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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 7 Documents
 1 
Search results for , issue "Vol 10 No 3 (2024): JuTISI" : 7 Documents clear
Simulasi Pengendalian Lift Menggunakan Manajemen Logika Fuzzy Qasimi, Mehr Ali; Nashir, Asmatullah
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.v10i3.8974

Abstract

The use of a permanent magnet synchronous motor as an actuator in a ropeless elevator presents a number of difficulties that must be overcome for the system to be secure and stable. Detent force, one issue with stabilization systems, will be examined in terms of how well it functions under a fuzzy logic controller using a nonlinear test like changes in load and distance to obtain a policy suitable for application in the industrial sector or other human endeavors. The elevator technologies are designed to provide the necessary passenger floors while taking into account the highest standards of elevator performance and passenger pleasure. This work addresses the problem by developing an elevator group controller using a fuzzy algorithm. This project is designed to handle the necessary passenger traffic density while maintaining acceptable passenger waiting times by integrating a fuzzy controller into an elevator system. Within a set of fuzzy rules, three important linguistic variables are added to improve the performance of the elevator group. These consist of load capacity, priority, distance, and average waiting time (AWT). The necessity of floor priority is lessened when there is a great volume of passenger traffic; instead, the expected arrival time should be decreased. While the real elevator prototype is being programmed using a PIC microcontroller acting as a controller, the simulation was completed to visually verify the fuzzy system's priority. Thus, a set of ambiguous guidelines was developed based on real-world issues, primarily the reduction of waiting times and energy usage. The elevator controller will select which elevator will service which incoming hall request when a few are registered. In order to maximize efficiency for financial reasons, high-rise buildings and the ensuing large number of elevators they require provide a significant logistical challenge in terms of time and space conservation. In order to run the elevators properly, complex elevator group control systems are built.
Aplikasi Surat Jalan Berbasis Android Lie, Steven; Wella, Wella; Tjahjana, David
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.v10i3.9029

Abstract

In the digital era with high internet penetration and rapid e-commerce growth, the need for digital solutions to increase business efficiency is increasingly urgent. The Android-based travel letter application is here as the right solution to help the sales team manage tasks, track and create reports easily and efficiently. This application offers various benefits, such as easier mobility, efficiency with automation features, increased productivity, and closer consumer relationships. Features such as a camera to upload proof of travel documents, search to make it easier to find travel documents, and travel history to view travel history of travel documents, further increase the value of this application. This application development uses the prototype method and has been tested with user acceptance testing (UAT) and user satisfaction surveys. The results show that 95.3% of the 95 UAT respondents and 99.6% of the 35 survey respondents were satisfied with the application performance. This shows that this Android-based travel document application is suitable for use and can help improve company performance.
Studi Perbandingan Evaluasi Kinerja Metode Pembelajaran Eager Learning versus Lazy Learning Lukman, Selvi; Loekito, Jimmy; Yapinus, Pin Panji
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.v10i3.9197

Abstract

The major revenue in banking sector is generated long term deposits from customers. Many marketing strategies are implemented to target potential customers by examining their impacted characteristics for decision making. Therefore, machine learning as a scientific computing has drawn many interest in finding best potential customers especially in predicting whether a long term deposit is subscribed or not. In this research, lazy and eager learning of K-Nearest Neighbours (KNN) and Random Forest (RF) is compared. The computation procedure of the prediction makes a sharp distinction between them and accordingly, RF is proven to be more superior than KNN in the term of Accuracy as much as 96%, Precision 93% and F1 score 0.97. Therefore, the ultimate performance of RF relies on the ability to handle non-linearities and its resistance to overfitting makes RF a suitable choice for many predictive applications. Keywords— Classification; Easy learning; Lazy Learning, Term Deposit
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.
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.

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