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All Journal al-Uqud : Journal of Islamic Economics JIM UPB (Jurnal Ilmiah Manajemen Universitas Putera Batam) Al-Hayat: Journal of Islamic Education Jurnal Manajemen dan Bisnis Antivirus : Jurnal Ilmiah Teknik Informatika ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Ekuilnomi Darmabakti : Junal Pengabdian dan Pemberdayaan Masyarakat International Journal of Economics, Management, Business, and Social Science Abimanyu: Journal of Community Engagement International Journal of Humanities Education and Social Sciences Malcom: Indonesian Journal of Machine Learning and Computer Science JOSAR (Journal of Students Academic Research) Journal of Digital Business and Innovation Management Indonesian Journal of Humanities and Social Sciences Jurnal Esensi Infokom : Jurnal Esensi Sistem Komputer dan Informasi
Riska Dhenabayu
Universitas Negeri Surabaya
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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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

Abstract

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.
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

Abstract

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

Abstract

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.  
Integration of WhatsApp Business API and Artificial Intelligence in a Assisted-Automated Letter Generation System for Village Governance Priambodo, Gumilang; Dewi, Renny Sari; Candra, Ika Diyah; Dhenabayu, Riska
ILKOMNIKA Vol 8 No 1 (2026): Volume 8, Number 1, April 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i1.876

Abstract

Conventional rural administration in Indonesia faces manual clerical bottlenecks, while existing digital solutions often fail due to the digital divide and an inability to process unstructured local dialects. To address these challenges, this study designed and evaluated an Intelligent Robotic Process Automation (IRPA) prototype for fully autonomous village correspondence, specifically handling seven distinct types of official letters. Utilizing the Design Science Research Methodology (DSRM), the proposed architecture integrates the WhatsApp Business API as an inclusive conversational interface, Google Gemini for cognitive intent classification, and the n8n low-code platform for cloud-based document orchestration. Functional evaluation using 20 test prompts, which represent real-world informal language, abbreviations, and typographical errors, demonstrated that the cognitive agent achieved 100% accuracy in intent recognition and boundary detection. Furthermore, the system significantly reduced administrative Turnaround Time (TAT) by approximately 77.5%, effectively transforming manual processes of 15 to 25 minutes into a 3 to 6 minutes automated cycle. Ultimately, this research offers three main contributions: an asynchronous architecture to lower user cognitive load, a deterministic prompt engineering method for public services, and empirical evidence of RPA efficiency in inclusive rural governance.
Sistem e-Learning pada Mata Pelajaran Matematika untuk SMP Berbasis Web Nanang Husin; Hujjatullah Fazlurrahman; Riska Dhenabayu
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

Abstract

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.
Evaluasi Augmentasi Generatif dan Oversampling pada Berbagai Arsitektur Model untuk Deteksi Penipuan Kartu Kredit : Evaluation of Generative Augmentation and Oversampling Across Model Architectures for Credit Card Fraud Detection Ramadhan, Gilang Bagus; Dhenabayu, Riska; Kautsar, Achmad; Fitro, Achmad; Ariyani, Nurul Fajrin
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2821

Abstract

Deteksi penipuan kartu kredit pada data dengan ketidakseimbangan kelas ekstrem merupakan tantangan karena model klasifikasi cenderung bias terhadap kelas mayoritas sehingga menurunkan kemampuan mendeteksi transaksi penipuan. Metode oversampling tradisional seperti Synthetic Minority Over-sampling Technique (SMOTE) banyak digunakan, tetapi sering belum mampu merepresentasikan kompleksitas distribusi data tabular. Sebaliknya, pendekatan generatif seperti Conditional Tabular Generative Adversarial Network (CTGAN) berpotensi menghasilkan data sintetis yang lebih realistis, meskipun evaluasinya pada berbagai model klasifikasi masih terbatas. Penelitian ini bertujuan membandingkan efektivitas empat metode augmentasi data, yaitu SMOTE, Borderline-SMOTE, SMOTE-ENN, dan CTGAN, pada model Extreme Gradient Boosting (XGBoost), TabTransformer, dan Deep Autoencoder. Evaluasi dilakukan menggunakan metrik yang sensitif terhadap kelas minoritas, meliputi Receiver Operating Characteristic–Area Under the Curve (ROC-AUC), Area Under the Precision–Recall Curve (AUPRC), serta metrik pendukung lainnya. Hasil menunjukkan bahwa kombinasi XGBoost dan Borderline-SMOTE memberikan keseimbangan precision–recall terbaik dengan F1-score 0,9269 dan AUPRC 0,8306, sedangkan TabTransformer dengan CTGAN mencapai kemampuan diskriminasi tertinggi dengan ROC-AUC 0,9846 dan precision 0,9795. Temuan ini menunjukkan bahwa efektivitas augmentasi bergantung pada kesesuaian karakteristik data sintetis dengan mekanisme pembelajaran model serta memberikan implikasi praktis bagi pengembangan sistem deteksi penipuan yang andal pada data tidak seimbang
RAG Agentic AI as Digital Frontline in Service-Based Organizations: A Systematic Literature Review Najmi Khairunnisa; Riska Dhenabayu; Syahda Hati Dendra
Journal of Digital Business and Innovation Management Vol. 5 No. 1 (2026): June 2026
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jdbim.v5i1.77596

Abstract

This study systematically reviews the development of Agentic AI as digital frontline systems within service-based organizations. The increasing adoption of artificial intelligence in organizational environments has accelerated the implementation of conversational systems capable of supporting consultation workflows, operational communication, and information delivery processes. However, conventional chatbot systems still experience limitations related to contextual understanding, reasoning capability, and multi-step task execution. To address these limitations, recent studies increasingly integrate Agentic AI and Retrieval-Augmented Generation (RAG) to support more adaptive and context-aware conversational systems. This study employed a Systematic Literature Review (SLR) approach following the PRISMA guidelines. Literature selection was conducted using several academic databases, including Scopus, IEEE Xplore, ACM Digital Library, ScienceDirect, and Google Scholar. After the screening and eligibility process, 61 articles published between 2020 and 2026 were selected for final review and analyzed using thematic synthesis. The findings identified three dominant research themes consisting of Agentic AI architectures and autonomous systems, Retrieval-Augmented Generation and conversational intelligence, and deployment of Agentic AI in digital frontline systems. The review demonstrates that Agentic AI integrated with retrieval-enhanced conversational mechanisms has strong potential to support contextual interaction, consultation workflows, and organizational communication processes within service-based environments. Nevertheless, several challenges remain related to hallucination, governance, trustworthiness, evaluation reliability, and real-world deployment complexity.
KREDIBILITAS MICRO INFLUENCER, PEMASARAN BUZZ, DAN CITRA MEREK TERHADAP NIAT BELI PRODUK F&B DI TIKTOK Thaariq Ayub Arrafah; Fresha Kharisma; Riska Dhenabayu; Ratih Amelia
International Journal Of Humanities Education and Social Sciences (IJHESS) Vol 5 No 5 (2026): IJHESS APRIL 2026
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhess.v5i5.2279

Abstract

This study aims to analyze the influence of micro-influencer credibility, buzz marketing, and brand image on consumer purchase intention for Food & Beverage (F&B) products on the TikTok platform. This research employs a quantitative approach with an explanatory method and uses purposive sampling to collect data from 150 TikTok users. Data were analyzed using multiple linear regression. The results show that micro-influencer credibility has a positive and significant effect on purchase intention (β = 0.193; t = 2.587; p = 0.011), buzz marketing has the strongest influence (β = 0.404; t = 5.409; p = 0.000), and brand image also significantly affects purchase intention (β = 0.346; t = 4.681; p = 0.000). Simultaneously, the three variables have a significant effect on purchase intention (F = 327.035; p = 0.000). These findings indicate that buzz marketing is the most dominant factor, followed by brand image and micro-influencer credibility. The study concludes that integrating credible micro-influencers, effective buzz marketing strategies, and a strong brand image can significantly enhance consumer purchase intention on TikTok. This research contributes to digital marketing literature and provides practical implications for F&B businesses in designing effective social media marketing strategies
Systematic Review on Breast Cancer Classification Using Random Forest and Extreme Learning Machine: Cost Sensitivity and Computational Complexity Perspectives Irsyad Budhiraja; Riska Dhenabayu; Erwan Nicolas
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 | DOI: 10.26740/jdbim.v4i2.70467

Abstract

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.
THE EFFECT OF PERSONALIZED ARTIFICIAL INTELLIGENCE INTERACTION QUALITY ON IMPULSIVE STREAMING INTENTIONS MEDIATED BY AFFECTIVE RESPONSES Irsyad Daffa Armmawadin; Riska Dhenabayu
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

Abstract

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.
Co-Authors Akbar, Muhammad Farhan Armawadin, Irsyad Daffa Candra, Ika Diyah Dendra Aptanata Nugraha Dwi Wahyuni Erta Erwan Nicolas Fazlurrahman, Hujjatullah Febrinita, Filda Fitro, Achmad Fresha Kharisma Hapsari Shinta Citra Puspita Dewi Hesty Puspita Sari Hujjatullah Fazlurrahman Husin, Nanang Ika Diyah Candra Arifah Irsyad Budhiraja Irsyad Daffa Armmawadin Kamila, Fanisa Ayu Cahya Kautsar, Achmad Kurnia Paranita Kartika Lestanti, Sri Muhammad Fajar Wahyudi Rahman Musdholifah Musdholifah Najmi Khairunnisa Nanang Husin Nurul Fajrin Ariyani Nurul Indawati Priambodo, Gumilang Purwohandoko Purwohandoko Qosimah, Rofiatul R.A. Sista Paramita Ramadhan Maruta Pradana Ramadhan, Gilang Bagus Ratih Amelia Renny Sari Dewi Rosmalia, Tria Rizky Ryan Ahmad Syah Putra Safitri, Anita Susi Handayani Syahda Hati Dendra Thaariq Ayub Arrafah Tias Andarini Indarwati Tri Sudarwanto Trias Madanika Kusumaningrum Ulil Hartono Wahyu Suci Saputri Yoga Yuniadi Yusniarsi Primasari