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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 266 Documents
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Aplikasi Android Untuk Pelaporan Perlengkapan Jalan di Kota Banjarmasin Imama, Muhammad Haykam; Arifin, Aridhanyati
Jurnal Teknologi Terpadu Vol 8 No 2 (2022): Desember, 2022
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v8i2.532

Abstract

As the highway organizer, the Department of Transportation of Banjarmasin City has complaints about web-based road equipment applications because it takes a long time to load web pages. This condition makes work efficiency go down. As a result, services to the community could be more optimal. This research designs and builds a system called “Sistem Informasi Perlengkapan Jalan” (Road Equipment Information System) with an android base. This research purpose is to create an efficient android-based road equipment system. The hope is that it will become an alternative solution to help the Department of Transportation of Banjarmasin City maintain road equipment. The research stages consist of three stages: data collection, literature study, and software development. Data collection uses two techniques, namely interviews and observation. The development method used is Extreme Programming (XP). The application is built using Android Studio tools, Java programming language, and MySQL database. The testing method used is black box testing. The result of the research is a system that can accurately accommodate road equipment data and accurately display the location of the intended road equipment on the map. In addition, the Android-based SIPJ application is more efficient than the web-based SIPJ based on access speed.
Penerapan Algoritma Naïve Bayes dalam mengklasifikasikan Media Sosial untuk mengamati Trend Kuliner Wilandini, Destaria; Purwantoro, Purwantoro
Jurnal Teknologi Terpadu Vol 8 No 1: Juli, 2022
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v8i1.535

Abstract

The influence of the internet and social media has accelerated the pace of business trends developing rapidly, and social media acts as a means of information and information warehouses in observing emerging trends. In this study, the Naïve Bayes algorithm was used to analyze and predict social media applications by classifying classes on the data to see which applications are most popular with the public in observing a culinary trend. Culinary business trends choose as the subject under study with the object of research on social media applications. Social media is researched to produce recommendations for social media applications that are suitable for use in marketing media based on their target market, the dataset used was 101 data with 80%:20% split data. The study results stated that using the Tiktok application is recommended, after Instagram, Twitter, Youtube, and finally, Facebook. This research was conducted for MSMEs to guide suitable social media applications in business marketing strategies. Based on the results of this study, research experiments using other methods and algorithms can be applied in other studies to be a comparison with the current research results.
Hybrid Machine Learning Model untuk memprediksi Penyakit Jantung dengan Metode Logistic Regression dan Random Forest Al Azhima, Silmi Ath Thahirah; Darmawan, Dwicky; Arief Hakim, Nurul Fahmi; Kustiawan, Iwan; Al Qibtiya, Mariya; Syafei, Nendi Suhendi
Jurnal Teknologi Terpadu Vol 8 No 1: Juli, 2022
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v8i1.539

Abstract

The heart is the main organ that must work properly and regularly. If there is interference, it will be fatal, namely the onset of a heart attack. Heart attack is included in the 10 diseases with a high risk of death. This is caused by stress factors, blood pressure, excessive work, blood sugar, and others. The purpose of this study is to predict heart disease using Machine Learning (ML) algorithms as an early preventive measure on desktop-based information systems. With Machine Learning models, the hybrid model can increase the accuracy value of an ML method that is added to other ML methods. The accuracy value obtained from the Hybrid Model Machine Learning using the Random Forest and Logistic Regression methods is 84.48%, which is an increase of 1.32%.  
Klasifikasi Penderita Diabetes menggunakan Algoritma Machine Learning dan Z-Score Karo Karo, Ichwanul Muslim; Hendriyana, Hendriyana
Jurnal Teknologi Terpadu Vol 8 No 2 (2022): Desember, 2022
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v8i2.564

Abstract

Diabetes is a deadly and chronic disease. It characterized by an increase in blood sugar. Many complications occur if diabetes does not treat and identified. The common identification process by visits to diagnostic centers and consulting physician. It makes bored patients. Machine learning approach can solve the problem of diabetic identification. However, the unbalanced range of diabetes variable values ​​affects the quality of machine learning results. This study predicts the likelihood of diabetes in diabetic patients from 768 Indian women, using three machine learning classification algorithms and Z-Score normalization method. The machine learning algorithms used are Decision Tree, Support Vector Machine (SVM) and Naive Bayes. Experiments were run on the Pima Indians Diabetes Database (PIDD). Dataset retrieved from the UCI Machine Learning Repository. The performance of the three algorithms was evaluated using accuracy, precision, F1, and recall based on confusion matrix. SVM algorithm is an algorithm that has the highest performance that both algorithm the Naive Bayes and Decision Tre algorithms, the accuracy and F1 is 80.73% and 76%. The Z-Score method has positively contribution to increasing the accuracy of the classification model. Furthermore, this study also managed to get a higher accuracy than previous studies.
Sistem Klasifikasi Karakter Kepribadian Siswa Sekolah Dasar berdasarkan Tipologi Hippocrates Galenus menggunakan Metode Naïve Bayes Sabri, Muhammad; Kasriadi, Dedy; Irsal, Irsal; Arifin, Suci Ramadhani
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.v9i1.577

Abstract

Schools must shape students' personalities through learning activities and programs that can make students become educated human beings with noble characters as the nation's next generation. Teachers as educators need to recognize students' character to develop more effective teaching methods. So far, the guidance process by teachers at SD Inpres 12/79 Palattae has been carried out face-to-face, namely by talking directly to students and carried out without knowing the student's character. As a result, the guidance and counseling that is carried out are sometimes ignored by students. Based on these circumstances, this research was then carried out to build an information system that implements the Naïve Bayes method in elementary school students' personality character classification system based on the Hippocrates-Galenus typology to know the personality types possessed by students. With this system, the teacher can assist in knowing the personality types possessed by students so that it makes it easier to build the mindset, attitude, and behavior of students so that they become positive, good-natured, noble-spirited, and responsible individuals.
Analisis perbandingan Algoritma Support Vector Machine, Naive Bayes dan Regresi Logistik untuk Memprediksi Donor Darah Hendriyana, Hendriyana; Karo Karo, Ichwanul Muslim; Dewi, Sri
Jurnal Teknologi Terpadu Vol 8 No 2 (2022): Desember, 2022
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v8i2.581

Abstract

Blood supplies and stocks are urgently needed. Regular donations from healthy volunteers are the only way to keep up with the blood supply. This research aims to develop and evaluate a machine-learning algorithm to predict whether a volunteer will donate or not. The machine learning algorithms are Naïve Bayes, Logistic Regression, and Support Vector Machine (SVM). This study also applies the process of normalizing data with a Z-score to standardize the dataset scale. The dataset is sourced from the Hsin-Chu City Blood Transfusion Service, Taiwan, and stored in the UCI repository. The evaluation methods are accuracy, precision, recall, and F-1 score. The research results with the Naïve Bayes algorithm were 89.90%, Logistic Regression 82.59%, and SVM 94.79%. The normalization process using the Z-Score method contributes positively to improving the performance of the classification model. Based on this performance, it provides predictive results for volunteers who will return to donate blood to offer blood reserves to those in need.
Sistem Informasi Pelayanan Surat Menyurat Di Kelurahan Desa Sriwijaya Lampung Tengah Siregar, Andronias; Satriansyah , Arief; Hidayat, Rachmat; Wijaya, Maya Septa
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.v9i1.588

Abstract

Information technology has progressed quite rapidly, and more and more years are increasingly developing technology worldwide. Information technology greatly benefits the world government by creating village websites and building computer-based information systems. With information technology, everyone can process and access data and information they want to convey to the whole community through the website so that everyone can access it easily without having to come and ask the staff or person concerned. The problem with the mailing service in the Village of Sriwijaya Village, Central Lampung, where the process of making letters is still done manually, there are still writing errors. The purpose of creating this correspondence information system is to overcome existing problems and help employees and staff make it easier to update village and community development information by easily accessing this information; with this system, staff can also improve skills in information technology. The method used by the waterfall in making this information system includes making correspondence and information systems in the Sriwijaya village, using the PHP program and MYSQL database. The results of this study are a web-based correspondence information system that is very helpful for community services in making certificates, KK, KTP, SKU, and more quickly and saves time.
Sistem Informasi Geografis Pencarian Layanan Vaksin dan PCR Covid-19 Menggunakan Google Maps API dan Jalur Terpendek Haromain, Imam; Munir, Sirojul; Wahyudi, Riyan
Jurnal Teknologi Terpadu Vol 8 No 2 (2022): Desember, 2022
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v8i2.596

Abstract

The need for information on the location of health services for Covid-19 vaccines, swabs and PCR in the era of the Covid-19 pandemic and pre-pandemic is something that the public is looking for. The ease and speed of getting the location and route of health services can help the community, so that the vaccine program and the enforcement of the Covid-19 diagnosis can run optimally. Geographic Information Systems (GIS) can be applied in the health sector such as spatial-based health service applications. This research is designing a web-based GIS using Google Maps API technology and the shortest path search algorithm. The software development method used is a prototype model and data collection techniques. The system prototype was tested using the blackbox testing method with the results of system testing obtained 100% all functional running well. This research has succeeded in making a prototype of the GIS application to find the nearest health service for vaccines, swabs and PCR Covid-19 as well as recommendations for the total travel costs that must be incurred to the location so that it can help the community, and the application is feasible to use.
Progressive Web Apps: Pengembangan dan Studi Penerimaan pada Mahasiswa Indonesia Menggunakan Scrum dan UTAUT Herman, Herman; Frederick, Frederick
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.v9i1.603

Abstract

Currently, the usage of mobile applications and the total activity on mobile phones through web browsing or native applications is very high. Most users use native mobile apps to browse the content of specific industries. Another way to do so is through a web browser. However, both have limitations. In web applications, users' experience is not that great compared to native applications. On mobile native applications, it requires higher development costs to ensure the app can be accessed across multiple platforms. To solve this problem, Google launched progressive web apps as an alternative, where progressive web apps can be accessed through different platforms, saving development costs but still providing a user experience almost the same as native apps. The focus of this research is to develop a progressive web app with React.js using the scrum method and also to research user acceptance of progressive web apps through qualitative methods/interviews based on the UTAUT research model to general students and lecturers of computer science. The result of this research, based on UTAUT points, shows positive responses toward accepting progressive web apps. Then with the rapid development of the web and browsers, progressive web apps will potentially have a promising future. With this research, it is hoped that can bring benefits to the decision to use the progressive web apps, become a reference for the next research, and increase the knowledge of the public and readers about the progressive web apps.
Penerapan Model Machine Learning Untuk Menentukan Klasifikasi Jenis Bantuan Sosial Rosanti, Nurvelly; Iqbal, Muhammad; Munir, Sirojul
Jurnal Teknologi Terpadu Vol 8 No 2 (2022): Desember, 2022
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v8i2.604

Abstract

The Provincial Government of DKI Jakarta has a social assistance program budgeted by the APBD in the form of the Jakarta Elderly Card (KLJ), Jakarta Persons with Disabilities Card (KPDJ) and Jakarta Child Card (KAJ) programs. The problems that occur at the Kelurahan level are related to social assistance, namely the difficulty in determining the right type of assistance to be received by residents according to the terms and criteria that have been determined by the Government and there is no overlapping of recipients of assistance. The registration factor and the lack of understanding of residents regarding the criteria for the type of social assistance resulted in the determination of recipients of social assistance not being on target, such as residents receiving assistance who did not meet the criteria, resulting in social jealousy. To help with this problem, research was carried out to determine the best model in classifying the types of social assistance based on recipient criteria by comparing three classification methods. This study uses 100 respondent data and 8 criteria used as determinants of recipients. Comparison of the Certainty Factor, Naïve Bayes and Decision Tree models will provide an overview of the best model based on the level of accuracy. The confusion matrix is used to test the accuracy for Naïve Bayes and Decision Tree and the output of the selected model is a web-based application that can provide recommendations for types of social assistance. The best accuracy results are Certainty Factor which is 98.4%, Naïve Bayes and Decision Tree is 93.3%.

Page 22 of 27 | Total Record : 266

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