cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota medan,
Sumatera utara
INDONESIA
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING
Published by Universitas Medan Area
ISSN : 25496247     EISSN : 25496255     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
JURNAL TEKNIK INFORMATIKA, JITE (Journal of Informatics and Telecommunication Engineering) is a journal that contains articles / publications and research results of scientific work related to the field of science of Informatics Engineering such as Software Engineering, Database, Data Mining, Network, Telecommunication and Artificial Intelligence which published and managed by the Faculty of Informatics Engineering at the University of Medan Area .
Arjuna Subject : -
Articles 464 Documents
 42   43   44   45   46 
Implementation of IoT-Based Smart Parking with Automated Barrier and QR Code Validation Asri Samsiar Ilmananda; Joshua Aldora Lian Djuk; Amadea Permana Sanusi
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16557

Abstract

The rapid growth of vehicles in urban areas has created significant challenges in parking management, particularly regarding operational efficiency and user experience. This research develops a smart parking system leveraging Internet of Things (IoT) technology, equipped with real-time monitoring capabilities, online reservation features, and automated validation using QR codes for four-wheeled vehicles. The hardware implementation utilizes ESP8266 NodeMCU as the main controller, infrared sensors for vehicle detection, and SG90 servo motors for automated gate operation. On the software side, a web-based application was developed using PHP and Node.js, serving as both user interface and control panel for parking operators. Testing results indicate that infrared sensors perform optimally within a 5-20 cm range, achieving response times between 100-153 ms. The QR code validation mechanism successfully identifies valid, expired, and unauthorized tickets with 100% accuracy under normal operating conditions. Servo motors demonstrate consistent performance with operation times ranging from 2.2-2.7 seconds for opening and closing the gate. Data synchronization between ESP8266 and the server achieves average response times of 120-135 ms on stable connections, complemented by automatic recovery capabilities following network disruptions. The implementation of this system proves effective in enhancing parking operational efficiency through validation automation, real-time monitoring, and reduction of human errors, positioning it as a viable smart parking solution within the smart city ecosystem.
A Hybrid IndoBERT-SERVQUAL Approach for Patient Satisfaction Evaluation in Hospital Services Nita Sari; A. Aviv Mahmudi; Fajar Sodiq
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16562

Abstract

The development of information and communication technology (ICT) provides opportunities for healthcare institutions to improve service quality through the digitisation of patient satisfaction evaluation processes. XYZ Hospital still uses manual methods to measure patient satisfaction, resulting in a slow and error-prone recapitulation process. This study aims to design and implement a sentiment analysis-based patient satisfaction system using the IndoBERT method integrated with quantitative Likert scale measurements based on the SERVQUAL dimensions. The IndoBERT model is used to classify positive and negative sentiments, while the Likert score provides a numerical representation of service quality. The study uses a hybrid approach by processing qualitative data in the form of 2,358 patient text reviews and quantitative data from the SERVQUAL questionnaire, which has been tested for validity and reliability. The IndoBERT model was trained and tested with an 80:20 data split and evaluated using accuracy, precision, recall, and F1-score metrics. The results show that the IndoBERT model is capable of classifying patient satisfaction sentiment with 91.10% accuracy and relatively balanced performance across both sentiment classes. The integration of sentiment analysis results and SERVQUAL scores is presented in an interactive dashboard to support decision-making at XYZ Hospital. This research contributes to the development of a more comprehensive, automated, and data-driven patient satisfaction evaluation system to support improvements in healthcare quality.
Improving Imbalanced Polycystic Ovary Syndrome Classification Using a Leakage-Free Machine Learning Pipeline Baiq Andriska Candra Permana; Zulkipli; Muhammad Wasil; Harianto
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16752

Abstract

Polycystic Ovarian Syndrome (PCOS) is a complex endocrine disorder affecting women of reproductive age and poses challenges for early diagnosis due to heterogeneous clinical presentations and imbalanced clinical datasets. This study aims to develop a data leakage–free machine learning pipeline to enhance the accuracy and reliability of PCOS classification using clinical data. The dataset underwent preprocessing and normalization, followed by stratified data splitting with an 80:20 ratio to maintain class proportions. The proposed pipeline was implemented within a unified computational framework integrating feature selection based on the ANOVA F-test, class imbalance handling using the Synthetic Minority Over-sampling Technique (SMOTE), and classification using a Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel. Hyperparameter tuning was performed using GridSearchCV combined with K-Fold Cross-Validation to ensure model robustness and consistency. The experimental results indicate that the proposed model achieved an accuracy of 0.9074, with precision, recall, and F1-score values of 0.8378, 0.8857, and 0.8611, respectively. Furthermore, ten dominant clinical features were identified, primarily related to hormonal profiles and ovarian morphology. These results demonstrate that the data leakage–free pipeline improves the validity and stability of PCOS prediction. The findings suggest that this approach may serve as a supportive tool for clinical decision-making, particularly in facilitating early and objective identification of PCOS.
Hybrid CNN-LSTM for Indonesian Cyberbullying Detection on Social Media X Muhammad Hafizh Fattah; Mochamad Alfan Rosid; Sukma Aji; Suprianto
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16938

Abstract

Cyberbullying on social media platform X has become a critical digital threat and requires automatic detection mechanisms to mitigate psychological impacts on victims. This study proposes a hybrid deep learning architecture that combines Convolutional Neural Network (CNN) for local feature extraction and Long Short-Term Memory (LSTM) for sequential context understanding in classifying Indonesian language cyberbullying comments. This study evaluates model performance using a dataset of 13,677 comments from social media X through a series of systematic testing scenarios, including the impact of regularization, utilization of FastText embeddings, and comparative studies against state-of-the-art models. Experimental results demonstrate that the Early Stopping mechanism is a critical factor in this architecture, where without this mechanism the model experiences accuracy degradation of up to 32%. The proposed CNN-LSTM model achieves 88.38% accuracy and 88.00% F1-Score, improving to 0.9559 AUC with FastText integration. This model achieves over 97% of IndoBERTweet's performance with 22 times lower computational complexity (4.97 million versus 110.88 million parameters) and outperforms machine learning methods such as SVM with an accuracy margin of more than 10 percentage points. This study concludes that the CNN-LSTM architecture offers a robust and efficient solution for cyberbullying detection, particularly for resource-constrained environments
Performance Evaluation of Augmented Reality-Based Smart Farming for Rice and Corn Pest Detection Ananda Faridhatul Ulva; Desvina Yulisda; Baidhawi Baidhawi; Nurhasanah Nurhasanah
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16996

Abstract

An Augmented Reality application for detecting pests and diseases in rice and corn has been developed to overcome the limitations of visual identification in the field, which still relies on subjective interpretation by users. This system utilises image processing and AR overlay based on smart farming to classify symptoms in real time, improving the precision of diagnosis and consistency in control decision-making. This study aims to design, implement, and evaluate the performance of an augmented reality (AR)-based smart farming system for the visual and interactive detection of pests and diseases in rice and corn crops. The research method uses an evaluative approach by assessing the performance of the Augmented Reality system in the field based on detection accuracy, operational reliability, and the suitability of the results to the predetermined performance indicators. Testing was conducted in Gampong Releut Barat, Dewantara District, North Aceh. The results showed that pest and disease detection accuracy increased from 42.4% to 66.7%, with a system response time of <2 seconds, accompanied by an 18% reduction in crop damage and a 24% increase in productivity, confirming the reliability of the system for field diagnosis. This achievement is significant because it meets the operational performance threshold for smart farming and demonstrates the system's readiness for adoption as an Augmented Reality-based decision support tool at the farmer level. The research conclusion indicates that Augmented Reality-based smart farming has the potential to improve detection accuracy, control efficiency, and crop productivity as a support for precision agriculture and sustainable village food security.
Decision Support System for Human Resource Program Prioritization Using AHP–SMART Nadia Afriza; Nanda Siti; ilham sahputra
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16999

Abstract

Human Resource (HR) programs are a crucial aspect of improving the quality of life in rural communities; however, they are often constrained by limited resources and urgent needs. Therefore, this study implements the Analytical Hierarchy Process (AHP) and Simple Multi-Attribute Rating Technique (SMART) methods within a decision support system. The AHP method is employed to determine the criteria weights based on the relative importance of various factors influencing program implementation, including cost, benefits, community participation, needs, sustainability, ease of implementation, and risk of failure. Subsequently, the SMART method is applied to rank program alternatives based on the evaluated criteria. Accuracy testing shows that the system produces results fully consistent with manual calculations, achieving an accuracy rate of 100%. Functional testing using the black-box method indicates that all system features operate properly without errors. Meanwhile, the User Acceptance Test (UAT) results demonstrate that all respondents provided positive evaluations (scores ranging from 3 to 5), with no reported dissatisfaction, indicating that the system is feasible and well accepted by users. The results reveal that the integration of AHP and SMART provides accurate program priority recommendations, with the Community Health Program (0.7150) ranked as the top priority and Food Security Training (0.6550) as the second priority. This decision support system is expected to enhance the efficiency and accuracy of human resource decision-making in Blang Pulo Village
Analisis Sentimen Tokoh Politik dalam Pemilihan Presiden Indonesia 2024 Menggunakan Support Vector Machine (SVM) Nazli Buyung Nasution; Susilawati Susilawati
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.13182

Abstract

Pemodelan machine learning yang mengandalkan pembagian data tunggal cenderung rentan terhadap bias pengacakan. Kelemahan komputasional ini dapat diatasi melalui optimalisasi algoritma Support Vector Machine (SVM) yang diintegrasikan dengan metode ekstraksi fitur Term Frequency-Inverse Document Frequency (TF-IDF). Penelitian ini bertujuan untuk mengimplementasikan skema 5-Fold Cross Validation pada arsitektur SVM dengan mengevaluasi tiga fungsi kernel (Linear, Polinomial, dan Gaussian). Evaluasi ini dilakukan untuk mengukur tingkat akurasi serta stabilitas performa model dalam mengklasifikasikan polaritas opini publik terkait Pemilihan Presiden Indonesia 2024. Dataset yang digunakan bersumber dari platform media sosial Twitter, yang didistribusikan secara dinamis ke dalam lima lipatan (folds), merepresentasikan proporsi 80% data latih dan 20% data uji pada setiap iterasi komputasinya. Hasil pengujian menunjukkan bahwa algoritma SVM dengan fungsi kernel Gaussian menghasilkan performa paling optimal, mencapai rata-rata akurasi sebesar 80,49% dengan tingkat standar deviasi yang sangat minim (±0,0213). Secara lebih spesifik, kinerja puncak model tercatat pada iterasi Fold 3 dengan akurasi sebesar 83,00%, sedangkan penurunan performa terendah terjadi pada Fold 5 dengan akurasi 77,69%. Temuan ini membuktikan bahwa algoritma SVM tidak hanya memiliki ketahanan terhadap bias pengacakan data, tetapi juga menunjukkan tingkat ketangguhan (robustness) yang tinggi dalam memproses derau (noise) dan variasi struktur teks tidak baku pada data media sosial. Model klasifikasi yang dihasilkan sangat relevan untuk diimplementasikan sebagai sistem cerdas pemantauan dinamika sentimen politik bagi lembaga penelitian dan analis kebijakan. Untuk penelitian selanjutnya, disarankan menggunakan korpus data empiris berskala lebih besar dari berbagai platform media sosial, serta mengintegrasikan model prediktif ini ke dalam antarmuka aplikasi yang interaktif.
Pruning-Based ShuffleNetV1 Optimization for Plant Disease Image Classification and Web-Based System Prototype Implementation: Indonesia Taufik Ismail Simanjuntak; Muhathir Muhathir; Fadlisyah Fadlisyah
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 10 No. 1 (2026): Issues July 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v10i1.15374

Abstract

Diseases and pest infestations on tea plants can significantly reduce the quality and quantity of production, necessitating an early detection system based on artificial intelligence. Although deep learning architectures are capable of providing high accuracy, their large model size remains a major constraint for deployment on resource-limited devices. This study aims to compress the ShuffleNet V1 architecture using an L2-Norm based structured pruning method for the classification of six classes of tea leaf conditions utilizing a dataset from Mendeley Data. The model evaluation is carried out using the 5-Fold Cross Validation method with a fine-tuning process for 5 epochs to restore the network representation capacity after pruning. The experimental results demonstrate that the application of structured pruning successfully reduces the total parameters and computational operations significantly without sacrificing model performance. The compressed model is able to maintain an optimal accuracy reaching up to 99% across various pruning scale scenarios from 10% to 50%, while simultaneously providing a noticeable inference speedup. In the final stage, the best compressed model file is integrated into a web interface program, enabling users to perform tea leaf disease classification practically and responsively directly through a web browser. This research proves that the combination of ShuffleNet V1 and structured pruning can produce a highly lightweight yet accurate model for web implementation needs
IS-OWC Point-to-Multipoint System Utilizing UDWDM Technology and NGPON Architecture Mulyono Mulyono; Rika Susanti
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 10 No. 1 (2026): Issues July 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v10i1.15384

Abstract

Inter-Satellite Optical Wireless Communication (IS-OWC) has emerged as a promising solution for long-distance satellite communications due to its high bandwidth capacity, low attenuation, and immunity to electromagnetic interference. However, most previous studies have focused on point-to-point communication systems with limited scalability and transmission capacity. This study aims to develop a point-to-multipoint IS-OWC system based on Ultra Dense Wavelength Division Multiplexing (UDWDM) and the Next-Generation Passive Optical Network (NGPON) architecture to improve transmission efficiency and the performance of multi-satellite communication networks. UDWDM is employed to enhance spectral efficiency through ultra-dense channel spacing, while the NGPON architecture enables efficient signal distribution to multiple satellite nodes. The proposed system is evaluated through simulations by varying the split ratio (1:2–1:64), transmission distance (200–40,000 km), input power (0–40 dBm), and bit rate (10 and 20 Gbps). System performance is assessed using the Bit Error Rate (BER) criterion based on the ITU-T G.698.1 recommendation, where acceptable performance is defined as BER ≤ 10⁻¹². The simulation results demonstrate reliable performance across all evaluated split ratios. At a 1:2 split ratio with an input power of 20 dBm, the proposed system achieves transmission distances of up to 16,000 km at 10 Gbps and 14,000 km at 20 Gbps while maintaining the required BER threshold. Increasing the split ratio reduces the maximum transmission distance, whereas higher input power extends the transmission range. These findings indicate that the proposed architecture is a scalable and effective solution for future high-capacity inter-satellite communication networks.
Explainable Fraud Detection in Mobile Banking Transaction Using Extreme Gradient Boosting (XGBoost) and Shapley Additive Explanations (SHAP) Rovita Indah Ayu Ningtias; Mahyuddin K M Nasution; Amalia
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 10 No. 1 (2026): Issues July 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v10i1.15505

Abstract

The development of mobile banking services has increased the convenience of financial transactions, but has also increased the risk of potentially fraudulent transactions. The complexity of transaction patterns and data integrity pose challenges in building an accurate and interpretable detection system. This study aims to analyze the risks of mobile banking transactions using XGBoost and provide an interpretation of factors that influence prediction results using Shapley Additive Explanations (SHAP). The data used are 6,898,889 mobile banking transactions in 2023 with six main attributes. The research stages include data preprocessing, feature extraction based on time and transaction behavior, normalization, data partitioning, anomaly detection using autoencoders, and classification using XGBoost. Autoencoders are used to generate pseudo-labels based on reconstruction error values, while SHAP is used to interpret the contribution of each feature to the model's prediction results. The results show that the autoencoder produces a threshold anomaly of 0.0345 and identifies 1,379 transactions as anomalies. The XGBoost model is able to recognize all anomalous transactions with a recall value of 1.00, although some normal transactions are still predicted as anomalies. The SHAP analysis results show that trx_value is the most influential feature on predictions, followed by channel_type and transaction_hour. This study demonstrates that the combination of Autoencoder, XGBoost, and SHAP can support the detection of potentially fraudulent transactions while providing a more transparent interpretation of the factors influencing the model's decisions.

Page 44 of 47 | Total Record : 464

 42   43   44   45   46 

Filter by Year

2017 2026


Reset
Filter By Issues
All Issue Vol. 10 No. 1 (2026): Issues July 2026 Vol. 9 No. 2 (2026): Issues January 2026 Vol. 9 No. 1 (2025): Issues July 2025 Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy Vol. 8 No. 2 (2025): Issues January 2025 Vol. 7 No. 2 (2024): Vol. 7 No. 2 (2024): Issues January 2024 Vol. 8 No. 1 (2024): Issues July 2024 Vol. 7 No. 1 (2023): Issues July 2023 Vol. 6 No. 2 (2023): Issues January 2023 Vol 6, No 1 (2022): Issues July 2022 Vol 5, No 2 (2022): Issues January 2022 Vol 5, No 1 (2021): EDISI JULY 2021 Vol 4, No 2 (2021): EDISI JANUARY 2021 Vol. 4 No. 1 (2020): ---> EDISI JULI Vol 4, No 1 (2020): ---> EDISI JULI Vol 3, No 2 (2020): EDISI JANUARI Vol 3, No 1 (2019): EDISI JULI Vol 2, No 2 (2019): EDISI JANUARI Vol 2, No 2 (2019): EDISI JANUARI Vol 2, No 1 (2018): EDISI JULI Vol 2, No 1 (2018): EDISI JULI Vol 1, No 2 (2018): Edisi Januari Vol 1, No 2 (2018): Edisi Januari Vol 1, No 1 (2017): Edisi Juli Vol 1, No 1 (2017): Edisi Juli More Issue