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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknoin Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmiah Informatika Komputer Jurnal Ilmiah Teknologi dan Rekayasa Prosiding KOMMIT TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnas Nasional Teknologi dan Sistem Informasi InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) J-SAKTI (Jurnal Sains Komputer dan Informatika) Journal of Information Systems and Informatics International Journal of Engineering, Science and Information Technology J-SAKTI (Jurnal Sains Komputer dan Informatika) International Journal of Engineering Continuity

Emy Haryatmi

Universitas Gunadarma

Author-ID : 277508

Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

Published : 26 Documents Claim Missing Document

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Articles

Title

Penerapan Algoritma Support Vector Machine Untuk Model Prediksi Kelulusan Mahasiswa Tepat Waktu Emy Haryatmi; Sheila Pramita Hervianti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

A University can have many student data in their database because many students did not graduate on time. Data mining technique can be used to process student data to predict student graduation on time. Support Vector Machine (SVM) algorithm is one of data mining techniques. Objectives of this research was implementation of SVM algorithm to model the prediction of student graduation on time in private university in Indonesia. This research was conducted using CRISP-DM (Cross Industry Standard Process for Data Mining) method. There are five steps in that method such as understanding business to predict student graduation in time which is not available, data understanding by choosing the right attribute for the next step, data preparation includes cleaning the null data and transforming data into category which has been specified, modeling was used by implementing data training and data testing on SVM algorithm and evaluation to validate and measure the accuracy of the model. The result of this research shown that accuracy value of data testing was 94,4% using 90% data training and 10% data testing. This concluded SVM algorithm can be used to model the prediction of student graduation on time.

Penerapan Convolutional Neural Network Deep Learning dalam Pendeteksian Citra Biji Jagung Kering Arum TiaraSari; Emy Haryatmi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Corn kernels detection can be implemented in industry area. This can be implemented in the selection and packaging the corn kernels before it is distributed. This technique can be implemented in the selection and packaging machine to detect corn kernels accurately. Corn kernel images was used before it is implemented in real-time. The objective of this research was corn kernel detection using Convolutional Neural Network (CNN) deep learning. This technique consists of 3 main stages, the first preprocessing or normalizing the input of corn kernels image data by wrapping and cropping, both modeling and training the system, and testing. The experiment used CNN method to classify images of dry corn kernels and to determine the accuracy value. This research used 20 dry corn kernels images as testing from 80 dry corn kernels images which used in training dataset. The accuracy of detection was dependent from the size of image and position when the image was taken. The accuracy is around 80% - 100% by using 7 convolutional layers and the average of accuracy for testing data was 0,90296. The convolutional layer which implemented in CNN has the strength to detect features in the input image.

Implementasi Teknologi Blockchain Proof of Work Pada Penelusuran Supply Chain Produk Komputer Annisya; Emy Haryatmi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 3 (2021): Juni 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

In recent times, the supply chain has developed into a large ecosystem. Various products moving from party to party require cooperation between stakeholders in managing the data generated. The problem is that every company has its own transaction records that can be inconsistent and their storage is centralized and not integrated between companies. This makes transaction records easy to falsify. Efficient data management is needed from the producer to the store so that consumers can trust the product. Therefore, the authors designed a product tracking system using blockchain by implementing proof of work (PoW) as the consensus algorithm, SHA-3 256 as data security, Mongo database as cloud-based data storage and QR Code as the output. As a result, transaction data from producers, distributors to retail stores are stored completely in MongoDB which is a cloud-based database, then the resulting QR Code can be used to view details of producers, distributors to retail stores that sell them. The simulation and trial results show the product tracing system design is successful as expected.

Implementasi Raised Cosine Filter Pada Sistem Penyiaran Televisi Digital Satelit 2 (DVB-S2) Rio Setiawan; Emy Haryatmi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The development of digital video broadcasting is still continue recently and was done by many parties. One of the project regarding this research was DVB project. There was three areas in digital video broadcasting. One of them was Digital Video Broadcasting Satellite Second Generation (DVB-S2). The development of this project is not focus only in video broadcasting but also focus in applications and mutlimedia services. The objective of this research was to implement raised cosine filter in DVB-S2 using matlab simulink in order to optimize SNR and BER value. Parameters used in this project was QPSK mode and LDPC with 50 iteration. Those parameters was chosen to maintain originality of data that sent in noisy channel. The result showed that by implementing raised cosine filter could optimized BER value of the system. The higher SNR value would give the lower BER value. In static video, the best SNR value when using a filter is 0.9 dB with a BER value of 0.000004810 while for dynamic video the SNR is 0.9 with a BER value of 0.00001030.

Co-Authors A. Benny Mutiara Abdul Rachman Rasyid Andhika Alif Arsadi Annisya Arini Islahul Nimah Arum TiaraSari Ary Bima Kurniawan Ayunda, Nur Azizah Bagaskoro Bagaskoro Bima Kurniawan, Ary Budi Prasojo Budi Prasojo, Budi Busono Soerowirdjo Busono Soerowirdjo Busono Soerowirdjo Dzulfikar, Laksamana Akbar E. Susy Suhendra Fathimah Nur Afifah Fauzi, Alfharizky Hanum, Fatmi Aulia Haryadi, Deni Ilham Wijaya Murniyati, Murniyati Musa, Purnawarman Ramadini, Febrin Ludia Randy Rahmanto Rio Setiawan Riyadi, Tri Agus Riza Adrianti Supono, Riza Adrianti Robbani, Fatih Dien Rosdiana Rosdiana Sheila Pramita Hervianti Teddy Oswari Teddy Oswari Tubagus Maulana Kusuma Yuhani, Ahmad Yunus Bakhtiar Arafat

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