The tourism sector become a promising country income. This is because its nature which is not easily run out if managed properly. This sector can be a great support especially for developing countries such as Indonesia, because usually developing countries income only rely on natural and human resources income. Museums are one of the tourism sectors that have not been explored properly. Museums in Indonesia are still few that using the latest technology such as smartphone application, wifi, bluetooth, RFID. So, that it makes the museum a less attractive place to visit, especially for young generation. This research tries to create an interactive broadcasting museum information by utilizing applications on smartphones, indoor positioning systems, and bluetooth low energy beacons to provide information to museum visitors about artifacts around museum visitor location. As a pilot project a testing was conducted at the Trowulan Museum, Mojokerto, East Java, Indonesia, which is a museum that exhibits the relics of the Majapahit kingdom. The application has been able to provide interactive information about artifacts around the visitor's location so as to create an interesting museum to visit
Dengue Fever is one of the viral diseases of the tropics that are easily spread in high density and humid area such as in Surabaya. Many researchers in various expertise have studied this disease. Some of them use statistical and machine learning approach to predict the outbreak of the disease, so that the government can prevent that incident. In this paper we use the geographically weighted regression for predicting the dengue fever outbreak in Surabaya. The geographically weighted regression has superiority in estimating the coefficient of the explanatory variables locally. So that, we can put more attention into the region with has high estimates coefficient parameters. Here, we look at the locally estimates of the dengue fever infected in the year 2016, 2017, population density and poverty percentage for predicting the dengue fever outbreak in the year 2018. In this study, the pattern of the predicted model can follow the pattern of the true dataset.
Abstract:In the classical economic ordering quantity (EOQ) model, it was assumed that products have no expire date. However, in practices, some products such as food and milk, have exact expire date and some vendors tries to reduce their lost by introducing quantity unit discount. In this paper, we develop multi items inventory models by considering product expire date and quantity unit discount for stochastic demand environment. Due to complexity of the models, simulation and Genetic Algorithm are used to solve the models. We then verify the models using a numerical example and sensitivity analysis. The results from sensitivity analysis show that inventory cost and backorder cost have significant effect to ordering time.Keywords: Expire date, unit discount, multi item, sthocastic demand. PendahuluanPengendalian persediaan bahan baku pada industri makanan atau produk-produk lain yang berkurang nilainya dengan berjalannya waktu serta memiliki waktu kadaluwarsa merupakan salah satu hal yang sangat penting untuk dilakukan. Hal ini dikarenakan perusahaan harus berusaha memenuhi permintaan konsumen yang bersifat tidak pasti dengan menggunakan bahan baku bersifat perishable (dapat mengalami penurunan nilai setelah waktu tertentu). Persediaan bahan baku yang berlebih pada kondisi ini dapat menimbulkan biaya kadaluwarsa yang besar. Kekurangan bahan baku dapat menimbulkan kerugian terjadinya kehilangan penjualan. Biaya yang ditimbulkan pada pengendalian persediaan (Elsayed et al. [3]) antara lain biaya pemesanan, biaya pembelian, biaya penyimpanan, dan biaya kekurangan.Penelitian mengenai pengendalian persediaan untuk barang yang dapat kadaluwarsa telah dilakukan secara intensif oleh beberapa peneliti. Hsu [4] menyusun satu model persediaan untuk barang-barang yang berkurang kuantitas dan kualitas dengan cepat dengan berjalannya waktu hingga kemudian mencapai waktu kadaluwarsa. Barang-barang jenis ini diantaranya adalah bunga, buah-buahan, dan produk bahan makanan lainnya. Penelitian berikutnya berkembang dengan memperhatikan diskon harga, hal ini disebabkan oleh perlunya pemberian diskon harga untuk meningkatkan penjualan bagi barang yang akan kadaluwarsa. Dalam penelitiannya, Bramorski [1] menemukan bahwa 98% respondennya selalu memperhatikan waktu kadaluwarsa 1 Fakultas Teknologi Industri, Program Studi Teknik Industri, Universitas Kristen Petra. Jl. Siwalankerto 121-131. Surabaya 60238. Email: tanti@petra.ac.id * Penulis korespondensi dari produk susu atau produk-produk lainnya yang memiliki waktu kadaluwarsa dan mereka melakukan pembelian dengan sistem LIFO (Last In First Out). Bramorski [1] juga menemukan bahwa dengan meningkatkan nilai diskon, responden memiki keinginan lebih untuk membeli produk yang lebih dekat dengan waktu kadaluwarsa. Oleh sebab itu faktor potongan harga (diskon) perlu dipertimbangkan dalam penyusunan satu kebijakan.Beberapa bahan baku memiliki potongan harga atau diskon ketika dibeli dalam jumlah tertentu. Jumlah pembelian bahan baku yang semakin banyak dapat menimbulkan diskon yang semakin ...
Generally, a museum has many locations and artifacts collection that display for visitors. Museum manager often have difficulty in obtaining information on visitors behavior such as, is there are particular locations/artifacts in the museum that are frequently/rarely visit by museum visitors, how long visitors spend their time in particular locations/artifacts, etc. The purpose of this study is try to build a suitable system in order to improve knowledge about the behavior of museum visitors by identifying the position of visitors in the museum. This study uses Bluetooth Low Energy (BLE) Beacon that place around the museum. The visitor mobile phone will detect BLE beacon signal, then the mobile phone application will calculated the visitor's mobile phone position using the signal strength from the BLE beacons that are detected. The application then sends it to the computer server to display it in as museum visitor heat map. From this information, the museum manager could find out the visitors behavior movement and know which areas/artifacts that frequently/rarely visit by museum visitors. According to distance error testing which compare real location and position of the calculation, it is show that the average of distance error is around 140 cm. So, it can be concluded that the information obtained is sufficient enough to represent the position of museum visitors.
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