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INDONESIA
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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Identifikasi Penyakit Daun pada Tanaman Solanaceae dan Rosaceae Menggunakan Deep Learning Faqih, Allan Bil; Avianto, Donny
Jurnal Teknologi Terpadu Vol 10 No 2 (2024): Desember, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i2.1440

Abstract

With a projected global population of 9.7 billion by 2050, agriculture faces significant challenges in ensuring food security. One major obstacle is plant diseases that reduce crop yields by 40% per year. Previous research is often limited to disease detection in a single plant species, thus poorly reflecting multi-species needs in real agricultural practices. This research aims to develop and evaluate deep learning-based plant disease detection system using Convolutional Neural Networks (CNN) applied to two plant families, Solanaceae and Rosaceae. The dataset used was PlantVillage, containing 54,306 leaf images in JPEG format downloaded from GitHub, with data outside two families discarded during pre-processing. Three deep learning models were tested: transfer learning with InceptionV3 architecture and two custom CNNs (DFE and LCNN). LCNN model showed the best performance with training, validation, and testing accuracies of 99%, 99%, and 95%, respectively. In contrast, InceptionV3 achieved 96% training, 98% validation, and 92% testing accuracy, while DFE with 86% training, 94% validation, and 82% testing accuracy. Confusion matrix analysis showed difficulty distinguishing between healthy potatoes and potatoes with late blight, as well as cedar apple rust. These results highlights importance of developing specific model architectures rather than complex models for accurate multi-crop disease detection.
Klasifikasi Motif Batik Yogyakarta Menggunakan Metode GLCM dan CNN Dani, Ananda Rizki; Handayani, Irma
Jurnal Teknologi Terpadu Vol 10 No 2 (2024): Desember, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i2.1451

Abstract

Yogyakarta batik motifs represent Indonesia’s cultural heritage, but automatic classification remains challenging. This study develops a Yogyakarta batik motif classification system using a combination of the Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction and Convolutional Neural Network (CNN) based on MobileNetV2 for image classification. GLCM was chosen for its ability to extract detailed texture features, while MobileNetV2 was used for its efficiency in visual pattern recognition with minimal computational resources. The dataset consists of 3,223 images from five batik motifs: Batik Ceplok, Batik Kawung, Batik Truntum, Batik Parang, and Batik Ciptoning, sourced from the Batik Keraton Museum Yogyakarta and Kaggle. The model achieved 99% accuracy, demonstrating the effectiveness of the approach in recognizing complex batik patterns. The results suggest that this system can be implemented into a mobile application with a client-server architecture for automatic motif detection. Despite promising results, the study is limited by dataset size and the complexity of specific motifs. Future research should expand the dataset and explore data augmentation techniques to improve classification accuracy for more complex motifs.
Analisis Faktor Kesuksesan E-Learning dalam Meningkatkan Kualitas Belajar Mengajar di Kota Batam Firmansyah, Muhamad Dody; Melati, Dini Sari
Jurnal Teknologi Terpadu Vol 10 No 2 (2024): Desember, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i2.1452

Abstract

Technology has had a significant impact on various aspects of human life, one of which is education through the implementation of e-learning as a modern learning solution. Technology makes the teaching and learning process easier thereby improving the quality of teaching and learning. The presence of the COVID-19 pandemic has also accelerated the transition of education to e-learning, although not all countries are fully prepared to implement it. This is also the case in Batam City, which has implemented e-learning in a number of schools and universities with support from academics and the local Education Office. The presence of e-learning offers learning flexibility but also presents challenges, such as learner dissatisfaction that affects low learning motivation. Therefore, this study aims to analyze the factors affecting e-learning user satisfaction among students with the research target of students in Batam City. The research was conducted using quantitative method and processed using SPSS. The results stated that e-learning satisfaction is influenced by system quality, internet quality, information quality and service quality.  Therefore, to optimize the satisfaction of e-learning users must pay attention to these four factors.
Implementasi Teknologi Augmented Reality dalam Sains berbasis Android dengan Kartu Interaktif Aminudin, Nur; Mutmainah, Mutmainah; A, Afnan Zalfa Salsabila
Jurnal Teknologi Terpadu Vol 10 No 2 (2024): Desember, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i2.1478

Abstract

Significant changes in education have resulted from the advancement of information technology, particularly in the area of abstract science teaching. Android-based Augmented Reality (AR) technology using interactive cards as educational materials is one possible breakthrough. Through interactive visualizations that enable in-depth subject analysis, this project seeks to increase student comprehension and engagement. The ASSURE model served as the basis for the research and development (R&D) methodology used in the study. Learning needs and technology are integrated in this paradigm, which consists of the following steps: Analyze Learners, State Objectives, Select Media and Materials, Utilize Media and Materials, Require Learner Participation, and Evaluate and Revise. Data were collected during a four-week observation period using pretest and posttest tests. An n-gain of 0.71 (high category) in the experimental class and 0.47 (moderate category) in the control class indicated a substantial increase, according to paired sample t-test analysis. The primary distinction was that the control class had less access to AR devices. The findings demonstrated that AR increased student comprehension and involvement. However, obstacles included infrastructural needs and sample limits. To investigate AR's efficacy on a broader scale and its influence on long-term learning results, more research is advised.
Implementasi Metode Case-Based Reasoning (CBR) dalam Sistem Pakar untuk Mendapatkan Diagnosis Anxiety Disorders Gunung, Tar Muhammad Raja; Lubis, Siti Sahara; Siregar, Manutur; Simanjuntak, Peter Jaya Negara; Jinan, Abwabul
Jurnal Teknologi Terpadu Vol 10 No 2 (2024): Desember, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i2.1480

Abstract

This research aims to develop an expert system based on the case-based reasoning method for diagnosing anxiety disorders. Anxiety Disorder is a mental health disorder that is often experienced by the public but is often not detected correctly. The case-based reasoning method was chosen because of its ability to utilise previous cases to solve new problems that have similarities. Case-based reasoning uses four main stages: retrieval, reuse, revise, and retain. The case-based reasoning method is implemented using case data obtained from psychology clinics and interviews with mental health experts. Testing the case-based reasoning method shows a high level of accuracy in diagnosing various types of Anxiety Disorders, such as Generalised Anxiety Disorder, Panic Disorder, and Specific Phobias. The results of this study show that the case-based reasoning method can be an effective tool in helping mental health professionals diagnose Anxiety Disorders more quickly and accurately. After searching using the symptoms obtained, the percentage of each type of disease is the percentage of Generalised Anxiety Disorder 35.7%, the percentage of Panic Disorder 30.7%, and the percentage of Specific Phobias 65%.
Implementasi Metode Double Exponential Smoothing untuk Sistem Peramalan Penjualan Alat Musik Ilham, Mohammad Akbar; Achmadi, Sentot; Sari, Karina Aulia
Jurnal Teknologi Terpadu Vol 10 No 2 (2024): Desember, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i2.1490

Abstract

EMC-Emmanuel Music Centre is a company that sells musical instruments and sound systems with the aim of advancing music and professional audio in Indonesia. This research aims to forecast the monthly sales of musical instruments to better manage sales data and develop more efficient sales strategies. The method used is Double Exponential Smoothing, a forecasting technique in data mining. The research subjects were the company's musical instrument sales data, with a population covering all sales transactions from January 2022 to January 2024. The sample was selected using purposive sampling, focusing on transaction data of top-selling products. Data was collected through interviews, direct observations of the sales process, and review of marketing strategy documents. The results show that the Double Exponential Smoothing method produces forecasts with an average MAE of 8.12%, categorized as very good. This study recommends using forecast results for inventory management and adjusting seasonal marketing strategies to improve efficiency. The results indicate that an alpha  of 0.1 provides better accuracy with an MAE of 17.08, compared to an alpha  of 0.2 with an MAE of 19.08. Therefore, an alpha  of 0.1 is recommended for improving the forecasting accuracy.
Sistem Pendukung Keputusan (SPK) Karyawan Tetap PT. Global Autoparts Pratama dengan Metode TOPSIS Asal, Oswaldus; Prayudhi, Risa; Riesmiyantiningtias, Ninuk; Ramadhani, Anjas
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.v11i1.1466

Abstract

PT. Global Autoparts is a company engaged in the distribution of car spare parts, the selection process for permanent employees still uses an assessment form. The purpose of this study is to determine the permanent employees who are in the first ranking order. The TOPSIS method is used because of its simple and easy-to-understand mathematical concept, its computational efficiency and its ability to measure the relative performance of decision alternatives into a simple mathematical form. The TOPSIS method uses ranking based on the results of the assessment form, so the system no longer needs to sort the data from the largest to the smallest values during the value data processing process and the results presented by the system can be printed in the form of a report. The final result of the selection value in determining the permanent employees who are in the first ranking order, namely A03 named "Paulina" with a preference value of 0.648 who gets the highest preference result value (Vi). The conclusion states that the employee named "Paulina" is entitled to become a permanent employee at PT. Global Autoparts Pratama from the 10 employee data that have been selected.
Pengembangan Sistem Informasi Akreditasi Program Studi Berbasis Web di Fakultas Teknik Universitas Udayana Utami, Ni Made Cyntia; Setiawati, Ni Luh Putu Lilis Sinta; Komaladewi, Anak Agung Istri Agung Sri; Setyawan, Ferdiansyah Pratama Putra
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.v11i1.1505

Abstract

The accreditation process is vital for study programs to gain recognition for their performance. Accreditation applications require supplementary data to support the Program Performance Report (LKPS) and develop the Self-Evaluation Report (LED). At the Faculty of Engineering, Udayana University (FT Unud), data collection is still conducted manually, leading to inefficiencies. This study aims to develop SIAP 4.0, a web-based accreditation support system for study programs at FT Unud, adhering to Decree No. 186/M/2021 issued by Kepmendikbudristek. Using the Waterfall methodology, the system was designed through stages including requirements definition, analysis, design, coding, testing, and maintenance. The system was built with the PHP Laravel framework, HTML, jQuery, and MySQL, and deployed on the Amazon Web Service EC2 Instance server. To ensure data security, user access to SIAP 4.0 is managed through registration and admin authorization. The system offers three core features: data input, visualization, and export, simplifying centralized and up-to-date data management. These functionalities enable efficient addition, editing, and deletion of accreditation data, improving the overall accreditation process at FT Unud.
Sistem Klasifikasi Berbasis Android untuk Penyakit Buah Kakao Menggunakan CNN NasNet-Mobile Gado, Gregorius Albertus Setu; Primandari, Putri Noraisya
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.v11i1.1821

Abstract

Cocoa is an important commodity in Indonesia that is susceptible to pathogen-induced diseases. These diseases reduce fruit quality and are difficult to recognise at an early stage. This research uses a Convolutional Neural Network (CNN) method with a transfer learning approach and NASNet-Mobile architecture to facilitate the classification of cocoa fruit diseases. The data consisted of 2000 images of diseased and non-diseased cocoa pods divided into four classes, namely Cocoa Pod Rot (Black Pod), Fruit Sucking Ladybugs (Helopeltis sp), Fruit Borer (Pod Borer) and Normal. Training was conducted for 25 epochs using Google Colab. The best model produced 99.11% training accuracy, 96.14% validation, and 94.88% testing. The model was implemented into an Android device and field tested with 93.33% accuracy, 98.5% recall, 57.1% precision, and 71.6% F1-score. This system is effective in helping early detection of cocoa pod disease in a practical, efficient manner without reducing the accuracy value.
Sistem Perangkingan Menentukan Fakultas Terbaik Penerapan Zona Integritas Menggunakan Metode SAW Dewi, Sri; Karo, Ichwanul Muslim Karo; Barus, Eviyona Laurenta Br
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.v11i1.1823

Abstract

The Integrity Zone (ZI) is defined as a designation given to government institutions that demonstrate a strong commitment from their leadership and all levels in realising a Corruption-Free Area (WBK) and/or a Clean and Serving Bureaucracy Area (WBBM). This commitment is realised through bureaucratic reform, particularly in encouraging the prevention of corrupt practices and improving the quality of public services. The purpose of this study is to build a system that will be used to see the ranking of the implementation of the faculty Integrity Zone in an effort to support corruption prevention and improve the quality of public services, so that it can assist stakeholders in decision making. Simple Additive Weighting (SAW) was applied in this study involving 7 Faculties at Medan State University, and 8 criteria were used for evaluation. The results of the study are a website-based Integrity Zone ranking system with several features that can be accessed by visitors, namely: Home, Ranking and Login, while the admin can access the Dashboard, Criteria, Alternatives, Simple Additive Weighting Calculation, and Decision Results features. The system was tested with Black Box Testing to see that the menu functions run well.

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