Asro Nasiri
Magister Teknik Informatika, Universitas AMIKOM Yogyakarta

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ANALISIS ARCHITECTURE TEKNOLOGI MENGGUNAKAN SABSA UNTUK MENINGKATKAN KEAMANAN DI RUMAH SAKIT QUEEN LATIFA Alvian Trias Kurniawan Alvian; Bambang Soedijono WA; Asro Nasiri
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 3 No. 2 (2022): Desember 2022
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v3i2.65

Abstract

Widespread use of Internet and increasing reliance on public packet-switched networks for e-commerce, telecommuting etc., has resulted in an increase in malicious attacks on security & malware in enterprises. Businesses in the healthcare sector use health information networks and information technology that work together to improve patient safety, increase treatment effectiveness, and increase efficiency. The purpose of this study is to address security and privacy concerns with integrated healthcare or medical records businesses. The security system implemented in several hospitals requires periodic improvements and reviews for the comfort and safety of the hospital and the patient. The use of the SABSA framework in this study focuses on IT security that can be applied in various industrial and organizational sectors as an EISA development. This paper discusses analyzing and improving EISA through synergistic integration of information security architecture into EA to improve data and information security. For its implementation using the TOGAF framework for Enterprise Architecture, while for information security architecture it will use the SABSA framework.
PENGARUH ARSITEKTUR CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI PENYAKIT DAUN TOMAT Rizky Arya Kurniawan; Andi Sunyoto; Asro Nasiri
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 4 No. 2 (2023): Desember 2023
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v4i2.111

Abstract

Plant disease is one of the crucial factors in plant survival. Tomato plants also need early help to be able to deal with disease problems. One of the organs of the tomato plant that is commonly attacked by diseases is the leaves. By providing assistance early on, it can prevent crop failure. Of course, having a trained system can reduce the cost of a farmer in dealing with diseases without expert assistance. In this research, we will test the ability of the CNN architecture to classify tomato leaf disease images. The dataset used is 4079 image data which are divided into 3 disease classes. From the results of experiments that have been carried out the InceptionV3 architecture gets the best results with an accuracy rate of 100%, ResNet50 has 97,36% accuracy and MobileNet 85,81%.
TATA KELOLA TEKNOLOGI INFORMASI MENGGUNAKAN FRAMEWORK COBIT 2019 DOMAIN ALIGN PLAN AND ORGANIZE STUDI KASUS: AKADEMI KOMUNITAS DARUSSALAM BLOKAGUNG BANYUWANGI Moh Abdul Aziz; Kusrini Kusrini; Asro Nasiri
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 4 No. 2 (2023): Desember 2023
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v4i2.112

Abstract

The role of information technology (IT)continues to experience a very significant increase in building and facilitating the performance of a company or institution. The use of IT in educational institutions is theoretically believed to be able to provide convenience and efficiency in administration. Akademi Komunitas Darussalam (AKD) Blokagung Banyuwangi is a relatively new higher education institution, with the 2019 Establishment Decree proving that the institution is not yet five years old. Like a new institution, many unresolved issues must be addressed, one of which is in the field of IT governance in optimizing the performance of human resources in the field of information technology. Therefore, it is necessary to design information technology governance related to the management of IT human resources. This research will design governance using the COBIT 2019 framework. The focus of this research is the Align, Plan, and Organize (APO) domain in the APO07 Manage Human Resources sub-domain. The results of the gap analysis carried out resulted in design recommendations on the people aspect in the form of adjustments to organizational structure, details of main tasks and functions, planning training and workshops, as well as communication in the form of meetings. In the process aspect, it produces SOPs, reporting schemes, and alignment of problem-solving. On the technology aspect, in the form of recommendations for human resource information system tools. This research is expected to assist the AKD Blokagung in carrying out recommendations based on the roadmap that has been adjusted to the AKD Blokagung long-term plan so that it can prioritize IT human resource management in the IT sector according to the needs of the AKD Blokagung. Keywords: Information Technology, COBIT 2019, IT Governance, APO07.
PEMANFAATAN DEEP LEARNING UNTUK SEGMENTASI PARU-PARU DARI CITRA X-RAY DADA Dinar Wakhid Putranto; Andi Sunyoto; Asro Nasiri
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 4 No. 2 (2023): Desember 2023
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v4i2.114

Abstract

Chest or thoracic X-ray examination is the most commonly used supporting examination in the diagnosis of lung diseases. In addition to being quick, X-rays are more economical than CT scans or laboratory blood tests. Before determining the disease that appears from the lung image in the chest X-ray image, the doctor first determines the boundaries of the lung area. Not every chest X-Ray image has a normal lung image, some display abnormal images that look white haze or morphological changes due to lung disease processes. As one of the CNN architectures that can be used in segmenting the lungs is UNET which is an encode-decoder architecture. This research tries to train a deep learning model with U-Net CNN architecture. From the results of our experiments, the proposed model can show the ability to recognize lung boundaries even though there are abnormal or foggy lung images. The performance of the model is calculated by measuring the pixel accuracy value and the overlap value with Jaccard Index (IoU), the values of both are 98.25% and 94.54% respectively.