Garuda - Garba Rujukan Digital

Sistem e-Learning pada Mata Pelajaran Matematika untuk SMP Berbasis Web Husin, Nanang; Fazlurrahman, Hujjatullah; Dhenabayu, Riska
Jurnal Esensi Infokom : Jurnal Esensi Sistem Informasi dan Sistem Komputer Vol 8 No 2 (2024): Jurnal Esensi Infokom : Jurnal esensi sistem informasi dan sistem komputer
Publisher : Lembaga Riset dan Pengabdian Masyarakat Institut Bisnis Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55886/infokom.v8i2.929

e-Learning merupakan suatu metode pembelajaran jarak jauh dengan menggunakan teknologi internet, sejauh ini e-Learning telah banyak di terapkan pada institusi dan perusahaan. e-Learning sudah menjadi suatu alternatif yang efektif dalam meningkatkan mutu atau keberhasilan proses belajar mengajar. Proses pembelajaran matematika pada Sekolah Menengah Pertama (SMP) masih menggunakan metode konvensional melalui tatap muka langsung antara guru dan siswa. Metode pembelajaran seperti ini akan membuat keterbatasan proses penyampaian ilmu pengetahuan. Penelitian bertujuan untuk merancang e-Learning yang berfokus pada pembelajaran matematika berbasis web di SMP, dan sistem dapat memudahkan siswa dalam mempelajari matapelajaran dimanapun dan kapanpun selama terhubung internet. ini juga menggunakan jenis penelitian diskriptif, dan metode pengumpulan data pada penulisan ini dengan observasi, wawancara dan kuesioner. Diharapkan dari hasil penelitian ini akan bisa didapatkan sistem e-Learning pembelajaran berbasis web di SMP dapat memudahkan siswa dalam mempelajari dan memahami matapelajaran matematika.

Systematic Review on Breast Cancer Classification Using Random Forest and Extreme Learning Machine: Cost Sensitivity and Computational Complexity Perspectives Budhiraja, Irsyad; Dhenabayu, Riska
Journal of Digital Business and Innovation Management Vol. 4 No. 2 (2025): December 2025
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Breast cancer remains one of the most common and deadly cancers affecting women worldwide. Early detection and accurate diagnosis are essential to improve patient survival rates and reduce long-term treatment costs. With the advancement of digital technologies, machine learning (ML) has emerged as a powerful tool in breast cancer classification. Among various ML algorithms, Random Forest (RF) and Extreme Learning Machine (ELM) have gained prominence due to their predictive capabilities. This systematic literature review aims to compare the classification performance of RF and ELM, focusing on cost sensitivity and computational complexity. Using PRISMA guidelines, 60 peer-reviewed articles published between 2013 and 2024 were analyzed. The findings show that RF generally offers high accuracy and robustness against overfitting, making it suitable for complex clinical datasets. Conversely, ELM excels in training speed and computational efficiency, making it ideal for real-time diagnostic systems. However, both methods face challenges in handling imbalanced data, where misclassification of malignant cases can be fatal. Cost-sensitive learning strategies are shown to improve model sensitivity toward minority classes, though their integration into ELM remains limited. Furthermore, computational efficiency is a critical factor, particularly in resource-constrained medical environments. This review provides a thematic synthesis of current research and highlights future directions, such as developing hybrid models combining RF’s accuracy with ELM’s efficiency, and implementing explainable AI for trustworthy clinical adoption.

Implementasi QRIS sebagai Media Transaksi Digital yang Transparan dan Akuntabel pada Organisasi Sosial Indawati, Nurul; Indarwati, Tias Andarini; Paramita, R.A. Sista; Purwohandoko, Purwohandoko; Dhenabayu, Riska
Abimanyu : Jornal of Community Engagement Vol 3 No 2 (2022): August 2022
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/abi.v3n2.p16-23

Covid-19 merupakan pandemi yang menjadi realitas global yang menerjang tatanan kehidupan umat manusia dari level internasional, hingga level terkecil yaitu rumah tangga. TPQ-Al-Aqsha dan Masjid Baitul Makmur I adalah contoh organisasi sosial yang terkena dampak dari pandemi covid-19. Masalah yang dihadapi mitra TPQ Al-Aqsha yaitu mitra mengalami kesulitan dalam menyampaikan laporan keuangan dan pertanggungjawaban kegiatan, serta mempromosikan TPQ Al-Aqsha kepada calon donatur akibat adanya aturan PSBB dan social distancing yang membatasi kegiatan masyarakat. Kurangnya jamaah yang signifikan pada Masjid Baitul Makmur I Unesa juga menyebabkan infaq yang diterima menurun. Program IbM ini ditujukan untuk memberikan solusi untuk meningkatkan pemahaman dan ketrampilan mitra dalam bentuk pelatihan penerapan teknologi QRIS dan aplikasi financial technology Bebas Bayar. Hasil dari kegiatan ini berupa publikasi ilmiah, publikasi pada media massa, dan pamflet/poster digital.

Sistem e-Learning pada Mata Pelajaran Matematika untuk SMP Berbasis Web Husin, Nanang; Fazlurrahman, Hujjatullah; Dhenabayu, Riska
Jurnal Esensi Infokom : Jurnal Esensi Sistem Informasi dan Sistem Komputer Vol 8 No 2 (2024)
Publisher : Institut Bisnis Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55886/infokom.v8i2.929

e-Learning merupakan suatu metode pembelajaran jarak jauh dengan menggunakan teknologi internet, sejauh ini e-Learning telah banyak di terapkan pada institusi dan perusahaan. e-Learning sudah menjadi suatu alternatif yang efektif dalam meningkatkan mutu atau keberhasilan proses belajar mengajar. Proses pembelajaran matematika pada Sekolah Menengah Pertama (SMP) masih menggunakan metode konvensional melalui tatap muka langsung antara guru dan siswa. Metode pembelajaran seperti ini akan membuat keterbatasan proses penyampaian ilmu pengetahuan. Penelitian bertujuan untuk merancang e-Learning yang berfokus pada pembelajaran matematika berbasis web di SMP, dan sistem dapat memudahkan siswa dalam mempelajari matapelajaran dimanapun dan kapanpun selama terhubung internet. ini juga menggunakan jenis penelitian diskriptif, dan metode pengumpulan data pada penulisan ini dengan observasi, wawancara dan kuesioner. Diharapkan dari hasil penelitian ini akan bisa didapatkan sistem e-Learning pembelajaran berbasis web di SMP dapat memudahkan siswa dalam mempelajari dan memahami matapelajaran matematika.

THE EFFECT OF PERSONALIZED ARTIFICIAL INTELLIGENCE INTERACTION QUALITY ON IMPULSIVE STREAMING INTENTIONS MEDIATED BY AFFECTIVE RESPONSES Armmawadin, Irsyad Daffa; Dhenabayu, Riska
JIM UPB (Jurnal Ilmiah Manajemen Universitas Putera Batam) Vol 14 No 1 (2025): Volume 14 Nomor 1 Tahun 2025
Publisher : Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jimupb.v14i1.10964

This study aims to analyse the effect of artificial intelligence-based interaction quality and personalisation on impulsive purchase intention in social commerce live streaming, with affective aspects as mediating variables. A quantitative approach was used through Partial Least Squares–Structural Equation Modelling (PLS-SEM) analysis involving 105 respondents who were students at Surabaya State University. The results show that the quality of interaction and artificial intelligence-based personalisation have a positive effect on the affective aspect, which in turn increases impulsive buying intention. Both independent variables also have a direct positive effect on impulsive buying intention, while the affective aspect acts as a mediator that strengthens this relationship. This study has limitations in its focus on the TikTok platform and student respondents, so further research is recommended to expand the research object and population coverage. A total of five hypotheses were tested in this study, and all were accepted based on the PLS-SEM analysis results.

Evaluasi Model Faktor Laten dalam Kondisi Kelangkaan Data: Studi Kasus Rendahnya Pembelian Ulang pada E-Commerce Rosmalia, Tria Rizky; Dhenabayu, Riska; Fazlurrahman, Hujjatullah; Dewi, Renny Sari
JOM Vol 6 No 4 (2025): Indonesian Journal of Humanities and Social Sciences , December
Publisher : Universitas Islam Tribakti Lirboyo Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33367/ijhass.v6i4.8431

The accuracy of recommendation systems is vital for successful personalization in e-commerce. However, the low frequency of repeat purchases creates hight data sparsity, limiting models in capturing user preferences. This study compares two latent factor-based algorithms. Matrix Factorization (MF) and Neural Matrix Factorization (NeuMF), using the Olist transaction dataset through data preparation, k-core filtering, and leave last out splitting. Performance was evaluated using HR@10 and NDCG@10. Results show that MF outperforms NeuMF, achieving HR@10 of 0,057 and NDCG@10 of 0,133. Although NeuMD is more complex and represents a deeper learning-based approach, MF can still be more suitable in certain data conditions, especially when interaction are limited. These findings highlight that simpler models may remain more efficient under sparse data, while NeuMF requires richer interactions histories. The study emphasizes repeat purchase frequency as a key factor in designing adaptive reommendations systems.

Model Ensemble untuk Prediksi Risiko Diabetes dengan Pertimbangan Efisiensi Biaya Qosimah, Rofiatul; Dhenabayu, Riska; Kautsar, Achmad; Safitri, Anita
JOM Vol 6 No 4 (2025): Indonesian Journal of Humanities and Social Sciences , December
Publisher : Universitas Islam Tribakti Lirboyo Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33367/ijhass.v6i4.8505

This study addresses the growing global burden of diabetes by evaluating whether ensemble-based machine learning models can support reliable and cost-efficient early risk prediction. Moving beyond accuracy-centered evaluation, the study integrates cost-sensitive threshold optimization and probability calibration to enhance clinical relevance. Random Forest and XGBoost are evaluated using two datasets with contrasting population characteristics. Model performance is examined in terms of discriminative ability, calibration quality, and total misclassification cost. The results indicate that while XGBoost remains competitive on small-scale datasets, Random Forest provides more stable calibration and more consistent cost efficiency. These findings suggest that cost-sensitive and calibrated ensemble approaches have the potential to support more rational and economically efficient diabetes screening policies.

Conceptual Review of Artificial Intelligence Over Reliance: Opportunities and Challenges for Employee Competencies Apida, Apida; Dhenabayu, Riska; Ardelia, Abidah; Kartika, Aulia; Mahfuzhah, Fathiya Fairuz Shafa; Zahra, Nadiya Nafisa Az; Arifah, Ika Diyah Candra
Studi Ilmu Manajemen dan Organisasi Vol 6 No 4 (2026): Januari
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/simo.v6i4.4764

Purpose: This study aims to conceptually examine the phenomenon of AI over-reliance and its impact on employee competence in the digital era, particularly in human resource management. Research Methodology: A Systematic Literature Review (SLR) was conducted using 15 scientific articles selected for their relevance to AI over-reliance and human resource competencies. Results: The findings revealed that over-reliance on AI stems from four main factors: the perception of AI as a neutral authority, low AI literacy, automation bias that fosters excessive trust in technology, and system designs that discourage reflective user engagement. These factors contribute to reduced cognitive abilities, such as critical thinking and independent judgment, while diminishing human involvement in decision making. Furthermore, over-reliance on AI raises concerns about job displacement anxiety and promotes deskilling across sectors. Conclusions: Overreliance on AI challenges employee competence and decision-making capacity, necessitating strategic responses in system design, digital literacy enhancement, and human-AI collaboration frameworks. Limitations: This conceptual study is based solely on a literature review, limiting its empirical generalizability and contextual depth. Contributions: This study contributes to a deeper understanding of AI over-reliance and its implications for human resource management. This study offers insights for organizations to mitigate the negative effects of AI while leveraging it to enhance employee competence.

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