Country of Publication : Indonesia Publisher : Department of Informatics, Faculty of Science and Technology EIC : Feri Fahrianto (SCOPUS ID: 56580340600) ISSN: 2549-7901 (online) ISSN: 1979-9160 (print) DOI: 10.15408/jti URL: http://journal.uinjkt.ac.id/index.php/ti Frequency: Biannual (April and Oktober) Advance Access : Yes Scope : ICT and Islam, Software Engineering, System Design Methodology, Data mining and Big Data, Human and Computer Interaction, Mobile Computing, Soft Computing, E-Learning, Multimedia and Image Processing, Parallel/Distributed Computing, Artificial Intelligence, Computational Lingustics, Data Comunication, Networking, Computer Engineering, and Cybersecurity. Type of Journal : Academic/Scholar Journals Open Access : Yes National Accredited : Sinta 2 Policy: Peer-Reviewer, Jurnal Teknik Informatika, a peer-reviewed scientific journal, is a significant publication managed by the Department of Informatics within the Faculty of Science and Technology at Universitas Islam Negeri Syarif Hidayatullah. With its biannual releases in April and October, this journal provides a valuable platform for researchers, educators, and students specializing in the field of informatics to contribute their insights and research findings. Furthermore, the journal's association with the Indonesian Association of Higher Education in Informatics and Computing (APTIKOM) enhances its standing as a recognized and influential platform for advancing knowledge and discourse within the informatics and computing domain in Indonesia. (Click here to download the MoU) Jurnal Teknik Informatika is indexed in:
Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah
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Application of the ELECTRE I and ELECTRE IS Method to Optimize Maize Seed Selection in Cameroon: A Multi-Criteria Approach
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
Copyright (c) 2025 Tanone Demas, Guidana Gazawa Fréderic, Yaboki Elisabeth https://creativecommons.org/licenses/by-sa/4.0
Optimizing Naïve Bayes Method for Felder-Silverman Learning Style Model Identification
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.
Copyright (c) 2025 Hanatyati Nur Asmi, Slamet Risnanto, Othman Bin Mohd https://creativecommons.org/licenses/by-sa/4.0
Hybrid Logistic Super Newton Model for Predicting Small Sample Size Data
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.
Copyright (c) 2025 Nurmalitasari Nurmalitasari, Zalizah Awang Long, Nurchim Nurchim https://creativecommons.org/licenses/by-sa/4.0
Syllable-Based Javanese Speech Recognition Using MFCC and CNNs: Noise Impact Evaluation
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
Copyright (c) 2025 Hermanto, Tjong Wan Sen https://creativecommons.org/licenses/by-sa/4.0
Design and Development of the Koperasi Bintang Tapanuli (KBT) Ticket Ordering System
The transportation industry has undergone a major transformation with the widespread adoption of online ticketing systems. However, Koperasi Bintang Tapanuli (KBT), a major player in regional transport, relies on a traditional manual booking system for its buses. The system suffers from inefficiencies such as long queue times and limited access to information. The project used a rigorous requirements gathering process, including stakeholder interviews to ensure the system met user needs and functionality. Passengers can conveniently search routes, compare timetables, book tickets and manage bookings online without the need for a physical ticket counter. The team built a website consisting of 28 functions. They are: registration, authentication (login and logout), profile viewing, profile editing, information viewing, adding information, information editing, information deleting, ticket viewing, ticket adding, ticket editing, ticket deleting, vehicle detailed information viewing, dashboard viewing, customer data viewing customer package information viewing, package payment viewing, ticket approval, review viewing, payment viewing, notification viewing, history viewing, ordering method viewing, payment viewing, ticket ordering, package delivery, check ticket order and add review. This website is built using the laravel framework and the waterfall software development methodology. The application we built helps KTB admins in managing ticket orders.
Copyright (c) 2025 Hernawati Samosir, Monica Silaban, Resa Halen Manurung, Elisabeth Uli Tambunan, Juan Saut Pandapotan Sitorus https://creativecommons.org/licenses/by-sa/4.0
Challenges and Strategies in Forensic Investigation: Leveraging Technology for Digital Security Using Log/Event Analysis Method
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.
Copyright (c) 2025 Ammar Yasir Nasution, Hartono Hartono, Rika Rosnelly https://creativecommons.org/licenses/by-sa/4.0
Online Shop Product Sales Prediction Using Multilayer Perceptron Algorithm
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.
Copyright (c) 2025 Erica Rian Safitri, Lili Tanti, Wanayumini https://creativecommons.org/licenses/by-sa/4.0
Anomaly Detection in Computer Networks Using Isolation Forest in Data Mining
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.
Copyright (c) 2025 Hartati Tammamah Lubis, Roslina, Lili Tanti https://creativecommons.org/licenses/by-sa/4.0
Impact of Hyperparameter Tuning on CNN-Based Algorithm for MRI Brain Tumor Classification
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.
Copyright (c) 2025 Muhammad Nasri Gea, Wanayumini, Rika Rosnelly https://creativecommons.org/licenses/by-sa/4.0
Adaptive Hint Generation for Educational Games Using Fuzzy Logic
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.
Copyright (c) 2025 Anggina Primanita, Hadipurnawan Satria, Muhammad Qurhanul Rizqie, Ananda Haykel Iskandar, Wibisena Nugraha https://creativecommons.org/licenses/by-sa/4.0
Comparison of Hyperparameter Tuning Methods for Optimizing K-Nearest Neighbor Performance in Predicting Hypertension Risk
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.
Copyright (c) 2025 Dimas Trianda, Dedy Hartama, Solikhun Solikhun https://creativecommons.org/licenses/by-sa/4.0
Evaluation of An Existing System Using The System Usability Scale (SUS) as A Guideline for System Improvement
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.
Copyright (c) 2025 M. Khairul anam, Susanti Susanti, Nurjayadi Nurjayadi, Fransiskus Zoromi, Atalya Kurnia Sari https://creativecommons.org/licenses/by-sa/4.0
Feature Extraction Using Mel-Frequency Cepstral Coefficients (MfCC) Technique For A Tajweed Guess Based on Android Application Development
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.
Copyright (c) 2025 Khodijah Hulliyah, Lilik Ummi Kultsum, Wahyu Hendarto Wibowo, Anif Hanifa Setianingrum, Arini, Yusuf Durachman https://creativecommons.org/licenses/by-sa/4.0
Classification of Coconut Fruit Quality Using The K-Nearest Neighbour (K-NN) Method Based on Feature Extraction: Color, Shape, and Texture
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%.
Copyright (c) 2025 Sucinda Kardena, Fildza Izzati,Rusdah https://creativecommons.org/licenses/by-sa/4.0
Detection of Vulgarity in Anime Character: Implementation of Detection Transformer
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.
Copyright (c) 2025 Amalia Suciati, Dian Kartika Sari, Andi Prademon Yunus, Nuuraan Rizqy Amaliah https://creativecommons.org/licenses/by-sa/4.0
Small Object Detection and Object Counting for Primary Roe Dataset Based on Yolo
This research offers an initial exploration into the effectiveness of three variations of the YOLOv8 model original, trimmed, and YOLOv8n.pt in combination with two distinct datasets characterized by tight and loose distributions of roe, aimed at enhancing small object detection and counting accuracy. Utilizing a primary roe dataset across 776 images, the research systematically compares these model-dataset configurations to identify the most effective combination for precise object detection. The experimental results reveal that the YOLOv8n.pt model combined with the loosely distributed dataset achieves the highest detection performance, with a mean Average Precision (mAP) of 53.86%. This outcome underscores the critical impact of both model selection and data distribution on the detection accuracy in machine learning applications. The findings highlight the importance of tailored model and dataset synergies in optimizing detection tasks, particularly in complex scenarios involving small, densely clustered objects. This research contributes valuable insights into the strategic deployment of neural network architectures for refined object detection challenges.
Copyright (c) 2025 Wahyu Andi Saputra, Nicolaus Euclides Wahyu Nugroho, Dany Candra Febrianto, Andi Prademon Yunus, Muhammad Azrino Gustalika, Yit Hong Choo https://creativecommons.org/licenses/by-sa/4.0