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Jurnal Teknik Informatika dan Teknologi Informasi
Published by Lembaga Pengembangan Kinerja Dosen
ISSN : 28279379     EISSN : 28279387     DOI : 10.55606
Core Subject : Science,
Computer Science & IT
Jurnal Teknik Informatika dan Teknik Informasi (JUTITI) adalah jurnal ilmiah peer review yang diterbitkan oleh Politeknik Pratama. Jurnal Teknik Informatika dan Teknik Informasi (JUTITI) terbit dalam tiga edisi dalam setahun, yaitu edisi Februari, Juni dan Oktober. Kontributor Jurnal Teknik Informatika dan Teknik Informasi (JUTITI) berasal dari peneliti, akademisi (dosen dan mahasiswa) di bidang Teknik Informatika dan Teknik Informasi. Jurnal Teknik Informatika dan Teknik Informasi. memiliki fokus dan ruang lingkup yang terdiri dari: Computer Architecture Parallel and Distributed Computer Pervasive Computing Computer Network Embedded System Human—Computer Interaction Virtual/Augmented Reality Computer Security Software Engineering (Software: Lifecycle, Management, Engineering Process, Engineering Tools and Methods) Programming (Programming Methodology and Paradigm) Data Engineering (Data and Knowledge level Modeling, Information Management (DB) practices, Knowledge Based Management System, Knowledge Discovery in Data) Network Traffic Modeling
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 197 Documents
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Penerapan Metode Logistic Regression untuk Memprediksi Potensi Penyakit Liver pada Pasien Tarmidzi Ibrahim; Imam Wahyudi; Vemi Januar Pratama; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6284

Abstract

Liver disease is a major global health concern that often goes undiagnosed in its early stages due to the absence of specific symptoms. Implementing data-driven approaches for early detection can significantly enhance diagnostic accuracy and improve clinical outcomes. This study aims to develop a predictive model using the Logistic Regression algorithm to identify individuals at high risk of liver disease. The data analysis process was conducted visually through data mining software, encompassing several stages such as data loading, feature selection, exploratory data analysis, and model evaluation. The dataset includes various clinical and laboratory attributes of patients, such as blood test results, liver function indicators, and demographic factors. The model’s performance was assessed using multiple evaluation metrics, with a focus on Classification Accuracy (CA) and the Area Under the ROC Curve (AUC) to measure predictive precision and classification ability. The results show that the Logistic Regression model achieved an accuracy of 71.8% and an AUC score of 0.746. These findings indicate that the model demonstrates good predictive performance and effectively identifies early-stage liver disease cases. However, further optimization is necessary to improve overall model efficiency and ensure more robust predictive capabilities in clinical applications.
Pengembangan Animasi Edukatif Berbasis Cerita Rakyat untuk Pelestarian Budaya Lokal Atiqah Noor Zhaafirah; Adhisa Nanda Kurnia; Purwadi Purwadi
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6286

Abstract

This study aims to analyze the development of educational animation based on Indonesian folklore as a medium for preserving local culture. The method used is a systematic literature review (SLR) of various national and international journal publications from 2021 to 2025 that discuss educational animation, local wisdom, and cultural preservation. Data were collected from ScienceDirect, ResearchGate, and SINTA databases using the keywords “educational animation,” “folklore,” and “cultural preservation.” The data were analyzed qualitatively using content analysis and thematic mapping techniques to identify key themes and recent research directions. The results show that educational animation based on folklore significantly contributes to enhancing cultural awareness, character education, and student engagement in learning. Most studies emphasize that digital media—especially animation—play an important role in transmitting cultural and moral values to younger generations. However, research gaps remain, as most existing studies still focus on the entertainment aspect rather than educational integration. In conclusion, the development of educational animation based on folklore has the potential to serve as a strategic medium for intangible cultural heritage preservation while promoting creative and contextual learning in the digital era.
Pelanggaran Keamanan Sistem Komputer: (Studi Kasus Unauthorized Access dan Dampaknya terhadap Privasi Data) Hana Khairunnas; Amelia Rachma Dita; Nuruzzahra Syaputri; Siti Zulaeha; Excelcis Novan Solomasi G; Yunita Yunita
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6290

Abstract

Unauthorized access incidents often occur stealthily, with password spraying attacks resulting in the misuse of legitimate credentials. This study reconstructs a real-world incident using system logs from Identity Provider/Single Sign-On (IdP/SSO), Security Information and Event Management/Endpoint Detection and Response (SIEM/EDR), and application-level sources. The attack techniques were mapped to the MITRE ATT&CK framework, focusing on T1110 (Brute Force) and T1078 (Valid Accounts). A Data Protection Impact Assessment (DPIA) was conducted based on the Indonesian Personal Data Protection Law (Law No. 27 of 2022), complemented by a gap assessment against ISO/IEC 27001 and 27002 controls. The results show that the attack’s success was driven by incomplete Multi-Factor Authentication (MFA) deployment, the continued use of legacy/basic authentication, weak adaptive rate-limiting and lockout mechanisms, and a monitoring system limited to alert-only functions. The DPIA identified exposure of thousands of personal data records with medium-to-high privacy risks, particularly concerning confidentiality breaches and identity impersonation, necessitating possible notification to authorities and affected data subjects. The study recommends enforcing MFA across all access channels, disabling legacy authentication, implementing risk-based or step-up authentication, activating automatic blocking for password spraying and impossible travel anomalies, extending DPIA coverage during control changes, and updating the Statement of Applicability to reflect modern security controls. Strengthening identity protection and adopting preventive monitoring are shown to significantly reduce privacy risks while easing compliance obligations.
Risiko Penggunaan Kecerdasan Buatan (AI) dalam Tugas Perkuliahan: Studi Kualitatif Mahasiswa Pranoto Effendi
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 4 No. 3 (2024): Desember : Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v4i3.6307

Abstract

The use of AI has become widespread among university students as a tool to improve the efficiency and quality of their studies. While AI aims to support the education system and enhance the learning process, its use, while providing benefits, is also perceived to pose risks. This article aims to uncover the use of AI and its associated risks by exploring perceptions from a student perspective. Qualitative methods were used with 53 student respondents, asking open-ended questions regarding AI use, their concerns, and the associated risks. Findings indicate that AI is primarily used to search for information and references, assist with completing coursework, and support independent learning. The concerns and risks identified by students include over-reliance, decreased critical and creative thinking skills, and ethical and data security issues. These findings suggest that AI must be used wisely for educational purposes, addressing these risks. Educational policies related to AI use must be improved and strengthened to maximize AI's benefits, aiming to support ethical and integrated education.
Simulasi Jaringan untuk Sistem Terdistribusi Website “Simple Restaurant App” dengan GNS3 Rafel Sutra Dharma; Tri Sutisno; Manatap Dolok Lauro; Ari Wijaya; George Wielianto; Michael Michael; Firzi Ilham Bagusti
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6313

Abstract

Distributed systems, integral to contemporary computing, involve the coordinated functioning of autonomous nodes across interconnected networks. Architectural models like client-server and peer-to-peer configurations define their structure, presenting both advantages and challenges. Challenges stem from the intricacies of managing geographically dispersed nodes, encompassing issues of data consistency, fault tolerance, scalability, and security. Overcoming these hurdles demands advanced algorithms and protocols, especially for achieving consistency and resilience in the face of failures. Scalability is a critical consideration, and security concerns add complexity to their design. Current trends in distributed systems, such as edge computing, serverless architectures, and blockchain technologies, aim to address these challenges and enhance system capabilities. Edge computing optimizes proximity to data sources, serverless architectures streamline resource utilization, and blockchain offers decentralized and tamper-resistant solutions. Understanding these architectures, addressing challenges, and embracing emerging trends are pivotal for constructing robust and efficient distributed systems that align with the demands of the interconnected digital landscape.
Sistem Deteksi Penggunaan Helm Pada Pengendara Sepeda Motor di Indonesia Menggunakan Perbandingan Model YOLOv8 dan RT-DETR Samuel Orief Rosario; Agustinus Aditya Bintara; Muhammad Rifki Zhaki; Rachmat Adi Purnama; Rame Santoso; Veti Apriana
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6314

Abstract

Road safety is an important aspect in reducing accident risks, especially for motorcycle riders. To improve compliance with helmet use, this study compares the performance of two deep learning–based object detection models, namely YOLOv8 and RT-DETR, using a Roboflow dataset consisting of 3,735 images with two classes: with helmet and without helmet. The research process includes data acquisition, preprocessing (512×512 pixels), model training conducted in Visual Studio Code using an Nvidia GTX 1070 Ti GPU with the Ultralytics framework (100 epochs, AdamW optimizer, 0.0005 learning rate, 25 patience), testing on images, videos, and real-time inputs using last.pt, as well as evaluation through precision, recall, mAP, and confusion matrix, followed by implementation of the best algorithm in a local Streamlit web application.The results show that RT-DETR achieved slightly better training performance in terms of mAP50–95, while YOLOv8 performed better during real-world testing with more stable accuracy, particularly for the with helmet class. YOLOv8 reached up to 100% accuracy in video and real-time testing, whereas RT-DETR performed better in the without helmet class, achieving 95% accuracy on image data and up to 100% in video testing. Overall, YOLOv8 was selected as the best model for implementation in the Streamlit-based helmet detection application because it is faster, more stable, and more accurate. This system has the potential to support intelligent ETLE enforcement to enhance traffic safety in Indonesia.
Perancangan dan Implementasi Sistem Informasi Nasabah pada BSI KCP Gunung Tua Studi Kasus Data Nasabah Laila Syarifah; Emmi Juwita Siregar; Afelia Anggraini; Rahmadhani Hasibuan; Riskha Armida Dewi
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6331

Abstract

The development of information technology has influenced the way various sectors work, including banking. Previously, customer data management was done manually, which often led to data duplication and time delays. This study aims to create and implement a more effective customer information system at Bank Syariah Indonesia (BSI) Gunung Tua Branch Office (KCP), so that the data management process becomes faster and more accurate. This study used the R&D method with the Waterfall model. The system was created using the PHP or Laravel programming language and relied on a MySQL database. The results showed that the system was successfully created and implemented. This system supports two main types of users: Admin/Customer Service who can log in, manage customer data (input, change, delete, and search), and print reports; and Customers who can view personal data, monitor savings status, and create new accounts. In conclusion, this system has an integrated database structure (Admin, Customer, Account, Transaction tables) and successfully meets user needs, thereby increasing efficiency and accuracy in customer data management at BSI Gunung Tua KCP.
Klasterisasi K-Means dan Model Markowitz dalam Pembentukan Portofolio Optimal Saham Syariah Muhammad Kildah Namariq
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6332

Abstract

This research seeks to construct an optimal portfolio of Sharia-compliant stocks listed in the Jakarta Islamic Index (JII) by integrating the Markowitz model with K-Means clustering. The dataset comprises the daily closing prices of JII constituent stocks and the risk-free return of BI-rate for the period of June–October 2025. The analysis begins with calculating the mean of stock returns and its corresponding standard deviation to identify the characteristics of each stock. The Silhouette Index is employed to determine the most suitable number of clusters, which indicates that three clusters provide the best separation. A representative stock is then selected from each cluster based on the Sharpe Ratio, resulting in BRPT, ASII, and UNVR as the primary candidates for portfolio construction. The Markowitz model is subsequently applied to determine the optimal portfolio weights, yielding an allocation of 16.29% for BRPT, 70.80% for ASII, and 12.91% for UNVR. The performance evaluation shows that the optimal portfolio achieves a Sharpe Ratio of 0.232872, higher than the JII index value of 0.118273, indicating superior risk–return efficiency. These findings demonstrate that a hybrid approach combining K-Means clustering and Markowitz optimization can enhance investment decision-making for Sharia-compliant stocks.
Klasifikasi Indikator Kesehatan Diabetes Menggunakan Algoritma Random Forest Haura Syahla; Haris Izzudin; Fariz Aditya Pratama; Beni Rahmatullah; Ahmad Jurnaidi Wahidin; Ika Kurniawati
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6338

Abstract

Diabetes continues to rise as a global health concern, highlighting the need for analytical methods that can assist in earlier and more accurate detection. This study aims to classify diabetes conditions using the Random Forest algorithm implemented through the Orange Data Mining platform. The dataset used contains various health-related attributes such as glucose levels, blood pressure, body mass index, age, and other clinical indicators associated with diabetes risk. Random Forest was selected due to its ability to produce stable models, handle large and complex datasets, and minimize overfitting by combining multiple decision trees. The research process includes data preprocessing, splitting the dataset into training and testing portions, building the Random Forest model, and evaluating its performance using metrics such as accuracy, precision, recall, F1-score, and confusion matrix. The results indicate that Random Forest delivers strong and consistent performance in classifying diabetes conditions based on the given health indicators. These findings suggest that employing data mining techniques especially Random Forest within Orange—can serve as a practical and reliable approach to support medical analysis and assist healthcare practitioners in achieving earlier and more accurate diabetes detection.                                                          
Sistem Pendukung Keputusan Berbasis Web untuk Penerimaan Bantuan Langsung Tunai Menggunakan Metode Multi Attribute Utility Theory (MAUT) di Desa Selemak Irham Nabiza Trisna; Asbon Hendra Azhar
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6359

Abstract

This study aims to develop a web-based decision support system to assist the selection process for Direct Cash Assistance (BLT) recipients in Selemak Village. The current manual selection process often leads to unfairness and subjectivity. This system was developed using the Multi-Attribute Utility Theory (MAUT) method, which is capable of providing selection results based on predetermined criteria and weighting. The research stages include collecting data on potential recipients, determining criteria, weighting, and implementing the system using PHP and MySQL. The system was evaluated using the K-Nearest Neighbor (KNN) algorithm to measure the accuracy of the calculation results. The results showed that the system was able to provide recipient recommendations with an accuracy of 96%, with 68 eligible families and 107 ineligible families. With this system, the process of determining BLT recipients is faster, more objective, and more transparent.

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