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The increasing demand for Indonesian workers in Taiwan has an impact on caregiver problems which can be triggered by the burden of caring for the elderly. Therefore, the aim of this study is to identify the characteristics of caregivers who are resilient to burdens based on Indonesian female caregivers who work in Taiwan data to be a guide for selecting prospective caregivers. The process includes analyzing the personal characteristics that have the most influence on the burden using multiple regression and then clustering caregiver data using K-Means with the Elbow Method and Silhouette Index. Then, segmentation in each cluster based on a comparison of the average values. The results of clustering accuracy on dimensions (PRDG) and modified dimensions (S+PRDG) were compared and the smallest error cluster was in case 4 in the S+PRDG dimension with the Elbow Method of 3.6%. Based on segmentation on that dimension, cluster 2 is a resilient caregiver cluster. Then the results of the multiple regression analysis (Number of Children, Education and Work Location) were studied further for each caregiver in cluster 2 and the conclusions are, their average number of children is 1, final education is in junior high school and their work location is in the capital of Taiwan.

Sebuah hunian merupakan kebutuhan yang paling penting bagi seorang mahasiswa, khususnya mahasiswa rantau yang berasal dari luar kota. Dalam penentuannya biasanya mempertimbangkan banyak kriteria. Misalnya, berapa besaran biaya yang harus dikeluarkan tiap bulanya, jarak hunian tersebut dari kampus, maupun fasilitas apa saja yang akan mereka dapatkan. Berbagai hal tersebut yang terkadang membuat mahasiswa menjadi bingung, sehingga dengan adanya suatu sistem pendukung keputusan akan lebih mempermudah mereka dalam mengambil keputusan. Pada artikel ini, penulis mencontohkan empat variabel pendukung keputusan dengan melibatkan dua belas contoh hunian kost dalam pengambilan keputusan. Metode yang digunakan dalam penelitian ini adalah Simple Additive Weighting (SAW). Penelitian ini dimulai dari tahapan penentuan kriteria, penentuan rating kecocokan, pembuatan matriks keputusan, normalisasi matriks dan diakhiri oleh proses perangkingan, serta menampilkan hasil akhir berupa rangking dari nilai preferensi setiap alternatif. Sehingga luaran dari penelitian ini merupakan suatu hasil perangkingan dan prototipe User Interface.

Dengue fever is still a crucial public health problem in Indonesia, with the highest fatality rate (CFR) is 1.01% in East Java, Malang Regency. One of the solutions to control the death rate and cases is to forecast the cases number. This study proposed ensemble forecasting that build from several penalized regressions. Penalized regressions are able to overcome linear regression analysis’ shortcomings by using penalty values, that will affect regression’s coefficient, resulting on regression model with a slight bias in order to reduce parameter estimations and prediction values' variances. Penalized regressions are evaluated and built as ensemble forecasting method to minimize the shortcomings of other existing model, so it could produce more accurate values comparing to single penalized regression model. The result showed that the ensemble model `consists of smoothly clipped absolute deviation (SCAD) and Elastic-net is sufficient to capture data patterns with root mean squared error (RMSE) 6.38.

This article presents a system for predicting a human's risk of contracting infectious diseases based on their personal attributes and environments (region, specific location features and climate contexts). This system is also intended to help human experts in the domain (i.e. epidemiologists) to represent their knowledge and ease their jobs related to personalized infectious disease risk prediction. The system consists of a knowledge representation to encode epidemiological knowledge about infectious disease risk, and an algorithm that auto-converts the encoded knowledge into a model that predicts the risk as a probability. The knowledge representation, Infectious Disease Risk (IDR), consists of an ontology and rules to represent the knowledge structure and its quantification in a way that allows auto-generation to a prediction model, Bayesian Network (BN). The algorithm, BN-Builder, converts the IDR knowledge-base to an infectious disease risk BN, including populating the basis of predictive reasoning from the IDR rules. A user interface facilitates encoding of epidemiological knowledge into the IDR knowledge-base. The system's output, personalized infectious disease risk prediction, is validated for three disease-country contexts: Dengue Fever and Tuberculosis in Indonesia, and Cholera in India. The personalized infectious disease risks are reliable (p values > 0.05) for each population parameter. The personalized infectious disease risk probability can be reliably predicted using this system. Inclusion of more granularity on contexts in this domain will be considered in further development of this system.

This article contains a literature review to seek knowledge representation for infectious disease risk prediction system. The knowledge representation should be able to encode knowledge related to infectious diseases and usable by experts. 188 articles are collected through several constraints. From these articles, 14 knowledge representations are gathered. Ontology comes out as the most used knowledge representation related to disease, followed by fuzzy and rules. This leads to the next step of the research, on how to encode knowledge using these representations.

E-Goverment atau situs pemerintahan saat ini mulai menjadi trend baru dan berkembang pesat di dunia pemerintahan. Hal ini dikarenakan situs pemerintahan (website) dapat berperan sebagai media informasi, media interaksi dengan masyarakat, menjadi tolak ukur bagaimana aktif atau tidaknya kegiatan pemerintahan, menjadi tempat masyarakat menyampaikan aspirasinya. Dengan website warga juga dapat informasi terpercaya karena website dikelola langsung dari kantor pemerintahannya. Kelurahan Gebang Putih sebagai kelurahan yang berbatasan langsung dengan ITS merupakan salah satu kelurahan yang menjadi percontohan dalam beberapa aspek. Meskipun kelurahan tersebut telah menjadi percontohan, akan tetapi penyebaran data-data serta informasi sebagian besar masih dilakukan dengan manual. Hal ini menjadi kurang efektif dan memakan banyak waktu. Kepala Kelurahan juga mengeluhkan bahwa tidak ada sarana yang memudahkan komunikasi yang efektif dengan warga sehingga banyak informasi yang berhenti tidak sampai ke tujuan, begitu juga dengan informasi yang ada di warga juga tidak bisa dnegan cepat sampai ke kelurahan. Untuk itu, artikel ini berfokus untuk pengembangan website kelurahan yang dapat digunakan sebagai sarana untuk memudahkan komunikasi dalam berbagai hal antara warga dengan pihak Kelurahan Gebang Putih. Hal ini ditujukan untuk meningkatkan efektifitas dan efisiensi perangkat kelurahan dalam melayani masyarakat.

The stock market is one of the investment choices that always have traction from time to time. Aside from being a means of corporate funding, investing in the stock market can benefit investors. Investing also has a higher risk because the pattern of stock prices is volatile, which is caused by internal and external factors. One external factor that affects stock prices is the macro-economic, where these factors are events that occur in a country where one of the economic sectors affected is stock prices. Investors often feel confused about the right time in decisions making related to buying or selling stock. One way to look at how the prospect of stock prices is a stock price forecasting activity. For this study, we will be making use of the recurrent neural network (RNN) to forecast stock prices for the next periods. This research involves two variables: the closing stock price and the rupiah exchange rate against the dollar for the daily period. We achieve a MAPE value of 1.546% for RNN model without the variable foreign exchange rate and 1.558% for the RNN model that uses the foreign exchange rate against the dollar.

The Moslem kids clothes demand in Habibah Busana are likely increase at certain times, especially near the Eid-holidays. that total demands in each year is varies and always increase when near the Eid holidays. The demand always increases when near Eid-holidays but decrease in next month. It's repeat every year but unfortunately it occur in different time every year. it means that Eid holidays occur in different time every year. It make the company in uncertainty condition. This research compare Arima method which make forecast in univariate data and Arimax as multivariate method which include independent variables such as different time of Eid holidays every year. The result show that Arimax method is better than Arima method in accuracy level of training, testing, and next time forecasting processes. There are minimum fourteen variables have to include in Arimax model in order to make accuracy level is not decrease.

Infectious diseases are a major cause of human morbidity, but most are avoidable. An accurate and personalized risk prediction is expected to alert people to the risk of getting exposed to infectious diseases. However, as data and knowledge in the epidemiology and infectious diseases field becomes available, an updateable risk prediction model is needed. The objectives of this article are (1) to describe the mechanisms for generating a Bayesian Network (BN), as risk prediction model, from a knowledge-base, and (2) to examine the accuracy of the prediction result. The research in this paper started by encoding declarative knowledge from the Atlas of Human Infectious Diseases into an Infectious Disease Risk Ontology. Automatic generation of a BN from this knowledge uses two tools (1) a Rule Converter generates a BN structure from the ontology (2) a Joint & Marginal Probability Supplier tool populates the BN with probabilities. These tools allow the BN to be recreated automatically whenever knowledge and data changes. In a runtime phase, a third tool, the Context Collector, captures facts given by the client and consequent environmental context. This paper introduces these tools and evaluates the effectiveness of the resulting BN for a single infectious disease, Anthrax. We have compared conditional probabilities predicted by our BN against incidence estimated from real patient visit records. Experiments explored the role of different context data in prediction accuracy. The results suggest that building a BN from an ontology is feasible. The experiments also show that more context results in better risk prediction.

Pelayanan publik merupakan aktivitas yang dibutuhkan oleh semua kalangan masyarakat di suatu kota. Sebagai negara yang sedang berkembang, Indonesia, mengalami peningkatan yang signifikan di bidang transportasi umum. Salah satunya dapat dilihat perbaikan sarana dan prasarana dari Terminal Bus Purabaya. Peningkatan ini hendaknya didukung dengan proses monitoring yang intensif demi tercapainya tujuan awal perbaikan sarana dan prasarana Terminal Bus Purabaya. Salah satu proses evaluasi yang dilakukan dalam paper ini adalah menilik kepuasan pengunjung dari Terminal Bus Purabaya. Makalah ini akan menyajikan sebuah studi awal mengenai kepuasan pengunjung yang diukur dari empat perspektif menggunakan statistika deskriptif. Data yang digunakan dalam penelitian ini dikumpulkan menggunakan kuesioner yang merupakan metode pengumpulan data yang memiliki reliabilitas yang rendah. Oleh karena itu, terdapat beberapa proses untuk meningkatkan reliabilitas dari data dengan cara menghilangkan data yang inkonsisten. Kuesioner terdiri dari dua tipe data yaitu skala Likert (ordinal) sebagai ukuran utama dan skala gabungan antara nominal, interval dan ratio untuk menggambarkan data demografi responden. Selain analisis reliabilitas, preproses data juga menggunakan uji validitas dengan dua cara yaitu manual dan komputerisasi. Hasil yang ditunjukkan dalam riset ini bersifat eksplanatori atau penggalian informasi pada faktor manakah pengunjung menunjukkan kepuasan yang tinggi dan rendah.

The World Health Organization (WHO) reported that deaths caused by cancer in the world these last four years has increased significantly. The data also reflected in the increase in breast cancer cases. In Indonesia, two cases also the highest cases of adult female deaths. Based on Hospital Information System, the number of breast cancer patients either inpatient or outpatient care amounted to 28.7%. This fact revealed more than 40% of all cancers can be prevented with early detection cancer. Role of Information Technology can implemented by data mining techniques to shorten the diagnosing time, accuracy and selection of factors early detection of breast cancer. Stepwise binary logistic regression method has the advantage to add and subtract the independent variables in accordance with level of significance of the model. Based on the analysis of weighting method, the highest four variables that should be more aware is the area of cancer (area), fineness (smoothness), the number of dots (concave points) or the nucleus of cancer and grayish level of cancer (texture). So the accuracy and processing speed of diagnosis of the severity of breast cancer can be improved through this method.