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International Journal of Advances in Intelligent Informatics
Published by Universitas Ahmad Dahlan
ISSN : 24426571     EISSN : 25483161     DOI : 10.26555
Core Subject : Science,
Computer Science & IT
International journal of advances in intelligent informatics (IJAIN) e-ISSN: 2442-6571 is a peer reviewed open-access journal published three times a year in English-language, provides scientists and engineers throughout the world for the exchange and dissemination of theoretical and practice-oriented papers dealing with advances in intelligent informatics. All the papers are refereed by two international reviewers, accepted papers will be available on line (free access), and no publication fee for authors.
Arjuna Subject : -
Articles 374 Documents
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A hybrid oversampling for imbalanced data using incremental SMOTE-KMeans and cluster-structured positive class-SVM Hartono Hartono; Muhammad Khahfi Zuhanda; Rahmad Syah
International Journal of Advances in Intelligent Informatics Vol 12, No 3 (2026): August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i3.2432

Abstract

Class imbalance remains a significant challenge in classification tasks, particularly when the minority class exhibits complex internal structures. This study proposes a unified framework that integrates Incremental SMOTE-KMeans with a cluster-structured positive class-SVM (CS-PC-SVM) to jointly address data imbalance and structural heterogeneity. The proposed method introduces structural alignment between oversampling and classification by generating synthetic samples only within reliable clusters and modeling the minority class as multiple subgroups. This design reduces noise, preserves local data structure, and enables more adaptive decision boundaries. Experimental results on five benchmark datasets demonstrate that the proposed approach achieves consistently strong and balanced performance, with Accuracy up to 0.981, G-Mean 0.969, Precision 0.967, and Recall 0.962, outperforming or remaining competitive with existing methods. These findings highlight the effectiveness of integrating structure-guided data generation with structure-aware classification for improving robustness in imbalanced learning scenarios.
Seq2Seq encoder–decoder with adaptive metaheuristic for scheduling optimization Arief Kelik Nugroho; Nurul Hidayat; Nofiyati Nofiyati
International Journal of Advances in Intelligent Informatics Vol 12, No 3 (2026): August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i3.2224

Abstract

The Job Shop Scheduling Problem (JSSP) is a combinatorial optimization problem that is NP-hard and highly complex, particularly in modern manufacturing environments associated with industry. Conventional metaheuristic methods such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are capable of generating solutions in a relatively short time; however, they often face limitations in solution quality due to premature convergence and limited adaptability to dynamic problem conditions. In contrast, deep learning approaches such as Sequence-to-Sequence (Seq2Seq) offer strong representational capabilities for modeling operation sequences, although they still encounter challenges related to training stability and generalization. This study proposes a hybrid approach that integrates a Seq2Seq encoder–decoder architecture with an adaptive metaheuristic mechanism to enhance scheduling optimization performance. The Seq2Seq model is utilized to learn underlying patterns in operation sequences, while the adaptive mechanism dynamically adjusts search parameters based on makespan evaluation. The experiments are conducted using datasets from the OR-Library, specifically the 10×10 and 15×15 scenarios, to evaluate the performance and scalability of the proposed method. The experimental results demonstrate that the Seq2Seq + adaptive metaheuristic approach consistently produces lower makespan values compared to GA and PSO. For the 10×10 dataset, the proposed method achieves a makespan of 932, outperforming GA (1095) and PSO (1047). Similarly, for the 15×15 dataset, it attains a makespan of 1050, which is better than GA (1250) and PSO (1200). Although the proposed approach requires slightly longer computational time, the improvement in solution quality indicates that it effectively balances exploration and exploitation.
Comparative study of energy and accuracy in spatio-temporal models for engagement classification Fahrur Aslami; Peeraya Sripian
International Journal of Advances in Intelligent Informatics Vol 12, No 3 (2026): August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i3.2402

Abstract

Energy consumption is a growing concern in deep learning, motivating the Green AI paradigm, where models are evaluated not only based on predictive performance but also on energy efficiency. Most existing evaluations focus on spatial tasks and do not fully capture the computational and temporal costs of video-based workflows. This study presents a systematic energy evaluation of spatio-temporal deep learning for video-based student engagement classification. Using the DAiSEE dataset, we compare EfficientNet variants (B0–B7) as frame-level feature extractors and model temporal dynamics with LSTMs on fixed-length sequences. GPU power consumption is measured during training using nvidia-smi, and energy efficiency is quantified with the Kappa Energy Index (KEI), defined as Cohen's Kappa divided by energy consumption (kWh). The results show a clear trade-off between accuracy and energy: EfficientNet-B7 achieves the highest accuracy (0.62) but incurs the highest energy cost (≈5 kWh), resulting in a low KEI, while EfficientNet-B0 achieves competitive accuracy (0.59) with the highest KEI (2.147) due to its low energy consumption (≈0.38 kWh). EfficientNet-B3 strikes a favorable balance (accuracy ≈ 0.61, KEI = 0.747), outperforming larger models under resource constraints. These findings suggest that deeper models do not always maximize energy efficiency, and the KEI value provides a practical metric to guide the selection of energy-efficient models.
Random perturbation bee colony optimized k-means approach for optimized MSME data clustering Lisna lisna Zahrotun; Ika Arfiani; Dwi Normawati
International Journal of Advances in Intelligent Informatics Vol 12, No 3 (2026): August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i3.1781

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play an important role in the economy and need strategic support to grow. The K-Means method is often used for cluster analysis, but has a weakness in determining the initial cluster centre. This research proposes the K-Means Random Perturbation Bee Colony Optimisation (RPBCO) method to overcome the problem. The test results show an increase in cluster accuracy, with the Silhouette Coefficient score increasing by 40.9% (from 0.171 to 0.245). Wilcoxon testing also showed a z-value of -1.342, confirming that RPBCO is superior to standard K-Means. This method proved effective in optimising clusters on a heterogeneous MSMEs dataset. The analysis revealed that creative MSMEs thrive on Instagram, while retail MSMEs perform best on Shopee. To expand the market reach of MSMEs beyond Java and internationally, marketing strategies tailored to the characteristics of the region and target market are required. Creative MSMEs can utilise Instagram Ads to target ASEAN countries, while retail MSMEs can focus on global platforms such as Amazon or Alibaba. Digital literacy training, platform algorithm workshops, and collaborative marketing campaigns can strengthen this strategy. These measures are expected to increase turnover and support the sustainable growth of MSMEs, positively impacting more than one million businesses.

Page 38 of 38 | Total Record : 374

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