Articles

Business intelligence through data visualization: a case study using marketing campaign dataset

10.12928/telkomnika.v23i6.27166

Aditi; Chandigarh College of Engineering and Technology Bansal , Ankit; Chandigarh College of Engineering and Technology Gupta

In today’s competitive business environment, data-driven marketing strategies are essential for successful campaign outcomes. This study presents a comprehensive analysis of marketing campaign data, emphasizing its role in enhancing customer engagement, improving decision-making, and increasing conversion rates. It explores the complexity of campaign dynamics and consumer behavior, demonstrating how business intelligence and data visualization techniques support informed marketing decisions and actionable insights. Advanced data science methods such as data cleaning, feature engineering, and cross-validation enhance predictive accuracy and campaign optimization. Visualization plays a central role in transforming raw data into interpretable insights, enabling businesses to identify trends in customer preferences and purchasing behavior. Key findings reveal that customers aged 51–70, particularly those with higher education and income levels, show the greatest purchasing power, especially for wine and meat products. These insights help align marketing strategies with data-driven understanding to design personalized campaigns that resonate with target audiences. By combining analytical methods with effective visualization, businesses can develop impactful campaigns that drive engagement, boost conversions, and foster revenue growth. The study concludes with directions for future research, including real-time data processing and automated decision-making systems to ensure continuous improvement in digital marketing strategies.

Volume: 23

Issue: 6

Page: 1466-1475

Publish at: 2025-12-01

Indonesian automated short-answer grading using transformers-based semantic similarity

10.11591/ijict.v14i3.pp1034-1043

Samuel Situmeang , Sarah Rosdiana Tambunan , Lidia Ginting , Wahyu Krisdangolyanti Simamora , Winda Sari ButarButar

Automatic short answer grading (ASAG) systems offer a promising solution for improving the efficiency of reading literacy assessments. While promising, current Indonesian artificial intelligence (AI) grading systems still have room for improvement, especially when dealing with different domains. This study explores the effectiveness of large language models, specifically bidirectional encoder representations from transformers (BERT) variants, in conjunction with traditional hand-engineered features, to improve ASAG accuracy. We conducted experiments using various BERT models, hand-engineered features, text pre-processing techniques, and dimensionality reduction. Our findings show that BERT models consistently outperform traditional methods like term frequency-inverse document frequency (TF-IDF). IndoBERTLite-Base-P2 achieved the highest quadratic weighted kappa (QWK) score among the BERT variants. Integrating handengineered features with BERT resulted in a substantial enhancement of the QWK score. Utilizing comprehensive text pre-processing is a critical factor in achieving optimal performance. In addition, dimensionality reduction should be carefully used because it potentially removes semantic information.

Volume: 14

Issue: 3

Page: 1034-1043

Publish at: 2025-12-01

Application of artificial intelligence in emission prediction for hybrid electric vehicles: integrating ANN and GPR

10.12928/telkomnika.v23i6.27128

In recent years, hybrid electric vehicles (HEVs) have emerged as a promising solution to mitigate vehicular emissions and improve fuel efficiency. This study focuses on the Toyota Prius HEV, employing advanced artificial neural networks (ANN) and Gaussian process regression (GPR) to develop a predictive model for vehicle emissions. The model considers multiple pollutants, including carbon monoxide (CO), carbon dioxide (CO₂), hydrocarbons (HC), and nitrogen oxides (NOx), measured under diverse driving conditions. The ANN model predicts emission trends, while GPR estimates prediction uncertainty, enhancing the model’s robustness. The GPR models achieved uncertainty levels of ±0.829 ppm for CO, ±9.978 ppm for HC, ±0.144 ppm for NOx, and ±411.256 ppm for CO₂, respectively, underscoring the robustness of the integrated approach for emission prediction. This research aims to support the development of more sustainable vehicle technologies and inform policy making for environmental sustainability (e.g., Euro 6/Euro 7 standards). Overall, the study addresses how artificial intelligence (AI) can be utilized to achieve accurate multi-pollutant emission predictions in HEVs. The findings reveal that an integrated ANN-GPR approach yields superior predictive performance (R² values approaching 1.0) with quantifiable uncertainty, outperforming a stand-alone ANN model and providing a robust solution to the emission prediction challenge.

Volume: 23

Issue: 6

Page: 1543-1554

Publish at: 2025-12-01

Cross-lagged panel insights into health, social, and economic in life satisfaction

10.11591/ijphs.v14i4.26696

Pachitjanut Siripanich , Titirut Philmolsri , Wasin Kaewchankha

This study examines life satisfaction in older Thai adults as shaped by dynamic interactions among health, social, and economic factors. Most prior research-both globally and in Thailand-has relied on cross-sectional designs, limiting understanding of cross-lagged relationships. Closing this gap led to the following research objectives: i) assess the stability of health, social, and economic factors and life satisfaction, ii) examine reciprocal and temporal relationships among these domains, and iii) explore age-related variations. Data were drawn from Wave III and IV of the Health, Aging, and Retirement in Thailand (HART) survey (n = 561) and analyzed using cross-lagged structural equation modeling. The findings demonstrated stability in social activity, physical health, mental health, and life satisfaction in both waves. Cross-lagged effects revealed reciprocal and temporal relationships, where mental health influenced future physical health, and life satisfaction impacted subsequent mental well-being. Age-specific differences emerged, with stronger effects in middle-aged adults, where life satisfaction had a greater effect on future mental health, and mental health more strongly influenced physical health over time. Among those aged 60 and older, physical health exhibited the highest stability, while life satisfaction and mental health effects weakened, suggesting age-related shifts in well-being.

Volume: 14

Issue: 4

Page: 1686-1699

Publish at: 2025-12-01

The effectiveness of bentonite in reducing soil resistance in acidic water swampland

10.12928/telkomnika.v23i6.27094

Dian; Universitas Sriwijaya Eka Putra , Muhammad; Sriwijaya University Irfan Jambak , Zainuddin; Sriwijaya University Nawawi

This study aims to evaluate the effectiveness of bentonite mixtures in reducing grounding resistance in acidic swampy areas. The method used is an experiment comparing resistance before and after the addition of bentonite in various compositions (25%, 50%, 75%, and 100%), supplemented with linear regression analysis. The results showed that bentonite significantly reduced soil resistance in three types of electrodes: iron rebar, copper-coated iron, and galvanised iron. The highest reduction in resistance was achieved in iron rebar electrodes, from 35.93 Ω to 22.46 Ω (a 37% reduction) with the addition of 25% bentonite. Linear regression analysis showed a consistent negative relationship between the percentage of bentonite and grounding resistance, with a coefficient of determination (R²) varying between 26.40% and 73.39%. These findings indicate that bentonite is effective as a natural grounding material in acidic swampy areas. This research makes an important contribution to the development of more efficient and safer electrical systems in swampy areas and challenging environments, while also supporting the use of natural materials to reduce dependence on synthetic chemicals.

Volume: 23

Issue: 6

Page: 1657-1665

Publish at: 2025-12-01

Polyaniline as a conductive polymer and its role in improving the efficiency and conductivity of perovskite solar cells

10.11591/ijpeds.v16.i4.pp2731-2743

Vahdat Nazerian , Mehran Hosseinzadeh Dizaj , Tole Sutikno

This article investigates the role of polyaniline as a conductive polymer in the active layer of perovskite solar cells. Samples were created by incorporating polyaniline into the transport layers to assess its impact on enhancing efficiency and conductivity. The application of this polymer across various layers of the cell structure led to improved stability and performance. Given its high doping capability, polyaniline was examined in detail, particularly focusing on two types of oxidation doping and its integration into the hole transport layer. Graphene oxide and reduced graphene oxide were chosen as comparative models, and their performance was evaluated against the standard polyaniline configuration. Laboratory results revealed that power conversion efficiency increased by 17.5% with graphene oxide and by 36.8% with reduced graphene oxide. Furthermore, short-circuit current density improved by 9.8% and 23.1%, respectively. These findings are consistent with existing studies in the field and support the validity of the approach.

Volume: 16

Issue: 4

Page: 2731-2743

Publish at: 2025-12-01

Design and simulation of an electric vehicle charging system with battery arrangement and control parameters optimization

10.11591/ijpeds.v16.i4.pp2521-2537

Nurmiati Pasra , Faizal Arya Samman , Andani Achmad , Yusran Yusran

The development of electric vehicle (EV) charging technology requires efficient, reliable, and economical systems to address users' concerns about battery drain. This study presents a simplification of EV charger design with an isolated model and optimal battery mode setting. The research method integrates step-up Y-Δ transformers, AC-DC converters, boost DC-DC converters, integral proportional control, and battery configurations. Series (S) - parallel (P) - series (S) battery arrangement pattern to maximize system performance. The test results using a 130 mF capacitor with the S40-P2-S6 and S80-P2-S3 array patterns produced an output voltage of 946 V, while the S100-P2-S3 array pattern achieved an output voltage of 1,182 V. The system is capable of fast charging with a time of 0.2 to 2 hours for a battery capacity of 30 to 100 kWh at a charging power of 50 to 150 kW with an efficiency of up to 97%. The combination of the use of an isolated model on the charger array and the EV battery setting pattern is proven to produce stable voltage values with minimal overshoot levels, thus addressing the complex charger design challenges and battery setting needs in the 800 to 1,100 V voltage range.

Volume: 16

Issue: 4

Page: 2521-2537

Publish at: 2025-12-01

Design of an EBNN-PID based adaptive charge controller for variable DC charging applications

10.11591/ijpeds.v16.i4.pp2634-2644

Mochammad Machmud Rifadil , Putu Agus Mahadi Putra , Amalia Muklis

This paper presents an adaptive charging system for lithium-ion batteries using an Elman backpropagation neural network (EBNN) integrated with a PID controller and a ZETA converter. The system dynamically identifies the battery type and adjusts the charging voltage accordingly. The EBNN model was trained using 1441 samples of initial current and voltage data, achieving a mean squared error (MSE) of 7.64×10⁻¹⁴. A ZETA converter enables both step-up and step-down voltage regulation, while the PID controller ensures stability toward the predicted setpoint. Simulations in Simulink were conducted on four lithium-ion battery types with setpoints of 4.4 V, 8.8 V, 14.4 V, and 21.6 V. The results show that the PID-regulated output voltage closely matches the target with a maximum deviation of ±0.05V and an average voltage error of 0.1725%. The system achieves fast response times between 0.015 and 0.033 seconds. Extended testing through 24 randomized trials confirmed consistent identification and regulation across varying battery types. These findings validate the proposed EBNN-PID-based charging system as a highly accurate, flexible, and efficient solution for managing lithium-ion battery charging in real-time embedded applications.

Volume: 16

Issue: 4

Page: 2634-2644

Publish at: 2025-12-01

Dehydration of Moringa leaves using microcontroller and IoT controlled electrical dryer

10.11591/ijpeds.v16.i4.pp2688-2698

Saifuddin Muhammad Jalil , Abubakar Dabet , Syarifah Akmal , Selamat Meliala , Muhammad Muhammad

The dehydration of Moringa Oleifera leaves is crucial to preserving their high nutritional value and extending shelf life for use in food and pharmaceutical applications. Traditional drying methods often result in nutrient degradation and lack precise environmental control. This study presents the design and implementation of an internet of things (IoT)- enabled electrical dryer system controlled by a microcontroller for the efficient dehydration of Moringa leaves. The system integrates temperature and humidity sensors, an Arduino Mega microcontroller, and a web-based interface for real-time monitoring and control. The electrical dryer maintains optimal drying conditions, significantly reducing moisture content while preserving essential nutrients. Data is logged and visualized through IoT connectivity, allowing for remote access and performance analysis. The dehydration of Moringa leaves requires approximately one kg of electricity for batteries in dual-energy dryers, which are based on microcontrollers and the IoT. The results demonstrate that the proposed system offers a reliable, energy-efficient, and scalable solution for the controlled dehydration of Moringa leaves, with potential applications in smart agriculture and postharvest processing. The excellent drying time is achieved in a greenhouse dryer, which maintains a temperature of 45 °C within the drying chamber, resulting in a median drying time of 6 hours. The standard moisture percentage of clean and dry Moringa leaves is measured at 18.5% (wb) and 8% (wb), respectively.

Volume: 16

Issue: 4

Page: 2688-2698

Publish at: 2025-12-01

Performance analysis of a cascaded dual full bridges 5, 7, and 9 levels inverter: experimental validation

10.11591/ijpeds.v16.i4.pp2464-2475

Nabil Saidani , Rachid El Bachtiri , Abdelaziz FRI , Karima El Hammoumi

Cascaded full-bridge inverter is a suitable topology for grid-connected applications due to its ability to generate an output voltage waveform that closely resembles a sine wave, resulting in lower total harmonic distortion (THD) factors. This article proposes the use of the selective harmonic elimination (SHE) technique to produce a 5-level voltage using a symmetrical inverter and 7 and 9-level voltage using an asymmetrical inverter composed of only two full bridges loaded by an RL circuit of 51.4 Ω and 200 mH. The study primarily focuses on analysing the impact of the number of levels on the power quality of the inverter. This includes investigating the effects of the fundamental magnitude on the produced power, as well as measuring losses in the inverter, power factor, THD factor, and fundamental magnitude for each level configuration. The study demonstrates that asymmetrical MLIs lower THD (10.9% vs. 16.7%) and increasing voltage levels enhance waveform quality but slightly reduce the fundamental voltage magnitude, impacting AC power output. The simulation analysis has been conducted using the PSIM environment, and the results have been validated through experimental measurements.

Volume: 16

Issue: 4

Page: 2464-2475

Publish at: 2025-12-01

Supervised learning for fast inverse motor control mapping: a comparative study on SRM and BLDC motors

10.11591/ijpeds.v16.i4.pp2419-2428

S. Sudheer Kumar Reddy , J. N. Chandra Sekhar

This paper investigates the application of machine learning (ML) models, specifically artificial neural networks (ANN) and XGBoost, for real-time motor control, focusing on switched reluctance motors (SRM) and brushless DC motors (BLDC). Traditional inverse dynamics mapping for motor control is compared with ML approaches to highlight advantages in speed, accuracy, and deployment efficiency. Datasets simulating the input-output behavior of both motor types are used to train and test the models. Key performance metrics such as mean squared error (MSE), R² score, training time, and latency are evaluated, with the goal of replacing traditional control methods in real-time applications. Results indicate that ML models outperform traditional methods in terms of prediction accuracy and deployment speed, suggesting a promising path toward more efficient and adaptive motor control systems. The novelty of this work lies in applying supervised learning directly for inverse motor control mapping, thereby eliminating the need for explicit analytical models and enabling a unified, data-driven benchmarking framework across SRM and BLDC.

Volume: 16

Issue: 4

Page: 2419-2428

Publish at: 2025-12-01

Approach to self-synchronization of a group of static power converters

10.11591/ijpeds.v16.i4.pp2342-2352

Victor Lavrinovsky , Nikita Dobroskok , Valery Bulychev , Ruslan Migranov , Yuriy Yu Perevalov , Anastasia Stotckaia

This study examines the control and synchronization of an orderly connected network of three-phase bidirectional power converters, serving as the grid interface for an energy storage system. The primary objective is to ensure stable operation under single-phase and non-symmetrical three-phase grid conditions. The control employs independent phase voltage regulation for compatibility. To achieve seamless coordination of an unlimited group of converters, the paper proposes a synchronization method based on a modified Kuramoto model. This method is designed to be compatible with independent phase control during asymmetric grid states. The proposed approach utilizes a structured connection graph, defined by phase shift magnitude, to synchronize the converter group. A brief overview of the tools for synchronizing oscillator groups is provided. A computer model was developed to study the operating modes of this converter class under both symmetrical and asymmetrical loads. Simulation studies confirmed the viability of the synchronization method. Furthermore, the research results were successfully applied in the design and implementation of a physical 10 kW grid - connected uninterruptible power supply prototype, demonstrating practical feasibility.

Volume: 16

Issue: 4

Page: 2342-2352

Publish at: 2025-12-01

Hybrid intelligent optimization algorithms-based power management for microgrid system

10.11591/ijpeds.v16.i4.pp2665-2676

R. Indumathi , S. A. Lakshmanan

The integration of the photovoltaic (PV) and wind sources of power in microgrids is a beneficial method toward decentralized, efficient, and sustainable energy management. This research endeavors to develop and implement a novel hybrid control strategy that efficiently combines grey wolf optimization (GWO) and particle swarm optimization (PSO) algorithms for the optimization of renewable energy-based microgrids. The proposed method addresses three critical tasks under one integrated control mechanism: i) maximum power point tracking (MPPT) for PV and wind systems under fluctuating environmental conditions, ii) smart management of energy storage systems for batteries, and iii) adaptive load scheduling based on real-time availability of energy. By leveraging the complementary strengths of GWO and PSO, the system enjoys improved convergence rate, global search, and decision-making robustness. The hybrid controller is tested and validated through thorough testing in MATLAB/Simulink under dynamic simulation scenarios that mimic sudden weather and load variations. Comparative performance with conventional methods and benchmarking based on IEEE 516 standards demonstrates the improved reliability, responsiveness, and energy efficiency of the proposed system. This research contributes to the state-of-the-art of intelligent microgrid control through an integrated, bio-inspired solution toward resilient and optimized energy management.

Volume: 16

Issue: 4

Page: 2665-2676

Publish at: 2025-12-01

Enhanced incremental conductance MPPT method for maximizing photovoltaic power generation

10.11591/ijpeds.v16.i4.pp2757-2767

Asnil Asnil , Refdinal Nazir , Krismadinata Krismadinata , Muhammad Nasir

This research proposes an enhanced maximum power point tracking (MPPT) algorithm that integrates the variable step size (VSS) method to significantly improve power extraction from photovoltaic (PV) systems. The primary objective is to optimize performance under dynamic environmental conditions. Through comprehensive experimental studies, the proposed algorithm’s performance was evaluated and directly compared against conventional incremental conductance (INC) and perturb and observe (P&O) algorithms. The results demonstrate a substantial increase in power generation, with the proposed algorithm delivering 18.79% more power compared to INC and 39.67% more power than P&O. These findings underscore the efficacy of the developed algorithm at improving the efficiency and robustness of PV power generation, particularly in variable operating environments.

Volume: 16

Issue: 4

Page: 2757-2767

Publish at: 2025-12-01

Design of a static synchronous compensator for the north-south high-speed railway system

10.11591/ijpeds.v16.i4.pp2369-2380

An Thi Hoai Thu Anh , Tran Hung Cuong

The modern high-speed rail system plays a crucial role in driving the nation’s economic development. The problem of voltage imbalance caused by intermittent load movements is a significant challenge for energy management and distribution. When electric trains are connected to the three-phase grid, power quality degradation occurs, resulting in distortion and imbalance of the three-phase grid current and voltage, which in turn increases operating costs. This paper has proposed a linear control method using a PI controller for a static synchronous compensator (STATCOM) to directly control the amount of reactive power loss for electric trains. This solution will also bring good and stable voltage quality to electric trains so that electric trains can operate for a long time. The STATCOM device in this paper is a three-phase voltage source converter with a simple structure and can be easily controlled. This is considered a simple and effective solution to balance voltage, improve power factor, and enhance harmonic quality for railway trains, thereby achieving an optimal operating solution. This discussion can be simulated using MATLAB/Simulink software to determine the operation and control steps for STATCOM, thereby improving the quality of the power system. The simulation results of current, voltage, and reactive power response are presented. The simulation results have demonstrated that the proposed algorithm successfully achieves the set goals of ensuring voltage stability and providing the necessary amount of reactive power for the train, thereby improving the quality of the power grid for the North-South high-speed train in Vietnam.

Volume: 16

Issue: 4

Page: 2369-2380

Publish at: 2025-12-01

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31,042 Article Results

Business intelligence through data visualization: a case study using marketing campaign dataset

Indonesian automated short-answer grading using transformers-based semantic similarity

Application of artificial intelligence in emission prediction for hybrid electric vehicles: integrating ANN and GPR

Cross-lagged panel insights into health, social, and economic in life satisfaction

The effectiveness of bentonite in reducing soil resistance in acidic water swampland

Polyaniline as a conductive polymer and its role in improving the efficiency and conductivity of perovskite solar cells

Design and simulation of an electric vehicle charging system with battery arrangement and control parameters optimization

Design of an EBNN-PID based adaptive charge controller for variable DC charging applications

Dehydration of Moringa leaves using microcontroller and IoT controlled electrical dryer

Performance analysis of a cascaded dual full bridges 5, 7, and 9 levels inverter: experimental validation

Supervised learning for fast inverse motor control mapping: a comparative study on SRM and BLDC motors

Approach to self-synchronization of a group of static power converters

Hybrid intelligent optimization algorithms-based power management for microgrid system

Enhanced incremental conductance MPPT method for maximizing photovoltaic power generation

Design of a static synchronous compensator for the north-south high-speed railway system

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