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Jurnal Teknologi Informasi Mura
Published by Universitas Bina Insan Lubuklinggau
ISSN : 20856156     EISSN : 26148722     DOI : https://doi.org/10.32767/jti.v15i1
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
Focus and Scope Manajemen TI dan Tata Kelola TI e-Government e-Kesehatan, e-Learning, e-Manufaktur, e-Commerce ERP dan Manajemen Rantai Pasokan Manajemen Proses Bisnis Sistem Cerdas Kota Pintar Teknologi Awan Cerdas Peralatan Cerdas & Perangkat Komputasi yang Dapat Dipakai Sistem Robot Jaringan Sensor Cerdas Infrastruktur Informasi untuk Smart Living Spaces Sistem Transportasi Cerdas Pemodelan Konseptual, Bahasa dan desain Rekayasa Perangkat Lunak Jaringan yang berpusat pada informasi Interaksi Komputer Manusia Media, Game, dan Teknologi Seluler Penambangan Data Pengambilan Informasi Informasi keamanan Pemrosesan Bahasa Alami
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 234 Documents
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PERANCANGAN UI/UX APLIKASI ABSENSI BERBASIS MOBILE MENGGUNAKAN METODE DESIGN THINKING Yudha Wibowo; Anis Lelitasari; Reza Ilyasa; Rangga Gading Satria; Mohammad Roffi Suhendry; Rizky Sabana
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 1 (2026): Jurnal Teknologi Informasi Mura
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i1.3191

Abstract

Perkembangan teknologi mobile mendorong kebutuhan akan sistem absensi yang lebih efektif dan efisien dibandingkan metode manual yang masih banyak digunakan. Proses absensi konvensional sering menimbulkan berbagai permasalahan, seperti ketidakakuratan data, potensi kecurangan, serta keterbatasan dalam pengolahan dan pelaporan data. Oleh karena itu, penelitian ini bertujuan untuk merancang user interface aplikasi absensi berbasis mobile yang mampu meningkatkan kemudahan penggunaan dan pengalaman pengguna. Metode yang digunakan dalam penelitian ini adalah Design Thinking yang terdiri dari tahapan empathize, define, ideate, prototype, dan testing. Melalui pendekatan ini, kebutuhan pengguna dianalisis secara mendalam untuk menghasilkan desain antarmuka yang intuitif, sederhana, dan sesuai dengan karakteristik pengguna. Hasil penelitian menunjukkan bahwa rancangan user interface yang dihasilkan mampu meningkatkan kemudahan navigasi, mempercepat proses absensi, serta memberikan pengalaman pengguna yang lebih baik. Dengan demikian, perancangan user interface berbasis metode Design Thinking memiliki peran penting dalam meningkatkan efektivitas penggunaan aplikasi absensi mobile.
PENDEKATAN COMPUTER VISION BERBASIS FUSION CNN DAN GRAD-CAM UNTUK IDENTIFIKASI PENYAKIT DAUN CABAI Andri Anto Tri Susilo; Lukman Sunardi; Budi Santoso
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 2 (2026): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i2.3033

Abstract

The rapid advancement of Artificial Intelligence (AI) technology has created new opportunities for modernizing the agricultural sector, particularly in the early detection and classification of plant diseases based on digital images. A Computer Vision-based approach has emerged as an effective solution, as it enables the automation of visual analysis that was previously reliant on manual observation. In this study, a method based on Fusion Convolutional Neural Networks (CNN) is proposed, combining the strengths of ResNet and DenseNet architectures to produce more robust and discriminative feature representations. In addition, this research integrates an Explainable Artificial Intelligence (XAI) technique using Grad-CAM to provide visual interpretations of the model’s decisions, thereby enhancing user trust in the developed system. The dataset used consists of three main classes of chili leaf conditions: Bacterial Spot, Curl Virus, and Healthy. Experimental results demonstrate that the proposed model achieves excellent performance, with an accuracy of 98%. Further analysis through the classification report indicates that the Healthy class attains perfect performance, with precision, recall, and f1-score all reaching 1.00. Meanwhile, the Bacterial Spot class achieves a recall of 1.00 and an f1-score of 0.97, indicating the model’s capability to correctly identify all samples in this class. The Curl Virus class also shows strong performance, with a precision of 1.00, recall of 0.95, and f1-score of 0.97. Overall, the macro average and weighted average f1-scores both reach 0.98, reflecting the model’s stability and consistency across all classes. Furthermore, the implementation of Grad-CAM is able to highlight specific regions on chili leaves that contribute to the model’s predictions, providing deeper insight into the disease patterns recognized by the model. This not only enhances interpretability but also supports visual validation by users. Therefore, this study demonstrates that the combination of Fusion CNN and Grad-CAM is not only effective in improving classification accuracy but also ensures transparency in the decision-making process, making it highly suitable for intelligent decision-support systems in precision agriculture
DIGITALISASI PENGELOLAAN SURAT PADA PT PUPUK SRIWIDJAJA MENGGUNAKAN METODE FAST Nurul Adha Oktarini Saputri; R.M. Nasrul Halim
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 2 (2026): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i2.3193

Abstract

Mail management is an important part of the company's administrative process, especially in terms of archiving, recording, and efficient distribution of documents. PT Pupuk Sriwidjaja Palembang as a large company requires a system that is able to handle letter management effectively and in a structured manner. So far, the letter management process is still mostly done manually, potentially causing problems such as delays, lost documents, and difficulties in searching for archives. This study aims to design and develop a web-based letter management system to support the digitalization process of administration at PT. Pupuk Sriwidjaja Palembang. The system built is a web-based application and uses the FAST (Framework for the Application of Systems Thinking) method, which is a structured approach in information system development. This study implements all FAST stages from needs analysis to system testing and evaluates the level of user acceptance using the System Usability Scale (SUS) method. The result of this study is a digital letter management application that supports the process of incoming and outgoing letters, recording, and searching for archives quickly and in a structured manner. The results of the system evaluation using the SUS method obtained a score of 84.0 which is in the Excellent category with an Acceptable user acceptance level. The implementation of the system is able to accelerate the document search process, improve the letter archiving process, and reduce the use of physical documents in the administrative process so that it can support the digitalization of administration at PT Pupuk Sriwidjaja Palembang.
PENERAPAN MACHINE LEARNING DALAM DETEKSI SERANGAN MALWARE PADA CIC IIOT DATASET 2025 Muhammad Fikri Akbar; Novi Lestari; Deni Nurdiansyah; Armanto Armanto
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 2 (2026): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i2.3300

Abstract

The development of the Industrial Internet of Things (IIoT) has increased the efficiency and productivity of industrial systems, but has simultaneously increased vulnerability to cyber threats, particularly malware attacks that can disrupt computer systems, networks, and IoT devices. This study aims to build and evaluate a machine learning model for classifying malware threats in IIoT infrastructure. The dataset used is the CIC IIoT Dataset 2025, containing normal (benign) network traffic and two types of malware attacks, namely Mirai SYN Flood and Mirai UDP Flood. The main problem in the dataset is class imbalance, so the SMOTE technique is applied to balance the data distribution, alongside the challenge of similar characteristics between attack classes. Three machine learning algorithms, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest, were used to build multi-class and binary classification models. Model performance was evaluated using accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC curves. The multi-class results show that Random Forest achieved the best performance with 87% accuracy and a weighted F1-score of 0.87, followed by SVM (86%; 0.86) and KNN (85%; 0.86), while in the binary classification scenario Random Forest reached 98.42% accuracy. These results are expected to provide insight into the most effective algorithm for detecting and classifying malware attacks in IIoT networks, thereby improving the security and reliability of IIoT-based industrial systems.

Page 24 of 24 | Total Record : 234

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