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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 28 Documents
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Search results for , issue "Vol 9 No 3 (2025): June 2025" : 28 Documents clear
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%.
Development of IoT-based Automatic Water Drainage System on Fishing Boat to Improve Operational Efficiency Zulfachmi, Zulfachmi; Zulkipli, Zulkipli; Rahayu, Vita; Saputra, Aggry; As Saidah, Muthiah
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.v9i3.6222

Abstract

The profession of fishermen requires a reliable system to remove stagnant water from fishing boats, as manual drainage is time-consuming and inefficient. This study proposes an IoT-based automatic water drainage system without using an inverter or ultrasonic sensor, offering a cost-effective alternative. The system utilizes a water level sensor and a DC water pump, controlled via a smartphone application. The research model used is the Research and Development (R&D) model, through several stages, namely potential and problems, initial data needs, prototype creation, prototype validation, prototype revision, validation, implementation. Problems occur at the prototype stage, problems that must be revised include aspects of wiring, Power Suitability, Water Level Sensor Test, and the configuration of the relay used. The IOT-based automatic water drainage system can function based on the results of white-box testing including Hardware Implementation, Software Implementation, Implementation of Application Usage, and Automatic Drainage System Testing. This is indicated by the results of the Liquid Water Level Sensor Functionality test, DC Water Pump Functionality Test, Solar Panel and Battery Functionality Test, and IOT Functionality Test. IOT-based automatic water discharge systems on fishing boats are more efficient and cost-effective in the long run, although diesel engines offer more reliability under adverse weather conditions or in places with limited access to sunlight.
UDAWA Gadadar: Agent-based Cyber-physical System for Universal Small-scale Horticulture Greenhouse Management System Suranata, I Wayan Aditya; Ketut Elly Sutrisni; I Made Surya Adi Putra
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.v9i3.6267

Abstract

Digitalization in agriculture is becoming increasingly important for improving efficiency and sustainability, but small-scale farmers often face difficulties in adopting digital technologies because of various constraints. This study proposes an open-source intelligent system platform called UDAWA (Universal Digital Agriculture Workflow Assistant) to assist small-scale farmers in digitizing greenhouse management processes. The first variant of this platform, UDAWA Gadadar, was designed as a cyber-physical agent to control and monitor greenhouse instruments. UDAWA Gadadar was built using a 5C architecture approach and farmer-centric design thinking, utilizing an ESP32 microcontroller and a power sensor module to ensure performance and energy efficiency. The UDAWA Gadadar prototype was tested in a small-scale greenhouse with promising results, with an average remaining memory of 175 KB in the non-SSL mode and 122 KB in the SSL mode. Cost analysis indicates that this platform is relatively affordable for small-scale farmers, with a total component cost of USD 33.7 per unit. A decision matrix analysis involving five different greenhouse models in Pancasari Village, Buleleng Regency, Bali, showed that UDAWA Gadadar has high relevance and potential for adoption, particularly in models GH3 and GH5, with compatibility scores of 0.27. This study contributes to the development of appropriate and accessible digitalization solutions for small-scale agriculture, with future work focusing on developing other physical agent variants and a digital twin for enhanced cultivation simulations.
Health Risk Classification Using XGBoost with Bayesian Hyperparameter Optimization Anam, Syaiful; Purwanto, Imam Nurhadi; Mahanani, Dwi Mifta; Yusuf, Feby Indriana; Rasikhun, Hady
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.v9i3.6307

Abstract

Health risk classification is important. However, health risk classification is challenging to address using conventional analytical techniques. The XGBoost algorithm offers many advantages over the traditional methods for risk classification. Hyperparameter Optimization (HO) of XGBoost is critical for maximizing the performance of the XGBoost algorithm. The manual selection of hyperparameters requires a large amount of time and computational resources. Automatic HO is needed to avoid this problem. Several studies have shown that Bayesian Optimization (BO) works better than Grid Search (GS) or Random Search (RS). Based on these problems, this study proposes health risk classification using XGBoost with Bayesian Hyperparameters Optimization. The goal of this study is to reduce the time required to select the best XGBoost hyperparameters and improve the accuracy and generalization of XGBoost performance in health risk classification. The variables used were patient demographics and medical information, including age, blood pressure, cholesterol, and lifestyle variables. The experimental results show that the proposed approach outperforms other well-known ML techniques and the XGBoost method without HO. The average accuracy, precision, recall and f1-score produced by the proposed method are 0.926, 0.920, 0.928, and 0.923, respectively. However, improvements are needed to obtain a faster and more accurate method in the future.
Automated Indonesian Plate Recognition: YOLOv8 Detection and TensorFlow-CNN Character Classification Windu Gata; Dwiza Riana; Muhammad Haris; Maria Irmina Prasetiyowati; Dika Putri Metalica
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.v9i3.6310

Abstract

The precise identification and reading of Indonesian vehicle number plates are important in many areas, including the enforcement of law, collection of charges, management of parking areas, and safety measures. This study integrates the implementation of the YOLOv8 object detection algorithm with three OCR methods: EasyOCR, TesseractOCR, and TensorFlow. YOLOv8 is capable of identifying license plates from images and videos at a high speed and reliability under different conditions and therefore is used in this study to perform plate detection in images and videos. After licenses are detected, OCR techniques are performed to segment and read the letters. Both EasyOCR and TesseractOCR performed moderately well on static images achieving accuracy rates of 70% and 68% respectively, but both suffered significantly lower performance in video scenarios. Of the 100 video frames, EasyOCR was able to correctly identify characters in 61 frames and TesseractOCR in 58 frames, while the TensorFlow-based model outperformed the other two with 75 correct recognitions. Furthermore, easy OCR and static images as input while the TensorFlow-based models completed them with 100% accuracy. This observation can be explained by its design, which utilizes a CNN with ReLU activation and Softmax outputs, trained on 10,261 annotated characters and was enhanced with five different data augmentation techniques. The model shows strong performance in its ability to handle dynamic conditions such as motion blur, changing light conditions, and rotation of the plate angle. The results underscore the drawbacks of one-size-fits-all OCR applications in real-world use cases and stress the need for bespoke model training, as well as hierarchical contouring, in the context of automatic license plate recognition (ALPR). This study provides additional insights into ALPR systems by delivering a robust, scalable, and real-time tool for plate and character recognition, which is essential for intelligent transportation systems.

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