Petra Christian University

About: Petra Christian University is a education organization based out in Surabaya, Indonesia. It is known for research contribution in the topics: Population & Customer satisfaction. The organization has 2412 authors who have published 2893 publications receiving 10429 citations.

Peramalan stok barang untuk membantu pengambilan keputusan pembelian barang pada toko bangunan xyz dengan metode arima

Abstract: Toko bangunan ”XYZ”, merupakan sebuah usaha dagang yang bergerak di bidang jual beli bahan bangunan. Dimana bahan bangunan yang dijual terdiri atas berbagai jenis barang dengan harga dan merek yang berbeda-beda. Hal ini menyebabkan informasi akan ketersediaan stok barang sesuai dengan penjualan menjadi sangatlah penting, mengingat banyak barang yang berharga cukup mahal dan membutuhkan tempat penyimpanan yang cukup besar. Sehingga bila terjadi salah perhitungan akan stok barang, maka akan terjadi kehilangan kesempatan untuk memenuhi penjualan (lost sales) karena kekurangan stok barang ataupun akan terjadi investasi yang terhenti karena barang tertimbun di gudang tanpa bisa terjual dengan lancar. Berdasar permasalahan di atas, dibutuhkan sebuah sistem peramalan guna menentukan persediaan barang yang ada sesuai dengan permintaan (demand). Guna memprediksi jumlah persediaan stok barang akan digunakan metode ARIMA yang akan dihitung dengan menggunakan sebuah aplikasi yang berjalan pada VB.Net dan SQL Server 2005. Hasil perhitungan nantinya akan ditampilkan dalam bentuk tabel dan grafik yang nanti akan divalidasi dan verifikasi secara manual berdasar data penjualan lampau yang telah terjadi. Dari penelitian dan pengujian yang dilakukan didapatkan bahwa sistem yang dibuat telah mampu menentukan model peramalan ARIMA yang tepat pada barang di toko bahan bangunan “XYZ” ini.

Sequence Matching Analysis for Curriculum Development

Abstract: Many organizations apply information technologies to support their business processes. Using the information technologies, the actual events are recorded and utilized to conform with predefined model. Conformance checking is an approach to measure the fitness and appropriateness between process model and actual events. However, when there are multiple events with the same timestamp, the traditional approach unfit to result such measures. This study attempts to develop a sequence matching analysis. Considering conformance checking as the basis of this approach, this proposed approach utilizes the current control flow technique in process mining domain. A case study in the field of educational process has been conducted. This study also proposes a curriculum analysis framework to test the proposed approach. By considering the learning sequence of students, it results some measurements for curriculum development. Finally, the result of the proposed approach has been verified by relevant instructors for further development.

Assessing the shrinkage and creep of alkali-activated concrete binders

Abstract: Shrinkage and creep are important long-term properties of concrete especially as a structural material. In order to be used as a structural material in applications, alkali-activated concrete should also possess good shrinkage and creep properties. An understanding of the importance of shrinkage and creep in concrete is presented first. The chapter then focuses more on those properties in alkali-activated concrete (AAC). Although the discussion concerns chiefly concrete, complete understanding is also developed based on studies of shrinkage and creep in mortars or pastes. Discussions on factors affecting shrinkage and creep are also introduced, followed by laboratory work and standard tests related to these long-term properties. An example of predicting shrinkage and creep of AAC using available prediction methods for OPC concrete is also presented.

Multivariate inputs for electrical load forecasting on hybrid neuro-fuzzy and fuzzy C-Means forecaster

Abstract: Multivariate inputs play important role in system with many dependent variables. By using some different inputs as input in neuro-fuzzy networks, complex nonlinear model can be modeled and also be forecasted with better results. This paper describes a neuro-fuzzy approach with additional fuzzy C-means clustering method before the input entering the networks. Afterwards, the network can be used to efficiently forecast electrical load competition data using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neuro-fuzzy network. The training algorithm is efficient in the sense that it can bring the performance index of the network, such as the sum squared error (SSE), down to the desired error goal much faster than the simple Levenberg-Marquardt algorithm (LMA).

Carbon Management in an Emissions-Intensive Industry in a Developing Economy: Cement Manufacturing in Indonesia

Abstract: Around 600 Mt carbon dioxide equivalents (CO2e) of anthropogenic greenhouse gases (GHG) emission originates from energy production and consumption in Indonesia annually. Of this output, 40 Mt CO2e comes from cement production. This makes the cement industry a key sector to target in Indonesia’s quest to reduce its emissions by 26% by 2020. Substantial opportunities exist for the industry to reduce emissions, mainly through clinker substitution, alternative fuels, and the modernization of kiln technologies. However, most of these abatement options are capital intensive and considered as noncore business. Due to this, the private sector is unlikely to voluntarily invest in emission reduction unless it saves money, improves revenue, enhances the strategic position of the firm, or unless governments provide incentives or force adoption through regulatory and policy controls. In this study, we review the profile of the Indonesian cement industry and assess the carbon management and climate policy actions available to reduce emissions. The case highlights opportunities for improved carbon management in emission-intensive industries in developing countries.