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INDONESIA
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 266 Documents
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Perbandingan Model Machine Learning pada Klasifikasi Tumor Otak Menggunakan Fitur Discrete Cosine Transform Prasetyo, Simeon Yuda; Nabiilah, Ghinaa Zain
Jurnal Teknologi Terpadu Vol 9 No 1 (2023): Juli, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v9i1.605

Abstract

Brain tumors are abnormal tissue growths characterized by excessive cell growth in certain brain parts. One of the reliable techniques currently available to identify brain tumors is using Magnetic Resonance Imaging (MRI) scans. The scanned MRI images are monitored and examined for tumor detection by a specialist. Developing more effective and efficient tools to help medical professionals identify brain tumors is urgent as the number of people suffering from brain tumors soars, and the death rate will reach 18,600 in 2021. In previous research, machine learning-based models demonstrated the ability to detect brain tumors with a classification accuracy of 92%, and this result is reliable. We computationally tested several hyperparameters using publicly available MRI datasets to obtain the most reliable binary classification accuracy in MRI brain images. A high level of model accuracy is achieved by testing various existing machine-learning model architectures and inserting a feature map extracted from the Discrete Cosine Transform (DCT). Classification of MRI images achieved the highest accuracy on test data at 93% using the Support Vector Machine (SVM) model.
Analisis Text Mining Klasifikasi Kegiatan Keluarga menggunakan Orange dengan Metode Naive Bayes Fathiarahma, Arsya; Voutama, Apriade; Ridwan, Taufik; Heryana, Nono
Jurnal Teknologi Terpadu Vol 9 No 1 (2023): Juli, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v9i1.606

Abstract

The dual roles performed by women often lead to conflict in the family. Disputes can occur due to the lack of family roles in helping balance the work at home and office experienced by career women and mothers. The Naïve Bayes algorithm is used in this study to determine the results of applying the Naïve Bayes algorithm classification based on role, activity, age, and interest in a family. A total of 287 data records generated from the survey were used in this study and a data split of 80:20 for training and testing data using orange data mining tools. The results show that the calculation accuracy with the Naïve Bayes algorithm reaches 93%, with the conclusion that the use of orange data mining tools results in good accuracy.
Penerapan K-Means dan Rank Order Centroid pada Proporsi Individu dengan Keterampilan Teknologi Informasi dan Komputer Nurfitriana, Diana; Voutama, Apriade
Jurnal Teknologi Terpadu Vol 9 No 2 (2023): Desember, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v9i2.608

Abstract

Technological developments occur so quickly, resulting in continuous changes that qualified human resources are needed to support the endless times that run. This study will classify individuals with information technology and computer skills in Indonesia based on region. This research used K-Means clustering, the Rank Order Centroid method, and the Davies-Bouldin Index clustering evaluation method to assess accuracy. K-means clustering is a simple algorithm and does not require a target class. There are areas for improvement in the K-Means process, namely at the initial centroid determination stage. Therefore, the ROC method is used. Based on data taken from the website of Badan Pusat Statistik Nasional about the proportion of productive age individuals 15-59 years who have Information and Computer Technology skills by the province during 2017-2021. It produces 3 clusters, including a high-level cluster in which there are 8 provinces, a medium-level cluster in which there are 22 provinces, and a low-level cluster in which there are 4 provinces, and obtained a DBI value of 0.163625 which is close to 0, meaning that the quality of the accuracy of the clustering results is good. Based on clustering results with good accuracy, using K-Means can be combined with ROC and is quite effective. The government can use the results of this study to prioritize improving the quality of human resources in areas with low-level information and computer technology skills. Suggestions for further research using other clustering algorithms and ROC as a comparison.
Analisis Sentimen Ulasan Aplikasi MyPertamina pada Google Play Store menggunakan Algoritma NBC Maulana, Rihan; Voutama, Apriade; Ridwan, Taufik
Jurnal Teknologi Terpadu Vol 9 No 1 (2023): Juli, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v9i1.609

Abstract

The MyPertamina application is a digital service application from Pertamina with various services initiated and created to carry out purchases in vehicle fuel transactions. The existence of the MyPertamina application made by PT Pertamina caused reactions and criticism from some application users. Each user has various opinions about the MyPertamina application, as evidenced by the different star ratings in Google Play Store reviews. This study aims to determine the sentiments of MyPertamina application users, which are divided into two classes, namely positive and negative. The dataset in this study uses scraping results from user reviews on the Google Play Store. The data taken to carry out sentiment analysis is from 1 July 2022 to 31 July 2022, and the dataset is taken randomly. The dataset is classified by rating: ratings 4 and 5 as positive sentiment and ratings 1, 2, and 3 as negative sentiment. In this study, Google Colab tools will be used using Python programming. The dataset used is 5722 data labeled positive and negative, with the division of training data by 80% and test data by 20%. The MyPertamina application sentiment analysis results tend to be hostile towards using the application. This study used classification with the Naïve Bayes Classifier (NBC) algorithm. By using the Naïve Bayes Classifier algorithm, it produces 87% accuracy, 86% precision, 90% recall, and 87% f1-score.
Pengembangan Aplikasi Emoticon Recognition dan Facial Recognition menggunakan Algoritma Local Binary Pattern Histogram (LBPH) dan Convolutional Neural Network (CNN) Haeruddin, Haeruddin; Herman, Herman; Hendri, Patrick Pratama
Jurnal Teknologi Terpadu Vol 9 No 1 (2023): Juli, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v9i1.613

Abstract

In the current modern era facial recognition technology can be found inside of everyday life, but said technology still has a big problem which is deepfake, where in which a deepfake can bypass security systems created with facial recognition as its base, one facial aspect that a deepfake cannot replicate perfectly is the emotion that can be observed from expression, which is why an emotion can be used as a tool to detect a deepfake, which is why an application that can detect both face and emotion at the same time is needed to add security to facial recognition technology, writer has succeeded in creating an application that can do both emotion recognition and facial recognition at the same time using LBPH (Local Binary Pattern Histogram) algorithm and purposive sampling technique for the facial recognition aspect with 67.5% accuracy and CNN (Convolutional Neural Network) algorithm using FER2013 (Facial emotion Recognition 2013) dataset for the emotion recognition aspect with 58.4% accuracy, with CRISP-DM method that can achieve the average accuracy rate of 63%, because currently not many research combine facial recognition using LBPH (Local Binary Pattern Histogram) algorithm and emotion recognition using CNN (Convolutional Neural Network) algorithm at the same time.
Design System pada Perancangan Antarmuka Perangkat Lunak Sistem Akses Digital Setiawan, Apriansyah Rizqi; Asfi, Marsani; Sevtiana, Agus; Pranata, Sudadi; Septian, Willy Eka
Jurnal Teknologi Terpadu Vol 9 No 1 (2023): Juli, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v9i1.619

Abstract

In developing software systems, there were no rules for making interface patterns in web base applications. The problem is the inconsistency of design styles between software, and the interface design development process takes a long time. This study aims to produce a design framework called Access Digital Design System. The system development method approach used in this research is Atomic Design which has several stages adapted to the investigation. In this study, the design system built is limited to product elements consisting of functional patterns and perceptual patterns. This research will produce a design system made up of perceptual and available marks as output artifacts in the form of code libraries, style guides, and pattern libraries. Based on the study, the designed Design System can reduce time spent creating user interface designs by using component guidelines and documentation with standardized foundation elements to increase productivity. Additionally, the final interface design becomes more consistent and uniform.
Pengaruh Pemasaran Media Sosial terhadap Keterlibatan Pelanggan (Survei pada Pengguna Halodoc di Indonesia) Ramadhani, Alif Ridha; Fauzi , Mochamad Ardan; Abdullah, Muhammad Mufti; Maesaroh, Syti Sarah; Herdiana, Oding
Jurnal Teknologi Terpadu Vol 9 No 1 (2023): Juli, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v9i1.622

Abstract

This study was conducted to obtain information about the magnitude of the influence of social media marketing on customer engagement. Social media marketing (X) is the independent variable, and customer engagement (Y) is the dependent variable in this study. Followers on the Halodoc Instagram account were selected to be the population in this study, and the sample was taken randomly (simple random sampling), with 120 respondents successfully obtained. The SEM method is used to analyze data with the help of IBM SPSS AMOS 21 software for Windows in the data processing. The results of data processing illustrate that there is an influence of social media marketing on customer involvement of (0.931) with a p-value (0.002) <0.05. Artificial Intelligence-Analitycs for social media is a tool for efforts to increase the competitiveness of official Instagram managers for a business against competitors owned by analyzing content optimization through various services in the form of statistics and metrics from Artificial Intelligence-Analitycs providers, which are generally website-based. The author's recommendation for Halodoc is to increase closeness with customers so that their bonds become stronger through planning content that is useful for the community and packaged attractively. Later it is hoped that there will be more user-generated content or posting word-of-mouth recommendation comments from customers to other customers.
Analisis dan Perbandingan Tools Forensik menggunakan Metode NIST dalam Penanganan Kasus Kejahatan Siber Yuladi, Achmad Iqbal; Indrayani, Rini
Jurnal Teknologi Terpadu Vol 9 No 2 (2023): Desember, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v9i2.636

Abstract

Cybercrime cases in Indonesia have increased yearly; during the COVID-19 pandemic as it is now, people rely on the internet to carry out daily activities such as teaching and learning activities, buying and selling online, working from home, etc. Therefore, cybercrime cases in Indonesia have increased. One of the most common examples is Cyberbullying cases on various social media networks with mobile platforms, one of which is WhatsApp Messenger. This study will analyze and compare the results of the MOBILedit Forensic Express and Magnet Axiom tools using the National Institute of Standards and Technology (NIST) method. This method can facilitate the investigation process in the case scenarios in this research. Researchers will also compare the results of the two tools used in this forensic process. The results of this study using the National Institute of Standards and Technology (NIST) method on the WhatsApp apps showed the Magnet Axiom tools were slightly superior with an accuracy of 82,8% compared to MOBILedit Forensics Express 72,7% in the condition that the object was not rooted.
Klasifikasi Jenis Burung menggunakan Metode Transfer Learning Pane, Yeremia Yosefan; Sihombing, Jeremia Jordan
Jurnal Teknologi Terpadu Vol 9 No 2 (2023): Desember, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v9i2.744

Abstract

Indonesia is known for its abundant natural resources, including its diverse bird fauna. The identification and classification of bird species is essential in maintaining biodiversity as well as for practical habitat management. Therefore, an efficient and accurate approach is needed to identify bird species. This study uses a deep learning approach to test and compare the MobileNetV2 architecture with architectures used in previous studies in recognizing bird species. We use a transfer learning approach that utilizes existing knowledge from pre-trained models and combines it with a Convolutional Neural Network (CNN) algorithm to detect and classify birds based on images with a total image data of 95376. Experimental results show that by using the MobileNetV2 architecture, we achieved an accuracy of 96.4% with a loss value of 0.241. Compared with the architecture used in previous research, our results show a significant improvement in accuracy and efficiency. The time taken to perform the classification at each step is about 646 ms. This study shows that using MobileNetV2 architecture in the transfer learning approach with CNN effectively performs bird species classification.
Smart Buildings menggunakan Hyperledger Fabric Blockchain untuk Manajemen Transaksi dan Pemodelan 3D Asmiatun, Siti; Novita Putri, Astrid; Zaman, Badroe
Jurnal Teknologi Terpadu Vol 9 No 2 (2023): Desember, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v9i2.751

Abstract

Home construction or home renovation must consider many complex factors. That is because there will be errors / human errors that occur. The impact of that will cause losses to architectural services and eliminate dissatisfied customer trust. Another problem is that customers use intermediaries or third parties in the home construction/renovation process, increasing funds. That is because the process of building/renovating the house is different fromations. This research utilizes a hyper ledger fabric blockchain and intelligent building technology to manage architect and consumer management without intermediaries. Technology can manage home construction/renovation through information on 3-dimensional house plans, initial home budgets, prices for building materials, and daily material needs. The purpose is to monitor the building/renovation of a house without a third party. This study uses the Multimedia Development Life Cycle (MDLC) Development Method to make its application. Meanwhile, blockchain technology is applied using Hyperledger Fabric Software. This research can increase trust and benefit both the customer and the developer. The results of this study are that by making block numbers 65 – 66, it is recorded that each transaction has a processing time from 2022-08-14 02:32:47 to 2022-08-14 02:32:50; it takes approximately.

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