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All Journal Proceeding International Conference on Information Technology and Business Annual Research Seminar Journal of Information Technology and Computer Science Indonesian Journal of Electrical Engineering and Computer Science
Ermatita Ermatita
Sriwijaya University
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Implementation of Autoregressive Integrated Moving Average Model to Forecast Raw Material Stock in The Digital Printing Industry Dwi Asa Verano; Husnawati Husnawati; Ermatita Ermatita
Journal of Information Technology and Computer Science Vol. 5 No. 1: April 2020
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3173.78 KB) | DOI: 10.25126/jitecs.202051117

Abstract

The technology used in the printing industry is currently growing rapidly. Generally, the digital printing industry uses raw materials in the form of paper production. The use of paper material with large volumes is clear badly in need of purchasing large quantities of paper stock as well. The purchase of paper stocks with a constant amount at the beginning of each month for various types of paper causes a buildup or lack of material stock standard on certain types of paper. During this time the purchase and ordering of raw materials only based on the estimates or predictions of the owner. In this paper proposed forecasting will be carried out in the digital printing industry by applying the ARIMA model for each type of raw material paper with the Palembang F18 digital printing case study. The ARIMA modeling applied will produce different parameters for each materials paper type so as to produce forecasting with the Akaike Information Criterion (AIC) value averages 13.0294%.
ANALISIS DAN PERANCANGAN SISTEM PENDUKUNG KEPUTUSAN PENENTUAN LOGAM BERAT PADA TANAMAN KELAPA SAWIT Ermatita Ermatita; Yudha Pratomo; Dedik Budianta
Annual Research Seminar (ARS) Vol 3, No 1 (2017): ARS 2017
Publisher : Annual Research Seminar (ARS)

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Abstract

Tanaman kelapa sawit merupakan salah satu tanaman yang banyak diproduksi menjadi berbagai macam bentuk makanan.  Sebagai bahan makanan, tanaman kelapa sawit  perlu terhindar dari logam berat.  Adanya logam berat dari tanah yang ditanami kelapa sawit dapat mengakibatkan tanaman kelapa sawit juga mengandung logam berat. Untuk menanggulangi adanya logam berat pada tanaman kelapa sawit perlu menganalisis dan menentukan kandungan logam berat, berdasarkan sifat kimia tanah. Penelitian ini menganalisis dan merancang sistem pendukung keputusan untuk menanggulangi kandungan logam berat pada tanaman kelapa sawit
PERANCANGAN SISTEM PAKAR UNTUK PEMILIHAN TANAMAN YANG BAIK BERDASARKAN SIFAT KIMIA TANAH MENGGUNAKAN METODE CASE-BASE REASONING Nurul Mufliha Eka Putri; Ermatita Ermatita
Annual Research Seminar (ARS) Vol 3, No 1 (2017): ARS 2017
Publisher : Annual Research Seminar (ARS)

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Abstract

Tanah merupakan bagian terpenting dalam kehidupn mahkluk hidup. Tanah memiliki 3 sifat yaitu sifat fisik, sifat kimia dan sifat biologi. Sifat yang terkandung dalam tanah sangat berpengaruh dalam pertumbuhan tanaman yang akan ditanam. Pemilihan tanaman yang tepat sangat berpengaruh terhadap hasil panen yang didapat. Salah satu metode dalam sistem pakar adalah Case Based Reasoning (CBR). CBR akan membantu menentukan pemilihan tanaman yang tepat berdasarkan kandungan kimia yang terkandung dalam tanah sehingga dapat menghasilkan hasil panen yang baik.
Efficient mobilenet architecture as image recognition on mobile and embedded devices Barlian Khasoggi; Ermatita Ermatita; Samsuryadi Samsuryadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 1: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i1.pp389-394

Abstract

The introduction of a modern image recognition that has millions of parameters and requires a lot of training data as well as high computing power that is hungry for energy consumption so it becomes inefficient in everyday use. Machine Learning has changed the computing paradigm, from complex calculations that require high computational power to environmentally friendly technologies that can efficiently meet daily needs. To get the best training model, many studies use large numbers of datasets. However, the complexity of large datasets requires large devices and requires high computing power. Therefore large computational resources do not have high flexibility towards the tendency of human interaction which prioritizes the efficiency and effectiveness of computer vision. This study uses the Convolutional Neural Networks (CNN) method with MobileNet architecture for image recognition on mobile devices and embedded devices with limited resources with ARM-based CPUs and works with a moderate amount of training data (thousands of labeled images). As a result, the MobileNet v1 architecture on the ms8pro device can classify the caltech101 dataset with an accuracy rate 92.4% and 2.1 Watt power draw. With the level of accuracy and efficiency of the resources used, it is expected that MobileNet's architecture can change the machine learning paradigm so that it has a high degree of flexibility towards the tendency of human interaction that prioritizes the efficiency and effectiveness of computer vision.
ASSOCIATION RULE METHOD FOR INFORMATION SYSTEM EPIDEMIC DENGUE MAPPING BASED ASSOCIATION OF RISK FACTORS IN PALEMBANG Ermatita Ermatita; Suci Destriatania
Prosiding International conference on Information Technology and Business (ICITB) 2015: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 1
Publisher : Proceeding International Conference on Information Technology and Business

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Abstract

Endemic diseases dangerous such as dengue fever must be handling seriously  for the risk minimize by the disease. Dengue Hemorrhagic Fever (DHF) is  disease  has not been found vaccine or cure is powerful. It is  necessary  treatment to prevent the occurrence of dengue fever, especially when it came to the incidence of dengue fever endemic in certain areas by doing Epidemiologist dengue fever. Epidemiology is identification of risk factors for DHF to find level of area  risk. Risk factors of hemorrhagic fever endemic must be identified  to prevent the occurrence of dengue fever. Identifying risk factors and  risk factors association  can  potential increase  the occurrence of dengue fever. This study developed  mapping information system Dengue epidemic through Association rule method of data mining. The information generated in the map of epidemic DHF level based association of potential risk factors that cause hemorrhagic fever endemic. Analysis with the Association Rule to determine level of  DHF epidemic area based data reporting system. KEY WORDS: information system mapping, data mining, Association Rule, endemic, Dengue Hemorrhagic Fever (DHF).
Co-Authors Barlian Khasoggi Dedik Budianta Dwi Asa Verano Husnawati Husnawati Nurul Mufliha Eka Putri Samsuryadi Samsuryadi Suci Destriatania Yudha Pratomo