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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 296 Documents
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Analisis Usability Sistem E-Voting Pemilihan Ketua dan Wakil Ketua OSIS dengan Metode Think Aloud Fajar Husain Asyari; Ellen Proborini; Sholihul Ibad
Jurnal Teknologi Terpadu Vol 12 No 1 (2026): Juli, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v12i1.2583

Abstract

The election of the OSIS (Student Council) chairman and vice-chairman is an important school agenda that requires a fast, accurate, and transparent voting process. With the advancement of information technology, e-voting systems have become a modern solution, but their success strongly depends on the system's usability. This study aims to evaluate the usability level of the e-voting system for the election of the OSIS (Student Council) president and vice-president using the Think Aloud method. The evaluation involved ten student respondents as the system’s target users, who were asked to complete six task scenarios—from entering the token to logging out—while verbalizing their thoughts and difficulties. The results showed that 90% of respondents successfully completed task scenarios T1–T5, while 60% encountered difficulties in task T6 (logging out). Based on the severity rating analysis, three main issues were identified: slow response time (score 4, critical), unclear navigation (score 3, major), and confusing input forms (score 3, major). Overall, the system achieved a task completion rate of 93.3% with an average completion time of 38 seconds per task. These results indicate that the e-voting system demonstrates good usability but requires improvements in navigation, system performance, and input guidance to enhance user experience. The Think Aloud method proved effective in directly identifying usability issues from the user perspective.
Implementasi Algoritma FP-Growth untuk Optimalisasi Strategi Pemasaran di Toko Pakaian: Studi Kasus Toko Trend Batara Mahardika Aryoko; Saiful Nur Budiman; Sri Lestanti
Jurnal Teknologi Terpadu Vol 12 No 1 (2026): Juli, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v12i1.2592

Abstract

TREND store is a retail outlet that sells various types of clothing and accessories. As market competition intensifies, the store needs to develop more efficient marketing strategies to remain competitive. One approach is to utilise sales data to analyse consumer purchasing patterns, given that such data has not been optimally used previously. This study aims to identify purchasing patterns and generate association rules as a basis for marketing strategies using the FP-Growth algorithm. The algorithm was chosen because it can identify frequent itemsets without candidate generation, making it more efficient than other methods in market basket analysis. The research data consist of 64 sales transactions from March 2025. In addition to pattern discovery, lift ratios were calculated to measure the strength of relationships between items. The results show that FP-Growth successfully identified significant purchasing patterns and generated relevant association rules. Several rules have lift ratios above 1, such as 1.2472 and 1.1463 for the combination K7, K1, C5, indicating positive relationships. These findings can be used to develop more data-driven and efficient marketing strategies, such as placing related items together to encourage impulsive purchases, supporting product recommendations, promoting planning, and informing other marketing decisions.
Klasifikasi Buah Kelapa Sawit dengan Convolutional Neural Network Arsitektur Inception-v4 Theresia Kurniati Seran; Septyan Eka Prastya; Muhammad Zulfadhilah; Rudy Ansari
Jurnal Teknologi Terpadu Vol 12 No 1 (2026): Juli, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v12i1.2597

Abstract

The palm oil industry plays a crucial role in Indonesia’s economy, making fruit classification by ripeness levels essential to ensuring the quality of palm oil production. This study aims to develop a classification system for oil palm fruits into two categories: ripe and unripe, using a Convolutional Neural Network with the Inception-v4 architecture. The dataset consists of 2,900 images, divided into training (2,000), validation (500), and testing (400) sets. The research stages include data collection, pre-processing (duplicate detection, augmentation, and normalization), model training with Inception-v4, evaluation, and result interpretation. Model performance was evaluated using accuracy, precision, recall, f1-score, and confusion matrix. Results indicate that Inception-v4 achieved the highest validation accuracy of 95% in classifying oil palm fruit. Further experiments were conducted using various optimizers (SGD, Adam, RMSprop, Adagrad, Adadelta) to enhance performance. This study confirms that Inception-v4 is highly effective for oil palm fruit classification and can be applied in plantation industries to improve harvest efficiency and production quality.
Pengembangan Sistem Deteksi Media Buatan AI menggunakan Arsitektur CNN ResNet-50 Guruh Pratama Putra; Adam Sekti Aji
Jurnal Teknologi Terpadu Vol 12 No 1 (2026): Juli, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v12i1.2610

Abstract

The rapid advancement of artificial intelligence (AI) technology has led to an increase in AI-generated media that is increasingly difficult to distinguish from authentic media. This phenomenon presents significant challenges in various fields, including digital security and the creative industry, necessitating a reliable automatic detection system. Manual identification is inefficient and error-prone, as it requires specialized expertise to recognize subtle digital media. This study proposes the development of an AI-generated media detection system using a Convolutional Neural Network (CNN) with the ResNet-50 architecture. The ResNet-50 model was chosen due to its proven ability to handle deep feature extraction and overcome the vanishing gradient problem. A sample of 4.600 images, 2.300 AI images and 2.300 real images, were used. The research methodology included data collection from various Kaggle datasets, data preprocessing including resizing and augmentation, model training, and performance evaluation based on accuracy, precision, and recall metrics. Experimental results show that the developed model achieved a high testing accuracy of 97.30%. This indicates the model's capability to effectively classify media as AI-generated or real with high precision. This research is expected to be a valuable reference for the development of more accurate and efficient AI systems in detecting synthetic media.
Audit Kredit Digital Berbasis Explainable Artificial Intelligence (XAI): Tinjauan Pustaka Sistematis Muhammad Arief Sutisna; Imam Riadi; Abdul Fadlil
Jurnal Teknologi Terpadu Vol 12 No 1 (2026): Juli, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v12i1.2614

Abstract

Digital transformation in the financial sector encourages the application of Artificial Intelligence (AI) in the credit audit process. While AI is capable of improving the speed and accuracy of risk assessments, modern models such as deep learning are black box, raising issues of transparency and accountability—two things that are critical in credit audits that must comply with regulations and build stakeholder trust. This study uses the Systematic Literature Review (SLR) approach to synthesize the scientific literature that discusses the application of Explainable Artificial Intelligence (XAI) in digital credit audits. The SLR process includes: Query formulation (e.g. "explainable AI", "credit audit", "interpretability model"), Screening of studies based on relevance, methodological quality, and year range of publication, Extraction of key data (XAI method, dataset type, prediction model, tools used, and key findings), and Comparative synthesis analysis. Based on the Systematic Literature Review, it was found that the main XAI methods are SHAP and LIME, the most commonly used prediction models are Random Forest and XGBoost and several repeated weaknesses were found, including limitations in the representativeness of the dataset, the risk of overfitting, and the trade-off between the level of accuracy and the level of interpretability. Thus, the proper integration of  XAI is expected to increase transparency, fairness, and trust in AI-based digital credit audits, while paving the way for more ethical audit practices and in line with regulatory demands.
Penerapan CNN dengan Arsitektur EfficientNet-B4 untuk Deteksi Penyakit Glaukoma Berbasis Web Rizal Rachman; Adi Karawinata Sataynegara
Jurnal Teknologi Terpadu Vol 12 No 1 (2026): Juli, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v12i1.2623

Abstract

Glaucoma is a leading cause of irreversible blindness and frequently goes undetected in its early stages due to its slow and silent progression. Many individuals remain unaware of the disease until significant optic nerve damage has occurred. Early screening is limited by low public awareness, insufficient access to eye-care facilities, and the need for specialists to interpret retinal images. Automated detection is further complicated by inconsistent image quality, illumination variations, and the subtle differences between healthy and glaucomatous retinas. This study develops a web-based early detection system using the EfficientNet-B4 Convolutional Neural Network. A total of 9,540 retinal images from the EyePACS dataset were utilised, including 8,000 for training, 770 for validation, and 770 for testing. Classification was performed using two expert-annotated categories: normal and glaucomatous. The model was trained for 30 epochs through transfer learning and fine-tuning to achieve stable performance. The results show validation accuracy between 90% and 92%, with well-converging loss. The final model was integrated into an interactive web platform that allows users to upload retinal images and receive preliminary predictions accompanied by basic glaucoma information. This system offers potential as an accessible tool for early community-level screening.
Persepsi Penggunaan ChatGPT sebagai Alat Bantu Belajar Mata Kuliah Pemrograman di Institut Widya Pratama Lisatri Makna; Victorianus Aries Siswanto; Ichwan Kurniawan
Jurnal Teknologi Terpadu Vol 12 No 1 (2026): Juli, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v12i1.2630

Abstract

The development of artificial intelligence technology, particularly ChatGPT, has created new opportunities to support programming learning in higher education. However, studies examining students’ perceptions of ChatGPT as a support tool in programming courses remain limited, especially regarding ease of use, usefulness in completing assignments, dependency levels, and learning effectiveness. This condition indicates the need for a focused study to understand the role of ChatGPT in student learning contexts. Therefore, this study aims to analyze students’ perceptions of ChatGPT in programming learning at Institut Widya Pratama Pekalongan. This study employed a quantitative survey approach. Data were collected through questionnaires distributed to 91 students selected through purposive sampling and analyzed using validity and reliability tests and descriptive statistics. The results show that ChatGPT's ease of use received an average score of 81.31%, indicating strong agreement. The usefulness of ChatGPT in assisting with programming assignments reached 78.63%, categorized as agree. Student dependency on ChatGPT was 54.79%, categorized as neutral. Meanwhile, the effectiveness of ChatGPT usage reached 77.30%, categorized as agree. Overall, these results indicate that ChatGPT has potential as an effective support tool when used wisely.
Penerapan Aplikasi Manten-Ku untuk Optimalisasi Layanan pada Khalisa Muslim Wedding Banjarnegara Ferry Febrianto; Khalimaturofi'ah Khalimaturofi'ah
Jurnal Teknologi Terpadu Vol 12 No 1 (2026): Juli, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v12i1.2639

Abstract

Khalisa Muslim Wedding Banjarnegara is a service business that offers wedding packages, bridal makeup, pre-wedding services, graduation services, and clothing rentals. Currently, the ordering process is still done conventionally, with customers having to come directly to the office to order packages. Customer data is still recorded on paper and summarised in a ledger. This often causes various problems, such as lost, damaged, or difficult-to-find data. The promotion process is still limited to telephone and social media such as Instagram. Customers see photos without getting detailed information about the contents and prices of available wedding packages. This study aims to develop a website-based wedding package booking application to facilitate the booking process and manage customer data. The methods used include data collection and the Waterfall system development. The application was designed using Flowchart, Data Flow Diagram (DFD), and Entity Relationship Diagram (ERD) modeling tools, and implemented using PHP and the MySQL database. Black Box testing results showed a 94% success rate, indicating that all system functions were running according to requirements. Meanwhile, the results of User Acceptance Testing (UAT), with an average score of 69.28%, indicate that the application is acceptable to users and considered helpful for ordering, promotion, and data processing.
Augmented Reality Pengenalan Peralatan Tradisional Bali pada Museum Subak Mascetti Berbasis Android I Gusti Ayu Agung Mas Aristamy; Putu Risanti Iswardani; Ni Ketut Utami Nilawati
Jurnal Teknologi Terpadu Vol 12 No 1 (2026): Juli, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v12i1.2642

Abstract

The Android-based Augmented Reality (AR) application for introducing Balinese traditional tools at the Subak Mascetti Museum was developed as an interactive information and educational medium. This research was motivated by limited information dissemination channels, damage to several traditional tools, and the low level of public knowledge about the history and functions of Balinese traditional tools. These conditions have led to the underutilisation of museum collections as cultural learning resources. This study aims to develop an AR application that presents information on traditional tools in a visual, interactive, and easily understandable manner. The method used is Marker-Based Tracking to display ten 3D models of traditional tools in real time, complemented by museum history information, audio explanations, and bilingual features in Indonesian and English. Application effectiveness testing was conducted to evaluate system performance. The results show an effectiveness score of 85.6%, categorised as very effective. Based on these results, it can be concluded that the developed AR application enhances user understanding and has strong potential as an educational medium to support the preservation of Balinese culture and traditional tools at the Subak Mascetti Museum.
Perbandingan Algoritma SVM dan Decision Tree untuk Analisis Sentimen Ulasan Google Maps Wisata Guci Gilang Fajar Al-Fatih; Reza Dwi Cahya Kurniawan
Jurnal Teknologi Terpadu Vol 12 No 1 (2026): Juli, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v12i1.2652

Abstract

Online reviews on digital platforms significantly influence tourist decision-making. This study performs sentiment analysis on visitor reviews of the Guci Hot Spring tourist destination using machine learning algorithms. A total of 1,127 reviews were collected from Google Maps and preprocessed through cleaning, case folding, normalization, tokenization, stop-word removal, and duplicate elimination. Sentiment labelling was performed using a lexicon-based approach, classifying reviews into positive, negative, and neutral categories. The dataset was split into 80% for training and 20% for testing. Two algorithms were compared: Support Vector Machine (SVM) and Decision Tree (DT). Performance evaluation employed accuracy, precision, recall, and F1-score metrics. Results show that SVM outperforms Decision Tree, achieving 69.9% accuracy and 0.680 macro-average F1-score, compared to Decision Tree's 64.2% accuracy and 0.622 macro-average F1-score. These findings demonstrate that SVM is more effective in handling high-dimensional text data, particularly in distinguishing ambiguous neutral-class reviews. This research provides valuable insights for tourism management to improve service quality based on visitor sentiment patterns.

Page 29 of 30 | Total Record : 296

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