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Jurnal Teknologi Terpadu
Published by STT Terpadu Nurul Fikri
ISSN : 24770043     EISSN : 24607908     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 266 Documents
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Komparasi Algoritma Machine Learning dalam Memprediksi Kapasitas Produksi Potensial Air Bersih di Indonesia Rohana, Tatang; Novita, Hilda Yulia; Nurlaelasari, Euis
Jurnal Teknologi Terpadu Vol 11 No 1 (2025): Juli, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Clean water availability is a key indicator of sustainable development, particularly in developing countries like Indonesia. Factors such as population growth, climate change, and urbanization contribute to fluctuations in clean water supply. This study aims to estimate the potential for clean water production in Indonesia using various machine learning algorithms, such as Linear Regression, Decision Tree, Random Forest, Multilayer Perceptron, XGBoost (Extreme Gradient Boosting), and Neural Network. Each algorithm was evaluated based on Mean Squared Error (MSE), Mean Absolute Error (MAE), R-squared (R²), and prediction accuracy. The results show that Linear Regression achieved the lowest MSE (9.31E-18), nearly zero, indicating extremely accurate predictions. Neural Network and Multilayer Perceptron also performed well, with MSE values of 0.00010898 and 0.00018004, respectively. Moreover, Linear Regression and Neural Network achieved R² scores of 1 and 0.9905, suggesting they can explain nearly all variability in the target data. These findings highlight the effectiveness of Linear Regression, Neural Network, and Multilayer Perceptron in modeling clean water production capacity. Therefore, these algorithms are recommended as the most reliable approaches for supporting data-driven decisions in clean water resource planning and management in Indonesia.
Rancang Bangun Website Smartbeez sebagai Platform Edukasi Parenting dan Calistung Anak Berbasis Waterfall Rasyade, Maura Aqlaila; Voutama, Apriade
Jurnal Teknologi Terpadu Vol 11 No 1 (2025): Juli, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v11i1.1732

Abstract

The development of digital technology has brought convenience to various aspects of life, including education. Websites have become one of the most effective solutions for providing access to learning, especially for parents and children. This study aims to design and develop the SmartBeez website as an educational platform for parenting and basic literacy (reading, writing, and arithmetic) for children, using the Waterfall model. System development consists of several stages, including requirements analysis, system design, implementation, testing, and maintenance. The website targets two main user groups, which are parents and children aged 5 to 12 years. Parents can access parenting materials, while children can learn basic literacy skills through interactive content. Data were collected through observation and literature. The system design is illustrated using Unified Modeling Language (UML) to represent the system flow visually and structurally. Testing was conducted using the black-box testing method to ensure that each feature works according to user needs. The results show that the SmartBeez website can serve as an educational platform that helps parents implement more effective parenting methods, while also providing an engaging and structured learning experience for children.
Klasifikasi Penyakit Daun Singkong Menggunakan Convolutional Neural Network (CNN) dengan Arsitektur VGG16 Berbasis Android Anggraeni, Annisa Mustika; Hermanto, Teguh Iman; Nugroho, Imam Maruf
Jurnal Teknologi Terpadu Vol 11 No 1 (2025): Juli, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

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Abstract

Cassava plants play an important role as a national food source. However, their productivity has declined in recent years due to leaf disease. Manual disease identification is often inaccurate and slow. This study aims to develop an automatic classification system based on digital images to detect cassava leaf disease quickly and accurately. The method used is a Convolutional Neural Network (CNN) with a VGG16 architecture. The system was developed following the CRISP-DM approach and uses tools such as Python, Keras, TensorFlow, and TensorFlow Lite for integration into Android. The model was trained to recognize five leaf conditions: brown spots, bacterial blight, green mite, mosaic, and healthy. Testing over 50 epochs showed an accuracy of 96%, with precision, recall, and F1-score ranging from 0.93 to 0.98. This approach is superior to the research by Setyanto and Ariatmanto, which only achieved an accuracy of 72.84%. This system helps farmers perform early diagnosis by taking or uploading photos of leaves, enabling more effective disease control.
Sistem Rekomendasi Kuliner Ikonik Kota Solo Menggunakan Metode Content Based Filtering Warta, Danu; Pramono, Pramono; Maulindar, Joni
Jurnal Teknologi Terpadu Vol 11 No 1 (2025): Juli, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v11i1.1870

Abstract

Content-Based Filtering is a user-independent method, meaning it does not rely on whether an item is new or previously selected by other users. This study aims to design and develop a recommendation system for iconic culinary places in Solo City using the Content-Based Filtering method. The system helps tourists find culinary options based on individual preferences such as food name, rating, and price. Culinary data was collected through web scraping from Google Maps using the Instant Data Scraper extension. The data is processed using the TF-IDF algorithm and cosine similarity to calculate the similarity between content features. The system development follows the Rational Unified Process (RUP) with four phases: inception, elaboration, construction, and transition. It is built using PHP with the Laravel framework and MySQL database. The system provides a list of culinary recommendations complete with images, names, ratings, addresses, and prices. Black-box testing on six main scenarios showed 100% success, proving the system meets functional requirements. The final recommendation results show a similarity score above 70%, indicating accurate and relevant suggestions. This system helps users discover Solo's iconic culinary spots more efficiently and according to their preferences.
Pemanfaatan IoT untuk Efisiensi Energi pada Pabrik Pintar: Tantangan, Solusi dan Tren Teknologi Suciana, Ewin; Nasrullah, Muhammad Hudzaifah; Christanto, Duta Arief; Cahyadi, Dede; Giantri, Lilik Tiara
Jurnal Teknologi Terpadu Vol 11 No 1 (2025): Juli, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v11i1.1897

Abstract

This study investigates the role of the Internet of Things in enhancing energy efficiency within smart factories by analyzing current trends, driving factors, and challenges. A Systematic Literature Review with the PRISMA framework was employed to ensure systematic and comprehensive selection of studies. The PICO framework guided the formulation of research questions, facilitating rigorous screening of data sourced from the Scopus database with strict inclusion and exclusion criteria. Findings reveal a substantial increase in IoT-related energy efficiency research in smart factories between 2019 and 2025. Key challenges identified include high sensor energy consumption, communication reliability, and network management complexity. Research limitations stem from the exclusive use of the Scopus database and English-language publications. The study highlights the necessity of interdisciplinary approaches and advanced technologies such as 5G and edge computing to address integration and data security issues, thereby supporting the effective and sustainable deployment of IoT in the manufacturing sector.
Sistem Penilaian Kinerja untuk Pengembangan SDM pada PT SIT Global Systems dengan Metode AHP Setiawati, Anjani; Primawati, Alusyanti; Akhirina, Tri Yani
Jurnal Teknologi Terpadu Vol 11 No 1 (2025): Juli, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v11i1.1913

Abstract

An objective and structured employee performance assessment is critical in supporting human resource (HR) development in the company. This study aims to design a decision support system (DSS) with the Analytical Hierarchy Process (AHP) method to assist PT SIT Global Systems in conducting comprehensive employee performance assessments. The AHP method was chosen because it produces consistent calculations based on the weighting of predetermined criteria and sub-criteria, thereby reducing subjectivity in the assessment process. This system is built using the NetBeans application with the Java programming language and uses MySQL as a database to manage and store all employee assessment data. This application allows users to manage employee data, calculate employee final grades, and generate assessment reports. With this system, the assessment result can be used as a basis for decision-making related to further HR development. This system is expected to support management in improving the company’s HR performance.

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