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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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Perancangan Model Referral dengan Pendekatan Design Science Research Methodology Linda, Mei; Imbar, Radiant Victor
Jurnal Teknik Informatika dan Sistem Informasi Vol 10 No 3 (2024): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v10i3.9215

Abstract

This research aims to create an effective referral model by applying the Design Science Research Methodology (DSRM) in the context of case X. This methodology focuses on integrating theory and practice to produce innovative solutions. By using this approach, the research proposes a model based on a theoretical framework that has proven effective. Data collection was conducted through surveys, interviews, and direct observation. The main focus of this research is the development of a model that not only enhances efficiency in the referral process but also improves service quality for stakeholders. The final results of the research include supporting artifacts for the solution development process, such as PRDs and flowcharts. It is hoped that the results of this study will provide practical guidance for companies or organizations facing challenges in managing complex referral processes. Therefore, this research is expected to make a significant contribution to the development of efficient and effective referral systems.
Dilated-Convolutional Recurent Neural Network untuk Klasifikasi Genre Musik Fatichin, Mochammad Rizqul; Hermawan, Alfado Rafly; Siahaan, Raynaldi Anggiat Samuel; Indraswari, Rarasmaya
Jurnal Teknik Informatika dan Sistem Informasi Vol 10 No 3 (2024): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v10i3.9347

Abstract

In the digital era, utilizing technology to automatically classify music genres has become very important, especially for applications such as music recommendation, music trend analysis, and digital music library management. This research evaluates the use of Dilated-Convolutional Recurrent Neural Network (D-CRNN) in classifying music genres. This method combines the advantages of Dilated-CNN in capturing longer temporal context with the temporal sequence recognition capability of CRNN. The data used is the GTZAN dataset consisting of 1,000 30-second audio recordings, categorized into 10 music genres. Data preprocessing involved converting the audio recordings into Mel-Frequency Cepstral Coefficients (MFCC) images. The model was tested using data without augmentation and with augmentation, resulting in a total of 15,991 images for training. The results show that the use of D-CRNN can improve the accuracy of music genre classification compared to the conventional CRNN method.
Pemanfaatan Teknik Peramalan Data Deret Waktu pada Inventori Farmasi di Rumah Sakit Mu'min, Aziz; Budi, Setia; Toba, Hapnes
Jurnal Teknik Informatika dan Sistem Informasi Vol 10 No 2 (2024): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v10i2.9352

Abstract

Good inventory management is essential in the hospital industry to overcome inventory problems. Ineffective inventory prediction methods can lead to shortages or excess stock inventory. Ultimately, this can impact the budget and availability of pharmaceutical items in the hospital. Previous traditional prediction methods often show inaccuracies. This research utilizes ARIMA (Autoregressive Integrated Moving Average) and FB Prophet methods to predict the demand for pharmaceutical items in hospitals. In an attempt to evaluate the effectiveness of both methods, an experiment was conducted on five pharmaceutical items. The results showed that the ARIMA method produced better performance compared to the FB Prophet method, with the smallest error of 0.07310.
Analisis Perbandingan Algoritma Machine Learning untuk Forecasting Persediaan Produk Barang Pokok Avinash, Avinash; Widjaja, Andreas; Karnalim, Oscar
Jurnal Teknik Informatika dan Sistem Informasi Vol 10 No 2 (2024): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v10i2.9357

Abstract

Abstract —In the era of continuously evolving technology, consumers' demands for everyday needs are becoming more complex.Retail companies must adopt sophisticated approaches to understand and meet consumer preferences. This research explores theeffectiveness of Machine Learning algorithms in forecasting inventory levels in various types of retail stores using historicaltransaction payment data and related variables. One approach used is data transformation using one standard deviation intervalto stationarize data, ensuring statistical consistency that is important for prediction algorithms. The research results show that theSeasonal Autoregressive Integrated Moving Average (SARIMA) algorithm performs best in predicting inventory levels for bothSMEs and modern retailers. For the original data, the Mean Absolute Percentage Error (MAPE) for SMEs is 1.11% and formodern retailers is 0.98%. For data modified with one standard deviation interval, the MAPE for SMEs is 0.74% and for modernretailers is 0.70%. These results indicate superior prediction accuracy, helping companies adjust their inventory levels moreaccurately according to market dynamics and consumer expectations. This research is expected to provide a solid guideline forimproving inventory management strategies, enabling companies to prepare inventory levels more accurately according to marketdynamics and consumer expectations.Keywords—Forecasting Optimization, Machine Learning Algorithm Comparison, Inventory Levels, Modern Retail, SMEs, Onestandard deviation interval
Evaluasi Hasil Neural Style Transfer Berbagai Gambar Pola Menggunakan Feature Similarity Index Metayani, Vanessa; Liliawati, Swat Lie; Widjaja, Andreas
Jurnal Teknik Informatika dan Sistem Informasi Vol 10 No 2 (2024): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v10i2.9380

Abstract

This research was conducted by applying the Neural Style Transfer method to various sets of content and style images, and then calculating the FSIM value for each pair of result and original images. Analysis was done on factors such as art style complexity, resolution and other special characteristics such as colour and texture that can affect the FSIM value. The purpose of this research is to identify whether there are factors that affect FSIM performance such as art style complexity, resolution, or other special characteristics such as colour and texture. This research is expected to be able to help artists who want to change the art style with Neural Style Transfer but still maintain the originality of the image and still be recognised by evaluating the results using FSIM and help artists to develop and produce artistic digital artworks with good quality. The results show that varying FSIM values can depend on the complexity of the art style and image resolution. Simple art styles and high-resolution images tend to produce higher FSIM values, indicating that the image structure is easily preserved. As long as the resolution and colours or textures do not change the main structure, the FSIM results will not decrease significantly. To support the research analysis, the Analysis of Variance (ANOVA) statistical test was used to measure the significance of the effect of complexity and resolution on FSIM and the Cronbach’s Alpha test to test the reliability of the general public and expert surveys. Based on the ANOVA statistical test results, there was insufficient evidence to reject the null hypothesis, so complexity and resolution did not have a significant influence on FSIM. From the Cronbach’s Alpha test results, the public assessment survey received a result of 0.94 and 0.91 for the expert assessment survey. These results indicate that the results from the surveys are reliable as subjective data for the research.
Penerapan Sentence BERT Untuk Similaritas Kompetensi Pekerjaan dan Mata Kuliah Agiharta, Kafka Febianto; Suteja, Bernard Renaldy; Ayub, Mewati
Jurnal Teknik Informatika dan Sistem Informasi Vol 10 No 3 (2024): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v10i3.9411

Abstract

This research focuses on the application of the Sentence BERT (S-BERT) model, a specialization of the BERT model and an adaptation of the Transformer architecture specifically designed for the Indonesian language, in exploring the concept of course credit transfer consolidation in accordance with the Merdeka Belajar – Kampus Merdeka program. The aim of this exploration is to develop an Indonesian-language S-BERT model and apply it to search and analyze the similarity between activity sequence descriptions and course syllabus (RPS) descriptions. The results of this similarity analysis are the identification of relevant courses based on the given query. The developed model has shown effective capabilities in searching and determining the similarity between activity sequence descriptions and course syllabus descriptions. Courses identified as relevant to the query demonstrate high similarity and compatibility, indicating that the S-BERT model can be relied upon in the process of course credit transfer consolidation within the context of Merdeka Belajar – Kampus Merdeka.
Deteksi dan Klasifikasi Tingkat Keparahan Jerawat: Perbandingan Metode You Only Look Once Veby Agustin, Giezka; Ayub, Mewati; Liliawati, Swat Lie
Jurnal Teknik Informatika dan Sistem Informasi Vol 10 No 3 (2024): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v10i3.9414

Abstract

Acne (Acne vulgaris) is one of the most common skin diseases, especially on the face. Accurate diagnosis and proper treatment are important for optimal care results and improving the accuracy of detection and classification of acne severity. YOLO (You Only Look Once) is a deep learning method used for object detection in images. This study compares the results and performance of YOLOv5 and YOLOv8 in detecting acne on the face. Several experiments were also conducted with data pre-processing, model size, and the use of different basic hyperparameters on both models to understand the impact and differences between YOLOv5 and YOLOv8. The results show that YOLOv5 overall has higher performance in detecting acne compared to YOLOv8, which requires larger hyperparameter values and model sizes to achieve the most optimal results. Conservative hyperparameters (with relatively smaller values or sizes) on YOLOv5 contribute to better performance.
Perancangan dan Implementasi E-Commerce Corrugated Carton Box Menggunakan Metode Rapid Application Development Takasili, Tevin; Kristiyanti, Dinar Ajeng
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.v11i1.9415

Abstract

PT Hora Cipta Jaya is a company specializing in the sale of corrugated cardboard boxes. PT Hora Cipta Jaya typically uses corrugated cardboard boxes to package goods or products for shipping. Due to their strength, durability, and recyclability, many industrial companies use them to package their products. PT Hora Cipta Jaya still conducts sales processes manually, starting from transactions, marketing, to recording and reporting. Employees still need to manually record all company data during sales using Excel. Errors often occur during the purchase and sale of products, such as mistakes in recording and pricing calculations, as well as errors in order data entry and shipping. The aim of this research is to assist the company in managing data and designing a system that features online ordering, payment, shipping, reporting, and product promotion. We developed this system using the RAD method, utilizing PHP programming language and MySQL database. The system development process is divided into four stages: requirements analysis, system design, system implementation, and application feasibility analysis. The research resulted in an e-commerce system that helps the company manage data, facilitates online product sales transactions and broader product marketing, and automatically records invoices and reports. All features of this system are now operational after successfully completing testing phases for both admin and customer parts using the black box testing method. The system scored 78 for the admin section and 79.5 for the customer section from each respondent using the System Usability Scale, achieving a class B rating (good category).
Aplikasi Pengenalan Sistem Isyarat Bahasa Indonesia dengan Tensorflow Lite dan Firebase Authentication Toyib, Rozali; Affandi Mussa, Anitya Putri; Wijaya, Ardi; Sonita, Anisya
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.v11i1.9678

Abstract

Deaf and mute individuals often face communication barriers with the general public due to limited understanding of sign language. This leads to a gap in social interaction and access to various public services. Government efforts to enhance social inclusion through various policies and programs need to be accompanied by practical solutions that can help the deaf and mute interact more easily with society. This study aims to develop a mobile application that can recognize and translate Indonesian Sign Language System (SIBI) into text or speech in real-time, thus helping the deaf and mute communicate more effectively with the general public. The application is designed using TensorFlow Lite for sign language recognition and Firebase Authentication for user authentication. The application was evaluated through questionnaires involving respondents from the general public and mobile experts. The results of the general user questionnaire showed an average satisfaction percentage of 86.65%, with positive ratings for ease of use, benefits, and application interface. Meanwhile, the results of the expert mobile questionnaire showed full satisfaction with an average percentage of 100%, indicating that all application features functioned well. The findings indicate that this application is effective in recognizing and translating sign language and is well-received by the deaf, mute, and the general public.
Perbandingan Kinerja Word Embedding dalam Analisis Sentimen Ulasan Pengguna Aplikasi Perjalanan Pahendra, Muhammad Agung Maugi; Anraeni, Siska; Ilmawan, Lutfi Budi
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.v11i1.9681

Abstract

Traveloka, as one of the leading travel booking platforms, has achieved more than 50 million downloads on Google Play Store. This achievement shows the high interest and trust of users in the services offered. However, user reviews indicate that there are some issues with the app's performance and stability that need to be taken into account. This research compares the performance of the Word2Vec and ELMo word embedding methods using the BiLSTM model in sentiment analysis of Traveloka application reviews. The research results show that the BiLSTM model with Word2Vec has an accuracy of 76.13%, precision 75.22%, and F1-measure 76.58%, better than the model with ELMo which has an accuracy of 74.38%, precision 70.49%, and F1-measure 74.40%. The BiLSTM model with Word2Vec is more effective in sentiment analysis of Traveloka reviews, helping identify and address user issues to improve service quality and user satisfaction.

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