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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Mantik
Putra, Adya Zizwan
Universitas Prima Indonesia, Indonesia
Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

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Pengenalan Sidik Jari Pada Absensi Guru & Pegawai Dengan Algoritma Perceptron Adya Zizwan Putra
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 2 No. 1 (2019): Jutikomp Volume 2 Nomor 1 April 2019
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v2i1.569

Abstract

Sidik jari adalah hasil reproduksi dari kulit permukaan jari, baik yang disengaja diambil / dicapkan dengan tinta maupun bekas yang ditinggalkan pada benda. Bentuk pokok sidik jari ditentukan oleh lapisan epidermal kulit, dimana apabila terjadi kerusakan pada lapisan ini akan bersifat sementara. Sidik jari yang ada pada seseorang adalah permanent dan karakteristik dan bentuknya terjaga dari lahir sampai mati. Selain itu berdasarkan hasil eksperimen Galton terbukti tidak ada dua orang mempunyai sidik jari yang sama, bahkan pada kembar identik pun ditemukan sidik jari yang berbeda meskipun mereka sama . Sehingga sidik jari dapat digunakan sebagai identitas seseorang dalam sistem keamanan. Perceptron merupakan bentuk jaringan syaraf yang sederhana, biasanya digunakan untuk mengklasifikasikan suatu tipe pola tertentu yang sering dikenal dengan pemisahan secara linear . Walaupun merupakan bentuk jaringan yang sederhana dan memiliki keterbatasan dalam pengenalan pola, perceptron dapat menyelesaikan masalah dengan baik dibanding jaringan lain apabila data dapat dipisahkan secara linear. Apabila data tidak dapat dipisahkan secara linear maka dapat dilakukan prepocessing terlebih dahulu untuk menghasilkan data yang dapat dipisahkan secara linear, atau menggunakan banyak perceptron dengan lapisan ganda, atau menggunakan jaringan jenis lain seperti back propagation. Pada tugas parket kerja lapangan ini penulis mencoba untuk menggunakan metode perceptron dengan representasi biner (0 /1). maksudnya, target yang diinginkan akan direprentasikan dengan nilai 1 dan target yang tidak diinginkan akan direprentasikan dengan nilai 0. Nilai target ini dapat dihasilkan oleh jaringan dengan fungsi aktivasi hardlim yang hanya dapat digunakan pada metode perceptron dan tidak dapat digunakan pada metode lain.
Implementation Of Secos Algorithm In Forecasting Gold Prices To Improve Business Intelligence Using Mse Accuracy Value Measurement Meri Anisa Br Purba; Krisdorin Lase; Alisha Indah Sari Sembiring; Lamhot M.E. Panjaitan; Adya Zizwan Putra
Jurnal Mantik Vol. 5 No. 2 (2021): Augustus: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

SECOS is one of the artificial neural networks that can be used in this study to make a prediction or forecasting of gold prices. This research will produce a combination of parameters, namely learning rate 1, learning rate 2, error threshold that will be carried out on the system then in the training process the data is quite influential on the resulting error value. From the results of the combination of parameters and testing with the SeCos algorithm shown in Figure 4.2, the smallest error value at layer 3 is 54262,375, which is obtained from the learning rate 1 parameter is 0.5, learning rate 2 is 1, learning rate 3 is 1.5. while the largest error value is 46023.9375. The results of the SECOS algorithm in forecasting the gold price can run well, which shows that the model from the implemented training and testing data can predict gold prices.
Klasifikasi Tingkat Penyebaran Pasien Covid-19 Berdasarkan Usia dan Wilayah Dengan Algoritma K-Means Adya Zizwan Putra; Ryan Wijaya Pinem; Sehat Silalahi; Fendianu Gulo; Juan Antonio Adityo Liukhoto
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2022): Article Research Volume 7 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11603

Abstract

The Covid-19 virus is a new type of disease, the first case of covid-19 was found in Wuhan Province, China in 2019 with general symptoms such as pneumonia. This virus can grow rapidly and can cause serious infections and even death. Due to the very fast transmission of the virus, the WHO declared the Covid-19 virus a pandemic on March 11, 2020. Anyone can be infected with the covid-19 virus, from small children to the elderly. However, various ways have been done, but the cases of covid-19 continue to increase. Various ways have been done to reduce the spread of COVID-19 so that the Covid-19 virus does not spread quickly. Then data mining techniques are needed by implementing the K-Means algorithm because the K-Means algorithm can group data. In this study, 790 patient data were used for COVID-19 patients. The test resulted in 3 clusters grouped based on low, medium, and high categories with a DBI value of -0.332. In cluster 0 with a low category there are 3 districts, in cluster 1 with a medium category there is 1 sub-district, in cluster 2 with a high category, there are 6 districts. From the results of the test, it can be seen that the age susceptible to COVID-19 is 26 to 45 years.
Coffee Quality Prediction with Light Gradient Boosting Machine Algorithm Through Data Science Approach Adya Zizwan Putra; Mawaddah Harahap; Achmad Nurhadi; Andro Eriel Tambun; Syahmir Defha
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2023): Articles Research Volume 8 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.12169

Abstract

In increasing sales by increasing consumer satisfaction with the quality of coffee sold. A way is needed to make it easier to predict the determination of quality coffee so as to increase the efficiency of the coffee sorting process which does not take a long time and can increase the productivity of companies that have competitiveness. Several developments have been made to improve the performance of the algorithm which has the potential to produce good quality predictions. Import Copy Data into a format that can be processed to a later stage or with a Machine Learning algorithm. Copy data that can be processed is then modified in such a way as to ensure that the data is suitable for use in Data Science or Machine Learning processes. By using coffee data specifications from the plantation to the coffee beans produced, it is expected that coffee quality can be predicted quickly without the need for manual calculations or analysis by humans. The working procedures for selecting the quality of coffee beans are coffee import data, coffee data processing, split test-train coffee data, light gradient enhancement machine, yield prediction, and Performance Prediction Evaluation. The amount of data used is 1,339 data. The dependent variable in this data is Coffee Quality while the rest will be cleaned and processed to serve as an independent variable. The accuracy rate of the algorithm in predicting coffee quality is 72%.
Pneumonia Classification Based on Lung CT Scans Using Vgg-19 Adya Zizwan Putra; D. V. M. Situmorang; G. wahyudi; J. P. K. giawa; R. A. Tarigan
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2023): Article Research Volume 8 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12778

Abstract

This research harnesses technology for critical health applications, specifically, pneumonia detection through medical imaging. X-ray photography allows radiologists to visualize the patient's health state, including the detection of lung infections signifying pneumonia. The study's centerpiece is the application of the VGG-19 model in classifying lung CT scan images, helping discern normal from pneumonia-indicative conditions. A comprehensive preprocessing procedure is employed, entailing pixel rescaling and data augmentation techniques. To address data imbalance, a critical issue in machine learning, we incorporate the Synthetic Minority Over-sampling Technique (SMOTE). The developed VGG-19 model demonstrates impressive performance, achieving a 94.6% accuracy rate in classifying lung CT scans. This finding underscores the potential of the VGG-19 model as a reliable tool for pneumonia detection based on lung CT scans. Such a tool could revolutionize the field, providing an efficient and accurate method for early pneumonia diagnosis, thereby allowing for timely treatment.
Fingerprint Identification for Attendance Using Euclidean Distance and Manhattan Distance Adya Zizwan Putra; Sallyana Yek; Shane Christian Kwok; Elovani Tarigan; William Frans Sego
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2023): Article Research Volume 8 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12844

Abstract

Attendance is an action to confirm that someone is present at the office, school, or event. The use of attendance in an agency or company is really important as it can improve the level of discipline and productivity. However, the traditional way of doing attendance is considered less effective, less secure, and more difficult to organize. Therefore, a modern attendance system that utilizes fingerprints can be the right solution, especially because every fingerprint is unique. In this research, we focus on designing a fingerprint identification system for attendance purposes by using two distance measure methods, namely Euclidean Distance and Manhattan Distance. The dataset used in the research contains 111 fingerprint images with 90 images for training the designed fingerprint identification system and the remaining 21 images for testing the system. Each fingerprint image has undergone image pre-processing stage before being used. We compare Euclidean Distance and Manhattan Distance based on their performances in identifying fingerprint. From the test results, the fingerprint identification accuracy obtained using Euclidean Distance is 76.19%, while the accuracy obtained using Manhattan Distance is 71.43%. In general, both algorithms succeed in providing the correct identification results. This proves that Euclidean Distance and Manhattan Distance can be utilized for fingerprint identification purposes.
Co-Authors Achmad Nurhadi Alisha Indah Sari Sembiring Andro Eriel Tambun D. V. M. Situmorang Elovani Tarigan Fendianu Gulo G. wahyudi J. P. K. giawa Juan Antonio Adityo Liukhoto Krisdorin Lase Lamhot M.E. Panjaitan Mawaddah Harahap Meri Anisa Br Purba R. A. Tarigan Ryan Wijaya Pinem Sallyana Yek Sehat Silalahi Shane Christian Kwok Syahmir Defha William Frans Sego