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Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Published by Ikatan Ahli Informatika Indonesia
ISSN : 25800760     EISSN : 25800760     DOI : https://doi.org/10.29207/resti.v2i3.606
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian Rekayasa Sistem, Teknik Informatika/Teknologi Informasi, Manajemen Informatika dan Sistem Informasi. Sebagai bagian dari semangat menyebarluaskan ilmu pengetahuan hasil dari penelitian dan pemikiran untuk pengabdian pada Masyarakat luas dan sebagai sumber referensi akademisi di bidang Teknologi dan Informasi. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) menerima artikel ilmiah dengan lingkup penelitian pada: Rekayasa Perangkat Lunak Rekayasa Perangkat Keras Keamanan Informasi Rekayasa Sistem Sistem Pakar Sistem Penunjang Keputusan Data Mining Sistem Kecerdasan Buatan/Artificial Intelligent System Jaringan Komputer Teknik Komputer Pengolahan Citra Algoritma Genetik Sistem Informasi Business Intelligence and Knowledge Management Database System Big Data Internet of Things Enterprise Computing Machine Learning Topik kajian lainnya yang relevan
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,046 Documents
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Optimizing Multilayer Perceptron for Car Purchase Prediction with GridSearch and Optuna Ginanti Riski; Dedy Hartama; Solikhun
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6328

Abstract

Multilayer Perceptron (MLP) is a powerful machine learning algorithm capable of modeling complex, non-linear relationships, making it suitable for predicting car purchasing power. However, its performance depends on hyperparameter tuning and data quality. This study optimizes MLP performance using GridSearch and Optuna for hyperparameter tuning while addressing data imbalance with the Synthetic Minority Over-sampling Technique (SMOTE). The dataset comprises demographic and financial attributes influencing car purchasing power. Initially, the dataset exhibited class imbalance, which could lead to biased predictions; SMOTE was applied to generate synthetic samples, ensuring a balanced class distribution. Two hyperparameter tuning approaches were implemented: GridSearch, which systematically explores a predefined parameter grid, and Optuna, an adaptive optimization framework utilizing a Bayesian approach. The results show that Optuna achieved the highest accuracy of 95.00% using the Adam optimizer, whereas GridSearch obtained the best accuracy of 94.17% with the RMSProp optimizer, demonstrating Optuna's superior ability to identify optimal hyperparameters. Additionally, SMOTE significantly improved model stability and predictive performance by ensuring adequate class representation. These findings offer insights into best practices for optimizing MLP in predictive modeling. The combination of SMOTE and advanced hyperparameter tuning techniques is applicable to various domains requiring accurate predictive analytics, such as finance, healthcare, and marketing. Future research can explore alternative optimization algorithms and data augmentation techniques to further enhance model robustness and accuracy.
Classification Model for Bot-IoT Attack Detection Using Correlation and Analysis of Variance Firgiawan Faira; Dandy Pramana Hostiadi; Roy Rudolf Huizen
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6332

Abstract

Industry 4.0 requires secure networks as the advancements in IoT and AI exacerbate the challenges and vulnerabilities in data security. This research focuses on detecting Bot-IoT activity using the Bot-IoT UNSW Canberra 2018 dataset. The dataset initially showed a significant imbalance, with 2,934,447 entries of attack activity and only 370 entries of normal activity. To address this imbalance, an innovative data aggregation technique was applied, effectively reducing similar patterns and trends. This approach resulted in a balanced dataset consisting of 8 attack activity points and 80 normal activity points. Feature selection using the ANOVA method identified 10 key features from a total of 17: seq, stddev, N_IN_Conn_P_SrcIP, min, state_number, mean, N_IN_Conn_P_DstIP, drate, srate, and max. The classification process utilized Random Forest, k-NN, Naïve Bayes, and Decision Tree algorithms, with 100 iterations and an 80:20 training-testing split. Random Forest showed superior performance, achieving 97.5% accuracy, 97.4% precision, and 97.4% recall, with a total computation time of 11.54 seconds. Pearson correlation analysis revealed a strong positive correlation (+0.937) between N_IN_Conn_P_DstIP and seq, as well as a weak negative correlation (-0.224) between N_IN_Conn_P_SrcIP and state_number. The novelty of this research lies in the application of a data aggregation technique to address class imbalance, significantly improving machine learning model performance and optimizing training time. These findings contribute to the development of robust cybersecurity systems to effectively detect IoT-related threats.
Sentiment Analysis of ChatGPT on Indonesian Text using Hybrid CNN and Bi-LSTM Prasetyo, Vincentius Riandaru; Naufal, Mohammad Farid; Wijaya, Kevin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6334

Abstract

This study explores sentiment analysis on Indonesian text using a hybrid deep learning approach that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). Due to the complex linguistic structure of the Indonesian language, sentiment classification remains challenging, necessitating advanced methods to capture both local patterns and sequential dependencies. The primary objective of this research is to improve sentiment classification accuracy by leveraging a hybrid model that integrates CNN for feature extraction and Bi-LSTM for contextual understanding. The dataset consists of 800 manually labeled samples collected from social media platforms, preprocessed using case folding, stop word removal, and lemmatization. Word embeddings are generated using the Word2Vec CBOW model, and the classification model is trained using a hybrid architecture. The best performance was achieved with 32 Bi-LSTM units, a dropout rate 0.5, and L2 regularization, which was evaluated using Stratified K-Fold cross-validation. Experimental results demonstrate that the hybrid model outperforms conventional deep learning approaches, achieving 95.24% accuracy, 95.09% precision, 95.15% recall, and 95.99% F1 score. These findings highlight the effectiveness of hybrid architectures in sentiment analysis for low-resource languages. Future work may explore larger datasets or transfer learning to enhance generalizability.
Efficient Hybrid Network with Prompt Learning for Multi-Degradation Image Restoration Muhammad Yusuf Kardawi; Laksmita Rahadianti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6381

Abstract

Image restoration aims to repair degraded images. Traditional image restoration methods have limited generalization capabilities due to the difficulty in dealing with different types and levels of degradation. On the other hand, contemporary research has focused on multi-degradation image restoration by developing unified networks capable of handling various types of degradation. One promising approach is using prompts to provide additional information on the type of input images and the extent of degradation. Nonetheless, all-in-one image restoration requires a high computational cost, making it challenging to implement on resource-constrained devices. This research proposes a multi-degradation image restoration model based on PromptIR with lower computational cost and complexity. The proposed model is trained and tested on various datasets yet it is still practical for deraining, dehazing, and denoising tasks. By unification convolution, transformer, and dynamic prompt operations, the proposed model successfully reduces FLOPs by 32.07% and the number of parameters by 27.87%, with a comparable restoration result and an SSIM of 34.15 compared to 34.33 achieved by the original architecture for the denoising task.
The Impact of Cancer on Poverty: An Analytical Study Using Big Data and OLS Regression Pratiwi, Heny; Muhammad Ibnu Sa’ad; Wahyuni, Wahyuni; Syamsuddin Mallala
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6112

Abstract

Cancer is one of the leading causes of death worldwide and has a significant impact on the economic condition of families, especially in developing countries. High medical costs and loss of work productivity often push families of patients with cancer into poverty. This study aimed to analyze the relationship between cancer mortality rates and poverty levels using the Ordinary Least Squares (OLS) regression method and big data covering various socio-economic indicators. The data in this study include cancer mortality rates and other socioeconomic indicators, which were then analyzed using the OLS regression method to understand the quantitative relationship between the two variables. The results of the analysis show a positive correlation between cancer mortality rates and increasing poverty, with the regression model explaining 73.8% of the variation in the target variable. The regression model demonstrated strong explanatory power and minimal error, with an R-squared value of 0.738, indicating that 73.8% of the data variability was explained by the model. Model quality was supported by low AIC (19070.4) and BIC (19110.4) values. Linearity was confirmed by a significant F-statistic of 1314.0 (p < 0.01), suggesting a robust linear relationship between independent and dependent variables. All parameters exhibited statistical significance (p < 0.05) at the 95% confidence level, with mean residuals close to zero, satisfying the unbiased expectation assumption. Although the model results show good performance, the model's estimators show low variance, as evidenced by small standard errors (e.g., Incidence_Rate: 0.009, Med_Income: 1.89e-05) and a Durbin-Watson statistic of 1.725, indicating no autocorrelation. These metrics collectively confirmed the reliability and stability of the regression model.
Improving Frame-based Engagement Classification in E-Learning Using EfficientNet and Normalized Loss Weighting Sugihdharma, Joseph Ananda; Bachtiar, Fitra; Yudistira, Novanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6161

Abstract

Engagement can be defined as how individuals are involved in and interact with a task that requires attention and emotional conditions. Engagement is an affective state positively correlated with learning processes. Engagement along with other affective states, such as boredom, confusion, and frustration must be analyzed to identify students’ learning behavior. Implementing proper prevention by measuring student engagement levels could increase students’ learning intake. Such implementation involves building an effective feedback system or rearranging the learning design. Several researchers have proposed deep-learning approaches using the DAiSEE dataset to classify student engagement levels. In addition, previous studies utilized various loss functions equipped with class weighting to assign higher importance to the minor classes, which are low and very low engagement classes. Most of the state-of-the-art models achieved high accuracy, but the f1-score was still low because of the minor class struggle. This research tries to solve engagement level classification on imbalance conditions by proposing a normalized loss function weighting based on the Inverse Class Frequency formula based on each class’ instances to give more importance and focus to the classes and trained on Vanilla EfficientNet model rather than experimenting on more advanced model to keep the efficient and suit the memory constraint on the e-learning implementation. Based on the conducted experiments, the normalized ICF obtained the highest accuracy of 51.64% and weighted f1-score of 50.86%, which is superior to the standard ICF performance, which received 50.32% accuracy and weighted f1-score of 50.49% using the same settings.
Performance Comparison of Monolithic and Microservices Architectures in Handling High-Volume Transactions Marieska, Mastura Diana; Arya Yunanta; Harisatul Aulia; Alvi Syahrini Utami; Muhammad Qurhanul Rizqie
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6183

Abstract

Monolithic and microservices are two distinct approaches for designing and developing applications. However, these architectures exhibit contrasting characteristics. In monolithic architecture, all components of an application form a unified entity with closely interconnected parts, whereas microservices decompose an application into independent, lightweight services that can be developed, deployed, and updated separately. Microservices are often regarded as superior to monolithic architectures in terms of their performance. This study aims to compare the performance of monolithic and microservices architectures in handling a high volume of transactions. It is important to observe how the two architectures behave when handling transactions from a large number of concurrent users. A prototype of an online ticketing system was implemented for both architectures to enable comparative analysis. The selected performance metrics were response time and error rate. The experimental results reveal that under high-load conditions, microservices outperform monolithic architectures, demonstrating 36% faster response times and 71% fewer errors. However, under overload conditions—when CPU usage exceeds 90%—the performance of microservices degrades significantly. This does not imply that microservices cannot handle a large number of concurrent users but highlights the necessity for enhanced resource management.
Obesity Status Prediction Through Artificial Intelligence and Balanced Label Distribution Using SMOTE Riyandi, Arif; Mahazam Afrad; M Yoka Fathoni; Yogo Dwi Prasetyo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6204

Abstract

Obesity, a global health challenge influenced by genetic and environmental factors, is characterized by excessive body fat that increases the risk of various diseases. With over two billion individuals affected worldwide, addressing this issue is crucial. This study investigated the application of Artificial Intelligence (AI) to predict obesity status using a dataset of 1,610 individuals, including demographic and anthropometric data. Four AI algorithms were analyzed: Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Random Forest, and Support Vector Machine (SVM). The Synthetic Minority Over-Sampling Technique (SMOTE) was applied to address dataset imbalance. The results demonstrate that SMOTE significantly enhanced the models' performance, especially in recall and F1-score for minority classes, such as obesity. Random Forest achieved the highest accuracy (92%) and recall (92%) post-SMOTE. The ANN showed substantial improvement in recall, increasing from 77% to 89%, whereas the SVM achieved the highest precision (89%), minimizing false positives. Despite these improvements, KNN remained the least effective. The findings underscore the critical role of SMOTE in improving AI model accuracy for obesity prediction and highlight Random Forest as the most reliable algorithm for clinical decision-making. Limitations, such as dataset representativeness, suggest future research directions, including expanding data diversity and advanced feature selection techniques. This study provides valuable insights into leveraging AI and preprocessing methods for obesity management.
Comparative Evaluation of Preprocessing Methods for MobileNetV1 and V2 in Waste Classification Aulia Afifah; Arumi, Endah Ratna; Maimunah, Maimunah; Setiya Nugroho
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6211

Abstract

Waste management remains a critical challenge for many countries, including Indonesia, which ranks as the world's second-largest contributor of waste. As tens of millions of tons are produced each year and the management system remains ineffective, environmental conditions and public health continue to deteriorate. To address this issue, it is imperative to develop more accurate and efficient solutions to enhance waste classification and management. This study investigates the influence of various image preprocessing techniques on the performance of MobileNetV1 and MobileNetV2 models in the classification of waste images. Preprocessing is crucial for enhancing data quality, particularly when dealing with real-world images that are affected by inconsistent lighting, texture, and clarity. Five preprocessing scenarios were evaluated: Baseline, CLAHE with Bilateral Filtering, CLAHE with Sharpening, Grayscale with CLAHE, and Gaussian Blur with Bilateral Filtering. Among these, the combination of CLAHE and Bilateral Filtering applied to MobileNetV1 achieved the best results, with 85% training accuracy, 96% validation accuracy, a training loss of 0.3178, and the lowest validation loss of 0.1630. Overall, MobileNetV1 benefited more significantly from preprocessing variations than MobileNetV2, particularly in terms of accuracy improvement and reduction in prediction error. These findings underscore the importance of effective preprocessing in enhancing model performance for waste image classification
Prediction of Financial Distress in Retail Companies Using Long-Short Term Memory (LSTM) Windasari, Wahyuni; Zakiyah, Tuti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6217

Abstract

Financial distress is a condition in which an entity struggles to meet its debt and operating obligations.. Financial distress can lead to bankruptcy or company closure if corrective action is not taken. This study aims to forecast financial distress in retail companies by utilizing key financial ratios, including Total Asset Turnover (TATO), Current Ratio (CR), Return on Assets (ROA), and Debt-to-Equity Ratio (DER). The analysis is based on secondary data from Indonesian retail companies listed on the Indonesia Stock Exchange (IDX) during the 2022–2024 period. The dataset exhibited missing values and class imbalance, which were addressed using mean imputation and the Synthetic Minority Oversampling Technique (SMOTE), respectivelyTo perform predictions, a Long Short-Term Memory (LSTM) model was implemented. The integration of SMOTE contributed to enhanced detection of the minority class; however, it was accompanied by a slight reduction in overall predictive accuracy. The model demonstrated a performance accuracy of 86%, with a recall rate of 85%, a precision of 100%, and an F1-score of 92%.

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