Jurnal Teknologi Informasi (JUTECH)

Perancangan UI/ UX Untuk Website Kasir Toko Sembako Agung Berbasis Prototype Menggunakan Metode Design Thinking

2025-09-16T08:14:47+00:00

Toko Sembako Agung faces challenges in recording transactions that are still done manually, which has the potential to cause errors in calculating and managing stock. To overcome this problem, this study aims to design a user interface (Ui) and user experience (Ux) for a prototype-based cashier website using the Design Thinking method. The Design Thinking method is applied through five main stages, namely Empathize, Define, Ideate, Prototype, and Test. In the Empathize stage, observations and interviews were conducted with users, namely cashiers and shop owners, to understand the needs and obstacles faced. The results of the study show that the application of the Design Thinking method in designing UI/UX is able to produce a cashier system design that is more user-friendly, efficient, and easy to use. This cashier website has main features such as transaction recording, stock management, and sales report generation. Testing of the prototype shows that the designed system can increase transaction efficiency and reduce recording errors compared to the manual method. Thus, UI/UX design based on the Design Thinking method can be an effective solution in developing a more optimal cashier system for grocery stores. Recommendations for further research are to develop this prototype into a system integrated with a real-time database and improve the security aspects of transaction data.

Implementasi Regresi Linear Berganda dalam Forecast Penjualan pada CV. Surya Kencana Sembako Berbasis website

2026-01-28T03:57:13+00:00

Perkiraan penjualan merupakan aspek penting dalam pengambilan keputusan bisnis, terutama dalam industri sembako yang memiliki permintaan tinggi dan fluktuatif. CV. Surya Kencana Sembako sebagai perusahaan distribusi barang kebutuhan pokok menghadapi tantangan dalam memprediksi jumlah penjualan secara akurat. Penelitian ini bertujuan untuk mengimplementasikan metode Regresi Linear Berganda dalam meramalkan penjualan berdasarkan beberapa variabel yang memengaruhi, seperti harga produk, jumlah promosi, dan musim penjualan. Sistem dirancang dalam bentuk aplikasi berbasis website agar memudahkan pengguna dalam menginput data dan memperoleh hasil prediksi secara real-time. Hasil dari implementasi menunjukkan bahwa model regresi mampu memberikan prediksi yang cukup akurat dengan nilai koefisien determinasi (R²) yang tinggi. Dengan adanya sistem ini, diharapkan CV. Surya Kencana Sembako dapat meningkatkan efisiensi dalam pengelolaan stok dan strategi penjualannya.

Integrasi Teknologi IoT dan Aplikasi Telegram Untuk Pemantauan Kadar Gula Darah Penderita Diabetes

2026-01-28T05:02:19+00:00

Blood sugar (glucose) is the body's main source of energy and is classified as a monosaccharide. Blood sugar levels are divided into low, normal, and high, with high levels potentially triggering diabetes, one of the main health issues in Indonesia. This research proposes an Internet of Things (IoT)-based health monitoring system to measure blood sugar levels using a photodiode sensor and a red LED as the light source. The system is equipped with real-time notifications via Telegram and data display on an OLED screen. The method used is a prototype with accuracy testing against a glucometer as a comparison tool. The test results showed an average error of 5.28% out of a total error of 105.77%. However, the device often displays the same measurement results repeatedly and shows a “finger not detected” notification due to the sensor's sensitivity to surrounding light.

Predictive Maintenance Berbasis Machine Learning dalam Smart Manufacturing

2026-01-28T05:04:13+00:00

The concept of predictive maintenance represents a significant change in traditional maintenance methods. The use of machine learning in manufacturing machine maintenance has the potential to offer unprecedented opportunities for predicting problems by uncovering hidden patterns in vast data sets. This study aims to examine four machine learning models in classifying maintenance needs in a smart manufacturing environment. Machine learning models such as Logistic Regression, Random Forest, XGBoost, and Multi-layer Perceptrong (MLP) are trained with 5-fold cross-validation. The dataset used is a public dataset from the kaggle website, which consists of 10000 rows and 13 features with the maintenance_required feature as the target feature. The model training results are evaluated using various metrics, such as accuracy, precision, recall, f1-score, and ROC-AUC. The test results show that Random Forest provides the best performance with an accuracy of 98.37%, precision of 99.97%, recall of 91.72%, f1-score of 95.67%, and ROC-AUC of 95.95%. The tree-based ensemble method Random Forest is able to capture patterns in the data better than linear and neural models. This indicates that Random Forest is a reliable model for detecting machine maintenance requirements. Further research can consider increasing dataset capacity, integration with deep learning techniques, examining the perspective of multivariate time-series structures.

Penerapan YOLO dan OpenCV dalam Klasifikasi Kendaraan pada Lalu Lintas Kota Depok

2026-01-29T05:00:34+00:00

The growth in the number of vehicles in Depok City has driven the need for an accurate and efficient traffic monitoring system. This study implements the You Only Look Once (YOLO) version 8 algorithm to automatically detect and classify vehicles based on Python and OpenCV. The focus of the study is on four types of vehicles, namely motorcycles, private cars, buses, and trucks. The dataset was obtained from CCTV recordings and field documentation, then annotated using LabelImg and processed into YOLO format. The training process was carried out using the pretrained YOLOv8 model, while the system testing was conducted on videos of Depok City roads. Model performance was evaluated using the metrics of mAP@0.5 and mAP@0.5:mAP95, precision, recall, and F1 score. The evaluation results show that the model achieved a mAP@0.5 of 91% and a mAP@0.5:mAP95 of 75.1%, precision of 88.5%, recall of 85.2%, and an F1-score of 86.8%. With these results, the model is capable of detecting and classifying vehicles in real time with high accuracy under various lighting conditions and camera angles. Additionally, this system is integrated with a web interface using Flask for direct visualization of detection results. This research contributes to supporting smart transportation systems in urban environments and provides a potential solution for data-based traffic management.

A Decision Support System for the Selection of Social Assistance Recipients: Comparison of SAW and TOPSIS Methods in Ponggol Village

2026-01-29T05:02:06+00:00

The selection process for prospective recipients of the Program Keluarga Harapan (PKH) social assistance in RW. 03, Ponggol Village, is currently conducted manually, which is time-consuming and prone to subjectivity. To address these issues, this research aims to develop a web-based decision support system (DSS). The system was designed using PHP and MySQL for the backend, along with HTML, CSS, and JavaScript for the frontend. Two DSS methods, namely Simple Additive Weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), were implemented to perform calculations and rank alternatives based on five criteria determined through an interview. System testing results show that both the SAW and TOPSIS methods successfully provided consistent ranking results for ranks 1 to 5. Therefore, the developed system can serve as an objective, transparent, and efficient tool for decision-makers in determining the most deserving recipients of social assistance.