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 tangerang selatan,
Banten
INDONESIA
TEKNIK INFORMATIKA
Published by Universitas Islam Negeri Syarif Hidayatullah Jakarta
ISSN : 19799160     EISSN : 25497901     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal Teknik Informatika merupakan wadah bagi insan peneliti, dosen, praktisi, mahasiswa dan masyarakat ilmiah lainnya untuk mempublikasikan artikel hasil penelitian, rekayasa dan kajian di bidang Teknologi Informasi. Jurnal Teknik Informatika diterbitkan 2 (dua) kali dalam setahun.
Arjuna Subject : -
Articles 262 Documents
 18   19   20   21   22 
Anomaly Detection in Computer Networks Using Isolation Forest in Data Mining Hartati Tammamah Lubis; Roslina Roslina; Lili Tanti
JURNAL TEKNIK INFORMATIKA Vol 18, No 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.44285

Abstract

The rapid growth of network data has increased the complexity of detecting anomalies, which are crucial for ensuring the security and integrity of information systems. This study investigates the use of the Isolation Forest algorithm for anomaly detection in network traffic, utilizing the Luflow Network Intrusion Detection dataset, which contains 590,086 records with 16 features related to network activities. The methodology encompasses data preprocessing (cleaning, normalization, and feature scaling), feature selection (bytes in, bytes out, entropy, and duration), model training, and performance evaluation. The results demonstrate that Isolation Forest can effectively identify anomalies based on feature patterns, isolating suspicious data points without the need for labeled datasets. However, performance metrics, such as accuracy (42.92%), precision (14.37%), recall (2.87%), and F1-score (4.79%), reveal challenges such as high false-positive rates and low sensitivity to true anomalies. These findings highlight the potential of the algorithm for dynamic, high-dimensional datasets but also indicate the need for further improvements through hyperparameter tuning, feature engineering, and alternative approaches. This study contributes to the development of adaptive anomaly detection frameworks for network security and suggests future integration into real-time systems for proactive threat mitigation. The study's findings are particularly relevant for enhancing network security in environments such as corporate and governmental networks, where real-time anomaly detection is crucial.
Hybrid Logistic Super Newton Model for Predicting Small Sample Size Data Nurmalitasari Nurmalitasari; Zalizah Awang Long; Nurchim Nurchim
JURNAL TEKNIK INFORMATIKA Vol 18, No 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.43929

Abstract

Logistic regression is a model commonly used for predicting data with large sample sizes. However, in real-world scenarios, many cases involve small datasets that need to be addressed using logistic regression. The aim of this research is to develop a hybrid logistic regression model to address issues with small sample sizes by combining the Newton Raphson and Super Cubic methods. This hybrid model is applied to predict student dropout at Universitas Duta Bangsa Surakarta. The performance of the hybrid model is evaluated using two main metrics: the convergence of the parameter approximation to measure the precision of parameter estimation, and the ROC curve to assess prediction accuracy. Experimental results show that the Hybrid Logistic Super Newton model outperforms the logistic regression Newton Raphson model, requiring only three iterations to converge, thus improving computational efficiency. Moreover, this model achieves higher accuracy, with an AUC of 0.8833. These findings suggest that the developed model has the potential to be applied in various fields, such as healthcare, finance, and others, offering an effective solution for accurate, real-time predictive analytics. Further research could focus on optimizing the model’s computational efficiency and exploring its application in other domains with small dataset challenges, such as healthcare and finance.
Feature Extraction Using Mel-Frequency Cepstral Coefficients (MfCC) Technique For A Tajweed Guess Based on Android Application Development Khodijah Hulliyah; Lilik Ummi Kultsum; Wahyu Hendarto Wibowo; Anif Hanifa Setianingrum; Arini Arini; Yusuf Durachman
JURNAL TEKNIK INFORMATIKA Vol 18, No 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.44721

Abstract

The development of information and communication technology today has had a significant impact on various aspects of life, including education. One notable example is the increasing number of applications designed for learning to recite the Quran with proper tartil. The growing trend of tahfidz (Quran memorization) is undoubtedly a positive development from a religious perspective. However, many individuals focus solely on memorization without acquiring the ability to recite the Quran properly and accurately. One discipline that supports proper Quran recitation is the knowledge of tajweed. Numerous applications have been developed in this field, especially on Android platforms. However, applications that utilize artificial intelligence (AI) to recognize tajweed rules and involve users in guessing tajweed readings are still in need of further development. The aim of this research is to develop a tajweed learning application using the concept of Automatic Speech Recognition (ASR). This study employs data collection methods such as literature review, quantitative methods, and testing. The design is represented using Unified Modeling Language (UML), while the application is tested using the Black Box Testing method. For data analysis and testing of the speech recognition model, the Hidden Markov Model (HMM) algorithm is employed, with Mel-Frequency Cepstral Coefficients (MFCC) used for feature extraction. The output of this research is an Android-based tajweed learning application that integrates speech recognition and allows users to guess tajweed rules interactively.
A Case Study: Comparison of LSTM and GRU Methods for Forecasting Oil, Non-Oil, and Gas Export Values in Indonesia Dian Kurniasari; Maydia Egi Nuraini; Wamiliana Wamiliana; Rizki Khoirun Nisa
JURNAL TEKNIK INFORMATIKA Vol 17, No 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i2.39098

Abstract

This study explores the forecasting of Indonesia’s oil, non-oil, and gas export values, highlighting its critical role in supporting national economic growth. Given the inherent volatility in export values, accurate forecasting is vital for informed economic decision-making. The research employs Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, both well-regarded for their ability to handle sequential data and complex temporal patterns. Model performance was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The findings indicate that although both models produced nearly identical MAPE values of 99.99% across the oil, non-oil, and gas sectors, the GRU model outperformed the LSTM model with RMSE values of 0.0655 for oil and gas exports and 0.0697 for non-oil and gas exports. Moreover, the GRU model’s forecasts align closely with data from the Central Bureau of Statistics (BPS), which reported an 11.33% decline in Indonesia’s export values by the end of 2023. These results suggest that the GRU model not only offers greater accuracy but is also applicable to other economic forecasting contexts, such as exchange rate and inflation predictions, thereby enhancing economic policy-making.
Impact of Hyperparameter Tuning on CNN-Based Algorithm for MRI Brain Tumor Classification Muhammad Nasri Gea; Wanayumini Wanayumini; Rika Rosnelly
JURNAL TEKNIK INFORMATIKA Vol 18, No 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.44147

Abstract

This study examines the impact of hyperparameter tuning on the performance of Convolutional Neural Networks (CNN) in classifying brain tumors using MRI images. The dataset, sourced from Kaggle, underwent preprocessing techniques such as normalization, augmentation, and resizing to enhance consistency and diversity. The study evaluates five hyperparameter configurations, analyzing their effects on classification accuracy, precision, recall, and F1-score. The optimal configuration (batch size: 16, epochs: 10, learning rate: 0.001) achieved an accuracy of 86%, precision of 81%, recall of 85%, and an F1-score of 0.83. Other configurations showed trade-offs, where larger batch sizes increased recall but reduced precision. These findings emphasize the importance of careful hyperparameter tuning to optimize medical imaging classification performance.
Utilization of the FP-Growth Algorithm on MSME Transaction Data:Recommendations for Small Gifts from The Padang Region Firman Noor Hasan; Riyan Ariyansah
JURNAL TEKNIK INFORMATIKA Vol 17, No 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i1.37966

Abstract

The existence of adequate transaction data turns out to have a similar sales transaction pattern for MSMEs, so it would be a shame if it were left like that. Moreover, this data can be used to increase efficiency in MSMEs in the culinary sector, one of which is as a recommendation for small gifts. The study uses the Association Rules technique, whereas fp-growth is used to obtain a combination of elements. The goal is to facilitate MSMEs' ability to suggest small gifts to clients. The fp-growth algorithm calculation was implemented to process 2043 data originating from transaction data in MSMEs, with the specified minimum support value being 15%, while the minimum confidence value determined was 55%. The results of the trial obtained the two best rules, namely, "If a customer buys a list of small gifts from Balado Sanjai Chips, then the customer will buy Jangek Crackers" and "If a customer buys Jangek Crackers, then the customer will buy Sanjai Balado Chips".
Syllable-Based Javanese Speech Recognition Using MFCC and CNNs: Noise Impact Evaluation Hermanto Hermanto; Tjong Wan Sen
JURNAL TEKNIK INFORMATIKA Vol 18, No 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.41067

Abstract

Javanese, a regional language in Indonesia spoken by over 100 million people, is classified as a low-resource language, presenting significant challenges in the development of effective speech recognition systems due to limited linguistic resources and data. Furthermore, the presence of noise is a significant factor that impacts the performance of speech recognition systems. This study aims to develop a speech recognition model for the Javanese language, focusing on a syllable-based approach using Mel Frequency Cepstral Coefficients (MFCC) for audio feature extraction and Convolutional Neural Networks (CNNs) methods for classification. Additionally, it will analyze how different types of colored noise: white gaussian, pink, and brown, when added to the audio, impact the model's accuracy. The results showed that the proposed method reached a peak accuracy of 81% when tested on the original audio (audio without any synthetic noise added). Moreover, in noisy audio, model accuracy improves as noise levels decrease. Interestingly, with brown noise at a 20 dB SNR, the model's accuracy slightly increases to 83%, representing a 2.47% improvement over the original audio. These results demonstrate that the proposed syllable-based method is a promising approach for real-world applications in Javanese speech recognition, and the slight accuracy improvement in noisy conditions suggests potential regularization effects
The Comparison of the Effectiveness and Efficiency of Fine-Tuning Models on Stable Diffusion in Creating Concept Art Abdul Bilal Qowy; Ahmad Nur Ihsan; Sri Hartati
JURNAL TEKNIK INFORMATIKA Vol 17, No 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i1.37942

Abstract

This research aims to overcome the limitations of the Stable Diffusion model in creating conceptual works of art, focusing on problem identification, research objectives, methodology and research results. Even though Stable Diffusion has been recognized as the best model, especially in the context of creating conceptual artwork, there is still a need to simplify the process of creating concept art and find the most suitable generative model. This research used three methods: Latent Diffusion Model, Dreambooth: fine-tuning Model, and Stable Diffusion. The research results show that the Dreambooth model produces a more real and realistic painting style, while Textual Inversion tends towards a fantasy and cartoonist style. Although the effectiveness of both is relatively high, with minimal differences, the Dreambooth model is proven to be more effective based on the consistency of FID, PSNR, and visual perception scores. The Dreambooth model is more efficient in training time, even though it requires more memory, while the inference time for both is relatively similar. This research makes a significant contribution to the development of artificial intelligence in the creative industries, opens up opportunities to improve the use of generative models in creating conceptual works of art, and can potentially drive positive change in the use of artificial intelligence in the creative industries more broadly. 
Detection of Vulgarity in Anime Character: Implementation of Detection Transformer Amalia Suciati; Dian Kartika Sari; Andi Prademon Yunus; Nuuraan Rizqy Amaliah
JURNAL TEKNIK INFORMATIKA Vol 18, No 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.46064

Abstract

Vulgar and pornographic content has become a widespread issue on the internet, appearing in various fields include anime. Vulgar pornographic content in anime is not limited to the sexuality genre; anime from general genres such as action, adventure, and others also contain vulgar visual. The main focus of this research is the implementation of the Detection Transformer (DETR) object detection method to identify vulgar parts of anime characters, particularly female characters. DETR is a deep learning model designed for object detection tasks, adapting the attention mechanism of Transformers. The dataset used consists of 800 images taken from popular anime, based on viewership rankings, which were augmented to a total of 1,689 images. The research involved training models with different backbones, specifically ResNet-50 and ResNet-101, each with dilation convolution applied at different stages. The results show that the DETR model with a ResNet-50 backbone and dilation convolution at stage 5 outperformed other backbones and dilation configurations, achieving a mean Average Precision of 0.479 and  of 0.875. The other result is dilated convolution improves small object detection by enlarging the receptive field, applying it in early stages tends to reduce spatial detail and harm performance on medium and large objects. However, the primary focus of this research is not solely on achieving the highest performance but on exploring the potential of transformer-based models, such as DETR, for detecting vulgar content in anime. DETR benefits from its ability to understand spatial context through self-attention mechanisms, offering potential for further development with larger datasets, more complex architectures, or training at larger data scales.
Adaptive Hint Generation for Educational Games Using Fuzzy Logic Anggina Primanita; Hadipurnawan Satria; Muhammad Qurhanul Rizqie; Ananda Haykel Iskandar; Wibisena Nugraha
JURNAL TEKNIK INFORMATIKA Vol 18, No 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.41893

Abstract

The increasing interest in programming education has led to a wide variety of learner abilities. However, existing learning media often remain fragmented, necessitating the development of adaptive tools to cater to learners of varying skill levels. This study employs fuzzy logic to generate dynamic hints for players struggling to solve programming challenges in an educational game. The effectiveness of the system was evaluated through both simulation and real-world experiments. Simulation results indicate that the fuzzy logic system successfully generates personalized hints, with the highest frequency of hints provided to beginner players. Real-world testing using the GUESS-18 framework demonstrated high playability and excellent usability scores for the game.

Page 20 of 27 | Total Record : 262

 18   19   20   21   22 

Filter by Year

2016 2025


Reset
Filter By Issues
All Issue Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA Vol 18, No 1: JURNAL TEKNIK INFORMATIKA Vol. 18 No. 1: JURNAL TEKNIK INFORMATIKA Vol 17, No 2: JURNAL TEKNIK INFORMATIKA Vol. 17 No. 2: JURNAL TEKNIK INFORMATIKA Vol 17, No 1: JURNAL TEKNIK INFORMATIKA Vol. 17 No. 1: JURNAL TEKNIK INFORMATIKA Vol. 16 No. 2 (2023): JURNAL TEKNIK INFORMATIKA Vol 16, No 2 (2023): JURNAL TEKNIK INFORMATIKA Vol 16, No 1 (2023): JURNAL TEKNIK INFORMATIKA Vol 15, No 2 (2022): JURNAL TEKNIK INFORMATIKA Vol. 15 No. 2 (2022): JURNAL TEKNIK INFORMATIKA Vol 15, No 1 (2022): JURNAL TEKNIK INFORMATIKA Vol 14, No 2 (2021): JURNAL TEKNIK INFORMATIKA Vol 14, No 1 (2021): JURNAL TEKNIK INFORMATIKA Vol 13, No 2 (2020): JURNAL TEKNIK INFORMATIKA Vol 13, No 1 (2020): JURNAL TEKNIK INFORMATIKA Vol 12, No 2 (2019): JURNAL TEKNIK INFORMATIKA Vol. 12 No. 1 (2019): JURNAL TEKNIK INFORMATIKA Vol 12, No 1 (2019): JURNAL TEKNIK INFORMATIKA Vol 11, No 2 (2018): Jurnal Teknik Informatika Vol 11, No 1 (2018): Jurnal Teknik Informatika Vol 10, No 2 (2017): Jurnal Teknik Informatika Vol. 10 No. 2 (2017): Jurnal Teknik Informatika Vol 10, No 1 (2017): Jurnal Teknik Informatika Vol 9, No 2 (2016): Jurnal Teknik Informatika Vol 9, No 1 (2016): Jurnal Teknik Informatika More Issue