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Jurnal Teknologi Terpadu
Published by STT Terpadu Nurul Fikri
ISSN : 24770043     EISSN : 24607908     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
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Search results for , issue "Vol. 7 No. 2: December, 2021" : 5 Documents clear
Penerapan Algoritma Genetika Untuk Mencari Optimasi Kombinasi Jalur Terpendek Dalam Kasus Travelling Salesman Problem Aldhiqo Yusron Mubarok; Umi Chotijah
Jurnal Teknologi Terpadu Vol. 7 No. 2: December, 2021
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v7i2.424

Abstract

In delivering packages, goods, and in doing a business, location is a critical variable to control. The number of cases is often found in the arrival of late packages because the courier cannot find the fastest or most efficient route. Determining the most effective distance in a shipment of goods or packages determines customer satisfaction. In this case, the authors make an alternative to search for the shortest path optimization in the Travelling Salesman Problem (TSP) using genetic algorithm methods. With this method, the author wants to analyze and calculate the optimal or shortest route with the data set used. With the principle of a genetic algorithm that resembles the selection of living things with the population as part of each individual, each individual will represent by a fitness value. The application used to make this application is Matlab 2020a. The research results show that the optimal value of generation is 200 generations with the optimal crossover probability of 0.8, and the optimal mutation probability is 0.005. By finding the optimal value of each required variable, the graph of the shortest route will see. This value can be said to be best because the fitness obtained from these results is 0.036 indicating the most optimal value.
Analisis Sentimen dan Emosi Vaksin Sinovac pada Twitter menggunakan Naïve Bayes dan Valence Shifter Bagus Muhammad Akbar; Ahmad Taufiq Akbar; Rochmat Husaini
Jurnal Teknologi Terpadu Vol. 7 No. 2: December, 2021
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v7i2.433

Abstract

The Sinovac vaccine is among the Covid-19 news in the world in early 2021. That information has led to public responses between the pros and cons. Through Twitter media, the public responds to the issue of the Sinovac; therefore, their opinions on Twitter can analyze to count the percentage of sentiment and emotion towards the Sinovac. This analysis hopes to provide a wise and objective reference, although the pros and cons information is still emerging. This study uses Rstudio for sentiment analysis through Twitter opinion classification using Naïve Bayes and the Valence Shifter Lexicon method to analyze emotions, also using the Naïve Bayes. The Data is 2000 English-language Twitter comments limited to the latest and most popular tweet based on the Sinovac keyword since February 1, 2021, from all Twitter users worldwide. The results showed that Naïve Bayes recognized 1433 (71.65%) positive sentiments, 403 (20.15%) negative sentiments, and 164 (8.2%) neutral sentiments. Meanwhile, Valence Shifter Lexicon recognized 903 (45.15%) positive sentiment, 437 (21.85%) negative sentiment, and 660 (33%) neutral sentiments. The Naïve Bayes also succeeded in recognizing emotions with the highest number 1727 (86.35%) mixed emotions and 141 (7.05%) joy emotion.
Penerapan Computer Vision Menggunakan Metode Deep Learning pada Perspektif Generasi Ulul Albab Arifin, Imamul; Haidi, Reydiko Fakhran; Dzalhaqi, Muhammad
Jurnal Teknologi Terpadu Vol. 7 No. 2: December, 2021
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v7i2.436

Abstract

Machine learning is one of the applications of artificial intelligence. The use of machine learning in computer vision is closely related to deep learning where computer scientists get inspiration about deep learning technology from the environment. The purpose of the research in this manuscript is to know and understand deep learning technology along with simple examples in processing image objects and to know and understand artificial intelligence technology from the perspective of the ulul albab generation so that it can provide comprehensive benefits for the world. The research conducted in this paper is a type of qualitative research with library research using various books and other reading literature such as journals and special websites so as to produce information on the topic under study. Artificial intelligence technology will always develop and lead to increasingly sophisticated directions, but technology also has a negative impact. The Ulul Albab generation must be able to struggle to have a positive impact on society because the Ulul Albab generation is the hope for the progress of Islamic civilization in various sectors of science and technology.
Pengembangan Absensi berbasis Mobile Aplikasi pada Badan Kepegawaian dan Pengembangan Sumber Daya Manusia Kabupaten Bone Syahrul Usman; Jeffry Jeffry; Firman Aziz
Jurnal Teknologi Terpadu Vol. 7 No. 2: December, 2021
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v7i2.437

Abstract

Since being designated a global pandemic by the world health agency (WHO), the Corona Virus Disease (Covid-19) outbreak has become a scourge worldwide; various standard transmission control procedures have been set by WHO to break the chain of transmission.  Bone District Government through the Circular of the Regional Secretary No. 800/1919/VI/BKPSDM/2020 dated June 4, 2020, regarding the work system of State Civil Apparatus Employees (ASN) in the new standard order regulates employee attendance using manual attendance and not using fingerprint attendance machines, and this will undoubtedly affect the recording of the performance of each ASN where the attendance data is already connected to the e-performance application that is applied to the Bone district. The purpose of this research is to create an online attendance application based on Android Mobile to be an alternative way of being absent by using the data communication method using the Representational State Transfer (Rest) web service architecture and utilizing the HTTP protocol with JavaScript Object Notation (JSON) format and the Java programming language as a language. Mobile Application programming. The results of this study are a mobile-based attendance application that has been tested for web service performance using the Apache JMeter application to ensure this application is ready to be used simultaneously by many ASN.
Sistem Pengenalan Benih Padi menggunakan Metode Light Convolutional Neural Network pada Raspberry PI 4 B Hermawan, Indra; Arnaldy , Defiana; Agustin, Maria; Widyono, M. Farishanif; Nathanael, David; Mulyani, Meutia Tri
Jurnal Teknologi Terpadu Vol. 7 No. 2: December, 2021
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v7i2.443

Abstract

Recently, Deep learning methods with Convolutional Neural Networks (CNNs) have been widely used for image classification tasks. CNN has an unrivaled advantage in extracting discriminatory image features. However, many existing CNN-based methods are designed to go deeper and more significant with more complex layers that make them challenging to implement on mobile devices or real-time devices that use microcontrollers like raspberry pi, Arduino, and immediately. This is overcome by using a Light Convolutional Neural Network (LCNN), so it needs to experiment to test the difference in LCNN performance on a personal computer and a raspberry pi four microcontrollers with a Raspbian operating system. Experiments will be carried out using several performance measures: accuracy, F-1 score, recall, precision, and time to get performance results from deep learning. As such, the results and model architecture will confirm performance differences across individual devices and show how the model performs on resource-constrained or real-time devices. Tests show that the performance of the raspberry pi, which is a tool with limited resources, does not affect the quality of image recognition but affects the recognition processing time because the raspberry pi requires a longer processing time to perform one data or photo recognition process. This will accumulate the time required for processing many data, so it can conclude that the raspberry pi and tools with limited resources are not very practical for conducting recognition training and carrying out a recognition process that contains a lot of data or photos in one process.

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