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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jiko (Jurnal Informatika dan komputer) 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)

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Penggunaan Recurrent Neural Network Dalam Mendeteksi Sentimen Berbahaya Pada Platform Media Sosial Chuanta, Roy Vidia; Harahap, Mawaddah; Putra, Adya Zizwan
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 7 No. 1 (2024): Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v7i1.4845

Abstract

Social media platforms in today's modern era make it easier for people to communicate and socialize. Behind that, on a hot topic, there must be sentiment. Every sentiment the community conveys varies; some are good and neutral, and some are bad or dangerous. To detect sentiment, start from crawling data, pre-processing, labeling, and then testing or training to get accuracy value, recall value, f1 value, and precision value using Long Short Term Memory. They obtained an accuracy value of 0.582, recall value of 0.582, f1 value of 0.428, and precision of 0.339. This LSTM model can be used to develop an analysis model that is successfully achieved.
Klasifikasi Tingkat Penyebaran Pasien Covid-19 Berdasarkan Usia dan Wilayah Dengan Algoritma K-Means Putra, Adya Zizwan; Pinem, Ryan Wijaya; Silalahi, Sehat; Gulo, Fendianu; Liukhoto, Juan Antonio Adityo
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 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 Putra, Adya Zizwan; Harahap, Mawaddah; Achmad Nurhadi; Andro Eriel Tambun; Syahmir Defha
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2023): Articles Research Volume 7 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 Putra, Adya Zizwan; Situmorang, D. V. M.; wahyudi, G.; giawa, J. P. K.; Tarigan, R. A.
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 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 Putra, Adya Zizwan; Yek, Sallyana; Kwok, Shane Christian; Tarigan, Elovani; Sego, William Frans
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 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.
Implementation Transfer Learning on Convolutional Neural Network for Tubercolosis Classification Putra, Adya Zizwan; Prayugo, Reynaldi; Siregar, Rizki Mudrika Alfanda; Syabani, Rizky; Simarmata, Allwin
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13723

Abstract

Tuberculosis (TB) is an infectious disease that can have serious effects on the lungs and is among the top 10 causes of death worldwide. This disease is caused by the transmission of Mycobacterium tuberculosis bacteria through the air when coughing or sneezing. Without treatment, pulmonary tuberculosis can result in permanent lung damage and can be life-threatening. Accurate and early diagnosis is crucial for effective treatment and control of the disease.The challenge lies in the accurate classification of tuberculosis from lung images, which is essential for timely diagnosis and treatment. Traditional diagnostic methods can be time-consuming and sometimes lack precision. To address this issue, this research aims to achieve high accuracy in classifying tuberculosis using the Convolutional Neural Network (CNN) algorithm through transfer learning methods. By utilizing visual images of tuberculosis-affected and normal lungs, we propose a solution that leverages advanced deep learning techniques to enhance diagnostic accuracy. This approach not only expedites the diagnostic process but also improves the reliability of tuberculosis detection, ultimately contributing to better patient outcomes and more effective disease management. The dataset applied consists of two labels: tuberculosis and normal. This dataset contains 4200 lung images of individuals with tuberculosis and normal lungs. By applying the transfer learning method, Transfer learning is a machine learning method where a pre-trained model is used as the starting point for a new, related task. it was found that the ResNet50 model achieved the highest accuracy at 99%, followed by InceptionV3 at 97%, and lastly, DenseNet121 at 91%.
Comparison of Tubercolosis Detection Using CNN Models (AlexNet and ResNet) Putra, Adya Zizwan; Amir Mahmud Husein; Nicholas; Frederico Wijaya; Aribel
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13979

Abstract

The bacterial infection caused by Mycobacterium tubercolosis, leading to tubercolosis is a prevalent contagious disease. This bacterium commonly targets the primary respiratory organs, particularly the lungs. Tuberculosis poses a significant global health challenge and necessitates early detection for effective management. In this context, to facilitate healthcare professionals in the early detection of patients, a technology capable of accurately identifying lung conditions is required. Therefore, CNN (Convolutional Neural Network) will be employed as the algorithm for detecting lung images. The research will utilize Convolutional Neural Network models, namely AlexNet and ResNet. The study aims to compare the performance of these two models in detecting TB through the analysis of chest X-ray images. The dataset comprises X-rays from both normal patients and TB patients, totaling 4.200 data points. The training process involves dividing the data into training and validation sets, with an 80% allocation for training and 20% for validation. The evaluation results indicate that the AlexNet model demonstrates higher detection accuracy, reaching 88.33% on the validation data, while ResNet achieves 83.10%. These findings suggest that the use of CNN models, especially AlexNet, can be an effective approach to enhancing early tuberculosis detection through the interpretation of chest X-ray images, with potential implications for improving global TB management and prevention efforts.
IMPLEMENTING SHA-256 IN BLOCKCHAIN FOR SECURE AND TRUSTED ONLINE TRANSACTIONS OF MSMEs Luis, Matthew; Dewantoro, Rico Wijaya; Crispin, Andrian Reinaldo; Putra, Adya Zizwan; Dharma, Abdi; Chrysia, Celine; Vagga, Cherry Piya
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 3 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10606

Abstract

The advancement of information technology has driven digital transformation in various sectors, including Micro, Small, and Medium Enterprises (MSMEs), which are vital to Indonesia's economy. However, local MSMEs still face challenges in online transactions, especially related to data security and low consumer trust. Issues like data manipulation, lack of transparency, and weak security systems hinder optimal digitalization. This study implements the SHA-256 cryptographic algorithm in a blockchain system to enhance security and trust in local MSMEs' online transactions. SHA-256 is chosen for its ability to produce unique, permanent, and tamper-resistant hashes. The system adopts a decentralized blockchain model, where transactions are recorded in encrypted, chronologically linked blocks. The testing results show that the SHA-256-based blockchain system functions effectively in maintaining data integrity and preventing manipulation. Black Box Testing confirmed that the system operates correctly from the user's perspective, including login validation, transaction recording, manipulation detection, and transaction history retrieval. White Box Testing validated the internal logic of the system, proving the correct implementation of SHA-256 hashing, block linking, Proof of Work (PoW), and transaction validation mechanisms. All test cases passed successfully, demonstrating that the system is stable, functional, and secure.
Co-Authors Abdi Dharma Achmad Nurhadi Alisha Indah Sari Sembiring Amir Mahmud Husein Andro Eriel Tambun Aribel Chrysia, Celine Chuanta, Roy Vidia Crispin, Andrian Reinaldo Dewantoro, Rico Wijaya Frederico Wijaya giawa, J. P. K. Gulo, Fendianu Krisdorin Lase Kwok, Shane Christian Lamhot M.E. Panjaitan Liukhoto, Juan Antonio Adityo Luis, Matthew Mawaddah Harahap, Mawaddah Meri Anisa Br Purba Nicholas Pinem, Ryan Wijaya Prayugo, Reynaldi Sego, William Frans Silalahi, Sehat Simarmata, Allwin Siregar, Rizki Mudrika Alfanda Situmorang, D. V. M. Syabani, Rizky Syahmir Defha Tarigan, Elovani Tarigan, R. A. Vagga, Cherry Piya wahyudi, G. Yek, Sallyana