cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Nisrina Akbar Rizky Putri
Contact Email
lppm@umkla.ac.id
Phone
-
Journal Mail Official
nisrinaakbar@umkla.ac.id
Editorial Address
Jl. Ir Soekarno Km.1 Buntalan Klaten Tengah Klaten Jawa Tengah
Location
Kab. klaten,
Jawa tengah
INDONESIA
JKTi - Jurnal Keilmuan Teknologi Informasi
Published by Universitas Muhammadiyah Klaten
ISSN : 31094163     EISSN : 31094163     DOI : https://doi.org/10.61902/jkti
Core Subject : Science,
Computer Science & IT
JKTI published by LPPM Universitas Muhammadiyah Klaten is a scientific journal that contains articles on research results, studies, and innovations in the field of information technology. JKTi invites academics and researchers to publish research results that demonstrate novelty, originality and current contributions with the scope of Data Mining, Software Engineering, IT Governance, Data and Cyber Security, Artificial Intelligence, Mobile Computing, Computer Graphics, Data Communication and Networking, Multimedia Technologies, Parallel/Distributed Computing and the Internet of Things.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 10 Documents
 1 
Integrasi 8 Golden Rules dalam Desain Antarmuka Sistem Informasi Penggajian Rahayu, Hartika; Putri, Nisrina Akbar Rizky
JKTI Jurnal Keilmuan Teknologi Informasi Vol 1 No 1 (2025)
Publisher : Universitas Muhammadiyah Klaten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61902/jkti.v1i1.1679

Abstract

The development of a system is done to help facilitate human work due to its effectiveness and efficiency. The XZY campus study program has used an integrated system in processing data. In terms of summarizing attendance and payroll data, it is still done manually, so a system is needed that can help users summarize this data. The interface design construction is made before system development. There are three previous steps in designing the system display: data collection (needs analysis), website design, and testing. The 8 golden rules are used as guidelines in designing a web display that is easy for users to learn.
Pendekatan Deep Learning untuk Deteksi Kantuk dengan YOLOv12 Hidayani, Diesti; Mustofa Romadhani; Ardiansyah, Ardiansyah
JKTI Jurnal Keilmuan Teknologi Informasi Vol 1 No 1 (2025)
Publisher : Universitas Muhammadiyah Klaten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61902/jkti.v1i1.1681

Abstract

Drowsiness while driving is a significant contributor to traffic accidents. To mitigate such occurrences, a precise and real-time drowsiness detection system is essential. This research aims to create a computer vision-based drowsiness detection system utilizing the YOLOv12 algorithm. The dataset was sourced from Kaggle and manually annotated with the help of Roboflow. It was categorized into two groups: drowsy and non-drowsy, with the original 5,000 images augmented to a total of 6,976 images. The model training utilized the AdamW optimizer (learning rate=0.001667, momentum=0.9) over 100 epochs and a batch size of 4. Performance assessment indicates that the model attained an mAP@50 of 0.732 and an mAP@50-95 of 0.62, alongside a precision of 0.648 and a recall of 0.928. These findings illustrate that YOLOv12 can successfully identify drowsiness in real-time. Nevertheless, the performance of the model is significantly influenced by the quality and balance of the dataset. Consequently, enhancing the structure and distribution of the dataset is vital for improving detection accuracy.
Deteksi Ekspresi Wajah Real-Time Menggunakan YOLOv12 Adimas, Rizal; Al Firmansyah, Garet; Ardiansyah, Ardiansyah
JKTI Jurnal Keilmuan Teknologi Informasi Vol 1 No 1 (2025)
Publisher : Universitas Muhammadiyah Klaten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61902/jkti.v1i1.1682

Abstract

This research focuses on the development of a real-time facial expression detection system using YOLOv12. The study utilizes a secondary dataset from Kaggle, consisting of 1000 images categorized into two classes: "Happy" and "Not Happy." The dataset undergoes preprocessing steps, including Gabor filter bank for key facial feature identification and geometric augmentation to enhance data quality. The YOLOv12 model is trained with 100 epochs, a batch size of 4, and the AdamW optimizer, achieving a mean Average Precision (mAP@0.5) of 0.89 for both expression classes. The system demonstrates real-time performance with an average processing speed of 15 FPS on CPU-based devices, adapting well to varying lighting conditions and angles, though accuracy decreases by 5-7% in low-light environments. The results highlight the model's potential applications in mental health, human-computer interaction, and security. Limitations include the restricted dataset and challenges with micro-expressions. Future work suggests expanding the dataset to include more expression classes and integrating post-processing models to reduce false positives.
Prediksi Analitik untuk Penyakit Ginjal Kronis: Perbandingan Metode Machine Learning Nuresa Qodri, Krisna; Rausan Fikri, Muhammad; Ardi, Luthfi
JKTI Jurnal Keilmuan Teknologi Informasi Vol 1 No 1 (2025)
Publisher : Universitas Muhammadiyah Klaten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61902/jkti.v1i1.1686

Abstract

Chronic kidney disease (CKD) is a progressive malady defined by reduced glomerular filtration rate, increased urinary albumin excretion or both, and is a major global public health concern with an extremely high unmet medical need. CKD is estimated to occur in 8-16% of the worldwide population and results in a substantially reduced life expectancy. Early detection and accurate prediction of CKD is crucial to reduce health complications such as hypertension, anemia, and premature death. This study aims to develop CKD prediction models using three machine learning methods: Random Forest, Naive Bayes, and Support Vector Machine, then compare the performance of each method. The dataset used is the CKD dataset from UCI Machine Learning Repository consisting of 400 instances with 24 attributes. Experimental results show that Random Forest achieved 90.50% accuracy, Naive Bayes achieved the highest accuracy of 94.21%, while SVM achieved 88.84% accuracy. The results indicate that Naive Bayes provides the best performance for chronic kidney disease prediction with superior accuracy compared to other methods. This prediction model can assist medical practitioners in early detection and appropriate clinical decision-making for CKD patient management.
Implementasi OCR berbasis Tesseract untuk Ekstraksi data kartu mahasiswa UMKLA Muhammad Nashiruddin; Noor Praditya, Fiusyam Dhaza; Agiel Faiz Mufazzal; Ardiansyah, Ardiansyah
JKTI Jurnal Keilmuan Teknologi Informasi Vol 1 No 1 (2025)
Publisher : Universitas Muhammadiyah Klaten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61902/jkti.v1i1.1688

Abstract

Manual data entry from student ID cards (KTM) is often inefficient and prone to errors. Therefore, automating this process is a crucial solution for educational institutions to improve accuracy and the speed of administrative services. This research aims to design and implement an Optical Character Recognition (OCR) system to automatically extract information from student ID card images of Universitas Muhammadiyah Klaten (UMKLA). The methodology involves image pre-processing using the OpenCV library to enhance image quality through grayscale conversion and Otsu's binarization. Subsequently, the Tesseract OCR Engine is used to convert the image into raw text, which is then parsed using Regular Expressions (Regex) to separate data fields such as Name, Student ID Number (NIM), and Program of Study. Test results indicate that the system can extract information with a good success rate, although accuracy is heavily influenced by image quality factors like lighting and text clarity. Fields with standard printed formats were found to have higher accuracy. In conclusion, this Tesseract-based system successfully demonstrates its feasibility for local automation of student ID card data. However, further development in the post-processing stage is required to handle more complex OCR output variations.
Analisis Penerimaan Tekonologi Internet of Things (IoT) Dalam Pertanian Menggunakan Technology Acceptance Model (TAM) Basiroh, Basiroh; Al Afifah Irwanto, Widya Novita
JKTI Jurnal Keilmuan Teknologi Informasi Vol 1 No 2 (2025)
Publisher : Universitas Muhammadiyah Klaten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61902/jkti.v1i2.2036

Abstract

This The advancement of digital technology has had a significant impact across various sectors, including agriculture. One of the innovations increasingly applied is the Internet of Things (IoT), which enables more effective and efficient monitoring and control of agricultural activities. However, the success of implementing this technology is highly influenced by the level of user acceptance, particularly among farmers. This study aims to analyze the acceptance of IoT in agriculture using the Technology Acceptance Model (TAM), which includes the variables Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Attitude Toward Using (ATU), Behavioral Intention (BI), and Actual Use (AU). The research employed a quantitative approach, with data collected through questionnaires distributed to farmers who are familiar with or have used IoT-based technologies in their agricultural practices. Data analysis was conducted using Structural Equation Modeling (SEM) with a Partial Least Squares (PLS) approach. The findings reveal that PEOU and PU significantly influence both ATU and BI, which in turn drive the actual use (AU) of IoT technology. Overall, the level of IoT acceptance in agriculture falls into the high category, with respondents’ acceptance percentage exceeding 70%. These results highlight that IoT holds substantial potential to enhance productivity, efficiency, and crop quality. However, its adoption needs to be supported by improved digital literacy, technical assistance, and government policies that facilitate infrastructure development and access to technology
Comparison of CNN Transfer Learning Models for Brain Tumor Detection Based on MRI Images noviyanto; Pamuja, Sintia Darma
JKTI Jurnal Keilmuan Teknologi Informasi Vol 1 No 2 (2025)
Publisher : Universitas Muhammadiyah Klaten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61902/jkti.v1i2.2185

Abstract

Brain tumors require early and accurate detection to support effective clinical decision-making. This study compares the performance of four transfer learning-based Convolutional Neural Network (CNN) models, namely DenseNet121, InceptionV3, MobileNet, and Xception, for brain tumor detection using MRI images. The dataset was preprocessed through resizing, normalization, and data augmentation, and all models were trained for 20 epochs using ImageNet pre-trained weights. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results show that all models achieved accuracies above 90%, with MobileNet outperforming the others by achieving an accuracy of 94.74% and precision, recall, and F1-score values of 0.95, 0.95 and 0,94. These findings indicate that lightweight CNN architectures can deliver superior performance for MRI-based brain tumor classification.
Optimasi Analisis Sentimen Ulasan Platform Pendidikan Daring Menggunakan Arsitektur ALBERT dan Teknik Augmentasi Kontekstual Pamuja, Sintia Darma; Noviyanto
JKTI Jurnal Keilmuan Teknologi Informasi Vol 1 No 2 (2025)
Publisher : Universitas Muhammadiyah Klaten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61902/jkti.v1i2.2256

Abstract

Online learning through global platforms like Coursera generates a massive volume of user reviews, which serve as vital information for educational quality improvement. However, these reviews often exhibit imbalanced label distributions, where positive sentiments significantly dominate negative and neutral ones, hindering traditional classification models. Advanced language models such as ALBERT (A Lite BERT) offer parameter efficiency through cross-layer parameter sharing while maintaining high performance in complex text understanding. This study aims to evaluate the ALBERT model's performance in classifying Coursera user reviews and addressing data imbalance using Contextual Word Embedding augmentation. The methodology involves collecting 10,000 reviews followed by preprocessing steps including case folding, punctuation removal, and tokenization. The augmentation technique utilizes language models to replace words based on context to balance minority classes. The results show that ALBERT provides highly consistent performance, achieving an F1-score of 0.9710 with the contextual augmentation scenario. The model proves effective in capturing linguistic variations and remains computationally efficient. In conclusion, the ALBERT model is highly effective for sentiment analysis on the Coursera dataset, where contextual augmentation significantly enhances the model's ability to recognize minority classes that were previously difficult to identify.
Evaluasi Permasalahan Layanan pada Sistem Reservasi Ruang Kelas Universitas XYZ Menggunakan COBIT 5 DSS02 Hidayani, Diesti; Nugroho Saputro, Fachruddin Edi
JKTI Jurnal Keilmuan Teknologi Informasi Vol 1 No 2 (2025)
Publisher : Universitas Muhammadiyah Klaten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61902/jkti.v1i2.2263

Abstract

The classroom reservation system is an information technology service that plays a vital role in supporting the smooth running of academic activities at XYZ University. However, many service issues still occur during operation. These include system delays, sudden class schedule changes, and suboptimal incident handling. The purpose of this study is to evaluate the capability level of XYZ University's Classroom Reservation system to manage service requests and incident handling, specifically the DSS02 (Manage Service Requests and Incidents) domain. The research method used is a case study with a quantitative approach through observation and the distribution of questionnaires based on the COBIT 5 Process Assessment Model (PAM). The results show that the DSS02 process capability level in its current condition is at Level 1 (Performed Process), while the expected condition is at Level 5 (Optimizing Process). Gap analysis shows a significant difference between the actual and expected conditions, especially in the aspects of process documentation, service performance measurement, and incident management. Therefore, to make the classroom reservation service support academic activities in a more efficient, effective, and reliable manner, planned and sustainable development efforts are needed.
Analisis Usability Antarmuka e-learning Menggunakan Metode System Usability Scale Pada Universitas Swasta Sri Widagdo, Adika; Pamuja, Sintia Darma
JKTI Jurnal Keilmuan Teknologi Informasi Vol 1 No 2 (2025)
Publisher : Universitas Muhammadiyah Klaten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61902/jkti.v1i2.2286

Abstract

Online learning systems, or e-learning, have become a crucial infrastructure in higher education, enabling a better learning process. However, their success depends heavily on the usability of the interface presented to students. This study aims to evaluate the usability of the e-learning system interface at a private university to identify barriers to user interaction and provide recommendations for improvement. The evaluation was conducted using the System Usability Scale (SUS) method as a standard and reliable quantitative measurement instrument. This study involved 45 student respondents selected using purposive sampling techniques to complete the SUS questionnaire consisting of 10 statements. The results showed that the average SUS score obtained was 68.2. Based on the SUS score interpretation criteria, this value places the system in the Acceptable category based on the acceptability ranges, receiving a Grade C predicate on the grade scale, and is in the Good category based on the adjective rating. Although the system is considered suitable for use, the score position that is at the marginal threshold indicates a need for optimization in the navigation aspect and the consistency of visual elements. These findings recommend simplifying the flow of material access and improving the layout of key features to increase efficiency and user satisfaction as a reference for improvements to improve the digital learning ecosystem in the future at the university.

Page 1 of 1 | Total Record : 10

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 1 No 2 (2025) Vol 1 No 1 (2025)