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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 16 Documents
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Search results for , issue "Vol 11 No 2 (2025): JuTISI" : 16 Documents clear
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.
Simulasi Dinamis Single Qubit dan Multi Qubit: Sebuah Pendekatan Python Setyawan, Muhammad Yusril Helmi; Harani, Nisa Hanum; Andriyanto, Achmad
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.v11i2.10075

Abstract

This study developed a dynamic simulation system for single qubit and multi qubit using a Python-based approach, leveraging quantum computing libraries such as Qiskit, NumPy, and Matplotlib. The system is designed to simulate various quantum operations, including Hadamard, Pauli-X, Pauli-Y, Pauli-Z, CNOT, and Toffoli, with integration into a Flask-based web interface for easy user interaction. The simulation results show a high level of accuracy, with a difference of only 0.2% in measurement probabilities for single qubit operations like Hadamard and less than 0.4% for multi qubit operations like CNOT and Toffoli. The tests also demonstrated efficient execution times, ranging from 12 to 25 milliseconds, even for complex quantum operations. Validation against established literature confirms that the system is accurate, efficient, and reliable, making it a valuable tool for supporting learning and research in quantum computing.
Implementasi Regularized Singular Value Decomposition dalam Sistem Rekomendasi Buku Collaborative Filtering Putra, I Made Alit Darma; Santiyasa, I Wayan
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.v11i2.10186

Abstract

At the school level, time is limited by the system of lesson hours. This makes students have to use their time wisely before changing lesson. However, choosing appropriate reading material often requires more time which results in wasted class hours. The development of a recommendation system using the Collaborative Filtering (CF) and Regularized Singular Value Decomposition (SVD) methods was chosen to solve the problem of students having difficulty finding books in the library. The data used is student interaction data with books in the form of ratings which are collected directly and processed to provide recommendations. The results of applying SVD in predicting ratings and looking for appropriate latent features to describe the characteristics of students and books produce MAE and RMSE values of 0.478 and 0.686. The research conducted also shows that the appropriate number of latent factors or features and the addition of regularization have an effect on increasing prediction accuracy. The predicted value of the rating is then used to provide personal book recommendations and the latent feature values of the books found are used in calculating cosine similarity to provide non-personal recommendations.
Perbandingan Kernel Convolutional Neural Network dalam Pengenalan dan Transliterasi Kata Aksara Lampung Utami, Desi Rahma; Murdika, Umi
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.v11i2.10406

Abstract

The study aims to create a system that can recognize and transliterate Lampung script image data and compare the Convolutional Neural Network (CNN) kernel to the Lampung script word recognition and transliteration system. The Lampung script recognition and transliteration system with the CNN learning model is implemented using the python 3.9.4 64 bit programming language, with a stride of 1 for convolution and 2 for pooling, the kernel size variations used are 2x2, 3x3 and 5x5 which are applied crosswise for feature extraction of the convolution and pooling processes. The 3x3 convolution kernel type and 3x3 pooling kernel showed the best performance in transliterating and recognizing Lampung script words with a test accuracy of 0.9 and a small test result data inequality, which is 2/10 or 0.2. The 3x3 Kernel Size shows ideal conditions for use, especially when the image features used have very few differences in features.
Deteksi Tingkat Kematangan Buah Mangga Berdasarkan Fitur Warna Menggunakan Pengolahan Citra Digital Aksa, Muhammad; Ranggareksa, Andi; Aras, Muh Riski Farukhi; Kaswar, Andi Baso; Andayani, Dyah Darma; Intam, Reski Nurul Jariah S
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.v11i2.10578

Abstract

The classification of mango Golek ripeness is crucial for ensuring product quality and its economic value, especially in industrial applications. Manual and subjective ripeness determination often leads to inconsistency, resulting in decreased harvest quality and market value. This study aims to classify the ripeness of Golek mangoes into three categories: unripe, semi-ripe, and ripe, using digital image processing based on HSV and LAB color features combined with the K-Nearest Neighbor (KNN) algorithm. The dataset consists of 300 images, split into 80% training data and 20% testing data. The proposed method includes image acquisition, preprocessing, segmentation, morphological operations, feature extraction, and classification. The results show that the combination of HSV and LAB color features is effective in distinguishing ripeness levels, with an accuracy of 81.67% on the testing data and an average precision, recall, and F1-Score of 82%. Consistent color patterns in the unripe and semi-ripe categories enhance accuracy, while fluctuations in color intensity in the ripe category pose challenges. This approach shows potential for implementation in automatic sorting systems in industry.
Perbandingan Performa Model Long Short-Term Memory dan Bidirectional untuk Prediksi Kabut Wiujianna, Atri; Sunarno, Sunarno; Iqbal, Iqbal
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.v11i2.10588

Abstract

Fog is a weather phenomenon that can significantly reduce visibility and impact transportation safety as well as public activities. The Citeko region in Bogor, located in a highland area, experiences a relatively high frequency of fog events, especially during the morning and rainy seasons. This study aims to develop and compare the performance of fog prediction models using Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) algorithms based on historical weather data from 2013 to 2023. The data, obtained from the Citeko Meteorological Station, includes weather parameters such as dry-bulb temperature, wet-bulb temperature, dew point, visibility, relative humidity, cloud cover, wind direction and speed, and hourly weather conditions. The data underwent several preprocessing steps, including missing value interpolation, fog classification based on weather parameters, normalization, and splitting into training and testing sets (80:20 ratio). The LSTM and BiLSTM models were then trained using a deep learning approach, both with and without early stopping. The results show that BiLSTM with early stopping achieved the best performance: 99.93% accuracy, 96.53% precision, 98.81% recall, and an F1-score of 97.66%, with only 9 false positives and 3 false negatives. This study contributes to the development of fog prediction systems based on artificial intelligence.
Klasifikasi Tingkat Kualitas Terung dengan Algoritma Backpropagation Berbasis Fitur Warna dan Tekstur R, Muh Raflyawan; Arifky, Reza; Tenriajeng, Andi Afrah; Kaswar, Andi Baso; Andayani, Dyah Darma; Azis, Putri Alysia
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.v11i2.10655

Abstract

Manual quality assessment of eggplant is often inconsistent, takes a long time, and is prone to errors due to worker fatigue. This research aims to develop an automated system based on digital image processing to assess eggplant quality efficiently and accurately. The stages begin with image capture using a mobile phone device designed to ensure stable lighting and uniform background. The acquired image is then processed through segmentation using the Otsu thresholding method as well as morphological operations to separate the main object from the background. Color and texture features are extracted through Gray-Level Co-occurrence Matrix (GLCM) analysis and RGB, HSV, and LAB color spaces. Training data amounting to 90% of the total dataset was used to train an artificial neural network-based classification model with a backpropagation algorithm, while the remaining 10% was used for testing. Experimental results showed that the combination of LAB, RGB, HSV, and texture features gave the best results, with a testing accuracy of 86%, recall of 85%, and precision of 92%. This model is very effective in detecting poor quality eggplants with 100% accuracy. This system can support the application of technology in the horticultural sector.

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