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Indonesian sentiment analysis in natural environment topics

10.11591/ijeecs.v38.i2.pp1353-1366
Christofer Octovianto , Muhammad Okky Ibrohim , Indra Budi
Indonesia is one of the countries that is rich in biodiversity and has a high population growth. This condition can cause Indonesia to have problems related to the natural environment that are more complex than other countries. Hence, this has created a lot of discussions regarding natural environmental issues in Indonesia on social media platforms. In this case, stakeholders like the government in general can utilize sentiment analysis (SA) to comprehend the public’s views to allow them to better fit the public’s expectations when formulating a particular policy that related to the environmental sustainability (ES) issues. This paper built the first open dataset of Indonesian SA dataset in ES topics collected from Instagram. As the benchmark of our dataset, we used IndoBERT model variant for constructing the model and the experiment result shows that model based on IndoBERT-large-p2 obtained the best performance with 72.44% of F1-score.
Volume: 38
Issue: 2
Page: 1353-1366
Publish at: 2025-05-01

Artificial intelligence approaches for cardiovascular disease prediction: a systematic review

10.11591/ijeecs.v38.i2.pp1208-1218
Jasim Faraj Hammadi , Aliza Binti Abdul Latif , Zaihisma Binti Che Cob
Cardiovascular disease (CVD) remains a top global cause of mortality, highlighting the critical need for precise prediction models to improve patient outcomes and optimize healthcare resource allocation. Accurate prediction of CVD is paramount for early diagnosis and reducing mortality rates. Achieving efficient CVD detection and prediction requires a deep understanding of health history and the underlying causes of heart disease. Harnessing the power of data analytics proves advantageous in leveraging vast datasets to make informed predictions, aiding healthcare clinics in disease prognosis. By consistently maintaining comprehensive patient-related data, healthcare providers can anticipate the emergence of potential diseases. Our study conducts a meticulous comparative analysis of CVD prediction methods, focusing on various artificial intelligence (AI) algorithms, particularly classification and predictive algorithms. Scrutinizing approximately sixty papers on cardiovascular disease through the prism of AI techniques, this study carefully assesses the selected literature, uncovering gaps in existing research. The outcomes of this study are expected to empower medical practitioners in proactively predicting potential heart threats and facilitating the implementation of preventive measures.
Volume: 38
Issue: 2
Page: 1208-1218
Publish at: 2025-05-01

An efficient load balance using virtual machine migration hybrid optimization technique in cloud computing

10.11591/ijeecs.v38.i2.pp1265-1272
Saravanan Madderi Sivalingam , Pavan Kumar Prathapagiri
Cloud computing is becoming increasingly important to developers and companies because to the rapid development of information technology and the wide availability of internet applications. Every information technology industry has a significant role for cloud computing. Numerous multinational technology businesses, like Google, Microsoft, and Facebook, have established data centers across the world to offer processing and storage capabilities. Customers can submit their jobs to cloud centers directly. Reducing overall power usage is the primary goal, which was overlooked in the early stages of cloud development. Using gene expression programming (GEP), symbolic regression models of virtual machines (VMs) are developed using measured VM loads and the corresponding resource parameters. In order to minimize resource use, multidimensional resource load balancing of all the physical machines within the cloud computing platform is the aim of this analysis. The VMH loads estimated and the genetic algorithm that considers the current and the future loads of VMHs and decides an optimal VM-VMH for migrating VMs and performing load-balance. Hence, an efficient load balance using virtual machine migration hybrid optimization technique (HOT) in cloud computing shows better results in terms of accuracy, energy consumption, migration cost.
Volume: 38
Issue: 2
Page: 1265-1272
Publish at: 2025-05-01

Geographic information system-based approaches for evaluating CO2 storage in Kalimantan basins, Indonesia

10.11591/ijeecs.v38.i2.pp904-914
Tri Muji Susantoro , Sugihardjo Sugihardjo , Suliantara Suliantara , Bambang Widarsono , Usman Usman , Herru Lastiadi Setiawan , Mohamad Romli , Panca W. Sukarno , Nurkamelia Nurkamelia , Rudi Suhartono
To achieve the energy transition towards more environmentally friendly energy, various approaches must be taken, one of which is CO2 source-to-sink matching. A basin evaluation study has been carried out through classifying, weighting, and scoring in the geographic information system (GIS) for screening and ranking basins for CO2 storage on the island of Kalimantan, Indonesia. The region covers 13 sedimentary basins that have the potential to serve as CO2 sinks. As many as 21 parameters have been analyzed through classification and weighting using a pairwise comparison matrix method to produce scores and ranks for each basin. The results show that the Kutai, Tarakan, and Barito basins are the top three basins for CO2 storage potential. Singkawang, Nangapinoh, Pangkalanbun Utara, and Embaluh Selatan basins have been found to have the lowest sink potential.
Volume: 38
Issue: 2
Page: 904-914
Publish at: 2025-05-01

Data mining and cardiac health: predicting heart attack risks

10.11591/ijeecs.v38.i2.pp1010-1023
Inoc Rubio Paucar , Laberiano Andrade-Arenas
In a context where heart attacks continue to be a global health concern, the lack of precision in predicting who is at higher risk poses a critical challenge due to the variability of risk factors and complex interactions among them. The research aims to develop predictive models for heart attack risks using data mining techniques, employing the knowledge discovery in databases methodology (KDD) and the k-means algorithm with RapidMiner studio. The primary objective is to identify patterns and risk profiles, allowing for early identification of at-risk individuals, considering factors like obesity, diabetes, alcoholism, and stress, to reduce preventable deaths and improve cardiac healthcare. This innovative approach combines cardiac health, data mining, and KDD methodology to address the challenge of predicting heart attack risks and has the potential to enhance medical care and save lives. The predominant results obtained were that cluster 1 with a fraction of 0.312 and a percentage of 31.2% of the attribute diabetes was one of the most prevalent causes of cardiac risk. Finally, the research concluded that people with diabetes are more likely to have cardiac risk associated with dietary factors or consumption of other substances.
Volume: 38
Issue: 2
Page: 1010-1023
Publish at: 2025-05-01

Discrete wavelet transform and convolutional neural network based handwritten Sanskrit character recognition

10.11591/ijeecs.v38.i2.pp1367-1375
Shraddha V. Shelke , Dinesh M. Chandwadkar , Sunita P. Ugale , Rupali V. Chothe
Sanskrit is one of the ancient languages from which the majority of present Indian languages are developed. Although the national mission for manuscripts (NMM) is digitizing handwritten Sanskrit manuscripts, there are still a lot of papers that need to be digitized. Recognition of handwritten script is a challenging task due to individual differences in writing styles and how those variations alter over time. The Sanskrit language is written in Devanagari script. A novel approach using discrete wavelet transform (DWT) and convolutional natural network (CNN) is proposed in this paper. Devanagari handwritten character dataset which includes 2000 handwritten images of 36 classes (2000*36=72000) is used in this research. Fine-tuned GoogLeNet model implemented here gave optimum values of epochs and learning rate of 15 and 0.01 respectively. Classification accuracy obtained by proposed DWT – CNN model is 98.97% with a loss of 0.098. Fine-tuned GoogLeNet model achieves 99.68% accuracy with a 0.0635 loss. Results obtained are also compared with existing approaches and found superior.
Volume: 38
Issue: 2
Page: 1367-1375
Publish at: 2025-05-01

Internet of things meteorological station for climate monitoring and crop optimization in Carabayllo-Perú

10.11591/ijeecs.v38.i2.pp755-766
Jeremy Jared Rumiche-Cardenas , Axel Walter Figueroa-Guevara , Deyvis Jhosmar Gamarra-Pahuacho , Josue Daniel Quiroz-Grados , Jamil Segovia-Ojeda , Maritza Cabana-Cáceres , Cristian Castro-Vargas
In the agricultural sector, monitoring environmental variables such as temperature, humidity, and atmospheric pressure is crucial for efficient and sustainable agriculture. However, conventional monitoring systems are expensive and need more autonomy, making their implementation difficult in small- and medium-scale agricultural operations. This study presents the design, implementation, and evaluation Internet of things (IoT)-based autonomous for watch remote critical climate variables in the Carabayllo region, Peru. The system uses a data acquisition, processing, and transmission architecture based on the ESP32 microcontroller, DHT22 sensors for measure climatic aspects, BMP180 for detection barometric, and the ThingSpeak cloud platform for data storage and visualization. Results show that the proposed system achieves accuracy comparable to commercial weather stations, making it accessible to small farmers. The implementation demonstrated the system’s ability to detect feasible local microclimates to monitor and predict weather patterns for proper crop growth. This approach enables farmers to monitor conditions in real time, receive early alerts on adverse weather events, and optimize agricultural practices such as irrigation and fertilization. The study concludes that the proposed IoT weather station represents a viable and cost-effective solution to improve agricultural decision-making in developing regions, potentially contributing to increasing crops.
Volume: 38
Issue: 2
Page: 755-766
Publish at: 2025-05-01

Android malware detection through opcode sequences using deep learning LSTM and GRU networks

10.11591/ijeecs.v38.i2.pp1106-1114
Annemneedi Lakshmanarao , Jeevana Sujitha Mantena , Krishna Kishore Thota , Pavan Sathish Chandaka , Chinta Venkata Murali Krishna , Madhan Kumar Jetty
Android malware detection was a complex task due to the intricate structure of Android applications, which consisted of numerous Java methods and classes. Effective detection required the extraction of meaningful features and the application of advanced machine learning (ML) or deep learning (DL) algorithms. This paper presented a novel approach to detecting Android malware by leveraging opcode sequences extracted from Android applications. These opcode sequences, which differed between malicious and benign apps, formed the basis of the detection model. The methodology involved extracting opcode sequences from decompiled Android APK files using the “Androguard” tool and applying recurrent neural networks (RNN) with long short-term memory (LSTM), Bi-LSTM, and gated recurrent unit (GRU) architectures to classify the apps as either malware or benign. The combination of these advanced DL techniques allowed for capturing temporal dependencies in opcode sequences, resulting in a significant improvement in detection capabilities. This work underscored the potential of using opcode sequences in conjunction with RNN, LSTM, and GRU for robust and accurate malware detection, while also highlighting the importance of further exploring additional features for comprehensive classification.
Volume: 38
Issue: 2
Page: 1106-1114
Publish at: 2025-05-01

Multi-camera multi-person tracking with DeepSORT and MySQL

10.11591/ijeecs.v38.i2.pp997-1009
Shashank Horakodige Raghavendra , Yashasvi Sorapalli , Nehashri Poojar S. V. , Hrithik Maddirala , Ramakanth Kumar P. , Azra Nasreen , Neeta Trivedi , Ashish Agarwal , Sreelakshmi K.
Multi-camera multi-object tracking refers to the process of simultaneously tracking numerous objects using a network of connected cameras. Constructing an accurate depiction of an object’s movements requires the analysis of video data from many camera feeds, detection of items of interest, and their association across various camera perspectives. The objective is to accurately estimate the trajectories of the objects as they navigate through a monitored area. It has several uses, including surveillance, robotics, self-driving cars, and augmented reality. The current version of an object tracking algorithm, DeepSORT, doesn’t account for errors caused by occlusion or implementation of multiple cameras. In this paper, DeepSORT has been extended by introducing new states to improve the tracking performance in scenarios where objects are occluded in the presence of multiple cameras. The communication of track information across multiple cameras is achieved with the help of a database. The suggested system performs better in situations where objects are occluded, whether due to object occlusions or person occlusions.
Volume: 38
Issue: 2
Page: 997-1009
Publish at: 2025-05-01

Optimizing photovoltaic system performance through MPPT synergetic adaptive control

10.11591/ijeecs.v38.i2.pp808-820
Kamel Hadjadj , Hadjira Attoui
This paper investigates enhancement of energy conversion through the implementation of new MPPT control strategy based on synergetic adaptive control (SAC) for a photovoltaic system. The architecture of this system encompasses a photovoltaic module, a DC-DC boost converter, a resistive load, and an MPPT controller. The controller amalgamates two distinct methodologies: the initial algorithm deduces the peak power current through a perturbation and observation (P&O) method, which serves as the reference point for the subsequent algorithm founded on synergetic adaptive control. The parameters for the latter are refined through the particle swarm optimization (PSO) technique This innovative method is employed to ascertain the optimal power output across varying weather conditions, aiming to enhance power transmission performance irrespective of meteorological variations. The efficacy of this strategy was affirmed through a comparative study with the conventional P&O method using MATLAB/Simulink simulations, which verified the superior performance of the proposed algorithm.
Volume: 38
Issue: 2
Page: 808-820
Publish at: 2025-05-01

Optimal land distribution for ambiguous profit vegetable crops using multi-objective fuzzy linear programming

10.11591/ijeecs.v38.i2.pp1162-1169
Pranav Dixit , Sohan Lal Tyagi
Decisions in agriculture had been driven by methodical planning to increase yields to cater to the needs of overwhelming populations while also allowing farmers to prosper. Allocating land to various crops by making use of limited resources is becoming a crucial challenge for achieving higher profits. To make cropping pattern decisions, farmers traditionally rely on experience, instinct, and comparisons with their neighbors. Since profit varies depending on many factors, intuition and experience usually cannot guarantee optimal (maximum) profits. A number of research studies on linear programming (LP) have shown optimum cropping patterns when crop prices (profits) are fixed. Vegetable crops, also known as cash crops, are subject to a high degree of price volatility owing to the fact that their production is costly and they carry a significant risk of not being profitable, despite the fact that they provide higher earnings than food crops. The net returns of crops in agriculture are greatly impacted by price uncertainty. With the use of the optimization tool TORA, a step-by-step process is shown in this paper to solve the model and manage the volatility in vegetable crop profitability using fuzzy multi-objective linear programming (FMOLP).
Volume: 38
Issue: 2
Page: 1162-1169
Publish at: 2025-05-01

Performance evaluation of transdermal optical wireless communication using spatial diversity techniques

10.11591/ijeecs.v38.i2.pp865-875
Rawan Almajdoubah , Omar Hasan
Active medical implants and other contemporary medical applications need a dependable, high-speed communication channel between external and internal transceivers. Optical wireless communications have demonstrated advantages over widely used radio frequency technology in terms of data speeds, bandwidth abundance, and immunity to interference. Regretfully, this advantage implies strict alignment requirements for both sending and receiving ends. This study focuses on the effects of using multiple transmitters or receivers under the influence of pointing error on the transcutaneous link's overall performance measured by the outage probability and outage rate. Spatial diversity techniques have demonstrated their viability in increasing the link's reliability in free space optical communications. This drives the investigation of improvement transdermal communication system by adding numerous transmitters or receivers. Various misalignment severities are used to represent different operating circumstances, and these analyses result in explicit closed-form formulas for the relevant metrics. The findings clearly show the benefits of employing multiple transmitters and receivers on the link's outage performances. A notable improvement in the average signal-to-noise ratio values for the outage probability and outage rate compared to the single input single output setup was achieved. Furthermore, the theoretical conclusions are subsequently confirmed by MATLAB-based Monte-Carlo simulation for several instructive cases.
Volume: 38
Issue: 2
Page: 865-875
Publish at: 2025-05-01

Emerging approaches of artificial intelligence tools for distance learning: a review

10.11591/ijeecs.v38.i2.pp1219-1230
Ghita Faouzi , Naila Amrous , Nour-Eddine El Faddouli , Mostafa Khabouze
Learning management system (LMS) is the best way to deliver educational content in the context of higher education, by settings students worldwide with high-quality educational material. This paper principally seeks to examine the use of e-learning platforms in the last years from 2019 to 2023, which has coincided with the pandemic period, by elucidating the benefits and limitations of e-learning platforms, analyzing the real-world artificial intelligence (AI) algorithms used and their operating context. A comprehensive literature search was conducted on different electronic databases to identify relevant studies related to e-learning and AI tools used during this period by applying inclusion, exclusion criteria and preferred reporting items for systematic reviews and meta-analysis (PRISMA) process. Based on this review the tools were necessary social media and free communication platforms that offer the flexibility and build autonomy to students. On the other hand, many challenges are arisen due to the lack of experience in the term of using those tools or due to technical problems, for this reason, the use of AI tools to enhance learning experience still one of the approved solutions.
Volume: 38
Issue: 2
Page: 1219-1230
Publish at: 2025-05-01

End-user software engineering approach: improve spreadsheets capabilities using Python-based user-defined functions

10.11591/ijeecs.v38.i2.pp1024-1032
Tamer Bahgat Elserwy , Tarek Aly , Basma E. El-Demerdash
End-user computing enables non-developers to handle data and applications, boosting collaboration and productivity. Spreadsheets are a key example of end-user programming environments that are extensively utilized in business for data analysis. However, the functionalities of Excel have limitations compared to specialized programming languages. This study aims to address this shortcoming by proposing a prototype that integrates Python's features into Excel via standalone desktop Python-based user-defined functions (UDFs). This method mitigates potential latency concerns linked to cloud-based solutions. This study employs Excel-DNA (dynamic network access) and IronPython; Excel-DNA facilitates the creation of custom Python functions that integrate smoothly with Excel's calculation engine, while IronPython allows these Python UDFs to run directly within Excel. Core components include C# and visual studio tools office (VSTO) add-ins, which enable communication between Python and Excel. This approach grants users the chance to execute Python UDFs for various tasks such as mathematical computations and predictions — all within the familiar Excel environment. The prototype showcases seamless integration, enabling users to invoke Python-based UDFs just like built in Excel functions. This study enhances the capabilities of spreadsheets by harnessing Python's strengths within Excel. Future work may focus on expanding the Python UDF library and examining user experiences with this innovative approach to data analysis.
Volume: 38
Issue: 2
Page: 1024-1032
Publish at: 2025-05-01

A tag-based recommender system for tourism using collaborative filtering

10.11591/ijeecs.v38.i2.pp960-974
Afef Selmi , Maryah Alawadh , Raghad Alotaibi , Shrefah Alharbi
Recommender systems have garnered significant attention from researchers due to their potential for delivering personalized recommendations in light of the vast amount of information available online. These systems have found applications in various domains, including financial services, movies, and research articles. Their implementation in the tourism industry is particularly promising. Travelers often face the daunting task of selecting the right tourist attractions from a plethora of options, which can consume considerable time and energy. By leveraging personalized recommendation technologies, it is possible to provide highly accurate travel suggestions tailored to individual preferences. This study proposes the development of a customized recommendation system (RS) aimed at assisting travelers in the Qassim region of the Kingdom of Saudi Arabia. By using this region as a case study, the proposed RS consists of two main modules: a user registration and login module and a recommendation technique and tag module. The system will capture users’ interests and prompt them to select from various options, subsequently presenting them with tailored recommendations based on their preferences. This approach aims to enhance the travel experience by offering relevant suggestions that align with the interests of each traveler.
Volume: 38
Issue: 2
Page: 960-974
Publish at: 2025-05-01
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