Hasil untuk "Islam"

Tahir Hussain, Saddam Hussain Khan

Accurate QA over classical Islamic texts remains challenging due to domain specific semantics, long context dependencies, and concept sensitive reasoning. Therefore, a new CGRA DeBERTa, a concept guided residual domain augmentation transformer framework, is proposed that enhances theological QA over Hadith corpora. The CGRA DeBERTa builds on a customized DeBERTa transformer backbone with lightweight LoRA based adaptations and a residual concept aware gating mechanism. The customized DeBERTa embedding block learns global and positional context, while Concept Guided Residual Blocks incorporate theological priors from a curated Islamic Concept Dictionary of 12 core terms. Moreover, the Concept Gating Mechanism selectively amplifies semantically critical tokens via importance weighted attention, applying differential scaling from 1.04 to 3.00. This design preserves contextual integrity, strengthens domain-specific semantic representations, and enables accurate, efficient span extraction while maintaining computational efficiency. This paper reports the results of training CGRA using a specially constructed dataset of 42591 QA pairs from the text of Sahih alBukhari and Sahih Muslim. While BERT achieved an EM score of 75.87 and DeBERTa one of 89.77, our model scored 97.85 and thus surpassed them by 8.08 on an absolute scale, all while adding approximately 8 inference overhead due to parameter efficient gating. The qualitative evaluation noted better extraction and discrimination and theological precision. This study presents Hadith QA systems that are efficient, interpretable, and accurate and that scale provide educational materials with necessary theological nuance.

Azwad Anjum Islam, Tisa Islam Erana

We describe our system for SemEval-2026 Task 5, which requires rating the plausibility of given word senses of homonyms in short stories on a 5-point Likert scale. Systems are evaluated by the unweighted average of accuracy (within one standard deviation of mean human judgments) and Spearman Rank Correlation. We explore three prompting strategies using multiple closed-source commercial LLMs: (i) a baseline zero-shot setup, (ii) Chain-of-Thought (CoT) style prompting with structured reasoning, and (iii) a comparative prompting strategy for evaluating candidate word senses simultaneously. Furthermore, to account for the substantial inter-annotator variation present in the gold labels, we propose an ensemble setup by averaging model predictions. Our best official system, comprising an ensemble of LLMs across all three prompting strategies, placed 4th on the competition leaderboard with 0.88 accuracy and 0.83 Spearman's rho (0.86 average). Post-competition experiments with additional models further improved this performance to 0.92 accuracy and 0.85 Spearman's rho (0.89 average). We find that comparative prompting consistently improved performance across model families, and model ensembling significantly enhanced alignment with mean human judgments, suggesting that LLM ensembles are especially well suited for subjective semantic evaluation tasks involving multiple annotators.

Momen Khandoker Ope, Akif Islam, Mohd Ruhul Ameen et al.

Cultural heritage restoration in Bangladesh faces a dual challenge of limited resources and scarce technical expertise. Traditional 3D digitization methods, such as photogrammetry or LiDAR scanning, require expensive hardware, expert operators, and extensive on-site access, which are often infeasible in developing contexts. As a result, many of Bangladesh's architectural treasures, from the Paharpur Buddhist Monastery to Ahsan Manzil, remain vulnerable to decay and inaccessible in digital form. This paper introduces Oitijjo-3D, a cost-free generative AI framework that democratizes 3D cultural preservation. By using publicly available Google Street View imagery, Oitijjo-3D reconstructs faithful 3D models of heritage structures through a two-stage pipeline - multimodal visual reasoning with Gemini 2.5 Flash Image for structure-texture synthesis, and neural image-to-3D generation through Hexagen for geometry recovery. The system produces photorealistic, metrically coherent reconstructions in seconds, achieving significant speedups compared to conventional Structure-from-Motion pipelines, without requiring any specialized hardware or expert supervision. Experiments on landmarks such as Ahsan Manzil, Choto Sona Mosque, and Paharpur demonstrate that Oitijjo-3D preserves both visual and structural fidelity while drastically lowering economic and technical barriers. By turning open imagery into digital heritage, this work reframes preservation as a community-driven, AI-assisted act of cultural continuity for resource-limited nations.

Mahmudul Islam Masum, Miad Islam

As local AI grows in popularity, there is a critical gap between the benchmark performance of object detectors and their practical viability on consumer-grade hardware. While models like YOLOv10s promise real-time speeds, these metrics are typically achieved on high-power, desktop-class GPUs. This paper reveals that on resource-constrained systems, such as laptops with RTX 4060 GPUs, performance is not compute-bound but is instead dominated by system-level bottlenecks, as illustrated by a simple bottleneck test. To overcome this hardware-level constraint, we introduce a Two-Pass Adaptive Inference algorithm, a model-independent approach that requires no architectural changes. This study mainly focuses on adaptive inference strategies and undertakes a comparative analysis of architectural early-exit and resolution-adaptive routing, highlighting their respective trade-offs within a unified evaluation framework. The system uses a fast, low-resolution pass and only escalates to a high-resolution model pass when detection confidence is low. On a 5000-image COCO dataset, our method achieves a 1.85x speedup over a PyTorch Early-Exit baseline, with a modest mAP loss of 5.51%. This work provides a practical and reproducible blueprint for deploying high-performance, real-time AI on consumer-grade devices by shifting the focus from pure model optimization to hardware-aware inference strategies that maximize throughput.

Yifan Wang, Muhammad Sakib Shahriar, Salma Soliman et al.

The exponential growth of edge artificial intelligence demands material-focused solutions to overcome energy consumption and latency limitations when processing real-time temporal data. Physical reservoir computing (PRC) offers an energy-efficient paradigm but faces challenges due to limited device scalability and reconfigurability. Additionally, reservoir and readout layers require memory of different timescales, short-term and long-term respectively - a material challenge hindering CMOS-compatible implementations. This work demonstrates a CMOS-compatible ferroelectric transistor using hafnium-zirconium-oxide (HZO) and silicon, enabling dual-memory operation. This system exhibits non-volatile long-term memory (LTM) from ferroelectric HZO polarization and volatile short-term memory (STM) from engineered non-quasi-static (NQS) channel-charge relaxation driven by gate-source/drain overlap capacitance. Ferroelectric polarization acts as non-volatile programming of volatile dynamics: by modulating threshold voltage, the ferroelectric state deterministically switches the NQS time constant and computational behavior between paired-pulse facilitation (PPF) and depression (PPD). This establishes a generalizable material-design principle applicable to diverse ferroelectric-semiconductor heterostructures, extending beyond silicon to oxide semiconductors and heterogeneously-integrated systems. The device solves second-order nonlinear tasks with 3.69 x 10^-3 normalized error using only 16 reservoir states - ~5x reduction - achieving 20 us response time (~1000x faster) and 1.5 x 10^-7 J energy consumption, providing an immediately manufacturable pathway for neuromorphic hardware and energy-efficient edge intelligence.

Indah Sari Dewi. Z

: Juvenile delinquency is behavior carried out by young children due to social neglect so that deviant behavior is formed. Delinquency does not appear without factors influencing it, one of which is when adolescents conform to their peers. The influence of peer conformity on juvenile delinquency is weakened when adolescents have emotional intelligence. By having emotional intelligence, adolescents can express their emotions appropriately and have adaptive emotional regulation. The purpose of this study was to find out whether emotional intelligence can moderate the influence of peer conformity on juvenile delinquency. This study is quantitative with research subjects of as many as 350 adolescents. Measurement using Self Report Delinquency (SRD), Peer Conformity Disposition Scale (PCSD), and Wong and Law Emotional Intelligence Scale (WLEIS) instruments. Data analysis using Moderated Regression Analysis (MRA). The results showed that emotional intelligence did not act as a moderator on the influence of peer conformity on juvenile delinquency (β=0.558; p=0.081).

Angga Yesaya, Vivi Arisandhy, David Try Liputra

Penelitian mengenai pemilihan atau penentuan supplier dengan menggunakan metode AHP maupun kombinasinya dengan metode TOPSIS dalam penentuan supplier di industri penghasil kasur pegas belum banyak dilakukan. Penelitian ini akan membahas penerapan gabungan metode AHP dan TOPSIS dalam penentuan supplier di industri penghasil kasur pegas sehingga diharapkan hasil yang diperoleh lebih akurat dibandingkan jika hanya menggunakan salah satu metode. PT XYZ melakukan pemesanan bahan baku kawat kepada 4 supplier dan mengalokasikan pesanan dengan jumlah yang sama rata. Langkah pertama yang dilakukan adalah penentuan kriteria dan subkriteria untuk pemilihan supplier dan penyusunan hierarki. Selanjutnya dilakukan penyusunan kuesioner perbandingan berpasangan untuk menentukan tingkat kepentingan kriteria dan subkriteria. Setelah dilakukan perhitungan dengan metode AHP, diperoleh bobot kriteria dan subkriteria yang menjadi input untuk metode TOPSIS. TOPSIS digunakan untuk mengetahui supplier yang memiliki kinerja terbaik. Hasil yang diperoleh adalah kriteria Price dan subkriteria Harga Bahan Baku mempunyai tingkat kepentingan tertinggi dalam penilaian kinerja supplier kawat karena memiliki bobot tertinggi. Perusahaan dapat tetap menggunakan kebijakan multi-supplier dengan memprioritaskan supplier 1, namun ada beberapa kriteria dari kinerja supplier 1 yang perlu ditingkatkan jika dibandingkan dengan supplier lainnya. Kata Kunci: AHP, Bahan Baku Utama, Kasur Pegas, Supplier, TOPSIS.

Usep Suherman, Eliva Sukma Cipta, Saeful Anwar et al.

In the face of increasingly complex educational challenges, there is a growing demand for leadership models that integrate ethical and humanistic values, particularly in Islamic elementary schools. This study explores the operationalisation of kindness-based leadership at MI Fitrah Insani, Leles, Garut, as a strategic response to the limitations of performance-oriented and hierarchical leadership paradigms. This study addresses the gaps in the literature regarding the implementation of ethical leadership grounded in Islamic values by examining how empathy, participatory communication, and ethical responsibility shape school culture and educational quality. Using a qualitative case study approach, data were collected through in-depth interviews, field observations, and a document analysis. Triangulation of these methods enabled a comprehensive understanding of institutional dynamics, leadership practices, and their impact on school climate, teacher motivation, and student engagement. The findings reveal that kindness-based leadership at MI Fitrah Insani fosters an emotionally safe and inclusive school environment. Through participatory decision-making, structured communication, and consistent appreciation practices, the leadership model contributes to improved teacher loyalty, pedagogical innovation, and heightened student participation. Despite structural, cultural, and operational barriers such as bureaucratic rigidity and limited professional development, adaptive strategies, including ethical leadership training, policy reform, and digital communication platforms, have enhanced the effectiveness and sustainability of this model. This study concludes that kindness-oriented leadership is not merely a normative ideal but a transformative practice that aligns with Islamic ethical traditions and addresses the academic and moral dimensions of education. The findings offer practical implications for Islamic schools seeking to cultivate character-driven and ethically grounded leadership.

Kintoko Kintoko, Ginanjar Zukhruf Saputri, Putri Rachma Novitasari et al.

Despite the advancements in conventional medicine, medicinal plants continue to play an important role in treating different human ailments, particularly in developing nations. This is based on locals' knowledge of medicinal plants for treating various ailments. Ethnomedicine is a branch of research investigating society's local wisdom for maintaining its health. According to diverse field studies, 40 different varieties of plants have been discovered that the native inhabitants of Kulon Progo Regency think are medicinal. The purpose of this research was to identify therapeutic herbs used by the Kulon Progo population. Traditional healers and members of the Kulon Progo village were interviewed as part of this study. The ethnomedicine data was analyzed using Use Value (UV), Frequency of Citation (FC), and Plant Part Value (PPV). The analytical results suggest that turmeric (0.78), galangal (0.67), and ginger (0.67) are the most important plants to society. Rhizomes (38.10%) and leaves (34.52) are the most commonly employed plant parts for medicinal purposes. Boiling it (47.06%) produces herbal medication from the plant extract.

Niful Islam, Mohaiminul Islam Bhuiyan, Jarin Tasnim Raya et al.

Brain tumors are one of the most common diseases that lead to early death if not diagnosed at an early stage. Traditional diagnostic approaches are extremely time-consuming and prone to errors. In this context, computer vision-based approaches have emerged as an effective tool for accurate brain tumor classification. While some of the existing solutions demonstrate noteworthy accuracy, the models become infeasible to deploy in areas where computational resources are limited. This research addresses the need for accurate and fast classification of brain tumors with a priority of deploying the model in technologically underdeveloped regions. The research presents a novel architecture for precise brain tumor classification fusing pretrained ResNet152V2 and modified VGG16 models. The proposed architecture undergoes a diligent fine-tuning process that ensures fine gradients are preserved in deep neural networks, which are essential for effective brain tumor classification. The proposed solution incorporates various image processing techniques to improve image quality and achieves an astounding accuracy of 98.36% and 98.04% in Figshare and Kaggle datasets respectively. This architecture stands out for having a streamlined profile, with only 2.8 million trainable parameters. We have leveraged 8-bit quantization to produce a model of size 73.881 MB, significantly reducing it from the previous size of 289.45 MB, ensuring smooth deployment in edge devices even in resource-constrained areas. Additionally, the use of Grad-CAM improves the interpretability of the model, offering insightful information regarding its decision-making process. Owing to its high discriminative ability, this model can be a reliable option for accurate brain tumor classification.

Vera Pavlova, Mohammed Makhlouf

The widespread use of large language models (LLMs) has dramatically improved many applications of Natural Language Processing (NLP), including Information Retrieval (IR). However, domains that are not driven by commercial interest often lag behind in benefiting from AI-powered solutions. One such area is religious and heritage corpora. Alongside similar domains, Islamic literature holds significant cultural value and is regularly utilized by scholars and the general public. Navigating this extensive amount of text is challenging, and there is currently no unified resource that allows for easy searching of this data using advanced AI tools. This work focuses on the development of a multilingual non-profit IR system for the Islamic domain. This process brings a few major challenges, such as preparing multilingual domain-specific corpora when data is limited in certain languages, deploying a model on resource-constrained devices, and enabling fast search on a limited budget. By employing methods like continued pre-training for domain adaptation and language reduction to decrease model size, a lightweight multilingual retrieval model was prepared, demonstrating superior performance compared to larger models pre-trained on general domain data. Furthermore, evaluating the proposed architecture that utilizes Rust Language capabilities shows the possibility of implementing efficient semantic search in a low-resource setting.

Azmine Toushik Wasi, Raima Islam, Mst Rafia Islam

Individuality and personalization comprise the distinctive characteristics that make each writer unique and influence their words in order to effectively engage readers while conveying authenticity. However, our growing reliance on LLM-based writing assistants risks compromising our creativity and individuality over time. We often overlook the negative impacts of this trend on our creativity and uniqueness, despite the possible consequences. This study investigates these concerns by performing a brief survey to explore different perspectives and concepts, as well as trying to understand people's viewpoints, in conjunction with past studies in the area. Addressing these issues is essential for improving human-computer interaction systems and enhancing writing assistants for personalization and individuality.

Azmine Toushik Wasi, Mst Rafia Islam, Raima Islam

Sense of ownership in writing confines our investment of thoughts, time, and contribution, leading to attachment to the output. However, using writing assistants introduces a mental dilemma, as some content isn't directly our creation. For instance, we tend to credit Large Language Models (LLMs) more in creative tasks, even though all tasks are equal for them. Additionally, while we may not claim complete ownership of LLM-generated content, we freely claim authorship. We conduct a short survey to examine these issues and understand underlying cognitive processes in order to gain a better knowledge of human-computer interaction in writing and improve writing aid systems.

Niful Islam, Md. Mehedi Hasan Jony, Emam Hasan et al.

Improper disposal of e-waste poses global environmental and health risks, raising serious concerns. The accurate classification of e-waste images is critical for efficient management and recycling. In this paper, we have presented a comprehensive dataset comprised of eight different classes of images of electronic devices named the E-Waste Vision Dataset. We have also presented EWasteNet, a novel two-stream approach for precise e-waste image classification based on a data-efficient image transformer (DeiT). The first stream of EWasteNet passes through a sobel operator that detects the edges while the second stream is directed through an Atrous Spatial Pyramid Pooling and attention block where multi-scale contextual information is captured. We train both of the streams simultaneously and their features are merged at the decision level. The DeiT is used as the backbone of both streams. Extensive analysis of the e-waste dataset indicates the usefulness of our method, providing 96% accuracy in e-waste classification. The proposed approach demonstrates significant usefulness in addressing the global concern of e-waste management. It facilitates efficient waste management and recycling by accurately classifying e-waste images, reducing health and safety hazards associated with improper disposal.

Tri Sugiarti Ramadhan, Nanik Wahyuningtiyas

Purpose: This study aims to determine and analyze the effect of price, brand image and product quality on iPhone purchasing decisions in the city of Malang. The population in this study are iPhone consumers who have purchased products at Fineapple id. Design/methodology/approach: The data collection method in this study used a questionnaire with a sample of 75 respondents. The independent variable in this study is the effect of price, brand image and product quality and the dependent variable is the purchase decision. The analytical method used in this study is multiple linear regression analysis and processed using SPSS 26. Findings: The results of this study simultaneously show that the effect of price, brand image and product quality has a positive and significant effect on purchasing decisions. Partially, Brand Image and Product Quality have a significant effect on iPhone Purchase Decisions, while Price has no effect on iPhone Purchase Decisions. Paper type: Research paper

Ahmed Abbas, Marif Daula Siddique, Shirazul Islam et al.

Abstract Galvanic isolation is an integral part for the grid connected solar PV system. With the advancement of multilevel inverters for the grid‐connected application, the multilevel inverters having isolation are not sufficiently discussed in the literature. Here, a 15‐level isolated multilevel inverter topology requiring only 13 switches is proposed. The proposed single‐phase isolated converter requires reduced switches to generate 15‐level ac output voltage with voltage gain of 7. Comparatively, the switches connected in the proposed converter undergo less voltage stress as compared to the MLIs reported in the literature. A comparison of the proposed converter structure to the state‐of‐the art MLIs described in the literature is included. The experimental results captured on a low‐power laboratory prototype are used to validate the performance of the proposed converter. The claimed efficiency of the converter calculated using simulation results is found to be 97.1%. However, the efficiency calculated using experimental results is 95.2% at 700 W.

Arif Kuswanto

Kemampuan awal pemahaman anak kelas VII yang masih belum sepenuhnya mengerti akan sistem kurikulum yang baru serta penyesuaian dan adaptasi dari jenjang sekolah dasar dan sekarang berada di jenjang menengah pertama menjadi salah satu faktornya. Mereka mengira bahwa pemisahan kelompok sesuai dengan hasil tes diagnostic adalah bagian dari pilih kasih guru karena perbedaan jumlah setiap kelompok diaggap mereka kurang adil sehingga pembagian tugas pemahaman akan lebih sulit dibanding kelompok lain. pendekatan kualitatif digunakan untuk memahami permasalahan tersebut secara mendalam. Metode literature review dengan penganalisisan buku dan jurnal digunakan untuk menyempurnakan penelitian ini. Data sekunder merupakan data yang diperoleh oleh pihak lain dan kita sebagai researcher tidak secara langsung melakukan pengumpulan data tersebut. Jadi, orang bilang data sekunder adalah data tangan kedua dimana literatur seperti jurnal,buku dan media pendukung lainnya, Dari hasil pembagian kelompok tersebut guru menemukan fenomena baru yang dapat diamati seperti mereka menganggap hal yang dilakukan guru ini termasuk salah satu bentuk pilih kasih guru terhadap siswa, ada juga yang merasa keberatan akan konsep ini mereka pikir hal ini menyulitkan mereka sebab perbedaan jumlah kelompok yang ada, namun guru menjelaskan sedikit demi sedikit konsep baru tersebut sesuai dengan kurikulum yang dipakai oleh satuan sekolah, penjelasan secara bertahap itulah yang akan memunculkan pemahaman baru peserta didik sehingga tidaak beranggapan buruk terhadap konsep yang sedang diterapkan.

Amanda Qomari Shekarsari, Sri Padmantyo

A teenager is someone who is in the transition period from child to adult. Many teenagers wrongly attribute it to their lack of knowledge in the field of finance. Teenagers often follow the times and impose lifestyles. A hedonic lifestyle can be defined as a lifestyle that values ​​pleasure. This kind of lifestyle requires a lot to remain a consumer. Therefore, teenagers need many boundaries, one of which is religious teachings or individual religiosity. This research is important because financial distress in teenagers can cause many negative things. This research aims to determine the effect of a hedonistic lifestyle on financial distress through another variable, namely religiosity. This research used a sample of 241 teenagers. The results of this research are that hedonism has a positive effect on financial distress, and religiosity increases the relationship between financial distress and hedonism. This research influences teenagers' lifestyles so they don't make mistakes in socializing. It is hoped that this research can be a guide for teenagers in managing their finances, especially in fulfilling their lifestyle. In future research, it is hoped that researchers will increase the number of samples and expand the research objects, they can choose research objects in Central Java or Indonesia. Researchers are aware of the limitations in data collection, it is hoped that future researchers can expand the research objects and subjects. The conclusion, the hedonistic lifestyle has a positive effect on financial distress and religiosity is able to moderate the influence of the hedonistic lifestyle on financial distress, namely weakening the relationship between the two

Alwan Azhari, Tuti Anggraini

Customers experience many problems in making direct transactions through ATM outlets such as waiting for queues at banks, remote home locations, checking bank statements and several other problems. The research aims to see the effect of virtual account billing, payment transactions, and e-statements on customer convenience in applying m-banking. This study used quantitative research methods and distributed questionnaires to 100 respondents of Sharia Banking study program students of North Sumatra State Islamic University 2019 and then processed on the SPSS application Version 29.0. This study shows that simultaneously all variables used, namely virtual account billing, payment transactions, and e-statements simultaneously have a significant influence shown by the results of the F value of the table of 20.782 > 2.70. Partially, payment transaction variables and e-statements have a significant effect on customer convenience. While the virtual account variable has no influence on the ease of customers in applying m-banking.

Md. Towfiqul Islam, Tanzim Ahmed, A. B. M. Raihanur Rashid et al.

Due to the massive explanation of artificial intelligence, machine learning technology is being used in various areas of our day-to-day life. In the world, there are a lot of scenarios where a simple crime can be prevented before it may even happen or find the person responsible for it. A face is one distinctive feature that we have and can differentiate easily among many other species. But not just different species, it also plays a significant role in determining someone from the same species as us, humans. Regarding this critical feature, a single problem occurs most often nowadays. When the camera is pointed, it cannot detect a person's face, and it becomes a poor image. On the other hand, where there was a robbery and a security camera installed, the robber's identity is almost indistinguishable due to the low-quality camera. But just making an excellent algorithm to work and detecting a face reduces the cost of hardware, and it doesn't cost that much to focus on that area. Facial recognition, widget control, and such can be done by detecting the face correctly. This study aims to create and enhance a machine learning model that correctly recognizes faces. Total 627 Data have been collected from different Bangladeshi people's faces on four angels. In this work, CNN, Harr Cascade, Cascaded CNN, Deep CNN & MTCNN are these five machine learning approaches implemented to get the best accuracy of our dataset. After creating and running the model, Multi-Task Convolutional Neural Network (MTCNN) achieved 96.2% best model accuracy with training data rather than other machine learning models.