Kasus positif covid-19 di Jakarta menunjukkan tren yang terus meningkat. Beberapa studi menyatakan bahwa salah satu faktor yang memengaruhi penyebaran penyakit menular (covid-19) adalah parameter meteorologi. Penelitian ini bertujuan untuk mengetahui pengaruh parameter meteorologi terhadap pertambahan kasus covid-19 di DKI Jakarta. Variabel respon pada penelitian ini adalah pertambahan kasus covid-19 di DKI Jakarta, sementara variabel prediktornya adalah data parameter meteorologi harian dari Badan Meteorologi Klimatologi dan Geofisika yang meliputi kelembapan rata-rata, temperatur rata-rata, temperatur maksimum, temperatur minimum, dan lamanya penyinaran matahari. Metode Generalized Additive Model (GAM) digunakan dalam penelitian ini karena varibel respon berdistribusi tidak normal. Penelitian ini berkesimpulan bahwa variabel temperatur dan kelembapan udara berpengaruh negatif terhadap fluktuasi kasus covid-19 di Jakarta. Semakin meningkat temperatur (rata-rata dan maksimum) maupun kelembapan udara maka semakin sedikit pertambahan kasus covid-19 di Jakarta, begitu juga sebaliknya. Hasil penelitian ini diharapkan dapat digunakan untuk menekan pertambahan kasus covid-19 di Indonesia.
This study uses Systematic Literature Review (SLR) to understand about research trends, methods, and data used in decision-making with GPS data. After reviewing 27 chosen journals and proceedings, this study concludes that there are several methods used to decide with GPS data such as decision tree, random forest, neural network, and support vector machine (SVM). Health, environment, Transportation, and agriculture are several fields of business that used GPS data to make a decision. The data analyzed consist of position, time, speed, track, and distance. Based on SLR result, we propose a method to validate the data collection process of a survey using three methods (SVM, decision tree and random forest) will be used to analyze the position, time, track and personal data of the surveyor.
Pertambahan kasus covid-19 di Jakarta dan Jawa Timur menjunjukkan tren yang saling berkesinambungan. Mobilitas penduduk yang tinggi merupakan salah satu faktor yang mempengaruhi penyebaran penyakit di berbagai wilayah. Charu (2017) melakukan studi mengenai penyebaran penyakit influenza di Amerika Serikat selama 2002-2010 dengan hasil bahwa setiap epidemi dapat dikaitkan dengan peristiwa penularan jarak jauh yang akan memicu transmisi selanjutnya. Penelitian ini bertujuan untuk mengetahui efek dinamika pertambahan kasus covid-19 di Jakarta dan Jawa Timur. Variabel yang digunakan pada penelitian ini adalah data pertambahan kasus covid-19 di Jakarta dan Jawa Timur dari @kawalcovid-19. Metode Vector Autoregressive (VAR) dengan Impulse Response Function (IRF) dan Variance Decomposition (VDC) dipilih karena mampu menjelaskan respon yang terjadi di suatu wilayah terhadap shock di wilayah itu sendiri dan wilayah lain. Penelitian ini membuktikan adanya pengaruh positif dan signifikan pertambahan kasus covid-19 di Jakarta terhadap pertambahan kasus covid-19 di Jawa Timur.
GPS data is an interesting thing to research. Various studies have been conducted to find information based on GPS data. In this paper, we propose a novel model for determining the stopping point on a GPS data for cases of human movement without using transportation modes. Further, this information can be used to determines human behavior such as fraud and favorite spot. The GPS data used in this research is the travel data of the SUSENAS survey officers at the time of updating the census block for 27 households. Density Based Spatial Clustering Of Application With Noise (DBSCAN) And Gaussian Mixture Model (GMM) Clustering model is used to create the model. The model made using a flowchart and applied to the GPS data that has been collected. The results of the developed model show that the stopping points generated using the DBSCAN cluster model are better than the stopping points generated using the GMM cluster model. Furthermore, the results of this study will be used to make model of surveyor fraud.
The development of IoT (internet of things) technology encourages increased use of smart devices and their supporting applications. The use of smart devices and applications that are growing rapidly and large will certainly produce large amounts of data and information. The next challenge is how to use the data and information generated to help people solve various problems that exist and improve the quality of human life. Data is analyzed into information, knowledge and in the end it will be used to make decisions. With all the features offered by IoT, decision making is expected to be easier and more precise, according to the expected goals. This paper will discuss the IoT architecture and decision-making framework with IoT. Furthermore, it provides an overview of the models, technology and development of the decision making process with IoT based on previously published papers (journals and conferences). The main purpose of making this paper is to provide insights for researchers about decision making with IoT.
The level of pollution in Indonesia is in the top 10 worst in the world. Judging from the Air Quality Index (IKU), there are 9 provinces that have KPI values below the expected target. This paper aims to perform IKU modelling using the population density variable as a predictor variable. Modelling using linear regression in the parametric method cannot be used because the model residuals are not normally distributed, so a nonparametric smoothing splines approach is carried out. However, the presence of outliers in the smoothing splines residual model causes the residuals of the model to be too large so that it affects the prediction accuracy, so the smoothing splines quantile regression is used in the IKU modelling. Apart from using the median (quantile τ = 0.5), the quantiles of 0.2 were also used; 0.4; 0.6; and 0.8 to generate models at various quantiles. The results of the analysis using the R package quantreg Software prove that the smoothing splines (median) quantile regression model is more robust against the presence of outliers seen from the lower RMSE value than the smoothing splines regression model (mean). In addition, it is concluded that there are 5 provinces that are below the quantile 0.2, which means that the IKU level is very low or there is very high pollution based on the level of population density. Likewise, there are 3 provinces with KPI values above the quantile of 0.8, which means they have very high IKU levels or areas with low levels of pollution.
Perkembangan perbankan syariah di Indonesia sangat pesat beberapa tahun belakangan ini. Namun, market share perbankan syariah masih jauh di bawah harapan. Kajian-kajian teori sebelumnya menyatakan bahwa lingkungan persaingan antar bank, dalam hal ini bank konvensional dan bank syariah, sangat mempengaruhi kinerja bank syariah. Penelitian ini ingin mengetahui bagaimana pengaruh kebijakan bank konvensional yang digambarkan dengan tingkat suku bunga (SB) terhadap keuntungan bank syariah yang digambarkan dengan Return on Assets (ROA). Metode analisis yang digunakan adalah Vector Autoregressive (VAR) dengan tambahan analisis Impulse Response Function (IRF) dan Variance Decomposition Analysis (VDC). Hasil analisis menggunakan VAR (1) menyatakan bahwa terdapat pengaruh negatif signifikan pada ) jika terjadi shock pada tingkat suku bunga tabungan bank konvensional (SB). Kondisi ini sejalan dengan hasil penelitian Haron dan Ahmad (2000); Haron (2004), serta Zainol dan Kassim (2012) yang mengemukakan bahwa apabila tingkat suku bunga tabungan bank konvensional meningkat, maka nasabah bank syariah akan beralih ke bank kovensional yang dianggap memberikan keuntungan lebih besar, sehingga keuntungan bank syariah mengalami penurunan. Hasil ini menunjukkan bahwa tidak semua konsumen bank syariah merupakan konsumen yang loyalis, perbankan syariah dituntut bertindak rasional, yaitu dengan cara menetapkan tingkat bagi hasil yang kompetitif terhadap tingkat suku bunga bank konvensional.
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