempting to revive the economy by implementing numerous recovery policies. These economic recovery policies, particularly in the tourism sector, must be backed by a policy evaluation conducted using tourism demand data, such as the number of international visitors visiting Indonesia. However, official data from the BPS-Statistics Indonesia has been released with a two-month delay and is sometimes revised the following month. Consequently, it is necessary to forecast the data for the current situation. In addition, the forecasting model must be modified to account for data conditions caused by the COVID-19 pandemic. This research proposes an ARIMAX forecasting model that utilizes Google Trends and an intervention forecasting model with data sources from BPS and Google Trends. Thus, this research aims to present an overview, develop a model and identify the best forecasting model, and calculate the influence of the intervention on the number of international visitors visiting Indonesia. Compared to the ARIMAX model, the results indicated that the intervention model provided the most accurate forecasts. Not only superior in forecasting results, but the intervention model also demonstrates the magnitude of the intervention's effect on the number of international visitors visiting Indonesia.
SUBDISTRICT CLUSTERING IN WEST JAVA PROVINCE BASED ON DISEASE INCIDENCE OF JKN PARTICIPANTS PRIMARY SERVICES
Authors: Nashir Husnun , Kurnia Anang , Fitrianto Anwar
Published: April 2023

One of the efforts that can be done to optimize health services and the distribution of facilities and infrastructure efficiently in a wide scope is by profiling and clustering areas in the province of West Java to the scope of sub-districts that have similar characteristics of disease category. The methods that will be compared to get the best clustering are hierarchical clustering and ensemble clustering. The data used as the object of research is the BPJS Kesehatan capitation primary service sample data for the 2017-2018 period. Some of the important variables used include: primary disease diagnosis data (ICD-10) of patients at the puskesmas, service time, type of visit, and location of service sub-district. This study uses several evaluation metrics Silhouette coefficient, Dunn index, Davies-Bouldin index, and C-index to determine the optimal number of clusters formed. In addition, descriptive analysis and visualization of the clustering results are also used as considerations in selecting the optimal cluster. Based on the evaluation results, the optimal method is hierarchical clustering with complete linkage. This method produces three clusters: cluster 1 consists of 5 sub-districts that have a high/dominant mean value in almost all disease categories, cluster 2 consists of 26 sub-districts that have a medium mean value, and cluster 3 consists of 589 sub-districts that have a low mean value. Most of the members of clusters 1 and 2 are sub-districts located in the districts/cities around the national capital (DKI Jakarta) and the provincial capital (Bandung) while the members of cluster 3 are mostly sub-districts located in suburban districts/cities or far from the central government.
LOGISTIC MODELING TO PREDICT THE INTEREST OF THE INDONESIAN PEOPLE FOR BUYING FLOOD IMPACTED INSURANCE PRODUCTS
Authors: Hikmah Yulial
Published: April 2023

Indonesia is a country located on the equator and in the form of an archipelago. It has a high potential for various types of hydrometeorological-related disasters, such as floods, flash floods, droughts, extreme weather, etc. Almost all cities in Indonesia experience flooding every year, including DKI Jakarta, the capital city of Indonesia. Based on data from the National Disaster Management Agency (BNPB) in 2020, East Jakarta is a city that is prone to flooding. According to BNPB (2013), flooding is a disaster that relatively causes the most losses. Losses caused by floods, especially indirect losses, may rank first or second after an earthquake or tsunami. Floods cause so many losses, and it is necessary to have disaster mitigation efforts to minimize the possibility of flood risks. One risk mitigation due to natural disasters is buying insurance products. However, not everyone buys flood-impact insurance products due to economic and social factors. This study aims to create a model with the Logistics Regression Model to determine the factors influencing Indonesian people's interest in purchasing flood-impact insurance products. The research data is from 140 households in East Jakarta, Indonesia, using a non-probability purposive sampling technique. Furthermore, with a significance level of 10%, the logistic regression model obtained 14 significant regression coefficients. In the end, the obtained model is evaluated based on its level of accuracy. The results showed that the accuracy rate was almost excellent, namely 89.3%.
DYNAMIC ANALYSIS OF THE MATHEMATICAL MODEL FOR THE CHOLERA DISEASE SPREAD INVOLVING MEDICATION AND ENVIROMENTAL SANITATION
Authors: Resmawan R , Yahya Lailany , Mahmud Sri Lestari , Nuha Agusyarif Rezka , Laita Nazrilla Hasan
Published: April 2023

This study aims to analyze the mathematical model of the cholera disease spread involving medicationnd environmental sanitation. The model was analyzed by determining the equilibrium point and the basic reproduction number. The next step was to analyze the equilibrium point, sensitivity, and simulate numerically. Analysis of the stability of the disease-free and endemic equilibrium points usedhe Routh-Hurwitz criteria and the Castillo-Chaves and Song Theorem. The Analysis resultf the model produced two equilibrium points; namely the disease-freequilibrium point  for local asymptotic stability  and the endemic equilibrium point  for local asymptotic stability if . Furthermore, the sensitivity analysis indicated the most sensitive parameters for basic reproductive number changes in succession are the parameters for natural birth rates , the transmission rate of bacteria from the environment to humans , the saturated concentration of bacteria in water , an increase in the bacterial population caused by environmental pollution rate by humans . Numerical simulations suggest an increase to give vaccine can contribute to slowing the transmission of cholera where as the reduction of a vaccine able to promote the transmission of cholera diseases.
ANALYSIS OF SPATIAL EFFECTS ON FACTORS AFFECTING RICE PRODUCTION IN CENTRAL SULAWESI USING GEOGRAPHICALLY WEIGHTED PANEL REGRESSION
Authors: Gamayanti Nurul Fiskia , Junaidi Junaidi , Fadjryani Fadjryani , Nur'eni Nur'eni
Published: April 2023

Fulfillment of rice stock in Indonesia to always be distributed based on demand in the community is certainly closely related to the results of rice production. The results of rice production in various regions of Indonesia are very different. This difference can of course be influenced by geographic location or spatial effects between regions. Central Sulawesi, which is one of the provinces with a large population compared to other provinces on the island of sulwesi, has a responsibility to meet the needs of its community, so it is necessary to take into account and increase the production of rice by relying on production in the province.Modeling of rice production that has spatial effects or heterogeneity between regions is needed as an analytical tool because if the modeling ignores spatial effects and generalizes the model, the modeling predictions will be biased. So we need an analytical model that can accommodate the problem of spatial effects using Geographically Weighted Panel Regression. The purpose of this study was to determine the factors that can affect rice production in central sulawesi. The data used comes from BPS Central Sulawesi province from 2014-2020. This study focus  to the spatial effect  factors that are considered to be able to affect the rice production production in Central Sulawesi. Tthe results of the study there area 8 districts/cities which are affected by land area, and 4 districts/cities are affected by land area and harvested are.
EXPERT SYSTEM DESIGN TO DIAGNOSE PESTS AND DISEASES ON LOCAL RED ONION PALU USING BAYESIAN METHOD
Authors: Junaidi Junaidi , Fadjryani Fadjryani , Setiawan Iman , Batara Mohammad , Hendra Syaiful , Ismail Nurmasita
Published: April 2023

Bayesian is a method that can be used to overcome the uncertainty of a situation or data. The information obtained must be continuously updated so that it can foster trust as a result of the uncertainty of those conditions. In this study, the application of the Bayesian method to detect early symptoms of diseases on local red onion Palu plants based on the symptoms that appear will be carried out. Information about pests and diseases that attack local red onion Palu is needed to help farmers. As a result, they can deal with attacked diseases quickly and precisely. This is crucial conducted by considering that this plant is one of the mainstay commodities for farmers in Central Sulawesi Province whose production must continue to be increased. Pests and diseases can be diagnosed through visible symptoms.The sample is local red onion Palu that affected by pests and disesases which planted in the AIAT of Central Sulawesi by experiment. As a result, through these symptoms an expert system can then be created to do a diagnosis. An expert system is a system that seeks to adopt human knowledge to a computer that is built to solve problems like an expert. The created expert system to diagnose diseases uses the Bayesian method to calculate the probability of an event occurring based on the obtained results from observations and experts. An expert system for diagnosis of pests and diseases is built on a web-based basis. This expert system has features and functions including the diagnosis of pests and diseases of the observed plants, viewing the results of the diagnosis and printing the results of the diagnosis. In addition, users can view information on pests and other diseases that attack plants. From the results of system testing that conducted by experts, this shows that the expert system is feasible to use to diagnose local red onion Palu plants which affected by pests and diseases with an accuracy point that has the largest percentage value.