PENGGUNAAN DATA MINING DALAM HIT RATE IMPORTASI JALUR MERAH DENGAN MODEL DECISION TREE

ABSTRACT: Customs and Excise faces a big challenge to be able to increase the hit rate of red line imports by 40% in accordance with the Blueprint for the 2014-2025 Ministry of Finance Institutional Transformation Program and international benchmarks. Through a qualitative study, this study aims to determine the use of data mining that is applied to the risk engine based on import data, people's experiences, and research results of customs institutions of other countries. The data mining method used is CRISP-DM, classification method, and decision tree model, using data imported from the red line KPU BC Type A Tanjung Priok for the period September – December 2019 and January 2020. The results show that the use of data mining can increase the hit rate of red line importation. The most relevant attribute in classifying data is the sending country which is categorized as a root node, while the import duty tariff attribute does not provide information on data classification. This research is expected to provide a new perspective for the KPU BC Type A Tanjung Priok in an effort to improve the risk engine targeting and risk engine routing of Customs and Excise. Keywords: CRISP-DM, data mining, decision tree, hit rate, the red line import. ABSTRAK: Bea dan Cukai menghadapi tantangan besar untuk dapat meningkatkan capaian hit rate importasi jalur merah sebesar 40% sesuai dengan Cetak Biru Program Transformasi Kelembagaan Kementerian Keuangan Tahun 2014 – 2025 dan benchmark internasional. Melalui studi kualitatif, penelitian ini bertujuan untuk mengetahui penggunaan data mining yang diterapkan dalam risk engine berdasarkan data importasi, pengalaman orang, dan data hasil penelitian institusi kepabeanan negara lain. Metode data mining yang digunakan adalah CRISP-DM, metode klasifikasi, dan model decision tree, dengan menggunakan data importasi jalur merah Kantor Pelayanan Utama (KPU) Bea dan Cukai (BC) Tipe A Tanjung Priok periode September – Desember 2019 dan Januari 2020. Hasil penelitian menunjukkan bahwa penggunaan data mining dapat meningkatkan capaian hit rate importasi jalur merah. Atribut yang paling relevan dalam mengklasifikasikan data adalah negara pengirim yang dikategorikan sebagai root node (akar), sedangkan atribut tarif bea masuk tidak memberikan informasi dalam klasifikasi data. Penelitian ini diharapkan dapat memberikan pandangan baru bagi KPU BC Tipe A Tanjung Priok dalam upaya perbaikan risk engine targeting dan risk engine penjaluran Bea dan Cukai. Kata Kunci: CRISP-DM, data mining, decision tree, hit rate, importasi jalur merah.

Risk of coronary heart disease and stroke based on United Kingdom prospective diabetes study in type 2 DM patients in Medan

Cardiovascular disease is one of the most prevalent diabetic consequences that can lead to death. The purpose of this study was to use The United Kingdom Prospective Diabetes Study (UKPDS) Risk Engine to determine the risk of CVD complications in type 2 DM patients. The study’s design is analytic using a cross-sectional approach, and the samples include 108 type 2 diabetes patients in Medan who fulfill the inclusion and exclusion criteria. The results showed that most patients had a high risk for CHD and a low risk for stroke. Education must be carried out intensively to patients that blood sugar is more controlled to reduce the risk of complications.

External validation of the UK Prospective Diabetes Study (UKPDS) risk engine in patients with type 2 diabetes identified in the national diabetes program in Iran

Background Cardiovascular diseases are the first leading cause of mortality in the world. Practical guidelines recommend an accurate estimation of the risk of these events for effective treatment and care. The UK Prospective Diabetes Study (UKPDS) has a risk engine for predicting CHD risk in patients with type 2 diabetes, but in some countries, it has been shown that the risk of CHD is poorly estimated. Hence, we assessed the external validity of the UKPDS risk engine in patients with type 2 diabetes identified in the national diabetes program in Iran. Methods The cohort included 853 patients with type 2diabetes identified between March 21, 2007, and March 20, 2018 in Lorestan province of Iran. Patients were followed for the incidence of CHD. The performance of the models was assessed in terms of discrimination and calibration. Discrimination was examined using the c-statistic and calibration was assessed with the Hosmer–Lemeshow χ2 statistic (HLχ2) test and a calibration plot was depicted to show the predicted risks versus observed ones. Results During 7464.5 person-years of follow-up 170 first Coronary heart disease occurred. The median follow-up was 8.6 years. The UKPDS risk engine showed moderate discrimination for CHD (c-statistic was 0.72 for 10-year risk) and the calibration of the UKPDS risk engine was poor (HLχ2=69.9, p<0.001) and overestimated the risk of heart disease. Conclusion This study shows that the ability of the UKPDS Risk Engine to discriminate patients who got CHD events from those who did not was moderate and the ability of the risk prediction model to accurately predict the absolute risk of CHD (calibration) was poor and it overestimated the CHD risk. To improve the prediction of CHD in patients with type 2 diabetes, this model should be updated in the Iranian diabetic population.

Descripción del riesgo cardiovascular en pacientes diabéticos tipo 1 en República Dominicana

Introducción: la diabetes tipo 1 es una enfermedad crónica de alto impacto económico con gran capacidad de ser controlada, la misma no tiene ninguna descripción local previa. Su principal causa de mortalidad es los eventos cardiovasculares y el manejo adecuado la disminuye considerablemente. Objetivo: determinar el riesgo cardiovascular en pacientes adultos con diabetes tipo 1 en la ciudad de Santiago de los Caballeros, República Dominicana. Método: se realizó un estudio descriptivo transversal multicéntrico con 39 pacientes en el período de junio a noviembre de 2019. La calculadora “Steno T1 Risk Engine” se utilizó para estimar el riesgo cardiovascular. Resultados: se obtuvo una relación significativa entre la albuminuria (p = 0.0127), presión arterial sistólica (p = 0.0002), tiempo de diagnóstico (p = 0.0037) y nivel de riesgo cardiovascular. La hemoglobina glucosilada (p = 0,7884) y la actividad física (p = 0.706) no mostraron una relación significativa con el riesgo cardiovascular. Conclusión: el nivel de riesgo cardiovascular promedio es bajo, con probabilidades <10 % de un evento cardiovascular agudo dentro de los 10 años. Esta herramienta permite incluir una evaluación cardiovascular rutinaria con datos que perfilen el tratamiento orientado a disminuir complicaciones vasculares, mortalidad y aumentar adherencia al tratamiento.

A retrospective study on the usefulness of the JJ risk engine for predicting the incidence rate of coronary heart disease in type 2 diabetes patients

Objective In 2018, we conducted a retrospective survey using the medical records of 484 patients with type 2 diabetes. The observed value of coronary heart disease (CHD) incidence after 5 years and the predicted value by the JJ risk engine as of 2013 were compared and verified using the discrimination and calibration values. Results Among the total cases analyzed, the C-statistic was 0.588, and the calibration was p < 0.05; thus, the JJ risk engine could not correctly predict the risk of CHD. However, in the group expected to have a low frequency of hypoglycemia, the C-statistic was 0.646; the predictability of the JJ risk engine was relatively accurate. Therefore, it is difficult to accurately predict the complication rate of patients using the JJ risk engine based on the diabetes treatment policy after the Kumamoto Declaration 2013. The JJ risk engine has several input items (variables), and it is difficult to satisfy them all unless the environment is well-equipped with testing facilities, such as a university hospital. Therefore, it is necessary to create a new risk engine that requires fewer input items than the JJ risk engine and is applicable to several patients.

A Retrospective Study on The Usefulness of The JJ Risk Engine for Predicting Complications in Type 2 Diabetes Patients

Objective: The JJ risk engine, developed in 2012, is an algorithm that predicts the 19 incidence of diabetes complications that may develop after 5 to 10 years. However, 20 studies validating the JJ risk engine have not yet been reported;we aimed to verify the 21 JJ risk engine. In 2013, we conducted a retrospective survey using medical records of 22 484 patients with type 2 diabetes. The observed value of coronary heart disease (CHD) 23 complicationsafter 5 years and the predicted value by the JJ risk engine as of 2013 were 24 compared and verified using the discrimination and calibration values.25Results: Among the total cases analyzed, the C-statistic was 0.588,and the calibration 26 was p <0.05; thus, the JJ risk engine could not correctly predict the risk of CHD. However, 27 in the group expected to have a low frequency of hypoglycemia, the C-statistic was 0.646; 28 the predictability of the JJ risk engine was relatively accurate. Further, in the group of 29 patients using high-dose insulin, segregated from the group expected to have a high 30 frequency of hypoglycemia, the C-statistic was 0.866; thus, the JJ risk engine correctly 31 predicted the risk of CHD. Hence, the above results were not consistent in trend.

Evaluation of cardiovascular risk in adults with type 1 diabetes: poor concordance between the 2019 ESC risk classification and 10-year cardiovascular risk prediction according to the Steno Type 1 Risk Engine

Background Patients with type 1 diabetes (T1D) have higher mortality risk compared to the general population; this is largely due to increased rates of cardiovascular disease (CVD). As accurate CVD risk stratification is essential for an appropriate preventive strategy, we aimed to evaluate the concordance between 2019 European Society of Cardiology (ESC) CVD risk classification and the 10-year CVD risk prediction according to the Steno Type 1 Risk Engine (ST1RE) in adults with T1D. Methods A cohort of 575 adults with T1D (272F/303M, mean age 36 ± 12 years) were studied. Patients were stratified in different CVD risk categories according to ESC criteria and the 10-year CVD risk prediction was estimated with ST1RE within each category. Results Men had higher BMI, WC, SBP than women, while no difference was found in HbA1c levels between genders. According to the ESC classification, 92.5% of patients aged < 35 years and 100% of patients ≥ 35 years were at very high/high risk. Conversely, using ST1RE to predict the 10-year CVD risk within each ESC category, among patients at very high risk according to ESC, almost all (99%) had a moderate CVD risk according to ST1RE if age < 35 years; among patients aged ≥35 years, the majority (59.1%) was at moderate risk and only 12% had a predicted very high risk by ST1RE. The presence of target organ damage or three o more CV risk factors, or early onset T1D of long duration (> 20 years) alone identified few patients (< 30%) among those aged ≥35 years, who were at very high risk according to ESC, in whom this condition was confirmed by ST1RE; conversely, the coexistence of two or more of these criteria identified about half of the patients at high/very high risk also according to this predicting algorithm. When only patients aged ≥ 50 years were considered, there was greater concordance between ESC classification and ST1RE prediction, since as many as 78% of those at high/very high risk according to ESC were confirmed as such also by ST1RE. Conclusions Using ESC criteria, a large proportion (45%) of T1D patients without CVD are classified at very high CVD risk; however, among them, none of those < 35 years and only 12% of those ≥ 35 years could be confirmed at very high CVD risk by the ST1RE predicting algorithm. More studies are needed to characterize the clinical and metabolic features of T1D patients that identify those at very high CVD risk, in whom a very aggressive cardioprotective treatment would be justified.