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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,338 Documents
 929   930   931   932   933 
The persistence of simplicity: why naive temporal baselines can outperform machine learning in short-term cancer incidence forecasting Samia Ferhane; Kies Karima
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp139-147

Abstract

Despite growing enthusiasm for artificial intelligence (AI) in epidemiology, its added value for short-term forecasting remains uncertain. In this method, using CI5plus international cancer registry data (1990–2017) enriched with World Bank urban environment indicators, we benchmark one-year-ahead cancer incidence forecasting under strict temporal validation (train 1990–2012; validation 2013–2015; test 2016–2017). We compare naive temporal base lines (last observation, three-year moving average), classical time-series models (ARIMA, exponential smoothing), machine learning (ridge regression, random forest), and a deep learning model (multilayer perceptron). Model differences are assessed using Diebold–Mariano tests and bootstrap confidence intervals, and robustness is verified through rolling-origin evaluation. Regarding the results, across country–sex–site strata, the last-observation baseline consistently achieved the best test performance, significantly outperforming all competing models (Diebold–Mariano p < 0.05 for all pairwise comparisons on MAE). These results were robust across three rolling-origin windows. Urban environment covariates provided negligible incremental predictive value beyond recent incidence history. In conclusion, for short-horizon cancer incidence forecasting with highly persistent series, strong naive baselines are difficult to beat. Rigorous temporal evaluation, statistical comparison, and appropriate baselines are essential for credible claims of AI benefit in epidemiological forecasting.
Analysis of PM2.5 pollutant sources in Jakarta using deep learning models and back trajectory approach Hendro Pratama Saragih; Imas Sukaesih Sitanggang; Hendra Rahmawan
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp325-334

Abstract

PM2.5 concentrations in Jakarta frequently exceed World Health Organization (WHO) air quality guidelines, indicating the need for an integrated approach for pollution prediction and source assessment. This study develops a spatiotemporal prediction framework using a long short term memory (LSTM) model integrated with the hybrid single particle Lagrangian integrated trajectory (HYSPLIT) model for backward trajectory analysis. Daily PM2.5 data from five monitoring stations were combined with meteorological variables from ERA5, Visualcrossing, and the global data assimilation system, with spatial context evaluated using Sentinel-2 land cover maps. After hyperparameter tuning, the optimized model demonstrated robust predictive capabilities, achieving a peak coefficient of determination (R2) of 75.87% on the test data. The framework exhibited exceptional relative accuracy, particularly at the Jagakarsa and Kebun Jeruk stations, which recorded mean absolute percentage error (MAPE) values of 13.34% and 17.80%, respectively. Backward trajectory analysis during selected pollution episodes indicates two dominant regional transport pathways that may influence PM2.5 levels in Jakarta. These pathways are associated with air mass transport over industrial and built-up areas in eastern and northern regions surrounding Jakarta. Land cover analysis shows limited vegetation along these pathways. Overall, elevated PM2.5 events are associated with combined local emissions, regional transport, and meteorological conditions that limit pollutant dispersion near the surface.
Smart panel design for renewable energy generation Rudi Syahputra; Nelly Safitri; Fauzan Fauzan; Yassir Yassir; Teuku Hasannuddin; Akhyar Akhyar; Radhiah Radhiah; Zulfikar Zulfikar
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp18-27

Abstract

The aim of this study is to design and develop a smart panel module specifically for solar power generation, which includes three critical functions: the automatic transfer switch (ATS), the automatic main failure (AMF), and capabilities for remote monitoring and control. The ATS and AMF features employ Haiwell AT12MOT Ethernet PLC control equipment in conjunction with the Haiwell B7H Ethernet IoT cloud HMI, both designed for remote operation through an IoT system and integrated with the Haiwell cloud application. The developed PLC program interacts with HMI software that is created using NB designer. Inputs from the PLC are monitored and managed via the Haiwell cloud application, which connects with the relay designated as input for the Haiwell AT12MOT PLC. The resulting design interfaces with the PLC output located within an electrical panel specifically designed to handle industrial loads. This research results in a smart panel capable of operating in an industrial context with a power capacity of 2,200 VA. It is noteworthy that during automatic operations, load transfers between solar power systems and PLN (the national grid company of Indonesia) do not occur instantaneously; instead, there is a delay of 10 seconds as the system stabilizes back to normal conditions.
A hybrid CNN-autoencoder-SVM/XGBoost model for polyphonic orchestral instrument classification Kelvin Wyeth; Iman Herwidiana Kartowisastro
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp114-126

Abstract

Polyphonic orchestral recordings pose significant challenges in music information retrieval (MIR) due to their overlapping frequency ranges and timbral similarities among instrument families, which complicate multi-label instrument classification. Prior studies have explored the integration of convolutional neural networks (CNN)-based feature extraction with classical machine learning (ML) classifiers, often on monophonic or simpler datasets like IRMAS. But the integration of deep learning (DL) feature extraction, Autoencoder (AE)-based dimensionality reduction, and ML classifiers for polyphonic orchestral instrument recognition remains underexplored. This study proposes a hybrid framework utilizing a pre-trained Inception V3 CNN for feature extraction from mel-spectrograms, followed by an optional 50% dimensionality reduction via AE, and finally, classification with support vector machines (SVM) or extreme gradient boosting (XGBoost). Experiments were run on two polyphonic datasets, OpenMIC-2018 and Orchset. The results demonstrate that non-AE configurations generally outperform AE variants. These results extend prior studies such using polyphonic datasets. The results highlight the practical value of hybrid CNN-ML pipelines and the trade-offs of feature compression in MIR.
FPGA based forest fires prediction system Faroudja Abid; Nouma Izeboudjen; Fatiha Louiz
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp78-92

Abstract

This paper describes the idea of designing and implementing an field programmable gate array (FPGA) based system on chip (SoC) for forest fires prediction (FFP). The FFP in the proposed system is based on the decision tree (DT) algorithm implemented as an intellectual property (IP) in the FFP Zynq-SoC architecture that constitutes the processing part of a smart sensor node. This latter processes the collected meteorological data and takes decision locally at sensor node level; without having to send massive data to the base station for decision. The performance of the decision-tree software classifier in terms of accuracy and recall are about 75% and 0.88, respectively. The hardware implementation results of the DT -based forest fires prediction SoC show that the developed DT IP core is area and power efficient.
Advanced state machine-sliding mode current control energy management for multi-source DC microgrid Hamza Rezigue; Mabrouk Khemliche; Samia Latreche; Badreddine Kanouni; Hamza Khemliche
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp63-77

Abstract

This work addresses effective power management in a multi-source DC microgrid. An innovative energy management technique utilizing a state machine control (SMC) integrated with a sliding mode current control (SMCC) has been developed. This approach offers advantages through its equitable distribution of power among sources, storage devices, and demand loads, thereby optimizing the flow rate, discharge, and charge cycles of energy storage devices; additionally, it improves the response time of PEMFC power across various states in comparison to a conventional PI controller. The SMC-SMCC has proposed nine scenarios, categorized into three state of charge (SOC) situations, to fulfill a predetermined set of parameters for the operation of the DC microgrid. Simulation studies conducted with a precise model in MATLAB/Simulink have demonstrated that the proposed SMC-SMCC is proficient in achieving effective power sharing, rapid DC link voltage control in terms of stability, and maintaining the SOC within its constraints; additionally, the SMC-SMCC exhibits a quicker response time compared to conventional SMC-PI.
A hierarchical scholar expertise information system based on publication profiles and research records: a case in Universitas Diponegoro Eko Didik Widianto; Hadiyanto Hadiyanto; Teddy Mantoro; Raka Sindu Wardoyo; Muhamad Irham Maulana
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp250-258

Abstract

One factor that boosts the reputation of higher education institutions (HEIs) in gaining competitive advantage is the research productivity of their scholars, including at Diponegoro University (Undip). However, existing studies focus on metrics and their measurement but lack discussion of systems for leveraging these metrics to promote these scholars’ expertise. This study proposes a scholars’ expertise information system at Undip based on their publications and research track records to bridge information between the campus and outside parties, namely industry, government, and society. It provides expertise searching and browsing facilities in Dewey’s hierarchy of subjects and the institutional structure hierarchy. Scholar publications and research data are retrieved using the science and technology index application program interface (SINTA API). The collected data included lecturer profiles, study programs, SINTA subjects, articles, research, community service, intellectual property rights (IPR), and books. It has been fully deployed and adopted online, presenting the expertise of Undip scholars based on their publications and research records. The finding shows that all pages perform well, with an average GTmetrix B grade and a performance score of 91%, a structure score of 88%, and a 1.8s loading time. With all the functionalities and performances of the system, this study contributes to developing an information system model to promote the expertise of university scholars, primarily based on their publications and research.
Automated drone-assisted detection system for rice leaf pathologies a deep learning approach Erfan Rohadi; Cahya Rahmad; Septian Enggar Sukmana; Aida Sartimbul; Kismet Anak Hong Ping; Dimas Rosiawan; Ahmad Afifuddin Zakki
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp148-156

Abstract

Early and accurate detection of plant diseases is vital for maintaining agricultural productivity. This study investigates an automated disease identification system specifically designed for the IR64 rice cultivar. By combining drone-captured aerial imagery (UAV) taken during the plant's vegetative stage with public datasets, we established a comprehensive training dataset. The study evaluates and compares four convolutional neural network (CNN) architectures, InceptionV3, ResNet50, EfficientNetV2S, and MobileNetV2, assessing their predictive accuracy and real-world computational efficiency. Our 10-fold cross-validation results indicate varying levels of inference speed and accuracy among the models. InceptionV3 and MobileNetV2 displayed the highest stability and minimal misclassification rates across multiple disease types. In contrast, the performance of ResNet50 and EfficientNetV2S fluctuated significantly depending on the detected pathogen. In conclusion, coupling UAV imagery with fine-tuned deep learning models provides a fast, scalable solution for continuous crop monitoring and precision agriculture.
Insights on routing and scheduling approaches in the IoT from the perspective of energy, QoS, and security–a systematic review Rajeshwari Kenchammana Hosakote Nanjappa; Manuvinakurike Narasimha Sastry Suma
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp335-344

Abstract

The internet of things (IoT) has been conceptualized to bring more efficient and seamless connectivity to a large number of low-power and low-cost embedded devices. In the context of IoT, limited radio resources create new challenges, such as collisions and access conflicts. Another challenge arises in dealing with energy constraints, as battery-powered IoT sensors have limited energy capacity in the sensing layer. Consequently, routing mechanisms play a fundamental role in dealing with route optimization and reliable data transmission problems in the transport layer, whereas broadcast and link scheduling are also considered as appealing solutions for fulfilling the energy efficiency, collision-free transmissions and latency requirements in the IoT. Also, security vulnerabilities are hard to identify in the IoT perception and transport layer due to its ad-hoc network topological factors. Thus, deploying efficient routing schemes in IoT demands effective collaborative solutions for cost-effective and secure route formation with energy-aware scheduling performance, which were not, explored much in the past. This investigation thereby analyses the strengths and limitations of existing efficient routing and scheduling solutions in IoT and extracts the critical findings which could provide quick survey to many researchers in this application area.
User-centered requirements elicitation for explainable AI transparency in recommendation and advertising systems Osama Dakhel Alsuhaimy; M. Rizwan Jameel Qureshi
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp271-280

Abstract

Modern recommendation and advertising systems increasingly rely on complex artificial intelligence (AI) models whose opaque decision-making processes limit user understanding and trust. While explainable artificial intelligence (XAI) techniques aim to improve transparency, excessive disclosure can increase privacy concerns and psychological discomfort. This study addresses the lack of structured approaches for regulating transparency in user-facing AI systems. We propose the optimal transparency and psychological safety (OTPS) framework, which regulates explanation depth, timing, and user control to balance interpretability with psychological safety. The framework is implemented through a modular architecture consisting of an explanation generation module, transparency controller, and user interface layer. A user survey involving 35 participants was conducted to evaluate perceptions of transparency, trust, and psychological comfort. Statistical analysis, including reliability testing and response distribution evaluation, indicates strong user preference for adaptive transparency mechanisms. The results demonstrate that regulated transparency improves user trust and usability without introducing significant system overhead, providing practical design guidance for explainable AI systems in recommendation and advertising platforms.

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