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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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Genetic Algorithm Optimization of Hybrid LSTM-AutoEncoder in Tourism Recommendation System Sanjaya, Bayu Surya Dharma; Setiawan, Erwin Budi
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.v17i2.39760

Abstract

The tourism industry has rapid growth and has become one of the world's leading economic industries in recent years due to advances in information technology, such as the internet and social media. However, the overwhelming amount of information often makes it difficult for travelers to decide on their preferred travel destination. To address these issues, this research proposes a tourism recommendation system that combines Content-Based Filtering and Hybrid LSTM-AE, which is optimized using Genetic Algorithm (GA). There is no research that has developed a recommendation system using a combination of these methods and optimized using GA. So that this research can contribute to providing personalized recommendations and higher accuracy. The dataset consists of 9,504 ratings collected from the Ministry of Tourism and Creative Economy, Twitter, and web sources. The system was able to achieve a rating prediction accuracy of 96.82% by applying SMOTE to handle data imbalance and implementing a GA approach to the Hybrid LSTM-AE model. Accuracy has increased by 18.7% from the baseline model without using SMOTE and optimization. These results underscore that a strong integration between natural language processing and genetically optimized deep learning provides more accurate recommendations.
Optimizing the Learning Rate Hyperparameter for Hybrid BiLSTM-FFNN Model in a Tourism Recommendation System Mustofa, Aufa Ab'dil; Setiawan, Erwin Budi
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.v17i2.40250

Abstract

Indonesia, with its abundant natural resources, is rich in captivating tourist attractions. Tourism, a vital economic sector, can be significantly influenced by digitalization through social media. However, the overwhelming amount of information available can confuse tourists when selecting suitable destinations. This research aims to develop a tourism recommendation system employing content-based filtering (CBF) and hybrid Bidirectional Long Short-Term Memory Feed-Forward Neural Network (BiLSTM-FFNN) model to assist tourists in making informed choices. The dataset comprises 9,504 rating matrices obtained from tweet data and reputable web sources. In various experiments, the hybrid BiLSTM-FFNN model demonstrated superior performance, achieving an accuracy of 93.36% following optimization with the Stochastic Gradient Descent (SGD) algorithm at a learning rate of about 0.193. The accuracy, after applying Synthetic Minority Over-sampling Technique (SMOTE) and fine-tuning the learning rate hyperparameter, showed a 14.3% improvement over the baseline model. This research contributes by developing a recommendation system method that integrates CBF and hybrid deep learning with high accuracy and provides a detailed analysis of optimization techniques and hyperparameter tuning.
Performance Analysis of Transfer Learning Models for Identifying AI-Generated and Real Images Arini, Arini; Azhari, Muhamad; Fitri, Isnaieni Ijtima’ Amna; Fahrianto, Feri
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.v17i2.40453

Abstract

This study aims to analyze and compare the performance of three transfer learning methods, namely InceptionV3, VGG16, and DenseNet121, in detecting AI-generated and real images. The background of this research is the unknown performance of transfer learning methods for detecting AI-generated and real images. This study introduces innovation by conducting 54 experiments involving three types of transfer learning, three dataset split ratios (60:40, 70:30, and 80:20), three optimizers (Adam, SGD, and RMSprop), two numbers of epochs (20 and 50), and the addition of dense and flatten layers during fine tuning. Performance evaluation was conducted using binary cross entropy loss and confusion matrix. This research provides significant benefits in determining the most effective transfer learning model for detecting AI-generated and real images and offers practical guidance for further development. The results show that the InceptionV3 model with the Adam optimizer, an 80:20 split ratio, and 20 epochs achieved the highest accuracy of 84.26%, with a loss of 39.54%, precision of 81.33%, recall of 82.43%, and an F1-Score of 81.88%.
Evaluation of An Existing System Using The System Usability Scale (SUS) as A Guideline for System Improvement Anam, M. Khairul; Susanti, Susanti; Nurjayadi, Nurjayadi; Zoromi, Fransiskus; Sari, Atalya Kurnia
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.v18i1.40766

Abstract

The e-Polvot system at the University of Science and Technology Indonesia (USTI) is a digital platform used for student elections, replacing traditional paper-based voting to enhance efficiency and minimize election fraud. This study evaluates the system using the System Usability Scale (SUS) to assess its usability, including efficiency, effectiveness, and user satisfaction. However, SUS alone does not determine failure points but provides a usability score that reflects user perception. A survey was conducted with 88 respondents from three different academic programs, which showed that while the system generally received a "Good" usability rating, certain areas require enhancement to improve user engagement and satisfaction. Based on the findings, this study recommends enhancing the user interface, providing targeted user training, and introducing additional features to broaden the system’s application across academic units. Additionally, the study highlights the potential for expanding the system's functionality beyond student elections, supporting activities such as departmental voting and organizational decision-making processes. These improvements aim to increase user satisfaction and usability, making the system a more effective tool for various academic and institutional contexts.
Using K-NN Algorithm for Evaluating Feature Selection on High Dimensional Datasets Silfana, Fina Indri; Barata, Mula Agung
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.v17i2.40866

Abstract

Data mining is the process of using statistics, mathematics, artificial intelligence and machine learning to identify problems that exist in data so as to produce useful information. Based on its function, data mining is grouped into description, estimation, classification, clustering, and association. K-NN is one of the best data mining methods and is widely used in research. K-NN algorithm was introduced by Fix and Hodges in 1951. K-NN algorithm is a simple algorithm and is often used to cluster supervised data. Feature selection attribute selection is a data mining technique used in the pre-processing stage. This technique works by reducing complex attributes that will be managed at the processing and analysis stage. In this study, the most effective feature selection to improve the accuracy of the K-NN algorithm by increasing accuracy by 95.12% on the breast cancer dataset and 88.75% on the prostate cancer dataset.
Optimizing Naïve Bayes Method for Felder-Silverman Learning Style Model Identification Asmi, Hanatyani Nur; Risnanto, Slamet; Mohd, Othman Bin
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.v18i1.40936

Abstract

One important issue in education institusion is the differences in students learning styles, which requires educators to pay attention to individual learning preferences. The manual learning style identification method is considered less effective in terms of time and data accuracy. This study aims to develop a student learning style identification system using the Felder-Silverman model and the Naïve Bayes method, This system is designed to assist lecturers in adjusting learning strategies according to student learning preferences, thus increasing the effectiveness of the learning process. The Naïve Bayes method was applied by analyzing student datasets and determining the accuracy of learning style identification. The validation results showed significant identification accuracy: 85% for the active-reflective dimension, 96% for the sensitive-intuitive dimension, 98% for the verbal-visual dimension, and 91% for the sequential-global dimension. The results of user validation show the effectiveness of the learning style identification application that has been tested based on the percentage value of each statement, and an average percentage value of 85.6% was obtained for all statements, indicating that the system functions well in identifying students' learning styles, while the results of expert validation state that the statements are in accordance with the indicators, the statements use simple and easy-to-understand language, and the identification results are appropriate. This study is expected to contribute to helping universities identify student learning styles efficiently, improve the quality of learning in higher education, and contribute to supporting an inclusive learning approach in higher education environments.
Human Fall Motion Prediction: Fall Motion Forecasting and Detection with GRU Yunus, Andi Prademon; Arifa, Amalia Beladinna; Choo, Yit Hong
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.v17i2.41027

Abstract

The human fall motion prediction system is a preventive tool aimed at reducing the risk of falls. In our research, we developed a deep learning model that utilizes pose estimation to track human body posture and integrated this with a Gated Recurrent Unit (GRU) to forecast human motion and predict falls. GRU, an enhancement of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models offers improved memorization and more efficient memory usage and performance. Our study presents the human fall motion prediction, which combines the forecasting and classification of potential falls.The CAUCAFall dataset is used as the benchmark of this study, which contains the image sequences of single human motion with ten actions conducted by ten actors. We employed the YOLOv8 Pose model to track the 2D human body pose as the input in our system. A thorough evaluation of the CAUCAFall dataset highlights the effectiveness of our proposed system. Evaluation using the CAUCAFall dataset demonstrates that the model achieved a Mean Per Joint Position Error (MPJPE) of 4.65 pixels from the ground truth, with a 70% accuracy rate in fall prediction. However, the model also exhibited a Mean Relative Error (MRE) of 0.3, indicating that 30% of the predictions were incorrect. These findings underscore the potential of the GRU-based system in fall prevention
Syllable-Based Javanese Speech Recognition Using MFCC and CNNs: Noise Impact Evaluation Hermanto, Hermanto; Sen, Tjong Wan
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
Comparison of Criteria Weight Determination Using MEREC and CRITIC Methods in Choosing The Best Student Accommodation with the MOORA Method Case Study: Coventry University Hilmi, M. Thosin Yuhaililul; Rosiani, Ulla Delfana; Astuti, Ely Setyo
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.v17i2.41097

Abstract

One of the challenges faced by IISMA Awardees and students in general in Coventry University is choosing a comfortable place to live. Although various student accommodations are provided, differences in facilities and considerations from other parties such as parents and friends make the selection process complicated. This study develops a decision support system to help students choose student accommodation objectively without any intervention from others and provides a comparison of the use of different combinations of methods as additional guidance in the decision-making process. Two methods, Method Based on the Removal Effects of Criteria (MEREC) and Criteria Importance Through Intercriteria Correlation (CRITIC), are used to weight the criteria. The Multi-Objective Optimization (MOORA) method is used to determine the best alternative after the weight calculation is known. The results using a combination of the MEREC-MOORA method and a combination of the CRITIC-MOORA method place Alternative 5 (A5) in first place, while the remaining alternatives show a similar ranking order. In this study, scenario testing was also carried out by deleting and adding criteria and alternatives which then provided ranking results with a positive correlation even though different combinations of methods were used in determining the ranking.
Classification of Coconut Fruit Quality Using The K-Nearest Neighbour (K-NN) Method Based on Feature Extraction: Color, Shape, and Texture Kardena, Sucinda; Izzati, Fildza; Rusdah, Rusdah
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.v18i1.41225

Abstract

In 2021, Indonesia was the world's largest coconut producer, with production reaching 17.1 million tons, according to the Food and Agriculture Organization (FAO). However, due to the long distribution time from farmers to consumers, the quality of coconuts often decreases, mainly due to manual classification. Coconuts that meet consumption standards are considered suitable, while coconuts that are overripe, damaged, or unripe are considered Non-standard. To overcome this problem, an automatic classification system was developed using machine learning with the K-Nearest Neighbor (K-NN) algorithm. The total required dataset is around 500, comprising 250 standard coconut datasets and 250 non-standard coconut datasets. The dataset was taken from coconut Images from Indragiri Hilir, Riau Province. Coconut features colour, shape, and texture.. The development process used the Cross Industry Standard Process for Data Mining (CRISP-DM). The evaluation used a confusion matrix .This study explores five training-test ratio data split scenarios of 90:10, 80:20, 70:30, 60:40, and 50:50. The highest accuracy, 96%, is achieved with a data split of 90:10 and a K value 5. Then, the K-NN model will be compared with other models,  for Support Vector Machine (SVM) with RBF kernel accuracy of 94%, SVM with Linear kernel of 90%, Random Forest with accuracy of 92%, and Convolutional Neural Network (CNN) with accuracy of 86%.

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