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bit-Tech
Published by Komunitas Dosen Indonesia
ISSN : 2622271X     EISSN : 26222728     DOI : https://doi.org/10.32877/bt
Core Subject : Science,
Computer Science & IT
The bit-Tech journal was developed with the aim of accommodating the scientific work of Lecturers and Students, both the results of scientific papers and research in the form of literature study results. It is hoped that this journal will increase the knowledge and exchange of scientific information, especially scientific papers and research that will be useful as a reference for the progress of the State together.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 642 Documents
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Rice Price Forecasting Using an Ensemble GRU–SVR Model with Enhanced Feature Engineering Faizi, Dandi Nur; Trimono, Trimono; Saputra, Wahyu Syaifullah Jauharis
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3532

Abstract

Rice price volatility significantly impacts economic stability and food security in Indonesia, particularly in East Java, where fluctuations in staple food prices affect household purchasing power and inflation management. This study addresses the limitations of existing rice price forecasting models, which often struggle to capture the complex, nonlinear dynamics of agricultural prices influenced by multiple factors such as climate variability and market conditions. Accurate and reliable price forecasting is essential to support effective policy formulation, market intervention, and food price stabilization strategies. This research develops an ensemble forecasting framework integrating Gated Recurrent Unit (GRU) and Support Vector Regression (SVR) with enhanced feature engineering to predict daily medium rice prices using historical price and weather data. The dataset comprises daily observations from 2021 to 2025, including rice prices, average temperature, relative humidity, rainfall, and sunshine duration. In this framework, GRU serves as a temporal feature extractor to learn complex temporal dependencies, while enhanced feature engineering generates complementary statistical features from sliding windows to enrich GRU's output. The combined feature set is provided to an SVR model with a Radial Basis Function kernel for final regression. Experimental results show that the proposed model achieves a high forecasting accuracy with an MAPE of 0.109%, demonstrating stable predictive behavior and making it a valuable tool for monitoring rice prices. The model's effectiveness in capturing temporal dependencies and nonlinear patterns suggests potential applicability beyond East Java, offering broader insights for agricultural price forecasting in other regions.
Bus Passenger Demand Forecasting Using A Hybrid ARIMA–MLP Model Moerrin, Naufal Baihaqi; Damaliana, Aviolla Terza; Diyasa, I Gede Susrama Mas
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3549

Abstract

Accurate passenger demand forecasting is crucial for operational planning and service reliability in public transportation systems. Despite the effectiveness of traditional models, existing approaches often struggle with nonlinear fluctuations in demand, which limits their ability to adapt to real-world variability. This study proposes a hybrid forecasting framework that combines the Autoregressive Integrated Moving Average (ARIMA) model with a Multi-Layer Perceptron (MLP) neural network for short-term passenger demand prediction. By using ARIMA to capture linear components like trend, seasonality, and autocorrelation, and MLP to model the residuals that contain nonlinear patterns, the proposed approach integrates the strengths of both models. This hybrid method addresses gaps in current forecasting techniques by improving adaptability and precision. Empirical analysis was conducted using daily passenger count data from Bus Trans Jatim during 2023–2024. Data preprocessing included exploratory time series analysis, variance stabilization, and outlier assessment to ensure compatibility with the modeling assumptions. Forecast performance was evaluated using the Mean Absolute Percentage Error (MAPE). The results show that the hybrid ARIMA–MLP model achieved a MAPE of 4.95%, outperforming the standalone ARIMA model in providing more adaptive and accurate short-term forecasts. These findings have practical implications for public transportation planning, enabling more responsive and efficient operations, particularly for forecasting demand fluctuations.
Cooking Oil Price Forecasting in East Java Using the Temporal Fusion Transformer Azis, Nauval Ihsani; Sugiarto, Sugiarto; Idhom, Mohammad
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3550

Abstract

Cooking oil is a critical staple commodity in Indonesia, where price fluctuations significantly impact household purchasing power and regional economic stability, especially in East Java. These fluctuations stem from complex, nonlinear interactions between crude palm oil prices, supply chain conditions, and market mechanisms. This study fills the gap in existing forecasting models, which often fail to address regional price dynamics and lack interpretability. We develop a short-term forecasting model using the Temporal Fusion Transformer (TFT), a deep learning architecture tailored for multi-horizon time series forecasting, to predict packaged and bulk cooking oil prices in East Java. Daily price data for packaged and bulk cooking oil, along with national palm oil prices, were sourced from official government records covering April 21, 2022, to October 2, 2024. The dataset was preprocessed with missing value interpolation, normalization, and transformation into a supervised multivariate time series format. The TFT model was trained using a 30-day historical window, with a seven-day forecasting horizon, optimized via quantile loss to generate probabilistic forecasts. Model performance was assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and quantile loss. Results show that the TFT model achieves high accuracy, low validation errors, and provides reliable uncertainty estimates. Short-term forecasts suggest stable price trends, with greater uncertainty for packaged cooking oil than bulk. This research demonstrates the TFT's potential for short-term forecasting, policy support, and its broader application to regional price monitoring.
Capability and Maturity Analysis of an Academic Final Project Information System Using COBIT Putra, Agung Surya; Indana, Luthfi
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3557

Abstract

The implementation of the Final Project Information System (SISTA) plays a critical role in supporting academic processes in higher education; however, inadequate information technology governance can lead to operational instability and service quality issues. This study addresses the limited application of the COBIT 2019 framework in evaluating academic information systems within Indonesian universities by assessing the capability level, maturity level, and governance gaps of SISTA at Universitas Merdeka Malang. A quantitative approach was employed, utilizing questionnaires, interviews, and documentation analysis to measure current and target capability levels across four COBIT 2019 domains: Align, Plan, and Organize (APO); Build, Acquire, and Implement (BAI); Deliver, Service, and Support (DSS); and Monitor, Evaluate, and Assess (MEA). The results show that most governance processes have achieved capability level 3 (Established), indicating consistent and standardized implementation, while the overall maturity level ranges from Defined to Managed. Gap analysis reveals that the DSS domain exhibits the largest discrepancy, particularly affecting operational stability and problem management, whereas the APO and MEA domains demonstrate relatively smaller gaps. These findings highlight critical governance weaknesses that directly impact system reliability and user experience. The study provides actionable insights for prioritizing governance improvements and offers a practical reference for higher education institutions seeking to strengthen sustainable IT governance through the COBIT 2019 framework.
A Hybrid CNN-LSTM Approach for Detecting Cross-Site Scripting Attacks in Web Applications Anugera, Eka Seftrian; Mirza, Anis
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3559

Abstract

Cross-Site Scripting (XSS) remains one of the most pervasive and persistent vulnerabilities in modern web applications, allowing attackers to inject and execute malicious scripts through user input fields. Conventional detection mechanisms based on static rules, blacklists, or handcrafted features often fail to recognize obfuscated or context-aware payloads, leading to severe gaps in real-time protection. This study proposes a hybrid deep learning architecture combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to overcome these limitations by simultaneously capturing spatial patterns and sequential dependencies in character-level input. The model was trained and validated using both public XSS datasets and sanitized internal web logs to ensure robustness across diverse payload structures. Experimental results demonstrate high classification performance, achieving 98.67% accuracy, 98.21% precision, 99.29% recall, and an ROC-AUC of 0.9990. The hybrid CNN-LSTM architecture’s novel integration of local feature extraction and temporal context modeling enables superior generalization compared to conventional CNN or LSTM-only approaches. Beyond quantitative metrics, the model was deployed in a Flask-based web simulation to assess its real-world applicability, where it successfully detected and mitigated live XSS payloads in real time without disrupting benign user operations. These findings highlight the potential of hybrid deep learning models as adaptive, low-latency defenses for strengthening modern web application security infrastructures.
Lightweight Model With Hyperparameter Optimization For Classification of Tomato Leaf Diseases Based On Plantvillage Fitriyandhi, Ari; Dwi Cahyani, Atika; Yunita, Risca; Perdana, Muhammad Ricky; Kristian, Taufik Aldri; Kusrini, Kusrini; Artha Agastya, I Made
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3566

Abstract

Tomato cultivation is a vital agricultural commodity in Indonesia, yet leaf diseases continue to pose a serious threat to crop quality and yield. While deep learning–based classifiers have achieved high accuracy in laboratory settings, most existing tomato leaf disease detection models rely on computationally intensive architectures that limit their practical deployment on resource-constrained devices commonly used in agricultural environments. To address this gap, this study proposes a lightweight Convolutional Neural Network (CNN) based on the MobileNetV2 architecture, explicitly combined with systematic hyperparameter optimization, for tomato leaf disease classification. Using 14,529 images from the PlantVillage dataset, the research involves image preprocessing, data augmentation, and structured tuning to improve performance while maintaining computational efficiency. The optimized model achieves an accuracy of 81% using a learning rate of 0.001, 128 units, a dropout rate of 0.3, and an alpha value of 0.35. Although this accuracy is slightly lower than that reported by heavyweight CNN models, it is competitive for lightweight architectures and represents a favorable trade-off between classification performance and computational efficiency. Despite its compact design, the model demonstrates reliable disease recognition and suitability for deployment on devices with limited resources. Furthermore, the trained model was implemented in a desktop-based application as a proof-of-concept system, demonstrating scalability and potential adaptation to mobile or edge-based agricultural decision-support platforms. This study highlights the novelty of integrating lightweight CNN design with systematic hyperparameter optimization and demonstrates that optimized lightweight deep learning models can provide effective, efficient, and deployable solutions for real-world precision agriculture applications.
A Deep Learning Approach Using Bidirectional-LSTM and Word2Vec for Fake News Classification Nur Hidayat, Fadhilah; Syaifullah J. S, Wahyu; Idhom, Mohammad
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3575

Abstract

The rapid growth of online news consumption in Indonesia has intensified the challenge of combating fake news, which undermines public trust and threatens social stability. Conventional approaches, including manual verification, are increasingly inadequate to address the scale and speed of digital information dissemination. This study aims to develop an automatic Indonesian fake news classification system using a deep learning framework that integrates Bidirectional Long Short-Term Memory (Bi-LSTM) with Word2Vec embeddings. Unlike many existing fake news detection models that rely on limited validation settings or focus predominantly on English-language data, this work explicitly addresses the linguistic characteristics and practical constraints of the Indonesian context, thereby strengthening model relevance for real-world deployment. The dataset comprises 6,000 balanced news articles, including 3,000 valid items from Detik.com and 3,000 hoax items from Turnbackhoax.id, collected between January and October 2024. Text preprocessing involved cleaning, stopword removal, tokenization, and padding. A 300-dimensional Word2Vec embedding model was employed, and the classifier was trained using stratified 3-fold cross-validation to ensure robust performance estimation. An ensemble inference strategy was further applied to reduce inter-fold variance and enhance generalization on unseen data, directly addressing a common limitation of prior single-model approaches. Experimental results show that the proposed model achieves an accuracy of 86.43% and an F1-score of 86.28%, alongside a high mean Average Precision of 0.927 during validation. Compared with previously reported deep learning baselines, this framework demonstrates competitive yet more stable performance under realistic evaluation settings, supporting scalable deployment.
Optimisation of Hyperparameter Tuning and Optimiser on MobileNetV2 for Batik Parang Classification Rafli, Muhammad; Prasetya, Dwi Arman; Hindrayani, Kartika Maulida
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3576

Abstract

Batik Parang is a prominent traditional motif in Indonesia, characterised by repetitive diagonal patterns and subtle visual variations across regional styles, such as Solo Parang and Yogyakarta Parang, which pose challenges for automated image classification. This study addresses this challenge by introducing an optimisation-focused framework that integrates hyperparameter tuning strategies with a lightweight convolutional neural network, extending the practical use of MobileNetV2 for fine-grained cultural motif classification. A balanced dataset of 160 batik images collected from Kaggle was employed and partitioned using an 80:20 stratified split to ensure class consistency. The model was evaluated on a limited yet representative dataset reflecting realistic small-scale cultural heritage scenarios. Two hyperparameter tuning methods, Bayesian Optimisation and Particle Swarm Optimisation, were applied to optimise learning rate, batch size, and dropout rate, while two optimisers, Adam and Adagrad, were compared to analyse their effects on convergence stability and generalisation. The training process followed a two-phase strategy consisting of transfer learning and selective fine-tuning of upper MobileNetV2 layers. Experimental results indicate that Adagrad-based configurations consistently outperform Adam-based models, which exhibited class collapse and poor generalisation. The optimal configuration, combining Adagrad with Bayesian Optimisation, achieved a validation accuracy of 91% with balanced precision, recall, and F1-score across both Parang classes. These findings demonstrate that careful optimisation enhances the reliability of lightweight CNNs and support extending the proposed framework to other cultural heritage classification tasks and resource-constrained real-time applications.
Design of Thesis Topic Recommendation System Using TF-IDF and Cosine Similarity Arrisalah, Muhammad Baihaqi; Haromainy, Muhammad Muharrom Al; Junaidi, Achmad
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3579

Abstract

Selecting a thesis topic is a critical stage in a student’s academic journey and frequently poses substantial cognitive and procedural challenges. This study reports the design and implementation of the Computer Science Thesis Recommendation System (SRSIK Hub), a web-based decision-support platform aimed at improving the efficiency and accuracy of thesis topic selection. The primary novelty of this research lies in the systematic integration of Term Frequency–Inverse Document Frequency (TF-IDF) and Cosine Similarity within a large-scale academic corpus to model fine-grained semantic relevance between student interests and prior thesis documents, enabling more precise and transparent recommendations than conventional keyword-based searches. The system adopts a content-based filtering approach and processes approximately 4,000 thesis records collected from multiple university repositories. Textual data are preprocessed and transformed using TF-IDF vectorization, while Cosine Similarity is employed to rank candidate topics according to relevance. System effectiveness was evaluated using the WebUse Framework involving 75 student respondents. The evaluation yielded an overall score of 4.44 out of 5, indicating high usability, strong information quality, and reliable system functionality. This performance score demonstrates that the proposed recommendation model is not only technically sound but also practically applicable in real academic settings, where it can significantly reduce topic selection time and uncertainty for students. The results confirm that SRSIK Hub effectively supports students in identifying research topics aligned with their academic interests and competencies. Beyond local deployment, the system is transferable to other institutions for scalable thesis recommendation support.
Implementation of Multiplex Leiden Algorithm for Clustering Ancol Visitors Wardani, Ajeng Puspa; Trimono, Trimono; Nasrudin, Muhammad
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3608

Abstract

Ancol is the largest recreational destinations, attracting visitors from diverse backgrounds. However, in 2024 the company experienced an 11.96% decline in visitor numbers. This condition highlights the urgent need for more accurate customer segmentation to support targeted and effective marketing strategies. Accordingly, this study investigates whether a Multiplex Leiden can produce coherent visitor segments, while also examining the relative contribution of each layer to community formation. Unlike prior multilayer segmentation studies, this study leverages the Multiplex Leiden algorithm, which guarantees well-connected communities and has been shown to achieve higher modularity. This is among the first applications of Multiplex Leiden for visitor segmentation, offering improved community coherence and interpretability in a multi-layer behavioral network. To balance network structures and reduce cross-layer density bias, kNN backbone preprocessing was applied before community detection. The results reveal 18 distinct visitor communities with substantial variation in size. Layer-wise quality analysis shows that the socioeconomic status layer contributes the strongest influence on the detected communities, followed by spending behavior and experiential preferences. The clustering quality was evaluated using multiple metrics. An Adjusted Rand Index (ARI) of 0.617 indicates a stable, non-random visitor segmentation, while a positive total quality score of 1.086 reflects strong cross-layer community structure. A mean conductance value of 0.548 suggests moderately well-separated yet realistically overlapping communities. Overall, the findings empirically confirm the effectiveness and interpretability of the Multiplex Leiden algorithm with backbone preprocessing for visitor segmentation in multi-layer networks. Future research may extend this framework by incorporating additional behavioral or temporal data.

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