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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 479 Documents
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Integrasi Convolutional Autoencoder dengan Support Vector Machine untuk Klasifikasi Varietas Almond Fadlullah, Rizal; Winarno, Sri; Naufal, Muhammad
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 1 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i1.9738

Abstract

This research aims to optimize almond variety classification by integrating Convolutional Autoencoder (CAE) as a feature extraction method and Support Vector Machine (SVM) for classification. The research process includes data collection from available datasets, preprocessing, and splitting data for training and testing. Features from almond images are extracted using CAE, which are then used in the SVM model for classification. Model evaluation shows a classification accuracy of 97% on the test data, a significant increase compared to the 48% accuracy of conventional SVM. The CAE-SVM approach offers more compact and informative feature representations, effectively improving almond variety recognition. This study highlights the potential of combining CAE and SVM advantages to enhance plant image analysis and encourages further advancements in machine learning applications in agriculture.
Implementasi Bidirectional Long Short-Term Memory untuk Identifikasi Entitas Saham Fatimah, Akmalia; Badieah, Badieah; Haviana, Sam Farisa Chaerul
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 1 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i1.9775

Abstract

One of the financial products in the capital market that is in great demand is stock. Shares are proof of ownership of a company that fluctuates and tends to have a high level of risk and nonlinear price changes. To make the right investment decision, investors are required to be able to analyze the abundant stock information carefully and quickly. In facing this challenge, Named Entity Recognition (NER) can be a potential solution in analyzing stock information by recognizing stock entities and grouping them into certain labels. In this research, NER is developed with the Bidirectional Long Short-Term Memory algorithm, which is used to identify five stock entities: company name, stock code, stock index, industry sector, and sub-sector. With an accuracy of 99.81% on the test data, the Bi-LSTM algorithm can identify the entities well and group each token into the five entities.
Peramalan Data Ekonomi Menggunakan Model Hybrid Vector Autoregressive-Long Short Term Memory Savada, A. Gilang Aleyusta; Nama, Gigih Forda; Yulianti, Titin; Mardiana, Mardiana
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 1 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i1.10066

Abstract

Fluctuations in stock prices and the Rupiah exchange rate create uncertainty for investors in their investment decision-making. One approach to minimizing investment risk is through forecasting utilizing a reliable method. Traditional forecasting models, such as Vector Autoregressive (VAR), are effective in capturing linear patterns but struggle to accommodate more complex patterns. On the other hand, modern deep learning models like Long Short Term Memory (LSTM) can handle dynamic patterns (both linear and nonlinear) but have limitations in consistently processing simultaneous relationships among variables. This research aims to develop a Hybrid forecasting model by integrating VAR and LSTM approaches to predict the Composite Stock Price Index (IHSG) and the Rupiah exchange rate against the US Dollar. The Hybrid VAR-LSTM model leverages the strengths of VAR for linear patterns and LSTM for nonlinear patterns in multivariate time series data. Using the OSEMN framework (Obtain, Scrub, Explore, Model, iNterpret), this study ensures a systematic and comprehensive analysis process. Data from January 2004 to December 2023 is used to build the model, while data from January to July 2024 is used for validation. The model's performance is evaluated using Mean Absolute Error (MAE) to measure the prediction error. The results indicate that the Hybrid VAR-LSTM model significantly improves prediction accuracy compared to the VAR model used independently, as evidenced by a reduction of 42.72 points in MAE for IHSG predictions and 55.82 points for Rupiah predictions.   Keywords — Composite Stock Price Index; Hybrid VAR-LSTM; OSEMN Framework; Rupiah Exchange Rate; Time Series Forecasting.
Perancangan Sistem Palang Parkir Otomatis Dengan Pengenalan Wajah Menggunakan Metode Eigenface Saputro, Yulius Dani Eko; Riti, Yosefina Finsensia
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 1 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i1.10215

Abstract

The main problem in this study is that the parking system on the campus of the Catholic University Of Darma Cendika still relies on manual methods, such as the use of cards and physical tickets, which are prone to human error, inefficient, easily misused, and raise security concerns. The main objective of this study is to improve the security and efficiency of parking area management by reducing dependence on card-based methods or physical tickets. This study collects facial data from individuals with various angles and facial positions, then the data is further processed to improve image quality. By applying the Eigenface model, the system is able to recognize faces with 100% accuracy under certain lighting and distance conditions. However, the performance of facial recognition is still affected by the quality of lighting and the distance between the camera and the object, indicating that further optimization is needed. Recommendations proposed include adjusting the lighting and camera position to obtain better facial image results. The Eigenface-based facial recognition technology applied in this study has great potential in improving the efficiency of the automatic parking barrier system. However, to achieve optimal results in various environmental conditions, further development is needed. Thus, it is expected that this system will not only be able to recognize faces accurately, but also be able to operate effectively and efficiently in real environments. In addition, this system also uses the Convolutional Neural Network method to distinguish between real faces and facial images from the cellphone screen, thereby increasing the overall security of the system.
Penerapan YOLOv5 untuk Klasifikasi Gambar dalam Sistem Estimasi Kandungan Kalori Masakan Indonesia Wiranata, Maximus Aurelius; Lestari, Caecilia Citra
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 1 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i1.10284

Abstract

In this era of continuously evolving technology, calorie counting applications have become crucial for individuals who are concerned about their eating habits and health. However, most of these applications have not fully accommodated the variety of dishes commonly consumed in Indonesia, especially the popular dishes in Java Island, which has the largest population in Indonesia. To address this limitation, this research introduces an innovative solution in the form of an Indonesian Cuisine Classification and Calorie Content Estimation System using YOLOv5 technology. In this approach, the YOLOv5 object classification technology is used to identify various types of Indonesian dishes, including eight classes such as satay, meatball soup, traditional soup, fried rice, mixed vegetables salad, fried chicken, beef soup, and beef stew. This system is not only capable of accurately classifying dishes but also provides calorie content estimation based on the composition of the classified food ingredients. The implementation of this research combines YOLOv5 to apply the Indonesian cuisine classification model using the nutrition API from API Ninjas to obtain the required nutrition data. This research uses datasets obtained from Kaggle website, Mendeley Data, and Roboflow, with a total of 303 images for each class of dishes. As a result, the model achieved an accuracy score of 94.2%, precision of 94.3%, recall of 93.8%, and an F1 Score of 93.8%.
Perancangan Index Learning Style untuk Pengembangan Personalisasi Learning Management System berbasis Moodle Sianipar, Helen Anjelica; Chaeruman, Uwes Anis; Tarjiah, Indina; Suteja, Bernard Renaldy
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 1 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i1.10418

Abstract

Differences in students' learning styles often pose challenges in online learning, particularly in personalizing learning materials to meet individual needs. This study developed an Index Learning Style (ILS) plugin based on the Felder-Silverman Learning Style Model (FSLSM) to support personalized learning on the Moodle Learning Management System (LMS). The plugin is designed to identify students' learning styles through 44 questions measuring four main dimensions: processing, perception, input, and understanding. The system development involved algorithms for learning style analysis, integration with Moodle's restricted access feature, and implementation in an Internet of Things (IoT) course. The implementation results show that the ILS plugin can effectively map students' learning styles to relevant Learning Object Materials (LOM). Moreover, personalized learning materials increase student engagement and facilitate material comprehension, particularly for those with dominant learning styles such as Active, Sensitive, Visual, and Sequential. The development of the ILS plugin provides a practical solution for enhancing the online learning experience to make it more adaptive. This plugin has the potential for widespread implementation in various technology-based education contexts to support more personal and effective learning.
Evaluasi Kebergunaan dan Pembangunan Website Online Library Information System Menggunakan Think Aloud Simbolon, Iustisia; Simanjuntak, Riski Yan Daniel; Siregar, Edrei Abiel Benaya
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 2 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i2.7206

Abstract

Usability refers to the quality of the user experience when interacting with a product or system, including websites, software, devices or applications. Usability is about effectiveness, efficiency and overall user satisfaction. OLIS (Online Library Information System) is an information system that functions as a catalog for managing the Del Institute of Technology library. Based on the usability value measurement results, the OLIS website has an SUS value of 41.25 or a “poor” level of usability. This shows that the usability aspect of the website must be improved. To achieve good usability, a usability evaluation is carried out using the think aloud method. This evaluation is carried out for at least 2 iterations until the OLIS usability value reaches a minimum value of 80. The results of the first iteration evaluation are 18 problem findings which are then analyzed to make the first iteration improvement design. Furthermore, the second iteration identifies problems from the first evaluation and produces 17 usability problems which will then be analyzed to make a second iteration improvement design that will be made into the final high fidelity prototype. The SUS measurement results on the OLIS website which have been evaluated by think aloud is 85.25 or the usability level is "excellent".
Analisis Kesuksesan Sistem Seleksi Mahasiswa Berprestasi dengan DeLone McLean Success Model Alfaprasetyan, Kefas; Tambotoh, Johan Jimmy Carter
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 2 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i2.8599

Abstract

Education is a very important thing where education can improve the quality and competitiveness of a person. Wonogiri Regency is one of the regions in Central Java that is currently struggling to improve the quality of human resources by increasing the interest in education of its people to the Higher Education level so that the Outstanding Student Scholarship program was launched. This study will analyze the success of the SIMAPRES website used for registration and selection of the scholarship program. The sample of this research is the applicants of the scholarship program who use the SIMAPRES website. The research model used is the DeLone & McLean IS Succes Model 2003 with 6 hypotheses and data processing using Partial Least Square-SEM which will show the correlation between variables from the model. This study tested a sample of 423 students applying for scholarship programs from various universities in Indonesia. From this sample, it was found that the six hypotheses showed a significant influence between variables with a T-Statistic value above 1.96, which also means that system quality, information quality, service quality have a good impact on the use and user satisfaction variables. From the results of the study it can be concluded that the SIMAPRES website has been quite successful in its use for registration and selection of the scholarship program.
Implementasi K-Means dalam Segmentasi Pelanggan Usaha Aluminium dan Kaca Berdasarkan Perilaku Pembelian Ramadhani, Salsabilla; Pandunata, Priza; Arifin, Fajrin Nurman
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 2 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i2.9533

Abstract

— Mulia Jasa Aluminium dan Kaca is a business in the retail and service sector, offering Aluminium and glass materials and services for manufacturing, installation, and repair. Currently, competition in this field is quite intense, leading the business owner to admit difficulties in increasing sales. Therefore, the business owner needs to implement marketing and service strategies to boost sales. However, the diversity of customers with varying characteristics and behaviors makes it challenging to establish effective marketing and service strategies. Thus, this study conducts customer segmentation based on purchasing behavior. The aim is to understand customer behavior and loyalty using sales report data from the business. The variables used to assess a customer's value are Length, Recency, Frequency, and Monetary (LRFM). These variables are grouped using the K-means clustering algorithm. The objective of this study is to group customers based on their purchasing behavior, thereby assisting the business in developing more effective marketing and service strategies, enhancing customer satisfaction, and ultimately increasing sales and loyalty. Using the Silhouette method to determine the optimal number of clusters, three customer groups were identified, with the highest coefficient value of 0.663063. Cluster 0 is the “Lost Customer Group”, Cluster 1 is the “New Customer Group”, and Cluster 2 is the “Core Customer Group”.  
Perbandingan Multifaktor Evaluation dan Fuzzy Analytic Hierarchy Process pada Kualitas Biji Kopi Meiyanti, Rini; Asrianda, Asrianda; Azmi, Win
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 2 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i2.9741

Abstract

The development of information technology in the agricultural sector is crucial, including in determining coffee bean quality. This research implements a comparison of decision support systems (DSS) using the Multifactor Evaluation Process (MFEP) and Fuzzy Analytic Hierarchy Process (FAHP) methods to assess coffee bean quality based on moisture content, Trase, defects, color, aroma, and bean size. The results show that FAHP has an accuracy of 77%, higher than MFEP with an accuracy of 71%. Thus, FAHP is more effective in determining the farmers with the best coffee beans, thereby helping to improve the economic well-being of farmers and cooperatives.

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