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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 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 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

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Assessment of Usability and Acceptance of An Academic Information System Using SUS And TAM Adaptation Nurlistiani, Rini; Romadona, Romadona; Kurniawan, Hendra; Nursiyanto, Nursiyanto
Prosiding International conference on Information Technology and Business (ICITB) 2023: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 9
Publisher : Proceeding International Conference on Information Technology and Business

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

Abstract

Organizations, companies, and the world of education carry out all learning activities using e-learning. There is an important part that requires an academic system with structured data, namely the system at private universities in Indonesia, for example,Informatics and Business Institute Darmajaya. Darmajaya is one of the educational institutes that uses online learning media information technology called e-learning for students and lecturers. The newest information system used at IIB Darmajaya is the academic information system (AIS) which consists of Darmajaya students and lecturers. Result from the assessment showing of lecturers understand how to use AIS with value 56.92, and 65.93 from students of IIB Darmajaya. Keywords :SUS,TAM, Evaluation, Acceptance, Usability
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.
OPTIMALISASI PENANGANAN SPARSITY MENGGUNAKAN RANDOM FOREST, DEEP LEANING, DAN HOT-DECK IMPUTATION Lestari, Sri; Satrio, Rafli Banu; Kurniawan, Hendra; Saleh, Sushanty
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7692

Abstract

Sparsity data dalam sistem rekomendasi dapat menurunkan akurasi prediksi dan relevansi saran. Penelitian ini membandingkan tiga metode imputasi—Random Forest Imputation, Deep Learning-Based Imputa-tion, dan Hot-Deck Imputation—dengan evaluasi menggunakan RMSE pada berbagai tingkat sparsitas. Hasil menunjukkan bahwa Random Forest Imputation consistently menghasilkan RMSE terendah di semua kondisi. Pada sparsitas 20%, metode ini lebih unggul dibandingkan Deep Learning-Based Imputation dengan selisih hingga 0.443 dan Hot-Deck Imputation hingga 0.338. Perbedaan RMSE se-makin meningkat seiring bertambahnya sparsitas, dengan selisih terbesar pada sparsitas tertinggi masing-masing dataset. Secara kese-luruhan, Random Forest Imputation terbukti paling efektif dalam me-nangani sparsitas dan meningkatkan akurasi rekomendasi.
Hybrid Recommendation System Based on Implicit Feedback with Collaborative Filtering and Gradient Boosting Kurniawan, Hendra; Zahra, Amalia
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 2 (2026): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.112368

Abstract

Recommendation systems are essential components in video streaming services as they assist users in selecting relevant content in line with the increasing availability of large-scale content. However, most recommendation systems still rely on explicit feedback data such as ratings, which are often unavailable on many platforms. This study aims to develop a hybrid recommendation system based on implicit feedback by constructing an interaction score derived from user behavior as a substitute for ratings. The proposed model integrates collaborative filtering methods (matrix factorization and k-nearest neighbor) with the CatBoost gradient boosting decision tree algorithm. The evaluation was conducted using empirical data from a video streaming service, with performance measured using root mean squared error (RMSE) and mean absolute error (MAE). The results indicate that the hybrid model achieves lower RMSE and MAE values compared to individual models. These findings confirm that the hybrid approach is effective in improving recommendation accuracy while also contributing to enhanced user experience quality in video streaming platforms without explicit rating data.
Implementasi Deep Learning Algoritma Convolutional Neural Network untuk Klasifikasi Kesegaran Buah dan Sayur Latifa, Annisa; Hikmah, Nor; Kurniawan, Hendra; Rohmat Hidayat, Kardilah; Larasati, Niken; Rumini
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 2: April 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026131

Abstract

Buah dan sayur merupakan sumber utama vitamin, mineral, dan serat yang sangat penting untuk menjaga kesehatan tubuh. WHO merekomendasikan konsumsi sebesar 400 gram per hari untuk gizi seimbang. Namun, kualitas dan kesegaran buah dan sayur sering kali sulit diidentifikasikan secara manual, terutama dalam skala besar, karena metode tradisional memiliki keterbatasan akurasi dan rentan terhadap kesalahan manusia. Kemajuan kecerdasan buatan, khususnya deep learning, memberikan solusi inovatif dalam klasifikasi citra. Convolutional Neural Network (CNN), telah terbukti efektif dalam pengenalan dan klasifikasi gambar. Penelitian ini menerapkan CNN dengan arsitektur Inception V3 dalam mengklasifikasikan kesegaran buah dan sayuran menjadi dua kategori utama, yaitu segar dan busuk. Model dikembangkan menggunakan dataset yang terdiri dari 11. 441 citra yang gambar, yang dibagi ke dalam tiga subset utama, yaitu data latih (±44.38%), data validasi (±11.07%), dan data uji (±44.55%). Dengan data kelas terbagi 14 kelas. Hasil penelitian  dengan menggunakan confusion matric  nilai accuracy sebesar 95%  dan hasil evaluasi validation accuracy  sebesar 100% pada epoch ke-4, dengan val_loss terendah sebesar 0.0260  serta nilai MAE  0.26, yang artinya model memiliki kinerja yang sangat baik  dalam mendekteksi kesegaran  buah dan sayur. Penelitian lanjutan disarankan untuk meningkatkan generalisasi model dengan menggunakan dataset yang lebih beragam, dan mengintegrasikan komputasi tepi (edge computing) untuk inspeksi kualitas langsung di Lokasi.   Abstract Fruits and vegetables are primary sources of vitamins, minerals, and fiber, which are essential for maintaining a healthy body. The World Health Organization (WHO) recommends a daily intake of 400 grams for a balanced diet. However, the quality and freshness of fruits and vegetables are often difficult to identify manually, especially at large scale, as traditional methods have limitations in accuracy and are prone to human error. Advances in artificial intelligence, particularly deep learning, offer innovative solutions in image classification. Convolutional Neural Networks (CNNs) have proven effective in image recognition and classification tasks. This study implements a CNN using the Inception V3 architecture to classify the freshness of fruits and vegetables into two main categories: fresh and rotten. The model was developed using a dataset consisting of 11,441 images, divided into three main subsets: training data (approximately 44.38%), validation data (approximately 11.07%), and test data (approximately 44.55%), with 14 distinct classes. The results of the study, based on the confusion matrix, show an accuracy of 95%, and a validation accuracy of 100% at the 4th epoch, with the lowest validation loss recorded at 0.0260 and a MAE of 0.26. These results indicate that the model performs very well in detecting the freshness of fruits and vegetables. Further research is recommended to improve model generalization using more diverse datasets and to integrate edge computing for on-site quality inspection.
Pemberdayaan Masyarakat Melalui Pengembangan Sistem Agrosilvopastura Berkelanjutan Berbasis Tanaman Multifungsi di Desa Noekele Kabupaten Kupang NTT Wahyuni, Dwi; Nalle, Catootjie L.; Telnoni, Sipora Petronela; Kurniawan, Hendra; Nurdianingsih, Aisyah; Pratiwi, Gadis Kartika; Oktaviani, Eva; Nababan, Badia Roy Ricardo; Gare, Kletus Florianus Sera; Al-Reza, Dimaz Danang; Syifa, Khozanah; Adu, Steven Jonathan
Jurnal Pengabdian UNDIKMA Vol. 7 No. 2 (2026): May
Publisher : LPPM Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jpu.v7i2.19653

Abstract

This community service activity aims to enhance the knowledge and capacity of the Berkat Farmer Group in Noekele Village, Kupang Regency, East Nusa Tenggara, through the implementation of a sustainable agrosilvopastoral system based on multifunctional crops, namely gmelina, Gamma Umami grass, and butterfly pea flowers. The program was implemented using an educative and participatory approach, with evaluation instruments in the form of assessment rubrics analyzed descriptively. The results of the program showed an improvement in community understanding of the agrosilvopastoral concept and the benefits of multifunctional crops in supporting sustainable livestock feed availability. The implementation of the integrated planting system was considered easy to apply and has the potential to improve land-use efficiency, livestock feed stability, and community welfare. This activity demonstrates that an agrosilvopastoral approach based on multifunctional crops is a relevant strategy for sustainable land and livestock management in East Nusa Tenggara.
The Effect of Compensation on Employee Performance Through Work Motivation as an Intermediary Variable (Case Study at Archa Beauty Clinic Bekasi) Shaura, Rizkiana Karmelia; Handoko, Melyani; Widyastuti, Reni; Swastika, Rahayu; Tambunan, Diana; Anggarini, Desy Tri; Kurniawan, Hendra
Journal of Management and Informatics Vol. 5 No. 1 (2026): April Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v5i1.335

Abstract

The rapid growth of digital payment systems has increased the complexity of financial transactions, making credit card fraud detection more challenging, particularly due to evolving fraud patterns and highly imbalanced datasets. Conventional machine learning approaches often struggle to capture temporal dependencies and adapt to new fraud behaviors, while centralized data processing raises privacy concerns. This study proposes a hybrid fraud detection framework that integrates Bidirectional Long Short-Term Memory (BiLSTM), Autoencoder, and Federated Learning to improve detection performance while preserving data confidentiality. The BiLSTM component models sequential transaction behavior from both forward and backward directions, while the autoencoder identifies anomalies based on reconstruction errors. Federated Learning enables collaborative model training across multiple institutions without sharing sensitive data. Experimental evaluation using benchmark datasets shows that the proposed model achieves high classification performance, with improved precision, recall, and overall stability compared to traditional and standalone deep learning models. The framework effectively handles class imbalance and detects both known and emerging fraud patterns. This study contributes a scalable and privacy-preserving solution for real-world fraud detection, supporting secure collaboration and enhancing model generalization in distributed financial environments.
The Influence of Marketing Mix (7ps) on Consumer Satisfaction Digital MSMEs Kurniawan, Hendra; Astuti, Miguna; Sembiring, Rosali; Nastiti, Heni
Dinasti International Journal of Economics, Finance & Accounting Vol. 7 No. 1 (2026): Dinasti International Journal of Economics, Finance & Accounting (March-April 2
Publisher : Dinasti Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/dijefa.v7i1.6683

Abstract

This study aims to analyze the influence of the marketing mix (7Ps), consisting of product, price, place, promotion, people, physical evidence, and process, on consumer satisfaction in digital SMEs. A quantitative approach was employed by distributing questionnaires to digital SME consumers, and the data were analyzed using the Structural Equation Modeling-Partial Least Square (SEM-PLS) method. The findings reveal that three variables, namely product, place, and physical evidence, significantly affect consumer satisfaction. Products are perceived to meet consumer needs with sufficient variety and clear information, place emerges as the most dominant factor by providing easy access to shipping and payment information, ratings, and reviews, while physical evidence supports satisfaction through clear platform design and informative product visuals. In contrast, price, promotion, people, and process do not significantly influence satisfaction. These results indicate that digital SME consumers prioritize information accessibility, visual quality, and platform reliability over low prices or excessive promotion. The study offers practical implications for digital SMEs to strengthen visual presentation and information clarity while suggesting future research to incorporate variables such as digital trust and user experience to achieve more comprehensive findings.
The Performance of Agricultural Extension Workers to Increase the Knowledge of Corn Farmers in Barakati Village, Batudaa District, Gorontalo Regency Kurniawan, Hendra; Abidin, Zainal; Ashari, Ulfira
International Journal of Business, Technology and Organizational Behavior (IJBTOB) Vol. 2 No. 5 (2022): International Journal of Business, Technology, and Organizational Behavior (IJB
Publisher : Garuda Prestasi Nusantara Consulting

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52218/ijbtob.v2i5.219

Abstract

Agricultural extension is a non-formal education for farmers. This study aims to determine the performance of agricultural extension workers on corn farmers in Barakati Village, Batudaa District, Gorontalo Regency. The method used in this study is data tabulation using a Likert scale. The sample in this study was 63 people. The results of this study, namely the performance of agricultural extension workers in the form of increasing knowledge of corn farmers in Barakati Village, Batudaa District, Gorontalo Regency, were quite good from the overall number of results, reviewed from the intensity of counseling with a percentage of 56.44%, reviewed the extension method with a percentage of 53.33%, in terms of counseling media with a percentage of 61.01
Co-Authors - Nurjoko Abdi Darmawan Abdullah Merjani, Abdullah Ade Moussadecq 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 ashari, ulfira Assatulaini Assatulaini Assyfa, Zahra Putri Astuti, Miguna Azima, Muhammad Fauzan Bagus Prihadi 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 Fajri, Ika Nur Fitria - Gare, Kletus Florianus Sera Halimah Halimah Handoko, Melyani Harijanto Wijaya Hasibuan, M.S. Heni Nastiti Hermanto HERMANTO Herwanto, Riko Herwanto, Riko Hikmah, Nor Irawan Setyabudi Irianto, Suhendro Y. Kurniawan, Tri Basuki Lilik Joko Susanto M Yusendra M. Zaky Fanany Zaky Maensya, Alendra Natuah Marbun, Elsa Agustin Maria, Okta Melda Agarina Mochammad Imron Awalludin Muhamad Ariza Eka Yusendra Muhammad Ariza Eka Yusendra Muhammad Fauzan Azima Muhammad Redintan Justin Muhammad Sahri Muji Lestari Muktiawan, Danang Ade Nababan, Badia Roy Ricardo Nadhir, Ahnaf Ronaldo Neni Purwati Niken Larasati 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 Ramadhan, Rizki Aditya Reni Widyastuti Rifqatunnisak, Rifqatunnisak Rini Nurlistiani Rizal, Ruki Rizkiana Karmelia Shaura Rohiman, Rohiman Rohmat Hidayat, Kardilah Romadona, Romadona Rosali, Rosali Rossa Wulandari Ruki Rizal Ruki Rizal Rumini Safitri, Egi Saputra, M Hardi Sasya Nadira Satrio, Rafli Banu Septiawan, Yuda Shofiyurrahman Shofiyurrahman Sipora Petronela Telnoni Siswahyudianto Solly Aryza Sri Karnila Sri Karnila Sri Karnila Sri Karnila Sri Karnila Karnila Sri Lestari Sri Rahayu Stefanus Rumangkit 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