Article Per Year (5 Year)
p-Index From 2021 - 2026
6.696
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Teknika Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business Jurnal Teknologi Informasi dan Ilmu Komputer Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) International conference on Information Technology and Business (ICITB) jurnal Teknologi Informasi Magister Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) JMM (Jurnal Masyarakat Mandiri) Prosiding Seminar Nasional Darmajaya JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Mathvision : Jurnal Matematika Jurnal Sistem Komputer & Kecerdasan Buatan Jurnal Komunitas: Jurnal Pengabidian Kepada Masyarakat Dinasti International Journal of Economics, Finance & Accounting (DIJEFA) Jurnal Pengabdian UNDIKMA Yumary: Jurnal Pengabdian kepada Masyarakat Journal of Applied Data Sciences International Journal of Business, Technology, and Organizational Behavior (IJBTOB) Jurnal Abdimas Bina Bangsa Patria Artha Journal of Community JUSTIN (Jurnal Sistem dan Teknologi Informasi) Prosiding Konferensi Nasional PKM-CSR Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Jurnal Publika Pengabdian Masyarakat Hijaz: Jurnal Ilmu-Ilmu Keislaman Journal of Management and Informatics Jurnal Informatika: Jurnal Pengembangan IT SWAGATI: Journal of Community Service JIMAD : Jurnal Ilmiah Multidisiplin
Hendra Kurniawan
Institute Of Informatics And Business Darmajaya
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 63 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Journal of Applied Data Sciences

 1 
Progressive Massive Fibrosis Detection Using Generative Adversarial Networks and Long Short-Term Memory Irianto, Suhendro Y.; Karnila, Sri; Hasibuan, M.S.; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Kurniawan, Hendra
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.707

Abstract

Contribution: Progressive Massive Fibrosis (PMF) is a severe form of pneumoconiosis, affecting individuals exposed to mineral dust, such as coal miners and workers in the artificial stone industry. This condition causes significant pulmonary impairment and increased mortality. Early and accurate detection is vital for effective management, yet traditional diagnostic methods face challenges in differentiating PMF from other pulmonary diseases due to variability in clinical presentations and limitations in imaging techniques. Idea: The study introduces a novel diagnostic framework that integrates Generative Adversarial Networks (GAN) and Long Short-Term Memory (LSTM) networks to enhance the detection and monitoring of PMF. The GAN generates high-fidelity synthetic imaging data to address the issue of limited datasets, while the LSTM network captures temporal patterns in patient data, enabling real-time monitoring of disease progression. Objective: The primary objective of this research is to develop an AI-driven model that improves the accuracy and efficiency of PMF detection and monitoring, facilitating early diagnosis and better treatment planning. Findings: The integrated GAN-LSTM model significantly outperformed traditional diagnostic methods. It proved high accuracy, a Dice coefficient of 0.85, and an Area Under the Curve (AUC) of 0.92, showing precise differentiation of PMF from other pulmonary conditions, such as lung cancer and tuberculosis. Results: The GAN-LSTM framework achieved an accuracy of 91.3%, suggesting that the fusion of GAN and LSTM technologies can effectively address the challenges of limited datasets and heterogeneous disease progression. The model showed promise in enhancing the non-invasive detection and ongoing monitoring of PMF. Novelty: This research stands for a significant advancement in PMF diagnostics by combining GAN and LSTM technologies in a single framework. This approach improves diagnostic accuracy and eases continuous disease monitoring, offering a non-invasive and highly precise solution for PMF detection.
SiMoI New Method to Solve the Sparsity Problem in Collaborative Filtering Kurniawan, Hendra; Lestari, Sri; Saleh, Sushanty; Satrio, Rafli Banu
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1015

Abstract

Sparsity data is a major challenge in collaborative recommendation systems, characterized by the predominance of missing values within the user-item matrix. When a substantial portion of data is unavailable, the estimation process becomes hindered, and prediction accuracy declines due to limited usable information. To address this issue, this study introduces a novel method called SiMoI (Similarity, Mode, and Minimum Imputation), which is adaptively designed to handle high levels of sparsity. The SiMoI method combines user similarity with imputation strategies based on mode and minimum values. By leveraging subsets of the most informative users and items, the method efficiently fills missing entries while maintaining prediction stability. Evaluation was conducted using both real and synthetic datasets with varying sizes and degrees of sparsity, including an extreme scenario with 93.7% missing data. Experimental results show that SiMoI consistently produces more accurate predictions than baseline methods. Under high-sparsity conditions, SiMoI achieved an RMSE as low as 0.823, outperforming KNNI (0.947) and MEAN (1.021). Moreover, SiMoI demonstrated resilience across different data scales and sparsity distributions, indicating its flexibility and scalability in diverse contexts. These findings suggest that SiMoI is an effective and stable approach for addressing sparsity and holds strong potential for implementation in user-based recommendation systems, particularly in real-world scenarios where data availability is frequently limited.
Co-Authors - Nurjoko Abdi Darmawan Abdul Rahman Abdullah Merjani, Abdullah Ade Moussadecq Adriana Adriana Adu, Steven Jonathan Adytama, Muhammad Rezky Agung Pradana Agus Rahardi Al-Reza, Dimaz Danang Amrullah, Ahmad Nur Hakim Anggreiny, Cut Dini Anita Dewi Purwati Annisa Anggun P Annisa Latifa Antoni Suseno Ardiansyah, Muhamad Iqbal Arianto, Jarot ashari, ulfira Assatulaini Assatulaini Assyfa, Zahra Putri Astuti, Miguna Azima, Muhammad Fauzan Bagus Prihadi Batubara, Naufal Akbar Catootjie L. Nalle, Catootjie L. Damayanti, Irah Dani Rofianto Denny Andreas Desi Ratna Sari, Desi Ratna Desy Tri Anggarini Dewi, Deshinta Arrova Diana Tambunan Dina Warsahanda Dona Yuliawati Dwi Wahyuni Edi Edi Pranyoto Egi Safitri Fadillah, Anggi Nanda Fajri, Ika Nur Fitria - Gare, Kletus Florianus Sera Habib, Muhammad Nurul Halimah Halimah Handoko, Melyani Harijanto Wijaya Hasibuan, M.S. Hasibuan, Rendi Sahputra Heni Nastiti Hermanto HERMANTO Herwanto, Riko Herwanto, Riko Hikmah, Nor Ihwani, Fadillah ILHAM Irawan Setyabudi Irianto, Suhendro Y. Kurniawan, Tri Basuki Lilik Joko Susanto Lombu, Fitroh Romodhin Syahputra M Yusendra M. Zaky Fanany Zaky Maensya, Alendra Natuah Manik, Sri Dame Marulina Marbun, Elsa Agustin Maria, Okta Melda Agarina Mochammad Imron Awalludin Muhamad Ariza Eka Yusendra Muhammad Ariza Eka Yusendra Muhammad Fauzan Azima Muhammad Hanafi Muhammad Redintan Justin Muhammad Sahri Muji Lestari Muktiawan, Danang Ade Nababan, Badia Roy Ricardo Nadhir, Ahnaf Ronaldo Neni Purwati Niken Larasati Nisa, Siti Khoirotun Novi Herawadi Sudibyo Nurdianingsih, Aisyah Nurjoko Nurjoko Nurlistiani, Rini Nursiyanto Oktaviani, Eva Oscar, Gusnanda Pedliyansah, Yogi Pratama, Raynaldo Syah Pratama, Wanda Andika Pratiwi, Gadis Kartika Putra, Rizky Samjaya Raden Abdurrahman Raihan Hasbid Raisa, Athaya Ramadhan, Rizki Aditya Reni Widyastuti Rifqatunnisak, Rifqatunnisak Rini Nurlistiani riyanto, diki Rizal, Ruki Rizkiana Karmelia Shaura Rohiman Rohiman Rohiman Rohiman Rohmat Hidayat, Kardilah Romadona, Romadona Rossa Wulandari Ruki Rizal Ruki Rizal Rumini Safitri, Egi Sainah, Sainah Saputra, M Hardi Sasya Nadira Satrio, Rafli Banu Sembiring, Rosali Septiawan, Yuda Shofiyurrahman Shofiyurrahman Sipora Petronela Telnoni Siswahyudianto Siti Aisyah Lubis, Siti Aisyah Solly Aryza Sri Karnila Sri Karnila Sri Karnila Sri Karnila Sri Karnila Karnila Sri Lestari Sri Rahayu Stefanus Rumangkit Sudarto, Sidiq Sumarya, Edi Supriyadi Susanti Susanti Susanti Susanti Susanto, Lilik Joko Sushanty Saleh Sutedi Sutedi Swastika, Rahayu Syahrizal Syahrizal Syidada, Amran Rahman Syifa, Khozanah Theresia, Sumini Tri Erri Astoeti Triyasri, Novita Valensia, Alda Caesar Wicakso Bandung Bondowoso Widijanto Sudhana Wijaya, Nanda Y, M Ariza Eka Y. Suhendro Yama, Tri Melda Yan Aditiya Pratama Yogi Pedliyansah Yuni Arkhiansyah Yusminar Yusminar Yusminar Yusminar Zahra, Amalia Zainal Abidin