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28,188 Article Results

Cloud computing needs to explore into sky computing

10.11591/csit.v6i3.p294-306
Arif Ullah , Hassnae Remmach , Hanane Aznaoui , Canan Batur Şahin , Amine Mrhari
This paper evaluates key issues in cloud computing and introduces a novel model, known as sky computing, to address these challenges. Cloud computing, a transformative technology, has played a critical role in reshaping modern operations—especially following the COVID-19 pandemic, when many human activities shifted to technology-driven platforms. It offers multiple service models, including Software as a Service, Hardware as a Service, Desktop as a Service, Backup as a Service, and Network as a Service, each tailored to user requirements. However, the rapid expansion of cloud-based technologies and interconnected systems has intensified infrastructure and scalability challenges. Sky computing, or the “cloud of clouds,” emerges as an advanced layer above traditional cloud models, enabling dynamically provisioned, distributed domains built over multiple serial clouds. Its core capability lies in offering variable computing capacity and storage resources with dynamic, real-time support, providing a robust and unified platform by integrating diverse cloud resources. This paper reviews related technologies, summarizes prior research on sky computing, and discusses its structural design. Furthermore, it examines the limitations of current cloud computing frameworks and highlights how sky computing could overcome these barriers, positioning it as a pivotal architecture for the future of distributed computing.
Volume: 6
Issue: 3
Page: 294-306
Publish at: 2025-11-01

Characteristics ransomware stop/djvu remk and erqw variants with static-dinamic analysis

10.11591/csit.v6i3.p283-293
Dodon Turianto Nugrahadi , Friska Abadi , Rudy Herteno , Muliadi Muliadi , Muhammad Alkaff , Muhammad Alvin Alfando
Ransomware has developed into various new variants every year. One type of ransomware is STOP/DJVU, containing more than 240+ variants. This research to determine changes in differences characteristics and impact between ransomware variants STOP/DJVU remk, which is a variant from 2020, and the erqw variant from 2023, through a mixed-method research approach. Observation, simulation using mixing static and dynamic malware analysis methods. Both variants are from the Malware Bazaar site. The total characteristics based on dynamic analysis, the remk variant has 177, and the erqw variant has 190, which increased by 1.8%. The total characteristics based on static analysis, the remk variants have 586, and the erqw variants have 736, which increased by 5.7%. All characteristics from remk to erqw increasing in dynamic analysis, except the number of payloads that decreased about 20%. In static analysis, all characteristics from remk to erqw increase except the number of sections decreased about 1.5%. It can be the affected CPU performance, because the remk variant affects performance by increasing CPU work by 3.74%, while the erqw variant affects performance by reducing CPU work by 1.18%, both compared with normal CPU. which will affect the ransomware's destructive work and require changes in its handling.
Volume: 6
Issue: 3
Page: 283-293
Publish at: 2025-11-01

Implementation of IoT-based water quality monitoring instruments in cantang grouper cultivation ponds

10.11591/csit.v6i3.p235-244
Hollanda Arief Kusuma , M Hasbi sidqi Alajuri , Anggarudin Anggarudin , Dwi Eny Djoko Setyono , Henky Irawan
Grouper fish farming in Indonesia has great potential, but water quality management remains a challenge. Manual monitoring at hatchery D-Marine aquaculture struggles to detect sudden changes, risking mass mortality. This study developed an IoT-based water quality monitoring system using an ESP32 microcontroller, DS18B20 temperature sensors, pH sensors, dissolved oxygen (DO) sensors, a micro-SD card, an organic light emitting diode (OLED) display, and the Ubidots platform. The methodology involved device design, sensor calibration, and field testing. Calibration showed sensor accuracy above 90%. Field tests recorded water temperatures of 26.84 °C (tank 1) and 27.74 °C (tank 2), with pH values of 6.73 and 6.87, which did not meet Indonesian national standard (SNI) standards. Data transmission to Ubidots had a 95% packet delivery ratio (PDR) for device 1 and 97% for device 2. The system successfully provided real-time water quality data, supporting effective farm management. However, improvements to the dissolved oxygen sensor and an automatic control system are needed for better stability and efficiency.
Volume: 6
Issue: 3
Page: 235-244
Publish at: 2025-11-01

Decision making with analytical hierarchy process algorithm and prototype model for exemplary teachers

10.11591/csit.v6i3.p225-234
Sumardiono Sumardiono , Norhafizah Ismail , Wiwit Priyadi , Agus Riyanto , Indra Martha Rusmana
The selection process for exemplary teachers in vocational schools in Bekasi City has so far been carried out subjectively without a structured system, relying on internal meetings and daily notes, thus causing problems of transparency, accuracy, and efficiency. To overcome this, this study developed an online decision support system (DSS) that makes use of the analytical hierarchy process (AHP) algorithm to create an objective and measurable selection method based on five criteria: discipline, travel costs, personality, teaching administration, and learning achievement. Quantitative methods were applied by collecting data through questionnaires and observations, while the system prototype was designed through the stages of problem analysis, design, implementation, and evaluation. The AHP algorithm was used to process the decision matrix, benefit-cost-based normalization, weighting, and pairwise comparisons, with a consistency test (CR =0.044) ensuring the reliability of the results. This system successfully identified Didi Saputra, S.Pdi., as the best exemplary teacher with the highest preference value (0.92), while providing a significant impact in the form of increased accuracy (reducing subjective bias), transparency (clear ranking reports), and efficiency (faster selection process). The research findings demonstrate the effectiveness of AHP as a structured solution for exemplary teacher selection, with potential for adoption by other educational institutions and sustainability through a web-based system.
Volume: 6
Issue: 3
Page: 225-234
Publish at: 2025-11-01

A machine learning approach for early prediction of mental health crises

10.11591/csit.v6i3.p335-345
Hassan Chigagure , Lucy Charity Sakala
The global mental health crisis, intensified by the COVID-19 pandemic, placed unprecedented strain on healthcare systems and highlighted the urgent need for proactive crisis prevention strategies. This study investigated the effectiveness of various machine learning (ML) models in predicting mental health crises within 28 days post-hospitalization, leveraging an eight-year longitudinal dataset. Multiple data preprocessing techniques, including feature selection (EFSA, RFECV), imputation, and class imbalance handling (SMOTE, Tomek links), were systematically applied to enhance model performance. Six traditional classifiers—logistic regression, support vector machine, k-nearest neighbors, naive Bayes, XGBoost, and AdaBoost—were evaluated alongside ensemble learning (EL) methods (bagging, boosting, stacking). Performance metrics such as accuracy, precision, recall, F1 score, and AUC-ROC were used for comprehensive assessment. Results demonstrated that ensemble methods, particularly boosting and bagging, consistently achieved high predictive accuracy (up to 93%), with XGBoost and AdaBoost emerging as top performers. Feature selection and class imbalance techniques further improved model robustness and generalizability. The findings underscored the potential of ML-driven approaches for early identification of at-risk patients, enabling more effective resource allocation and timely interventions in mental health care. Recommendations for integrating these predictive tools into clinical workflows were discussed to support data-driven decision-making.
Volume: 6
Issue: 3
Page: 335-345
Publish at: 2025-11-01

Optimizing diplomatic indexing: full-parameter vs low-rank adaptation for multi-label classification of diplomatic cables

10.11591/csit.v6i3.p274-282
Dela Nurlaila , Abba Suganda Girsang
Accurate classification of diplomatic cables is crucial for Mission’s evaluation and policy formulation. However, these documents often cover multiple topics, hence a multi-label classification approach is necessary. This research explores the application of pre-trained language models (CahyaBERT, IndoBERT, and MBERT) for multi-label classification of diplomatic cable executive summaries, which align with the diplomatic representation index. The study compares full-parameter fine-tuning and low-rank adaptation (LoRA) techniques using cables from 2022-2023. Results demonstrate that Indonesian-specific models, particularly the IndoBERT, outperform multilingual models in classification accuracy. While LoRA showed slightly lower performance than full fine-tuning, it significantly reduced GPU memory usage by 48% and training time by 69.7%. These findings highlight LoRA’s potential for resource-constrained diplomatic institutions, advancing natural language processing in diplomacy and offering pathways for efficient, real-time multi-label classification to enhance diplomatic mission evaluation.
Volume: 6
Issue: 3
Page: 274-282
Publish at: 2025-11-01

Optimizing energy distribution efficiency in wireless sensor networks using the hybrid LEACH-DECAR algorithm

10.11591/csit.v6i3.p262-273
Muhammad Abyan Nizar Muntashir , Vera Noviana Sulistyawan , Noor Hudallah
Wireless sensor network (WSN) is a network system consisting of various supporting components that integrate information to the base station. In its operation, delivery is greatly influenced by energy usage because limited battery supply causes variability in energy consumption on node activity factors, communication distance, and environmental conditions. So, in order to increase performance and energy efficiency, a routing protocol is required by selecting the best path through cluster head. The technique of determining the cluster head (CH) based on energy is used to avoid irregularity (randomness). In this study, the hybrid routing protocol selects CH based on the remaining energy, considering distance, coverage radius, and energy metrics. The system test evaluation compares the implementation of low-energy adaptive clustering hierarchy (LEACH) and hybrid LEACH- Distributed, energy and coverage-aware routing (DECAR). The results of 300 rounds show that the hybrid achieves a packet delivery ratio close to 100% and a throughput of 78.22 Kbps, while LEACH achieves a packet delivery ratio of 92.18% and a throughput of 247.15 Kbps. The average energy consumption of LEACH is 99.27%, while the hybrid shows much greater efficiency at 30.55%. This study emphasizes the significance of maintaining equilibrium performance and energy consumption in the development of future routing protocols.
Volume: 6
Issue: 3
Page: 262-273
Publish at: 2025-11-01

Javanese and Sundanese speech recognition using Whisper

10.11591/csit.v6i3.p253-261
Alim Raharjo , Amalia Zahra
Automatic speech recognition (ASR) technology is essential for advancing human-computer interaction, particularly in a linguistically diverse country like Indonesia, where approximately 700 native languages are spoken, including widely used languages like Javanese and Sundanese. This study leverages the pre-trained Whisper Small model an end‑to‑end transformer pretrained on 680,000 hours of multilingual speech, fine tuning it specifically to improve ASR performance for these low resource languages. The primary goal is to increase transcription accuracy and reliability for Javanese and Sundanese, which have historically had limited ASR resources. Approximately 100 hours of speech from OpenSLR were selected, covering both reading and conversational prompts, the data exhibited dialectal variation, ambient noise, and incomplete demographic metadata, necessitating normalization and fixed‑length padding. with model evaluation based on the word error rate (WER) metric. Unlike approaches that combine separate acoustic encoders with external language models, Whisper unified architecture streamlines adaptation for low‑resource settings. Evaluated on held‑out test sets, the fine‑tuned models achieved Word Error Rates of 14.97% for Javanese and 2.03% for Sundanese, substantially outperforming baseline systems. These results demonstrate Whisper effectiveness in low‑resource ASR and highlight its potential to enhance transcription accuracy, support language preservation, and broaden digital access for underrepresented speech communities. 
Volume: 6
Issue: 3
Page: 253-261
Publish at: 2025-11-01

Mediterranean and northern european archaeology: a computational comparison

10.11591/csit.v6i3.p326-334
Hamza Kchan , Saira Noor
Despite the proliferation of computational tools in archaeology, few studies systematically compare their regional adaptations or explore the epistemological assumptions guiding their application. This paper addresses four critical research gaps: (i) the lack of comparative regional analysis between the Mediterranean and Northern Europe in computational archaeology, (ii) the insufficient integration of philosophical and epistemological frameworks in predictive modeling, (iii) the underexplored application of artificial intelligence (AI) and network theory in spatial analysis, and (iv) the limited interdisciplinary synthesis of biological, geospatial, and digital data. By examining representative case studies from both regions, we highlight the methodological innovations, theoretical orientations, and institutional dynamics that shape regional practices. The study underscores the necessity of integrating computational methods with interpretive depth and interdisciplinary collaboration to foster a more reflective and inclusive digital archaeology. 
Volume: 6
Issue: 3
Page: 326-334
Publish at: 2025-11-01

Hybrid feature fusion from multiple CNN models with bayesian-optimized machine learning classifiers

10.11591/csit.v6i3.p315-325
Dewi Rismawati , Sugiyarto Surono , Aris Thobirin
Information technology advancements have created big data, necessitating efficient techniques to retrieve helpful information. With its capacity to recognize and categorize patterns in data, especially the growing amount of picture data, deep learning is becoming a viable option. This research aims to develop a medical image classification model using chest X-Ray with four classes, namely Covid-19, Pneumonia, Tuberculosis, and Normal. The proposed method combines the advantages of deep learning and machine learning. Three pre-trained CNN models, VGG16, DenseNet201, and InceptionV3, extract features from images. The features generated from each model are fused to enhance the relevant information. Furthermore, principal component analysis (PCA) was applied to reduce the dimensionality of the features, and Bayesian optimization was used to optimize the hyperparameters of the machine learning algorithms support vector machine (SVM), decision tree (DT), and k-nearest neighbors (k-NN). The resulting classification model was evaluated based on accuracy, precision, recall, and F1-score. The results showed that FF-SVM, which is the proposed model, achieved an accuracy of 98.79% with precision, recall, and F1-score of 98.85%, 98.82%, and 98.84%, respectively. In conclusion, fusing feature extraction from multiple CNN models improved the classification accuracy of each machine-learning model. It provided reliable and accurate predictions for lung image diagnosis using chest X-Ray.
Volume: 6
Issue: 3
Page: 315-325
Publish at: 2025-11-01

Prospective applications of assistive robotics for the benefit of population groups

10.11591/ijece.v15i5.pp4531-4541
Anny Astrid Espitia-Cubillos , Robinson Jimenez-Moreno , Javier Eduardo Martínez-Baquero
The development of robotics has reached various fields of application such as the assistance field, where robots support people with different abilities in different activities to provide independence, comfort and interaction, even improving their self-esteem and quality of life. The objective is to identify the main benefits of the application of assistive robotics achieved to project its future fields of action. For this purpose, the Scopus database is used to find documents related to assistive robotics, which are filtered by publication date and according to the elimination criteria determined by the authors, and then bibliometric networks are constructed using VOSviewer. Finally, the main findings are analyzed and presented according to their area of application. Five areas of application of assistive robotics are identified that benefit children, the elderly, provide hospital assistance, help people with disabilities or support therapy and rehabilitation work, developments that allow the formulation of areas for future study. It is concluded that there are many advances in assistive robotics that demonstrate robotic development and provide assistance to a particular population, but more work is still needed to increase the number of beneficiaries, reduce costs and expand research in the areas mentioned and to be developed.
Volume: 15
Issue: 5
Page: 4531-4541
Publish at: 2025-10-01

Dynamic head pose estimation in varied conditions using Dlib and MediaPipe

10.11591/ijece.v15i5.pp4581-4592
Rusnani Yahya , Rozita Jailani , Nur Khalidah Zakaria , Fazah Akhtar Hanapiah
This paper presents the formulation and validation of a dynamic head pose estimation (HPE) algorithm, addressing challenges related to diverse conditions, complex poses, and partial obstructions. The study aims to create a robust algorithm that maintains high accuracy in real-time applications across varying conditions. The algorithm was implemented and assessed using Dlib and MediaPipe models. The study involved 30 participants in face and head without obstacles, face with obstacles and head with obstacles conditions. The results demonstrated impressive performance in both controlled and spontaneous head movement categories. The algorithm achieved an average accuracy of 93% for head pose estimation and 88% in detecting visual attention under spontaneous head movement categories. A correlation coefficient of 0.866 indicates a strong positive linear association between performance and attention accuracy, indicating that performance improvements are intricately linked to proportional increases in attention accuracy. However, this does not necessarily imply causation. The findings provide valuable insights into the effectiveness of the proposed algorithms in assessing visual attention and demonstrate their potential applications in healthcare monitoring, educational intervention, and driver monitoring systems. The significance of these results lies in the ability to advance human-computer interaction, enhance healthcare diagnostics, and offer innovative solutions across various domains.
Volume: 15
Issue: 5
Page: 4581-4592
Publish at: 2025-10-01

Field-programmable gate array-based voltage-feedback-driven battery charging with DC-DC buck converter

10.11591/ijece.v15i5.pp4993-5002
Afarulrazi Abu Bakar , Suhaimi Saiman , Tharnisha Sithananthan , Muhammad Nafis Ismail , Saidina Hamzah Che Harun
This paper presents the design and development of a reference-driven field-programmable gate array (FPGA)-based controllable battery charging system featuring a buck converter. The controller tracks and adjusts the system's duty cycle based on output voltage feedback. The primary goal was to introduce a digital pulse-width modulation generator program using a Hardware Description Language within a feedback loop. To enhance the buck converter's accuracy, the system's switching frequency was set to 20 kHz with an 8-bit counter, achieving a resolution of 0.390625% per clock cycle. An 8-bit parallel analog-to-digital converter provided feedback by measuring the output voltage and comparing it with the reference setpoint. The simulation model was developed using MATLAB/Simulink, while the Quartus II software was employed for controller programming. The resultant data was meticulously analyzed to assess the circuit's performance across various voltage and control parameters. To validate the proposed controller's effectiveness, a 400 W system prototype comprising a step-down transformer, rectifier, and buck converter was constructed and tested for voltage ranging from 24 to 72 V. Through FPGA-based digital control, this system demonstrated a voltage regulation accuracy of ±0.39 per clock cycle and the capability to continuously track and regulate the duty cycle with each clock trigger, ensuring precise control over the charging process.
Volume: 15
Issue: 5
Page: 4993-5002
Publish at: 2025-10-01

Discount factor-based data-driven reinforcement learning cascade control structure for unmanned aerial vehicle systems

10.11591/ijece.v15i5.pp4542-4554
Ngoc Trung Dang , Quynh Nga Duong
This article investigates the discount factor-based data-driven reinforcement learning control (DDRLC) algorithm for completely uncertain unmanned aerial vehicle (UAV) quadrotors. The proposed cascade control structure of UAV is categorized with two control loops of attitude and position sub-systems, which are established the proposed discount factor-based DDRLC algorithm. Through the analysis of the Bellman function's time derivative from two perspectives, a revised Hamilton-Jacobi-Bellman (HJB) equation including a discount factor is developed. Then, in the view of off-policy consideration, an equation is formulated to simultaneously solve the approximate Bellman function and approximate optimal control law in the proposed DDRLC algorithm with guaranteed convergence. According to the modified state variables vector, the development of the discount factor-based DDRLC algorithm in each control loop is indirectly implemented by transforming the time-varying tracking error model into the time invariant system. Finally, a simulation study on the proposed discount factor-based DDRLC algorithm is provided to validate its effectiveness. To validate the tracking performance of the quadrotor, four performance indices are considered, including IAE_p=3.0527, IAE_Ω=0.1175, ITAE_p=1.8408, and ITAE_Ω=0.0144, where the subscript p denotes position tracking error and Ω denotes attitude tracking error.
Volume: 15
Issue: 5
Page: 4542-4554
Publish at: 2025-10-01

Development and testing of a dedicated cooling system for photovoltaic panels

10.11591/ijece.v15i5.pp4387-4394
Omar Elkhoundafi , Rachid Elgouri
Solar energy is a viable alternative to fossil fuels, but its efficiency is limited by photovoltaic panel overheating, which causes a decrease in efficiency. This paper suggests a passive cooling method that incorporates aluminum heat sinks beneath the solar cells. This simple, low-cost device maximizes heat dissipation using natural convection. It requires no external energy. The goal is to provide a solution to the challenge of selecting an effective, sustainable, and flexible cooling system while considering technological, economic, and environmental constraints. Experimental results demonstrate that modules fitted with heatsinks experience an average 8.13 °C drop in temperature, as well as a 0.51 V rise in open-circuit voltage when compared to the reference panel. This increase demonstrates how well-designed passive solutions can dramatically improve the energy performance of solar panels. The study emphasizes the relevance of thermal design in photovoltaic system optimization and provides specific opportunities for the development of more efficient solar technologies, particularly in high-temperature situations.
Volume: 15
Issue: 5
Page: 4387-4394
Publish at: 2025-10-01
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