Articles
<![CDATA[Implementasi Teknologi Blockchain pada Sistem Presensi Staff VM LePKom Berbasis Web ]]>
<![CDATA[Ilham Wijaya]]>;
<![CDATA[Emy Haryatmi]]>;
<![CDATA[Ary Bima Kurniawan]]>
<![CDATA[ InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 5, No 1 (2020): InfoTekJar September ]]>
Publisher : <![CDATA[Universitas Islam Sumatera Utara ]]>
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DOI: 10.30743/infotekjar.v5i1.2932
<![CDATA[Attendance for staff is one of data collection in the institution as a part of reporting and recording activity. Blockchain technology or distributed ledger has become registration system used in the network. Blockchain technology is one of the technologies that has robust security system and minimizing the lost of data because each node has the copy of data. Blockchain technology can be implemented in many aspects for example in the attendance system in LePKom Gunadarma University based on virtualization. There is an attendance for staff application in LePKom using web application but the database used in the application is centralized. Using centralized database has many disadvantages such us the lost of data or data can be changed by others. The objective of this research is to analyze the implementation of blockchain technology for staff attendance based on web application in VM LePKom. Method of this research used System Development Life Cycle (SDLC). The result of this research was blockchain technology can be implemented in attendance application and distributed database. Data was secured using Proof of Work (PoW) consensus Algorithm and can be validated. ]]>
<![CDATA[Penggunaan Metode Haar Cascade Classifier dan LBPH Untuk Pengenalan Wajah Secara Realtime ]]>
<![CDATA[Febrin Ludia Ramadini]]>;
<![CDATA[Emy Haryatmi]]>
<![CDATA[ InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 6, No 2 (2022): InfoTekJar Maret ]]>
Publisher : <![CDATA[Universitas Islam Sumatera Utara ]]>
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DOI: 10.30743/infotekjar.v6i2.4714
<![CDATA[Pengenalan wajah manusia menjadi sebuah topik penelitian biometric yang cukup banyak diminatai karena pada wajah manusia terdapat banyak informasi terutama mengenai identitas seseorang. Setiap orang memiliki bentuk wajah yang berbeda yang dapat dilihat dari mata, hidung, telinga dan juga mulut. Pada penelitian ini penulis menggabungkan dua metode haar cascade classifoer dan LBPH untuk membuat sistemm yang dapat mengenali wajah seseorang. Metode haar casecade classifier digunakan untuk mendeteksi adanya wajah manusia sedangkan metode LBPH digunakan untuk mengenali wajah seseorang. Pada sistm ini terdapat beberapa proses untuk dapat mengenali wajah seseorang, yaitu: proses deteksi wajah, proses pengambilan dataset, proses pelatihan wajah dan proses pengenalan wajah. Proses pengambilan dataset dilakukan secara otomatis, saat sistem sudah mendeteksi adanya wajah manusia dan diambil sebanyak 40 foto untuk setiap satu wajah user. Sistem akan mencocokkan wajah yang terdeteksi dengan indentitas wajah yang telah dimasukkan ke dalam dataset. Selanjutnya sisstem akan mengenali wajah yang dideteksi dan menampikan nama sesuai dengan nomer user ID yang terdapat di dataset. Tampilan pengenalan wajah mnggunakan sistem realtime dimana nama yang ditampilkan sesuai dengan orang yang tepat berdiri didepan kamera laptop pada saat itu. Keberhasilan sistem ini sebesar 88,42%.]]>
<![CDATA[Design and Control System Monitoring of Water Quality on Tilapia Cultivation Farm based Internet of Things (IoT) with NodeMCU ]]>
<![CDATA[Fathimah Nur Afifah]]>;
<![CDATA[Emy Haryatmi]]>
<![CDATA[ InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 4, No 2 (2020): InfoTekJar Maret ]]>
Publisher : <![CDATA[Universitas Islam Sumatera Utara ]]>
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DOI: 10.30743/infotekjar.v4i2.2398
<![CDATA[Fish cultivation has a high potential to improve the welfare of the community. One of the important factors influencing the success of fish cultivation is the aspect of water quality in the pond which is illustrated by several physical parameters such as temperature, degree of acidity (pH) and turbidity of the water. The use of water for cultivation must always be maintained at turbidity level, the impact of turbid water causes disruption to the physical growth of the fish and even death. Due to the limitations of humans who cannot monitor ponds 24 hours, a system is created that can monitor and control water quality in real time. To get good water quality for cultivation, the authors make this system using a turbidity sensor to detect water turbidity, a pH sensor to detect the acidity of the water and a temperature sensor to detect the temperature of the water on the pond. While processing and control using a microcontroller, namely NodeMCU. Periodically the sensor will send the measurement results to the Cayenne application in real time which can be viewed via a smartphone / PC. The control system is also made with two modes, namely automatic and manual modes to fill and drain water on the pond. Based on the results of system testing, turbidity, pH and temperature sensors are very good in detecting any changes in pond water conditions. Cayenne application with IoT technology is able to provide action on the water pump in the process of draining and filling water in ponds with manual mode. NodeMCU is able to send data with a good internet connection.]]>
<![CDATA[Penerapan Algoritma Support Vector Machine Untuk Model Prediksi Kelulusan Mahasiswa Tepat Waktu ]]>
<![CDATA[Emy Haryatmi]]>;
<![CDATA[Sheila Pramita Hervianti]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v5i2.3007
<![CDATA[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.]]>
<![CDATA[Penerapan Convolutional Neural Network Deep Learning dalam Pendeteksian Citra Biji Jagung Kering ]]>
<![CDATA[Arum TiaraSari]]>;
<![CDATA[Emy Haryatmi]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v5i2.3040
<![CDATA[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.]]>
<![CDATA[Implementasi Teknologi Blockchain Proof of Work Pada Penelusuran Supply Chain Produk Komputer ]]>
<![CDATA[Annisya]]>;
<![CDATA[Emy Haryatmi]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 3 (2021): Juni 2021 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v5i3.3068
<![CDATA[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.]]>
<![CDATA[Implementasi Raised Cosine Filter Pada Sistem Penyiaran Televisi Digital Satelit 2 (DVB-S2) ]]>
<![CDATA[Rio Setiawan]]>;
<![CDATA[Emy Haryatmi]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v5i6.3442
<![CDATA[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.]]>
<![CDATA[PURWARUPA ALAT PENDETEKSI BAYI KUNING DAN SUHU TUBUH PADA BAYI BARU LAHIR BERBASIS SENSOR WARNA DAN SENSOR SUHU ]]>
<![CDATA[Bagaskoro Bagaskoro]]>;
<![CDATA[Emy Haryatmi]]>;
<![CDATA[Tri Agus Riyadi]]>
<![CDATA[ Jurnal Ilmiah Informatika Komputer Vol 27, No 3 (2022) ]]>
Publisher : <![CDATA[Universitas Gunadarma ]]>
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DOI: 10.35760/ik.2022.v27i3.7725
<![CDATA[Jaundice adalah perubahan warna kuning pada kulit atau bagian putih mata yang sering terlihat pada bayi baru lahir. Perubahan warna ini disebabkan oleh zat kuning yang disebut bilirubin. Bayi dengan kadar darah tinggi bilirubin, yang disebut hiperbilirubinemia, berkembang menjadi warna kuning ketika bilirubin terakumulasi di kulit. Jaundice juga membutuhkan perawatan khusus agar bayi tersebut dapat segera ditangani. Alat ini dirancang menggunakan mikrokontroler ESp8266, serta dengan menggunakan Aplikasi Blynk yang berfungsi untuk media interaksi antara user dengan alat itu sendiri. Tujuan dari penelitian ini adalah merancang dan melakukan uji coba terhadap alat pendeteksi warna kuning pada kulit bayi baru lahir dan suhu berbasis mikrokontroler. Metode penelitian yang digunakan adalah melakukan studi literature, disain, pengujian dan analisis alat. Tahap pengujian alat dilakukan terhadap sensor warna yang dapat mendeteksi kadar bilirubin pada kulit bayi serta menggunakan sensor suhu mendeteksi suhu bayi tersebut. Berdasarkan hasil pengujian, sensor suhu tubuh dapat bekerja dengan baik dengan mendeteksi suhu tubuh pada orang dewasa ataupun bayi berada pada kisaran 32-36°C. Pengujian terhadap kulit manusia dan berdasarkan warna referensi menunjukkan bahwa terdapat empat kondisi yaitu normal, ringan, berat (severe) dan critical (kritis).]]>
<![CDATA[Herbal plant leaves classification for traditional medicine using convolutional neural network ]]>
<![CDATA[Fauzi, Alfharizky]]>;
<![CDATA[Soerowirdjo, Busono]]>;
<![CDATA[Haryatmi, Emy]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i3.pp3322-3329
<![CDATA[The classification of herbal plant leaves can be implemented in agriculture and traditional medicine. Primarily, sorting leaves was done before it was processed into medicinal ingredients. Currently, the sorting was still done manually by writing it on notes. Sometimes there were errors in the selection of leaves for medicinal ingredients. Herbal plants had various forms and are very greatly. Artificial intelligence technology was needed to have fast-paced time efficiency in sorting leaves. In the field of artificial intelligence, there was a specific or detailed learning process known as deep learning. The objective of this research was to classify herbal plant leaves images by applying and combining the convolutional neural network (CNN) deep learning method with data augmentation methods without the pre-trained architecture such as MobileNet and LeNet. This technique consisted of 4 main stages such as collecting data, preprocessing or normalizing data, building a model, and evaluating. The dataset used in this research were 4 types of herbal plants that do not flower and do not bear fruit including gulma siam, piduh, sirih, and tobacco. Each class had 250 images with total dataset used in this research was 1,000 images of herbal plant leaves and divided into 2 data, namely 80% data training 20% data testing, and validation. The data was trained with the epoch of 100 for the best training. It had an accuracy score of 98.74%. Without the data augmentation process it had an accuracy score of 91.43%.]]>
<![CDATA[Classification of Tomato Ripeness Based on Convolutional Neural Network Methods ]]>
<![CDATA[Ayunda, Nur Azizah]]>;
<![CDATA[Haryatmi, Emy]]>;
<![CDATA[Riyadi, Tri Agus]]>
<![CDATA[ Journal of Information System and Informatics Vol 5 No 4 (2023): Journal of Information Systems and Informatics ]]>
Publisher : <![CDATA[Universitas Bina Darma ]]>
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DOI: 10.51519/journalisi.v5i4.613
<![CDATA[Sorting system for tomato is one of the important things to deploy to achieve better quality of tomato. Nowadays, many sorting system is done manually and this could spend a lot of time and become inefficient. One method can be implemented in the sorting system by using Convolutional Neural Network (CNN) method to classify the ripeness of tomatoes. The objective of this research is to classify the ripeness of tomatoes based on the color of tomatoes. There are three categories of color level such as green for raw tomato, turning for half-ripe tomato and red for ripe tomato. Research methodology of this research is data collection, data pre-processing and image maintenance, CNN model, and training data. The image used in this research are 1148 images. These images were taken manually using smartphone camera in outdoor environment. These images were used to build CNN model. The results of this research show that by testing 10 images of tomatoes achieved raw tomatoes close to 90%, ripe tomatoes close to 90% and half-ripe tomatoes close to 80%. Based on the results, CNN can be used as a good alternative in image classification tasks.]]>
