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Hapnes Toba
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JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Published by Universitas Kristen Maranatha
ISSN : 24432210     EISSN : 24432229     DOI : https://doi.org/10.28932/jutisi
Core Subject : Science,
Computer Science & IT
Paper topics that can be included in JuTISI are as follows, but are not limited to: • Artificial Intelligence • Business Intelligence • Cloud & Grid Computing • Computer Networking & Security • Data Analytics • Datawarehouse & Datamining • Decision Support System • E-Systems (E-Gov, E-Health, E-Commerce, etc.) • Enterprise System (SCM, ERP, CRM) • Human-Computer Interaction • Image Processing • Information Retrieval • Information System • Information System Audit • Enterprise Architecture • Knowledge Management • Machine Learning • Mobile Computing & Application • Multimedia System • Open Source System & Technology • Semantic Web & Web 2.0
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 17 Documents
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Search results for , issue "Vol 8 No 3 (2022): JuTISI" : 17 Documents clear
Penerapan Machine Learning untuk Penentuan Mata kuliah Pilihan pada Program Studi Informatika Fathorazi Nur Fajri; Abu Tholib; Wiwin Yuliana
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 3 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i3.3990

Abstract

Informatics study program at Nurul Jadid University does not have a general concentration of knowledge, so that sometimes the selection of elective courses by students is not quite right. This study aims to classify the concentration of knowledge with a data mining approach which can then be used as a recommendation for selecting elective courses by students. In this study, we implement a machine learning algorithm to provide recommendations to students regarding what interests are more suitable to be taken based on the values ​​of prerequisite courses in previous semesters. Student data was obtained from the Head of the Center for Data and Information Systems (PDSI) at Nurul Jadid University with 70 student data from Nurul Jadid University batch 2018. The machine learning algorithm used is Neural Network with Python programming language, the tools used are Google Collab. At the beginning of data collection, then pre-processing is carried out to prepare the dataset in order to get good results, and model training is carried out. After training on the model, then further testing is carried out on the model to determine the performance of the model. The result of the accuracy value in the training model process is 0.83 or 83% and the accuracy of the test data is 0.79 or 79%.
Pengembangan Website untuk Menampilkan Harga Koin Kripto dengan Antarmuka Pemrograman Aplikasi Rizky Parlika; Achmad Yuneda Alfajr; Alif Ernanda Putra; Ahmad Dendy Prasongko Putra
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 3 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i3.4285

Abstract

Cryptocurrency is one of the investment instruments and trading assets chosen by old and new traders today. Lots of people claim that the information from the platforms they use is just numbers or trivial information. However, many ordinary people who really need it are still confused about this information. In this study, an application programming interface was developed to help new traders using the instrument easier.
Perancangan Aplikasi Pembukuan Menggunakan Metode Agile Scrum Dhandy Joenathan Kurnia Putra; Penidas Fiodinggo Tanaem
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 3 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i3.5060

Abstract

The product and transaction management section's business process in the Sepatu Kaki Kaki has not maximized the existing system application. This makes the business processes carried out by the store less effective. The method used in this study uses one of the agile software development models, namely Scrum. Which has advantages, including flexible requirements, speed up application production time, and require fewer team members. This research aims to produce application development that can manage products effectively and help staff analyze in accordance with the expected targets in the future. The application development uses the Kotlin programming language - Android Native, with BaaS (Backend as a Service) Firebase. The results of this research are an android application to speed up and simplify product management, the latest product stock can be known, and minimize manual recording errors.
Pengembangan Dashboard Informasi Gereja Tangguh Bencana dengan Metode User Centered Design Ebentera Santosa; Kristian Adi Nugraha; Agata Filiana
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 3 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i3.5119

Abstract

Indonesia is a country prone to natural disasters, from earthquakes to annual flood. These can affect, and even at times, endanger people's lives. JAKOMKRIS PBI or the Christian community for disaster management in Indonesia has the duty to aid with Christian churches and institutions to actualize the concept of disaster-resilient church. One of the ways that can help JAKOMKRIS PBI's main goal is to create a website-based dashboard displaying information related to the concept of disaster-resilient church. This research applies the principles of User-Centered Design which in its development focuses on the user. User-Centered Design requires information about the appearance of the interface desired by the user. The results of the research are expected to be able to assist users in preparing churches for disaster events by providing provisions and disaster risk reduction based on managed data. This research uses usability tests using performance metrics and System Usability Scale (SUS) methods. The performance metrics test results for the efficiency value of 90.95% for the user role and 100% for the admin role, while the effectiveness value is 96% for the user role and 100% for the admin role and the results of the System Usability Scale (SUS) test are 81.5 for the user role and 81.25 for admin roles.
Menentukan Aksi Lawan Komputer Pada Game Strategi Menggunakan Algoritma K-Nearest Neighbour Michael Freddy; Teady Matius Surya Mulyana
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 3 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i3.5137

Abstract

Advances in computer technology allow various devices to complete complex computing, especially in the entertainment industry and the biggest example is games. Strategy game is the type of game that most often gets an Artificial Intelligence or AI system implemented to imitate human behaviour when playing games. Many game AI systems are predictable so players get bored quickly, so adaptive and simple AI is needed to make it easier for game developers. K-Nearest Neighbour is a classification algorithm with supervised learning, this algorithm will be used in this study. The research method tests the level of accuracy in determining the class by providing a sample of data which is divided into training data and test data. The measure of the level of accuracy is calculated using the confusion matrix after the test table is obtained. The results of the study concluded that the K-Nearest Neighbour algorithm can determine computer opponents fairly well. More data samples are needed as data training to increase the level of classification accuracy.
Rancang Bangun Aplikasi Koleksi Resep Makanan Berbasis Sistem Operasi iPhone Jessica Wienadi; Yosua Setyawan Soekamto
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 3 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i3.5140

Abstract

Food is a necessity that is needed by anyone. One way to obtain food is to cook. Many people choose to cook their own food, even more so during the Covid-19 pandemic which caused cooking preferences to increase. One of the things used when cooking is recipes. Recipes need to be saved for easy reuse. Hence built a food recipe collection app that helps store recipes on mobile devices. The application is built on iOS. To find out the problems and needs in recipe saving, interviews were conducted, and problems were found regarding separated storing location and diverse storing format, limited search capabilities on current saving method, time-consuming input processes, and disturbing automatic-lock on device. Analysis of similar applications were also conducted and found shortcomings related to the flow and application interface. Based on these results, the application is designed with two main features, namely recipe storage and cooking mode. In recipe storage, users can add, change, and delete recipes. Once a recipe is saved, users can view a list of saved recipes, view recipes by category, and search for recipes by title. In cooking mode, users are given a checklist to prepare ingredients and then given a step-by-step. Application development uses the Swift programming language, with Swift User Interface to build the app's interface. Application data is stored locally using Core Data. Testing of application implementation results is carried out using alpha test and beta test. Alpha tests are performed using black-box test, to ensure the accuracy of inputs and outputs of various application functions. The beta test is done by conducting application trials and interviews with cooks to ensure application usability and to obtain feedbacks from users. Based on the test results, all functions in the application run as expected and this application helps users save food recipe collection.
Implementasi Deep Convolutional Generative Adversarial Network untuk Pewarnaan Citra Grayscale Muhammad Ricky; Muhammad Ezar Al Rivan
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 3 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i3.5218

Abstract

The process of adding color to a grayscale image is needed so that improvements to the image can be done quickly and without special knowledge. Image coloring using Deep Convolutional Generative Adversarial Network (DCGAN) and Generative Adversarial Network (GAN) methods. The model training uses the Places365 dataset, which contains 98,721 training data and 6,600 test data. The image is converted into the CIELAB color space, using the L channel as grayscale input and the AB channel as the other input. The test is done by comparing the accuracy values ​​using the Mean Absolute Error (MAE) and Structural Similarity Index Matrix (SSIM) methods. The calculation results of the MAE method show that the average MAE value of the DCGAN method is smaller than the GAN method, with a score of 10.18 and 10.81. The results of the calculation of the SSIM method show that the DCGAN method has a higher average with a score of 91.54% and 68.32% for the GAN method. The results of the questionnaire conducted on 30 respondents showed that the DCGAN method was chosen by more respondents than the GAN method, respectively 88.40% and 11.60%.
Klastering Sayuran Unggulan Menggunakan Algoritma K-Means Lina Mardiana Harahap; Wahyu Fuadi; Lidya Rosnita; Eva Darnila; Rini Meiyanti
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 3 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i3.5277

Abstract

Horticulture, especially vegetables, has great potential to be developed because it becomes a source of income for the community and small farmers in each region because Indonesia is called an agrarian country with most of them working in agriculture. Mandailing Natal Regency is the district with the largest area in North Sumatra province, but Mandailing Natal has not been able to outperform vegetable crop production in North Sumatra. Data mining methods can find interesting and invisible patterns in data sets. One of the methods is the K-Means clustering algorithm which groups data into clusters based on the similarity of data characteristics. In this study, vegetable data was clustered which aims to determine the potential commodities in each area in Mandailing Natal Regency, plants that have potential in the area will be maintained and their production increased, while vegetable crops whose production is still low will be a priority to increase their production. The research method used in this study was to collect vegetable data from the Badan Pusat Statistik in the form of data on harvested area, production, plant area, and new planting area. In addition, data collection was carried out by conducting theoretical studies in journals. The results of clustering superior vegetables using the K-Means Algorithm are in the form of potential grouping into 3 clusters, namely low, medium, and high clusters and the output is a web-based system in its application. The results of the clustering analysis were obtained with each total data of 69 data, namely big chili with C1 81%, C2 16% and C3 3%. Cayenne C1 29%, C2 48% and C3 23%. Long Beans C1 26%, C2 38% and C3 36%. Kale C1 39%, C2 36% and C3 25%. Eggplant C1 43%, C2 29% and C3 28%. Tomato C1 41%, C2 58% and C3 1%.  
Perancangan User Interface dan User Experience Sistem Informasi E-learning Menggunakan Design Thinking Devi Karlina; Dwi Rosa Indah
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 3 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i3.5412

Abstract

The COVID-19 pandemic has caused various activities to be shifted online, one of which is learning activities at school. Because they don't have their own e-learning information system, online learning activities at SMA Tunas Bangsa Palembang utilize various media such as Classrooms to create classes, Google Forms to fill absences, and YouTube to deliver material. Lots of media and there are still perceived shortcomings related to the features of the media used cause the implementation of learning activities become less effective and efficient. Thus, to provide comfort and convenience in these learning activities, an e-learning information system user interface and user experience (UI/UX) design is required. This study uses design thinking as a method that has 5 stages, starting with problem exploration (empathize) to testing the solution design prototype (test). Prototype testing is carried out using the usability testing method, using task scenarios and the System Usability Scale (SUS) and User Experience Questionnaire (UEQ) questionnaires. The results of usability testing using SUS task scenarios and questionnaires include aspects of learnability and efficiency of teacher and student user groups, namely 100% and 0.04 goals/sec, for teacher user satisfaction aspects is 93 with grade scale "A" and for student users is 85 with a grade scale of “B”, the UEQ assessment scores for the two user groups were above 2.0 in all rating categories namely “Attractives”, “Perspicuity”, “Efficiency”, “Dependability”, “Stimulation”, “Novelty”. So, it can be concluded that the e-learning prototype design has had a good user experience.
Serverless Named Entity Recognition untuk Teks Instruksional Pertanian Kota Trisna Gelar; Aprianti Nanda; Akhmad Bakhrun
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 3 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i3.5447

Abstract

The evolution of document documentation, classification, and information retrieval includes named entity recognition (NER). The implementation of NER in the agricultural domain, in particular instructional texts or transcriptions of tutorial videos, will make it easier for the general public to understand the specific concepts and terms of urban agricultural activities such as crop production processes and procedures, agricultural methods and tools, harvest cycles, and handling plant pests or diseases. Spacy is an NLP tool, has two methods of developing NER models, namely with Toc2Vec and Transformer. Both methods have advantages and disadvantages, namely different sizes, performance and prediction speeds according to needs. The NER model can be implemented into a Serverless application, using the Functional as Services (FaaS) and Backend as Services (BaaS) approaches. For the subtopic of cultivating fruit crops in agricultural instructional literature, three NER models have been built in this study. First, the IndoBERT-based model, the Toc2Vec-based model with efficiency optimization, and the Toc2Vec-based model with accuracy optimization. The most efficient toc2vec model, with a f1-score of 0.71, is followed by the effective toc2vec model, with a f1-score of 0.60. The COUNT, PERIOD, and VERIETAS entities are consistently predicted incorrectly by the Toc2Vec model, which is unable to forecast numeric entities well. In addition, the Toc2Vec Model's better efficiency optimization directly relates the size of the model to the speed of word prediction per second, and the model is simple to integrate into a FaaS- and BaaS-based Serverless. The capabilities of Serverless M have been successfully tested using the black box method.

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