University of Riau

Clustering is a fundamental technique for identifying structures within datasets, with Fuzzy C-Means (FCM) being widely used due to its simplicity and ease of implementation. However, FCM suffers from sensitivity to noise, outliers, and initialization issues. This study introduces an enhanced model, IFCM with Chebyshev, which integrates fuzzy Chebyshev distance and intuitionistic fuzzy sets. Data are first normalized using MinMax scaling, and dimensionality reduction is applied to handle high-dimensional datasets. The optimal cluster number is determined using the Elbow method. The proposed algorithm is evaluated against standard FCM, FCM with Chebyshev, and IFCM. A genomic dataset related to prostate cancer is used as a numerical example. Results show that IFCM with Chebyshev achieves the highest clustering accuracy (88.9%), outperforming IFCM (85.7%), FCM with Chebyshev (81.2%), and FCM (78.5%). It also yields superior cluster validity indices, recording the highest Partition Coefficient (0.74) and the lowest Partition Entropy (0.52), indicating clearer cluster separation. Although IFCM with Chebyshev incurs higher computational cost (1.58s), sensitivity analysis demonstrates faster convergence around five clusters, suggesting an optimal structure. Memory consumption remains consistent at 295 MB across cluster settings, highlighting efficiency for large-scale applications. Overall, combining Chebyshev distance and IFCM enhances clustering robustness and accuracy, particularly in noisy or complex data environments, making it a promising approach for advanced data analysis tasks.

Plastic waste remains one of the most pressing environmental challenges due to its persistence and the lack of effective recycling solutions. This study explores the use of recycled low-density polyethylene (LDPE) plastic waste as a binder substitute for cement in the production of eco-friendly paving blocks. The aim is to evaluate compressive strength, water absorption, density, and thermal resistance, as well as to determine the optimal mix composition that meets the Indonesian National Standard (SNI 03-0691-1996). A quantitative experimental approach was employed, using six variations of LDPE-to-sand ratios and standard laboratory testing procedures. The results showed that the paving block made from a 40

In this work, bio-colloidal silver nanoparticles (AgNPs) were synthesized using Matoa (Pometia pinnata) leaf extract as a reducing and stabilizing agent under microwave irradiation to promote rapid AgNPs formation. The influence of microwave irradiation powers (360, 540, and 720 W) on the optical, structural, and morphological properties of the synthesized AgNPs was systematically investigated. The AgNPs formation was characterized by yellowish-brown colloids formation with surface plasmon resonant (SPR) bands centered around 430 nm, as detected by UV-Vis spectroscopy. FTIR analysis revealed the presence of C-H and O-H functional groups from polyphenols and flavonoids in Ag+ ion reduction. XRD pattern revealed a face-centered cubic (FCC) crystalline structure of AgNPs, while TEM images showed predominantly quasi-spherical morphology with average diameter of 58–62 nm. The colorimetric sensing performance was examined over Hg2+ concentration ranging from 1–100 ppm. Upon Hg2+ exposure, the AgNPs colloids demonstrated a progressive decrease in SPR intensity along with visible discoloration in the 350–550 nm region. Nanoparticles synthesized at 720 W exhibited the highest sensitivity of AgNPs, which displayed obvious discoloration observed in the range 40–100 ppm of Hg2+ with steepest slope at 8 × 10–4. These findings highlight the potential of microwave-assisted Pometia pinnata-derived AgNPs as a green and eco-friendly approach for the colorimetric sensing of mercury ions.

This study reports on the synthesis and characterization of the Fe3O4/activated carbon composites derived from oil palm empty fruit bunch (EFB) waste and natural iron sand for copper (Cu2+) adsorption. Composites with ratios of 1:1, 5:3, and 3:5 (EFB carbon: iron sand) were prepared. Iron sand was processed by co-precipitation to produce Fe3O4 magnetite, while EFB carbon was activated using 1 N CH3COONa. The structural and functional properties were analyzed by XRD, FTIR, and SEM-EDS, while adsorption efficiency was measured using AAS. Results confirmed Fe3O4 formation with a cubic structure (a = 8.3750 & Aring;), particle sizes of 79.97-98.91 mu m, and excellent copper removal efficiency of 99.8% with an adsorption capacity of 0.377 mg/g.

This article reconsiders Sunan Kalijaga's Lir Ilir as a distinct form of Islamic educational philosophy emerging from Javanese cultural thought. Rather than viewing the song merely as religious preaching, it is approached here as a pedagogical practice that embodies a philosophy of learning through art, metaphor, and feeling. Lir Ilir invites ethical awakening and spiritual renewal through aesthetic experience, offering a model of education grounded in the cultivation of the self and the community. Such an approach contrasts sharply with textual and rationalist traditions that have dominated modern and colonial frameworks of education. By examining Lir Ilir within the broader discourse of Islamic philosophy of education, this article argues that Kalijaga's pedagogy challenges epistemic injustice the systematic exclusion of local, embodied, and poetic ways of knowing from legitimate educational theory. Reclaiming Lir Ilir as a philosophical and educational text reopens space for alternative epistemologies within Islamic and global educational thought. It demonstrates how indigenous forms of pedagogy can reconfigure what counts as knowledge, teaching, and learning in postcolonial contexts.