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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
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Enhancing Speech-to-Text and Translation Capabilities for Developing Arabic Learning Games: Integration of Whisper OpenAI Model and Google API Translate Khairani, Dewi; Rosyadi, Tabah; Arini, Arini; Rahmatullah, Imam Luthfi; Antoro, Fauzan Farhan
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.v17i2.41240

Abstract

This study tackles language barriers in computer-mediated communication by developing an application that integrates OpenAI’s Whisper ASR model and Google Translate machine translation to enable real-time, continuous speech transcription and translation and the processing of video and audio files. The application was developed using the Experimental method, incorporating standards for testing and evaluation. The integration expanded language coverage to 133 languages and improved translation accuracy. Efficiency was enhanced through the use of greedy parameters and the Faster Whisper model. Usability evaluations, based on questionnaires, revealed that the application is efficient, effective, and user-friendly, though minor issues in user satisfaction were noted. Overall, the Speech Translate application shows potential in facilitating transcription and translation for video content, especially for language learners and individuals with disabilities. Additionally, this study introduces an Arabic learning game incorporating an Artificial Neural Network using the CNN algorithm. Focusing on the “Speaking” skill, the game applies to voice and image extraction techniques, achieving a high accuracy rate of 95.52%. This game offers an engaging and interactive method for learning Arabic, a language often considered challenging. The incorporation of Artificial Neural Network technology enhances the effectiveness of the learning game, providing users with a unique and innovative language learning experience. By combining voice and image extraction techniques, the game offers a comprehensive approach to enjoyably improving Arabic speaking skills.
Adaptive Hint Generation for Educational Games Using Fuzzy Logic Primanita, Anggina; Satria, Hadipurnawan; Rizqie, Muhammad Qurhanul; Iskandar, Ananda Haykel; Nugraha, Wibisena
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.
Application of the ELECTRE I and ELECTRE IS Method to Optimize Maize Seed Selection in Cameroon: A Multi-Criteria Approach Demas, Tanone; Fréderic, Guidana Gazawa; Elisabeth, Yaboki
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.v18i1.41903

Abstract

This study aims to help Cameroonian farmers choose the best maize seeds to improve their yields. To achieve this, we evaluated 15 varieties based on five essential criteria: cycle length, yield per hectare, cob quality, stem height, and grain weight. Using the ELECTRE I and ELECTRE IS multi-criteria decision-making methods, we selected four particularly high-performing varieties: CLH103, CMS8602, CMS9015, and CMS 8501. These seeds offer a good balance between productivity and adaptation to local conditions, with potential yields of up to 10 tons per hectare. In-depth analyses have confirmed the reliability of these results, assuring farmers of a robust and effective choice. These recommendations can help improve food security and the profitability of farms in Cameroon
Comparison of Hyperparameter Tuning Methods for Optimizing K-Nearest Neighbor Performance in Predicting Hypertension Risk Trianda, Dimas; Hartama, Dedy; Solikhun, Solikhun
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.v18i1.42260

Abstract

Hypertension is a major cause of cardiovascular disease, making early risk prediction essential. According to WHO, hypertension cases are estimated to reach 1.28 billion by 2023. This study aims to optimize the K-Nearest Neighbor (KNN) algorithm for predicting hypertension risk through hyperparameter tuning. Three methods Grid SearchCV, Bayes SearchCV, and Random SearchCV are compared to determine the best parameter configuration. The dataset, obtained from Kaggle, consists of 520 balanced samples (260 positive and 260 negative) with 18 health-related features such as age, gender, blood pressure, cholesterol, glucose, and others. After preprocessing, the KNN model is tuned using each method by testing combinations of neighbors (k), weight types, and distance metrics. Results show Bayes SearchCV achieved the highest accuracy of 92%, outperforming the baseline KNN model, which had 85% accuracy. The ROC AUC score of 0.96191 also indicates excellent classification performance. In conclusion, Bayes SearchCV significantly improves KNN's predictive ability in hypertension risk classification.
Challenges and Strategies in Forensic Investigation: Leveraging Technology for Digital Security Using Log/Event Analysis Method Nasution, Ammar Yasir; Hartono, Hartono; Rosnelly, Rika
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.v18i1.42815

Abstract

Cybersecurity threats continue to evolve, necessitating advanced techniques for network anomaly detection. This study developed a comprehensive methodology for detecting network anomalies by leveraging sophisticated log and event analysis using machine learning algorithms. By employing a Naive Bayes classification approach on a synthetic cybersecurity dataset comprising 40,000 entries with 25 unique features, the research aimed to enhance anomaly detection precision. The methodology involved meticulous data preprocessing, feature selection, and strategic model validation techniques, including cross-validation and external benchmarking. Comparative analysis with K-Nearest Neighbors and Support Vector Machine algorithms demonstrated the Naive Bayes method's superior performance, achieving a classification accuracy of 94.8%, an Area Under the Curve (AUC) of 0.949, and a Matthews Correlation Coefficient of 0.896. The study identified critical parameters influencing anomaly detection, such as source port characteristics and attack signatures. These findings contribute significant insights into machine learning-based network security strategies, offering a robust framework for early threat identification and mitigation.
Hybrid Logistic Super Newton Model for Predicting Small Sample Size Data Nurmalitasari, Nurmalitasari; Awang Long, Zalizah; 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.
Impact of Hyperparameter Tuning on CNN-Based Algorithm for MRI Brain Tumor Classification Gea, Muhammad Nasri; Wanayumini, Wanayumini; Rosnelly, Rika
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.
Anomaly Detection in Computer Networks Using Isolation Forest in Data Mining Lubis, Hartati Tammamah; Roslina, Roslina; Tanti, Lili
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.
Online Shop Product Sales Prediction Using Multilayer Perceptron Algorithm Safitri, Erica Rian; Tanti, Lili; Wanayumini, Wanayumini
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.v18i1.44286

Abstract

This study aims to develop a predictive model for forecasting product sales using the Multilayer Perceptron (MLP) algorithm. The model's performance was evaluated using key metrics, including the Mean Absolute Error (MAE), Mean Squared Error (MSE), and R² score. The model achieved an MAE of 0.861, an MSE of 9.521, and an impressive R² score of 0.999, demonstrating its ability to accurately predict product sales with minimal error. Feature correlation analysis identified key variables related to the target prediction, which is the number of products ready for shipment, underscoring the importance of feature selection in enhancing model performance. Prediction results revealed variability among product sales, with products like Foodpak Matte 245 (Code 49) predicted to sell approximately 244.31 units, while others like Stiker Kertas (Code 90) showed lower sales forecasts. The findings suggest that strategic interventions may be necessary to boost sales for underperforming items and capitalize on the demand for popular products. Future improvements, such as optimizing the network architecture, experimenting with activation functions and optimization algorithms, and incorporating external factors such as market trends, could further enhance the model’s accuracy and predictive power. Overall, the MLP model demonstrates strong potential for product sales forecasting, providing valuable insights for business decision-making.
Feature Extraction Using Mel-Frequency Cepstral Coefficients (MfCC) Technique For A Tajweed Guess Based on Android Application Development Hulliyah, Khodijah; Kultsum, Lilik Ummi; Wibowo, Wahyu Hendarto; Setianingrum, Anif Hanifa; Arini, Arini; Durachman, Yusuf
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.

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