cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Sularno
Contact Email
soelarno@unidha.ac.id
Phone
+6281377008616
Journal Mail Official
jteksis@unidha.ac.id
Editorial Address
Jl. Sawahan No.103, Simpang Haru, Padang Tim., Kota Padang, Sumatera Barat 25000
Location
Kota padang,
Sumatera barat
INDONESIA
Jurnal Teknologi Dan Sistem Informasi Bisnis
Published by Universitas Dharma Andalas
ISSN : -     EISSN : 26558238     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Jurnal Teknologi dan Sistem Informasi Bisnis merupakan Jurnal yang diterbitkan oleh Prodi Sistem Informasi Universitas Dharma Andalas untuk berbagai kalangan yang mempunyai perhatian terhadap perkembangan teknologi komputer, baik dalam pengertian luas maupun khusus dalam bidang-bidang tertentu yang terkait dengan teknologi informatika komputer. Naskah yang diterima untuk diterbitkan berupa hasil penelitian lapangan, penelitian kepustakaan, pengamatan serta karya ilmiah yang berhubungan dengan topik yang relevan dengan situasi Teknologi Komputer.Jurnal Teknologi Komputer terbit 2 kali dalam satu tahun yaitu bulan Januari dan Juli.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 447 Documents
 41   42   43   44   45 
Implementasi Algoritma K-Means Pada Pengolahan Citra Untuk Deteksi Bentuk Dan Material Gelas putri, kamila amaliah; Ramadhanu, Agung
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 7 No 4 (2025): Oktober 2025
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v7i4.2267

Abstract

Digital image processing is a branch of computer science that plays a significant role in automating object identification processes. This study presents the implementation of the K-Means Clustering algorithm for detecting the shape and material of drinking glasses based on digital images. The research methodology involves several stages, including image data collection, color space conversion from RGB to Lab, image segmentation using K-Means Clustering, and feature extraction of shape and texture. The K-Means algorithm is employed to cluster image pixels into multiple groups according to color similarity and texture patterns, thereby enabling the classification of glasses based on their material (glass, plastic, or clay) and shape. The experimental results demonstrate that the proposed method achieves a high level of accuracy in object identification and can be effectively implemented within a Matlab-based system. Consequently, this approach offers a potential solution for the automation of drinking container identification in various industrial and research applications.
Topik Modelling Skripsi Prodi Teknik Lingkungan di Jawa Timur Menggunakan Metode LDA khoirinnisa', nurul; Khalid, Khalid; Wahyudi, Noor; Ardilla, Yunita
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 7 No 4 (2025): Oktober 2025
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v7i4.2278

Abstract

In higher education, the undergraduate thesis represents a tangible contribution of students to the development of scientific knowledge. In the field of Environmental Engineering, student research often focuses on issues that are closely related to both local and global environmental challenges. This study aims to analyze the main themes of undergraduate theses written by Environmental Engineering students in East Java using the Latent Dirichlet Allocation (LDA) method. The research data were collected from thesis titles and abstracts obtained from several universities. Through the application of LDA, several dominant themes were identified, including waste management, water quality, air pollution reduction, and the application of environmental treatment technologies. The results indicate that LDA is capable of uncovering research patterns and clustering topics that reflect the primary concerns of students in this field. These findings not only provide insights into the current research trends among students but also serve as a reference for curriculum development, research planning, and academic decision-making. Thus, this study contributes to improving the quality of education while mapping future research directions in Environmental Engineering.
Penerapan Image Processing untuk Identifikasi RAM, SSD, dan Webcam Menggunakan Metode K-Means Clustering Hikmi, Zakiya; Ramadhanu, Agung
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2266

Abstract

The development of computer hardware requires appropriate automatic identification methods to assist in inventory, maintenance, and learning processes. Manual identification methods for hardware such as RAM, SSD, and webcams are often ineffective due to the difficulty of distinguishing their visual forms, especially for those who are unfamiliar with them. This study aims to apply image processing techniques using the K-Means clustering method to identify these three types of devices. The system was created using MATLAB with a graphical user interface (GUI) for ease of use. The process begins by capturing images in RGB format, which are then converted to Lab* color space. Segmentation is performed using the K-Means clustering method, which divides objects from the background into two clusters. The segmentation results are then refined using morphological operations. Next, shape features and texture features are extracted using Gray Level Co-occurrence Matrix (GLCM), which includes contrast, correlation, energy, and homogeneity. The features obtained are compared with the database using Euclidean distance to determine the type of hardware. The test results show that the system is able to accurately distinguish between RAM, SSD, and webcams. In conclusion, the use of K-Means clustering, GLCM, and distance-based classification can be an effective solution in identifying computer hardware through images.
Analisis Performa Komparatif Algoritma Machine Learning untuk Deteksi Fraud dalam Transaksi Blockchain Apriyanthi, Ni Putu Eka; Dhewanty, Civica Moehaimin; Ayu, Putu Desiana Wulaning; Nugroho, I Made Riyan Adi; Wijaya, I Wayan Rizky
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2285

Abstract

The decentralized finance (DeFi) and blockchain environment encounters substantial security threats, particularly complex and expensive fraudulent activities. Conventional detection methods frequently prove insufficient when dealing with enormous transaction volumes and datasets characterized by unbalanced class distributions. This research seeks to examine and evaluate the effectiveness of three widely used machine learning techniques Logistic Regression, Random Forest, and XGBoost in identifying fraudulent activities within blockchain transactions. The investigation utilized an Ethereum transaction dataset sourced from Kaggle, where the imbalanced data distribution was addressed through the application of SMOTE methodology. Performance assessment was carried out using precision, recall, F1-score, and ROC-AUC measurements on testing data. The findings demonstrate XGBoost's superiority among the algorithms, delivering an accuracy rate of 99.46%, precision of 99.69%, recall of 97.86%, and ROC-AUC score of 99.97%, while maintaining minimal false positive occurrences (only 1 instance). These results exceeded those achieved by both Random Forest and Logistic Regression models, demonstrating that gradient boosting methodologies excel at detecting intricate fraudulent behaviors. The study's outcomes offer significant contributions toward creating resilient and autonomous fraud detection frameworks. Keywords: Blockchain, Fraud, Machine Learning, Logistic Regression, Random Forest, XGBoost.
Sistem Informasi Uji Kelayakan Kendaraan Bermotor Berbasis Android (Studi Kasus pada CV. Axlindo Telematika Purwokerto) Setyawati, Endang; Adilla, Axl; Purwono, Purwono; Wibowo, Adhi; Santoso, Muhammad Hery
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2286

Abstract

This study discusses the development of a motor vehicle roadworthiness test information system at CV. Axlindo Telematika Purwokerto, which was previously carried out manually through the registration process, testing, recapitulation, and issuance of KIR certificates. This manual system made the service ineffective and time-consuming. The solution developed was an ECU (Electronic Control Unit)-based Electronic Scanner with NodeMCU RS232 support that can automatically read test data and store it on a server for access via the web or Android applications. The development method used a prototype with REST API integration. The test results showed an increase in effectiveness of 98.9%, efficiency of 86.6%, usefulness of 82.2%, and a difference in data transmission time from 19.2 seconds to 1.39 seconds. The main contribution of this study is the design of hardware and software integration that can improve the accuracy and speed of KIR testing based on an intelligent information system.
Sistem Kecerdasan Buatan Untuk Deteksi Kondisi Daun Berbasis Metode Klasifikasi Fahrozi, Habil; Adiansyah, Rifky Ramadhan; Samit, Zaidan; Sujiliani, Sujiliani; Santoso, Rame; Apriana, Veti
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2315

Abstract

Plant diseases pose a significant threat to agricultural productivity. This study aims to develop and evaluate an artificial intelligence system capable of automatically detecting leaf health conditions and comparing the performance of two different deep learning architectures. Leaf image data obtained from the Kaggle dataset were processed and classified using Convolutional Neural Network (CNN) and MobileNetV2, while the YOLOv8 algorithm was applied to detect leaf objects within the images. The main evaluation metric used was classification accuracy to assess the model’s ability to identify whether a leaf is healthy or diseased. The results demonstrate the efficiency and comparative performance of both classification methods. The best-performing model was then implemented into a Python-based web application, enabling users to upload leaf images and obtain real-time health detection results. This implementation provides a practical contribution to the development of precision agriculture systems.
Business Process Reengineering pada Layanan Perpustakaan SMKN 13 Malang Menggunakan Uji Efisiensi Throughput Rahmah, Anisa Ayu Nabila Nur; Suharso, Wildan
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2323

Abstract

This study applies Business Process Reengineering (BPR) to improve the operational efficiency of the library at SMKN 13 Malang. The manual borrowing process was mapped and analyzed using Business Process Model and Notation (BPMN) and evaluated with the ASME-based throughput efficiency test. Data were collected through interviews and direct observation of library staff and activities. The initial borrowing process required 40,5 minutes with a throughput efficiency of only 62,96%, indicating long queues and a high risk of recording errors. The reengineered process introduces barcode-based automation for visitor identification and book lending, eliminating four non–value-added steps and automating two others. The redesigned workflow reduced the total borrowing time to 14,5 minutes and improved throughput efficiency to 100%. These results demonstrate that BPR significantly accelerates service delivery, reduces administrative burdens on staff, and enhances the overall user experience.
Komparasi SVM dan Random Forest Berbasis Histogram Warna untuk Deteksi Penyakit Anggur Faqihuddin, Muhammad; Purnama, Rachmat Adi
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2340

Abstract

The decline in grape (Vitis vinifera) productivity is often caused by leaf diseases such as Black Rot, which are challenging to detect accurately through manual visual inspection The key point of this research is to compare the performance of two Machine Learning classification algorithms, namely Support Vector Machine (SVM) and Random Forest, to identify the most optimal model for disease detection. The methodology employs digital image processing with Histogram Color (HSV) feature extraction, which is chosen for its efficiency in representing color changes caused by infection. The grape leaf disease image dataset was classified and evaluated. The comparative results demonstrate that Random Forest achieved the highest accuracy of 95.32%, slightly surpassing SVM which reached 94.48%. These findings prove that both algorithms perform excellently, but Random Forest is more recommended for this dataset due to its superior robustness in accurately predicting disease classes.
Analisis Dan Perancangan Sistem Pencatatan Dan Pemantauan Kekeruhan Air Berbasis Website Rivaldi, Ikvi Akmal; Utomo, Pradita Eko Prasetyo; A, Muhammad Razi
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2355

Abstract

The utilization of web-based information systems has been widely adopted to facilitate easier user access to information. Specifically, PAM Tirta Tempino requires a system to manage customer meter recording data and monitor water turbidity quickly and accurately. Therefore, this study aims to design and develop a web-based Information System for Recording and Monitoring Water Turbidity that ensures accessibility for users anytime and anywhere. The system development utilizes the Rapid Application Development (RAD) method. The result of this research is a web-based information system architectural design modeled using Unified Modeling Language (UML), allowing users (Head, Officer, and Customer) to manage and view data according to their respective access rights. Furthermore, the design was verified using the Requirement Traceability Matrix (RTM), yielding valid results which demonstrate that the design fulfills all specified requirements.
Analisis Keamanan Sistem Informasi RKSchool Depok Menggunakan Metode VAPT Oktavian, Ardika; Imaduddin, Zaki
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2363

Abstract

This study aims to evaluate the security level of the information system used by RKSchool, a micro-scale educational institution that relies on digital platforms for daily operations. The increasing number of cyberattacks targeting the education and MSME sectors highlights the need for systematic security assessments to minimize risks related to personal data exposure and service disruption. This research integrates penetration testing and vulnerability assessment based on the OWASP Web Security Testing Guide with perception analysis using the Technology Acceptance Model to examine organizational readiness in adopting the recommended security controls. The assessment was conducted through five main phases, including reconnaissance, scanning, exploitation, post-exploitation, and reporting. The findings indicate several vulnerabilities in server configuration, such as missing security headers, the absence of CSRF protection, and the lack of a Content Security Policy. TAM analysis shows that perceived usefulness and perceived ease of use have not yet formed a consistent behavioral intention toward security practices. The integration of both technical and behavioral findings demonstrates that the major risks do not solely originate from system weaknesses but also from limited organizational preparedness in implementing comprehensive security measures.

Page 44 of 45 | Total Record : 447

 41   42   43   44   45 

Filter by Year

2019 2026


Reset
Filter By Issues
All Issue Vol 8 No 1 (2026): Januari 2026 Vol 7 No 4 (2025): Oktober 2025 Vol 7 No 3 (2025): Juli 2025 Vol 7 No 2 (2025): April 2025 Vol 7 No 1 (2025): Januari 2025 Vol 6 No 4 (2024): Oktober 2024 Vol 6 No 3 (2024): Juli 2024 Vol 6 No 2 (2024): April 2024 Vol 6 No 1 (2024): Januari 2024 Vol 5 No 4 (2023): Oktober 2023 Vol 5 No 3 (2023): July 2023 Vol 5 No 2 (2023): April 2023 Vol 5 No 1 (2023): Januari 2023 Vol 4 No 2 (2022): Juli 2022 Vol 4 No 1 (2022): Januari 2022 Vol 3 No 2 (2021): Juli 2021 Vol 3 No 1 (2021): Januari 2021 Vol 3 No 2 (2021): Jurnal Teknologi Dan Sistem Informasi Bisnis (Jteksis) Vol 2 No 2 (2020): Juli 2020 Vol 2 No 1 (2020): Januari 2020 Vol 2 No 2 (2020): Jurnal Teknologi Dan Sistem Informasi Bisnis (Jteksis) Vol 2 No 1 (2020): Jurnal Teknologi Dan Sistem Informasi Bisnis (Jteksis) Vol 1 No 2 (2019): Juli 2019 Vol 1 No 1 (2019): Januari 2019 More Issue