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Renewable energy conversion systems for global emission neutralization

10.11591/ijeecs.v39.i1.pp79-88
Suwarno Suwarno , Catra Indra Cahyadi , Pardamean Manurung , Abdul Rahim , Farhan Tanjung , Herman Birje , Fadly Syafni , Muhammad Ridho Kurnia , Ismail Faruqi
Fossil fuel power plants still play an essential role in providing energy worldwide, but their environmental impact will contribute significantly to emissions and environmental pollution. To reduce these emissions, renewable energy offers a solution to reduce global emissions. This study proposes a renewable energy modeling system using hybrid optimization of multiple energy resources (HOMER) simulation on renewable energy systems for economic savings. This simulation can combine photovoltaic (PV), wind power (WP), and converter systems. The hybrid combination of PV and WP is the most appropriate and economical choice at the research location. The results showed that the modeling of the renewable energy hybrid system made a significant contribution, with an initial investment cost of IDR 107,474.43 million and an annual operating cost of IDR 22,540.23 million, 41% lower on condition now with an estimated return on investment of 11 years. The results of this study can be used as recommendations for similar conditions in other places. Policymakers can use this model to provide incentives and have a positive impact on hybrid power plants (HPS) in neutralizing global emissions.
Volume: 39
Issue: 1
Page: 79-88
Publish at: 2025-07-01

A comparative analysis of hybrid of traditional load flow methods for IEEE distributed power generation networks

10.11591/ijeecs.v39.i1.pp33-44
Muhammad Hafeez Mohamed Hariri , Noor Dzulaikha Daud , Nor Azizah Mohd Yusoff , Syed Muhammad Zakwan Syed Zaman , Mohd Khairunaz Mat Desa
Analyzing power flow or load flow is crucial for planning, operating, maintaining, and controlling electrical power systems. Two traditional power flow methods namely the Newton-Raphson (NR) method are known for their accuracy and robustness nevertheless high computational intensity, and the fast decoupled load flow (FD) method, is valued for its computational efficiency and speed, however, generating less accurate data. This research aims to develop a hybrid load flow technique that integrates both strengths, achieving higher accuracy and faster convergence. The validation processes are based on several IEEE standard bus systems, including the 3-bus, 9-bus, 14-bus, and 30-bus systems. These systems, with different bus types and interconnections, represent real-world operations and help generate comprehensive data on iteration count, execution time, and the accuracy of the output data results. A new hybrid method generated from this research work compared to traditional load flow methods, provides a substantially well-balanced number of iteration counts, the fastest execution times, improved by 41.55%, and produces a similar accuracy of the data set. These improvements make the hybrid method highly advantageous in practical real-time applications and large-scale systems where both accuracy and speed are critical.
Volume: 39
Issue: 1
Page: 33-44
Publish at: 2025-07-01

Robust k-NN approach for classifying Aquilaria oil species by compounds

10.11591/ijeecs.v39.i1.pp178-189
Noor Aida Syakira Ahmad Sabri , Nur Athirah Syafiqah Noramli , Nik Fasha Edora Nik Kamaruzaman , Nurlaila Ismail , Zakiah Mohd Yusoff , Ali Abd Almisreb , Saiful Nizam Tajuddin , Mohd Nasir Taib
Accurate classification of Aquilaria oil species is essential for ensuring the quality and authenticity of agarwood oils, which are widely used in perfumes and traditional medicine. This study investigated the effectiveness of the k-nearest neighbours (k-NN) machine learning model for classifying Aquilaria oil species based on four significant chemical compounds: dihyro-βagarofuran, δ-guaiene, 10-epi-γ-eudesmol, and γ-eudesmol. The dataset comprised 480 samples of Aquilaria oil, which were analyzed using gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detector (GC-FID). The k-NN model, with an optimal k-value of 10 and using euclidean distance as the distance metric, achieved 100% accuracy, sensitivity, specificity, and precision in both training and testing datasets. These results demonstrate the robustness of k-NN in species identification, highlighting the discriminative power of the selected compounds. This study verifies that the integration of chemical profiling with machine learning offers a scalable solution for accurate species identification in the essential oil industry. Future work could explore hybrid models and data expansion techniques to further enhance the classification performance in more complex environmental conditions.
Volume: 39
Issue: 1
Page: 178-189
Publish at: 2025-07-01

Smart brake pad early warning system: enhancing vehicle safety through real-time monitoring

10.11591/csit.v6i2.p122-135
Afif Syam Fauzi , Giva Andriana Mutiara , Muhammad Rizqy Alfarisi , Tedi Gunawan , Muhammad Aulia Rifqi Zain
A contributing factor to traffic accidents is brake pad failure, which diminishes braking system performance and extends braking distance. This work develops a prototype utilizing internet of things (IoT) to measure brake pad thickness, hence enhancing driver awareness through real-time monitoring. The system establishes the thickness detection threshold at 75% (3-4 mm) and 50% (5–6 mm) as a cautionary parameter. The thickness parameter employs an American wire gauge (AWG) 18 cable to connect to the ESP32 microcontroller. The web-based IoT monitoring interface employs Laravel. This method enables drivers to get prompt notifications regarding the decrease in brake pad thickness, hence permitting urgent preventative maintenance to mitigate the risk of accidents. The system underwent testing through friction at a rotational speed of 600 to 6,000 rpm. The test findings indicated that the sensor precisely measured the brake pad thickness with a prototype response time of a second. This system is suitable for implementation on old model vehicles that do not have an early warning system. The installation of this technology is anticipated to enhance driver knowledge of the state of the brake pads, hence potentially diminishing the danger of brake system failure caused by unmonitored pad wear.
Volume: 6
Issue: 2
Page: 122-135
Publish at: 2025-07-01

Artificial intelligence-powered robotics across domains: challenges and future trajectories

10.11591/csit.v6i2.p176-199
Tole Sutikno , Hendril Satrian Purnama , Laksana Talenta Ahmad
The rise of artificial intelligence (AI) in robotic systems raises both challenges and opportunities. This technological change necessitates rethinking workforce skills, resulting in new qualifications and potentially outdated jobs. Advancements in AI-based robots have made operations more efficient and precise, but they also raise ethical issues such as job loss and responsibility for robot decisions. This study explores AI-powered robotics in both of their challenges and future trajectories. As AI in robotics continues to grow, it will be crucial to tackle these issues through strong rules and ethical standards to ensure safe and fair progress. Collaborative robots in manufacturing improve safety and increase productivity by working alongside human employees. Autonomous robots reduce human mistakes during checks, leading to better product quality and lower operational expenses. In healthcare, robotic helpers improve patient care and medical staff performance by managing routine tasks. Future research should focus on improving efficiency and accuracy, boosting productivity, and creating safe environments for humans and robots to work safely together. Strong rules and ethical guidelines will be vital for integrating AI-powered robotics into different areas, ensuring technology development aligns with societal values and needs.
Volume: 6
Issue: 2
Page: 176-199
Publish at: 2025-07-01

An ensemble learning approach for diabetes prediction using the stacking method

10.11591/csit.v6i2.p102-111
Elliot Kojo Attipoe , Alimatu Saadia Yussiff , Maame Gyamfua Asante-Mensah , Emmanuel Dortey Tetteh , Regina Esi Turkson
Diabetes is a severe illness characterized by high blood glucose levels. Machine learning algorithms, with their ability to detect and predict diabetes in its early stages, offer a promising avenue for research. This study sought to enhance the accuracy of predicting diabetes mellitus by employing the stacking method. The stacking method was chosen because it integrates predictions from various base models, resulting in a more precise final prediction. The stacking method enhances accuracy and generalization by utilizing the varied strengths of multiple base models. The Pima Indians diabetes dataset, a widely used benchmark dataset, was utilized in the study. The machine learning models used for the studies were logistic regression (LR), naïve Bayes (NB), extreme gradient boost (XGBoost), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM). LR, KNN, and SVM were the best-performing models based on accuracy, F1-score, precision, and area under the curve (AUC) score, and were consequently used as the base model for the stacking method. The LR model was utilized for the meta-model. The proposed ensemble approach using the stacking method demonstrated a high accuracy of 82.4%, better than the individual models and other ensemble techniques such as bagging or boosting. This study advances diabetes prediction by developing a more accurate early-stage detection model, thereby improving clinical management of the disease.
Volume: 6
Issue: 2
Page: 102-111
Publish at: 2025-07-01

Optimizing EfficientNet for imbalanced medical image classification using grey wolf optimization

10.11591/csit.v6i2.p112-121
Khusnul Khotimah , Sugiyarto Surono , Aris Thobirin
The advancement of deep learning in computer vision has result in substantial progress, particularly in image classification tasks. However, challenges arise when the model is applied to small and unbalanced datasets, such as X-ray data in medical applications. This study aims to improve the classification performance of fracture X-ray images using the EfficientNet architecture optimized with grey wolf optimization (GWO). EfficientNet was chosen for its efficiency in handling small datasets, while GWO was applied to optimize hyperparameters, including learning rate, weight decay, and dropout to improve model accuracy. Random cropping, rotation, flipping, color jittering, and random erasing, were used to expand the diversity of the dataset, and class weighting is applied to overcome class imbalance. The evaluation uses accuracy, precision, recall, and F1-score metrics. The combination of EfficientNetB0 and GWO resulted in an average 4.5% improvement in model performance over baseline methods. This approach provides benefits in developing deep learning methods for medical image classification, especially in dealing with small and imbalanced datasets.
Volume: 6
Issue: 2
Page: 112-121
Publish at: 2025-07-01

Bibliometric analysis and short survey in CT scan image segmentation: identifying ischemic stroke lesion areas

10.11591/csit.v6i2.p91-101
Wahabou K. Taba Chabi , Sèmèvo Arnaud R. M. Ahouandjinou , Manhougbé Probus A. F. Kiki , Adoté François-Xavier Ametepe
Ischemic stroke remains one of the leading causes of mortality and long-term disability worldwide. Accurate segmentation of brain lesions plays a crucial role in ensuring reliable diagnosis and effective treatment planning, both of which are essential for improving clinical outcomes. This paper presents a bibliometric analysis and a concise review of medical image segmentation techniques applied to ischemic stroke lesions, with a focus on tomographic imaging data. A total of 2,014 publications from the Scopus database (2013–2023) were analyzed. Sixty key studies were selected for in-depth examination: 59.9% were journal articles, 29.9% were conference proceedings, and 4.7% were conference reviews. The year 2023 marked the highest volume of publications, representing 17% of the total. The most active countries in this area of research are China, the United States, and India. "Image segmentation" emerged as the most frequently used keyword. The top-performing studies predominantly used pre-trained deep learning models such as U-Net, ResNet, and various convolutional neural networks (CNNs), achieving high accuracy. Overall, the findings show that image segmentation has been widely adopted in stroke research for early detection of clinical signs and post-stroke evaluation, delivering promising outcomes. This study provides an up-to-date synthesis of impactful research, highlighting global trends and recent advancements in ischemic stroke medical image segmentation.
Volume: 6
Issue: 2
Page: 91-101
Publish at: 2025-07-01

Effects of hyperparameter tuning on random forest regressor in the beef quality prediction model

10.11591/csit.v6i2.p159-166
Ridwan Raafi'udin , Yohanes Aris Purwanto , Imas Sukaesih Sitanggang , Dewi Apri Astuti
Prediction models for beef meat quality are necessary because production and consumption were significant and increasing yearly. This study aims to create a prediction model for beef freshness quality using the random forest regressor (RFR) algorithm and to improve the accuracy of the predictions using hyperparameter tuning. The use of near-infrared spectroscopy (NIRS) in predicting beef quality is an easy, cheap, and fast technique. This study used six meat quality parameters as prediction target variables for the test. The R² metric was used to evaluate the prediction results and compare the performance of the RFR with default parameters versus the RFR with hyperparameter tuning (RandomSearchCV). Using default parameters, the R-squared (R²) values for color (L*), drip loss (%), pH, storage time (hour), total plate colony (TPC in cfu/g), and water moisture (%) were 0.789, 0.839, 0.734, 0.909, 0.845, and 0.544, respectively. After applying hyperparameter tuning, these R² scores increased to 0.885, 0.931, 0.843, 0.957, 0.903, and 0.739, indicating an overall improvement in the model’s performance. The average performance increase for prediction results for all beef quality parameters is 0.0997 or 14% higher than the default parameters.
Volume: 6
Issue: 2
Page: 159-166
Publish at: 2025-07-01

HepatoScan: Ensemble classification learning models for liver cancer disease detection

10.11591/csit.v6i2.p167-175
Tella Sumallika , Raavi Satya Prasad
Liver cancer is a dangerous disease that poses significant risks to human health. The complexity of early detection of liver cancer increases due to the unpredictable growth of cancer cells. This paper introduces HepatoScan, an ensemble classification to detect and diagnose liver cancer tumors from liver cancer datasets. The proposed HepatoScan is the integrated approach that classifies the three types of liver cancers: hepatocellular carcinoma, cholangiocarcinoma, and angiosarcoma. In the initial stage, liver cancer starts in the liver, while the second stage spreads from the liver to other parts of the body. Deep learning is an emerging domain that develops advanced learning models to detect and diagnose liver cancers in the early stages. We train the pre-trained model InceptionV3 on liver cancer datasets to identify advanced patterns associated with cancer tumors or cells. For accurate segmentation and classification of liver lesions in computed tomography (CT) scans, the ensemble multi-class classification (EMCC) combines U-Net and mask region-based convolutional network (R-CNN). In this context, researchers use the CT scan images from Kaggle to analyze the liver cancer tumors for experimental analysis. Finally, quantitative results show that the proposed approach obtained an improved disease detection rate with mean squared error (MSE)-11.34 and peak signal-to-noise ratio (PSNR)-10.34, which is high compared with existing models such as fuzzy C-means (FCM) and kernel fuzzy C-means (KFCM). The classification results obtained based on detection rate with accuracy-0.97%, specificity-0.99%, recall-0.99%, and F1S-0.97% are very high compared with other existing models.
Volume: 6
Issue: 2
Page: 167-175
Publish at: 2025-07-01

Effective methods for employee performance assessment

10.11591/ijeecs.v39.i1.pp509-522
Agatha Beny Himawan , Rinta Kridalukmana , Toni Prahasto
This study aims to select the most effective multi-criteria decision-making method used in an employee performance appraisal system. The approach used in this study is a comparative experiment where three multi-criteria decision-making methods simple additive weighting (SAW), analytical hierarchy process (AHP), and technique for order preference similarity to an ideal solution (TOPSIS) are compared. The dataset involves 16 employees, considering input data such as work behavior scores, and performance targets (SKP). The criteria for evaluating work behavior include service quality, accountability, competence, harmony, loyalty, adaptability, collaboration, and achievement of targets. The comparison results were tested using a one-way ANOVA to evaluate whether there are significant differences among the three methods, as well as to provide supporting evidence for the conducted research. The results indicated that the SAW method provides the most accurate and relevant performance assessments while AHP yields less precise rankings as some employees received the same scores despite having different workloads. TOPSIS also produced rankings that did not accurately reflect the relative workloads. Implementing the SAW method in the employee performance information system enhances the assessment process, making it faster, more objective, transparent, and credible. Thus, SAW emerges as the most effective method for aligning performance scores with employee roles and responsibilities.
Volume: 39
Issue: 1
Page: 509-522
Publish at: 2025-07-01

Design and development of an automated spirulina (Arthrospira platensis) algae cultivator

10.11591/ijeecs.v39.i1.pp139-147
Miguel Q. Mariñas II , Mark Joseph B. Enojas , Daryll C. Balolong , Charissa Zandra B. Correa , Lemmuel Keith C. Roldan , Mark Lester Teves , Christian Mari Dela Cruz
The cultivation of algae has gotten more attention from algae enthusiasts who have seen the benefits of algae in many uses. To maximize productivity, the parameters for growth of this algae must be controlled, such as pH, turbidity, light intensity, and the mixture solution for optimal growth. In this paper, an automated spirulina algae cultivator is designed and developed in a small-scale pond to replace the existing manual process. The system developed is composed of compact and modular cultivation unit, ph sensor, water level sensor, turbidity sensor, light intensity sensor, and motor actuators for mixing solutions. Each parameter was controlled individually in an on-off control system. A simple nutrient addition program (SNAP) solution is also used for better growth productivity by maximizing its nutrient contents. The pH is maintained at 9 to 12 for a healthy biomass output. Daily weight measurement was conducted using an analytical balance to monitor its growth. Using the developed prototype recorded a 33% higher rate of productivity over the manual process. This setup can potentially be used as a model for mass production of spirulina algae.
Volume: 39
Issue: 1
Page: 139-147
Publish at: 2025-07-01

Blockchain technology for optimizing security and privacy in distributed systems

10.11591/csit.v6i2.p210-220
Wisnu Uriawan , Adrian Putra Pratama , Shafwan Mursyid
Blockchain technology is increasingly recognized as an effective solution for addressing security and privacy challenges in distributed systems. Blockchain ensures information security by validating data and defending against cyber threats, while guaranteeing data integrity through transaction validation and reliable storage. The research involves a literature study, problem identification, analysis of blockchain security and privacy, model development, testing, and analysis of trial results. Furthermore, blockchain enables user anonymity and fosters transparency by utilizing a distributed network, reducing the risk of fraudulent activities. Its decentralized nature ensures high reliability and accessibility, even in node failures. Blockchain enhances security and privacy by offering features like data immutability, provenance, and reduced reliance on trust. It decentralizes data storage, making tampering or deletion extremely challenging, and ensures the invalidation of subsequent blocks upon any changes. Blockchain finds applications in various domains, including supply chains, finance, healthcare, and government, enabling enhanced security by tracking data origin and ownership. Despite scalability and security challenges, the potential benefits of reduced costs, increased efficiency, and improved transparency position blockchain as a promising technology for the future. In summary, blockchain technology provides secure transaction recording and data storage, thus enhancing security, privacy, and the integrity of sensitive information in distributed systems.
Volume: 6
Issue: 2
Page: 210-220
Publish at: 2025-07-01

Hybrid energy storage solutions through battery-supercapacitor integration in photovoltaic installations

10.11591/ijeecs.v39.i1.pp11-22
Abdelkader Yousfi , Fayçal Mehedi , Youcef Bot
Batteries integrated into renewable energy storage systems may experience multiple irregular charge and discharge cycles due to the variability of photovoltaic energy production characteristics or load fluctuations. This could negatively impact the battery’s longevity and lead to an increase in project costs. This article presents an approach for the sharing of embedded energy between the battery, which serves as the main energy storage system, and the supercapacitors (SC), which act as an auxiliary energy storage system. By delivering or absorbing peak currents according to the load requirements, supercapacitors increase the lifespan of batteries and reduce their stresses. An maximum power point tracking (MPPT) algorithm regulates the connection of the photovoltaic (PV) cells to the DC bus through a boost converter. A buck-boost converter connects supercapacitors and batteries to the DC bus. A DC-AC converter connects the inductive load to the DC bus. The system regulates static converters connected to batteries and supercapacitors based on current. An energy management block supervises the system components. We implement the entire system in the MATLAB/Simulink environment. We present the simulation results to demonstrate the effectiveness of the proposed control strategy for the entire system.
Volume: 39
Issue: 1
Page: 11-22
Publish at: 2025-07-01

Torque ripple minimization and performance enhancement of switched reluctance motor for electric vehicle application

10.11591/ijeecs.v39.i1.pp70-78
Yogesh B. Mandake , Deepak S. Bankar , Amit L. Nehete
Switched reluctance motors (SRMs) are an attractive choice for electric vehicle (EV) applications but suffer from certain limitations, such as high torque ripple and acoustic noise. This paper presents ongoing research and development activity details to enhance the performance of SRMs for EV applications. The poor performance of a conventional SRM which is available in market with a rating of 8/6 poles, 48 V, 500 W, and 2,000 rpm is tested. A motor model of the same rating is developed using ANSYS Maxwell software. Motor performance parameters important for EV applications, such as efficiency, rated torque and torque ripple are compared with the conventional motor. One novel technique to reduce the torque ripple of SRM is discussed along with the results. Torque ripple of developed software model is reduced by 24.52% without a reduction in the efficiency and rated torque of the motor. The performance of the developed SRM software model is better compared to conventional SRMs available in the market. 2D and 3D models of SRM were presented using ANSYS Maxwell software.
Volume: 39
Issue: 1
Page: 70-78
Publish at: 2025-07-01
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