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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Semantik PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic CommIT (Communication & Information Technology) Seminar Nasional Informatika (SEMNASIF) Register: Jurnal Ilmiah Teknologi Sistem Informasi Creative Information Technology Journal SISFOTENIKA Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Indonesian Journal on Software Engineering (IJSE) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Bina Insani ICT Journal Informatics for Educators and Professional : Journal of Informatics Jurnal Komputer Terapan JTERA (Jurnal Teknologi Rekayasa) Jurnal Ilmiah Matrik Conference SENATIK STT Adisutjipto Yogyakarta ILKOM Jurnal Ilmiah Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Penelitian dan Pengabdian Kepada Masyarakat UNSIQ Jurnal Informasi dan Komputer Aptisi Transactions on Management Aptisi Transactions on Technopreneurship (ATT) Informasi Interaktif CCIT (Creative Communication and Innovative Technology) Journal Building of Informatics, Technology and Science Progresif: Jurnal Ilmiah Komputer SENSITEK E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Indonesian Journal of Business Intelligence (IJUBI) Journal of Robotics and Control (JRC) JATI (Jurnal Mahasiswa Teknik Informatika) Respati ICIT (Innovative Creative and Information Technology) Journal Journal Sensi: Strategic of Education in Information System CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics IJIIS: International Journal of Informatics and Information Systems Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Kajian Ilmiah JINAV: Journal of Information and Visualization Journal of Applied Data Sciences International Journal for Applied Information Management Jurnal Ekonomi dan Teknik Informatika Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Dinamika Informatika (JDI) Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Universal Raharja Community (URNITY Journal) Blockchain Frontier Technology (BFRONT)
Henderi .
Raharja University
Aerospace Engineering Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Neuroscience Social Sciences Transportation Other

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Optimalisasi Database 3.0 untuk Verifikasi Data Pelatihan Pelaut Nugraha, Rizal Fitrah; Henderi, Henderi; Sudaryono, Sudaryono
JTERA (Jurnal Teknologi Rekayasa) Vol 9, No 2: December 2024
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31544/jtera.v9.i2.2024.101-112

Abstract

This study explores the optimization of Database 3.0 to enhance the registration of training participants and data verification in seafarer training programs. The increasing complexity of managing and verifying vast training data demands advanced database technologies. Database 3.0, with its capabilities for real-time updates, automated data entry, and system integration, presents a solution to these challenges. The research employs SmartPLS to model the relationships between Database Optimization, Data Accuracy, Verification Efficiency, and User Satisfaction, aiming to assess how optimization impacts the overall effectiveness of training data management. The study fills a gap in the literature by focusing on Database 3.0 optimization within the maritime training context, an underexplored area. The results indicate that optimized databases significantly improve data accuracy and verification efficiency, leading to higher user satisfaction among administrators and trainers. The findings suggest that integrating Database 3.0 into seafarer training programs can streamline data verification processes, ultimately enhancing certification reliability and operational efficiency in maritime education. These insights offer a novel perspective on utilizing advanced database technologies in specialized sectors like maritime training.
Utilization of Testimonials Menu as Submission Media Information on Buyer Satisfaction on the Website E-Commerce Raharja Internet Café Henderi, Henderi; Zcull, Harph; Putri, Cheetah Savana
Aptisi Transactions On Technopreneurship (ATT) Vol 1 No 1 (2019): March
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v1i1.12

Abstract

Raharja Internet Cafe is a facility at Raharja College, which provides various needs for Raharja's private lecture activities. Raharja Internet Cafe is used to help lecture activities by facilitating students such as computers, printers and scanners. Also helps students to install or service iPad. However, sometimes the facilities available at Raharja Internet Cafe are still experiencing problems so students are less interested in visiting Raharja Internet Cafe. This study uses 2 (two) methods, namely the literature review method and questionnaire. As a result, to make it easier for sellers and buyers to know the quality of services provided by Raharja Internet Cafe to Pribadi Raharja, Raharja Internet Cafe's website is used by adding testimonials menus. In the testimonials menu, there are many testimonials that have been given by Raharja Internet Cafe users. And the results obtained from the research conducted are that Raharja Internet Cafe is very helpful in lecturing activities
Scalable Machine Learning Approaches for Real-Time Anomaly and Outlier Detection in Streaming Environments Dewi, Deshinta Arrova; Singh, Harprith Kaur Rajinder; Periasamy, Jeyarani; Kurniawan, Tri Basuki; Henderi, Henderi; Hasibuan, M. Said
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.444

Abstract

The prevalence of streaming data across various sectors poses significant challenges for real-time anomaly detection due to its volume, velocity, and variability. Traditional data processing methods often need to be improved for such dynamic environments, necessitating robust, scalable, and efficient real-time analysis systems. This study compares two advanced machine learning approaches—LSTM autoencoders and Matrix Profile algorithms—to identify the most effective method for anomaly detection in streaming environments using the NYC taxi dataset. Existing literature on anomaly detection in streaming data highlights various methodologies, including statistical tests, window-based techniques, and machine learning models. Traditional methods like the Generalized ESD test have been adapted for streaming data but often require a full historical dataset to function effectively. In contrast, machine learning approaches, particularly those using LSTM networks, are noted for their ability to learn complex patterns and dependencies, offering promising results in real-time applications. In a comparative analysis, LSTM autoencoders significantly outperformed other methods, achieving an F1-score of 0.22 for anomaly detection, notably higher than other techniques. This model demonstrated superior capability in capturing temporal dependencies and complex data patterns, making it highly effective for the dynamic and varied data in the NYC taxi dataset. The LSTM autoencoder's advanced pattern recognition and anomaly detection capabilities confirm its suitability for complex, high-velocity streaming data environments. Future research should explore the integration of LSTM autoencoders with other machine-learning techniques to enhance further the accuracy, scalability, and efficiency of anomaly detection systems. This study advances our understanding of scalable machine-learning approaches and underscores the critical importance of selecting appropriate models based on the specific characteristics and challenges of the data involved.
Utilizing Sentiment Analysis for Reflect and Improve Education in Indonesia Henderi, Henderi; Asro, Asro; Sulaiman, Agus; Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; AlQudah, Mashal
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.527

Abstract

This study explores the potential of sentiment analysis in providing valuable insights into education in Indonesia based on comments from the YouTube platform. Utilizing the Naive Bayes Classifier method, this research analyzed 13,386 processed comments out of 17,920 original comments. The results show that 53.8% of comments were negative, while 28.5% were positive, and 17.7% were neutral, reflecting diverse perspectives on existing educational issues. The Accuracy of this model reached up to 72.51% with testing on various sample sizes (10%-30%), indicating the model's effectiveness in identifying sentiments. Although the model tends to classify comments as unfavorable, this opens opportunities for introspection and improvement within the educational system. Further analysis with a word cloud revealed dominant keywords, indicating areas that require more attention in public discussions about education. By leveraging this sentiment analysis, the study offers practical and valuable guidance for policymakers to reflect on and enhance educational strategies and policies in Indonesia. This research measures public reactions and aims to foster more constructive and inclusive discussions about the sustainable development of education in Indonesia.
Model Sistem Pendukung Keputusan Dosen Berprestasi di Bidang Tri Dharma Menggunakan Metode Simple Attribute Rating Technique Nugraha, Rizal Fitrah; Henderi, Henderi; Sudaryono, Sudaryono
ICIT Journal Vol 11 No 1 (2025): Februari 2025
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/icit.v11i1.3593

Abstract

Penelitian ini menyoroti pentingnya pemilihan dosen berprestasi sebagai langkah strategis untuk meningkatkan motivasi dan kualitas akademik di perguruan tinggi. Penelitian ini bertujuan untuk mengembangkan Sistem Pendukung Keputusan (SPK) berbasis metode Simple Multi Attribute Rating Technique (SMART) untuk menentukan dosen berprestasi berdasarkan kinerja penelitian dan pengabdian masyarakat. Metode SMART digunakan karena kemampuannya dalam mengolah data multi-atribut melalui pembobotan kriteria dan normalisasi. Penelitian ini mengatasi gap yang ada pada sistem evaluasi yang saat ini dengan menawarkan solusi yang objektif dan transparan dalam memilih dosen yang memiliki kinerja terbaik. Novelty dari penelitian ini terletak pada penerapan metode SMART dalam konteks akademik, dengan memanfaatkan berbagai kriteria kinerja seperti hibah penelitian, publikasi jurnal, hak kekayaan intelektual, dan kegiatan seminar, untuk memberikan rekomendasi yang akurat dan efektif mengenai dosen berprestasi. Kesimpulan dari penelitian ini menunjukkan bahwa sistem yang dihasilkan dapat meningkatkan proses pengambilan keputusan, memberikan kemudahan bagi LPPM dan kepala program studi dalam evaluasi dosen, serta berkontribusi dalam meningkatkan motivasi dosen untuk menghasilkan karya akademik berkualitas. Oleh karena itu, penelitian ini diharapkan dapat menjadi referensi untuk pengembangan sistem pendukung keputusan berbasis SMART di institusi pendidikan lainnya.
PENGEMBANGAN PROTOTYPE SISTEM DIAGNOSA UNTUK PENANGANAN PENYAKIT IKAN PADA DINAS PERIKANAN KOTA TANGERANG SELATAN Henderi, Henderi; Haekal Simangunsong, Fikri Muhammad; Maulidina, Muhammad Muflih; Mulyana, Muhamad; Kartawinata, Dea
Universal Raharja Community (URNITY Journal) Vol 5 No 1 (2025): URNITY (Universal Raharja Community)
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/urnity.v5i1.3648

Abstract

Abstrak Penelitian ini bertujuan untuk merancang prototipe sistem berbasis web yang dapat membantu masyarakat, khususnya pembudidaya pemula, dalam mengidentifikasi gejala, mendiagnosis penyakit ikan, serta memberikan rekomendasi penanganan secara cepat dan tepat. Sistem ini dikembangkan menggunakan pendekatan design thinking untuk menghasilkan solusi berbasis data yang sesuai dengan kebutuhan pengguna. Prototipe ini dilengkapi fitur-fitur seperti cek diagnosis, daftar jenis penyakit ikan, serta forum interaksi pengguna untuk berbagi pengalaman. Hasil penelitian menunjukkan bahwa sistem berbasis web ini dapat diakses dengan mudah dan memberikan informasi yang komprehensif terkait penyakit ikan. Diharapkan, sistem ini dapat meningkatkan pengetahuan pembudidaya, meminimalkan kerugian akibat penyakit ikan, dan mendukung keberlanjutan industri perikanan di Kota Tangerang Selatan. Kata Kunci: Budidaya ikan, penyakit ikan, sistem berbasis web, diagnosis penyakit, design thinking.
Design and Development of Interactive Media in Vocational High Schools Using the Multimedia Development Life Cycle Method Based on Android Yusuf, Inayatul Izzati Diana; Jahiri, Muhamad; Henderi, Henderi; Ladjamudin, Al-Bahra Bin
JINAV: Journal of Information and Visualization Vol. 5 No. 1 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav2883

Abstract

This research aims to develop interactive media as a learning medium. Now Indonesia is entering the era of industrial revolution 5.0 which prioritizes technology in all fields, including education. However, unfortunately, learning media, especially in vocational schools, is not sufficient, and teachers must use interactive media to teach students using technology. The method used is the Multimedia Development Life Cycle (MDLC) method with six main stages, namely: Concept, Design, Material Collection, Assembly, Testing and Distribution. From the results of the review, revisions were made according to suggestions from media and material experts. At the Distribution stage, the product was tested on students, the test subjects were class X SMK Yanisba Boarding School Vocational School. Data is collected through surveys. After that, the data is examined, and recommendations are used to update the final product. The aim of this research is to: (1) Create interactive media using Kodular at SMK Yanisba Boarding School (2) Determine the feasibility of interactive media using Kodular at SMK Yanisba Boarding School. Validator assessment of interactive media using Kodular.
Development of Android Application-Based E-Learning Learning Media Using the Borg and Gall Method Jamaludin, Dieng Asep; Henderi, Henderi; Bin Ladjamudin, Al Bahra
JINAV: Journal of Information and Visualization Vol. 5 No. 1 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav2915

Abstract

The problems of this study are twofold. Namely, the need for the development of learning media with a student-centered learning model (Student Centered Learning), because the traditional learning process is inefficient and very difficult to apply to current training participants. The research method is needs analysis, using an industry-based development model (Borg and Gall) where research findings are used to design learning products, which are then systematically tested in the field, so that specific products can be produced and the effectiveness of the product can be tested. The results of the validator evaluation for e-learning learning media are 79.00% which can be interpreted as quite effective to use; for the evaluation of the benefits of e-learning learning media as learning materials, the results obtained are (1) 81.00 The results of the practicality test evaluation from the display aspect are 86.08% which can be interpreted as practical to use. The results of the effectiveness test evaluation from aspects (1) 87.77% and (2) 87.38% can be interpreted as Very Good to Use, because the effectiveness value of e-learning learning media as a learning resource is 87.60%. The development of e-learning media has become a major focus in schools, especially in SMK Negeri 7 Kota Serang, which is a learning aid that is in accordance with the learning needs of the school. Therefore, a well-structured e-learning media was created.it can be seen on correlation result where factor Education facility has the highest negative correlation value is -0.526.
Incorporate Transformer-Based Models for Anomaly Detection Dewi, Deshinta Arrova; Singh, Harprith Kaur Rajinder; Periasamy, Jeyarani; Kurniawan, Tri Basuki; Henderi, Henderi; Hasibuan, M. Said; Nathan, Yogeswaran
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.762

Abstract

This paper explores the effectiveness of Transformer-based models, specifically the Time-Series Transformer (TST) and Temporal Fusion Transformer (TFT), for anomaly detection in streaming data. We review related work on anomaly detection models, highlighting traditional methods' limitations in speed, accuracy, and scalability. While LSTM Autoencoders are known for their ability to capture temporal patterns, they suffer from high memory consumption and slower inference times. Though efficient in terms of memory usage, the Matrix Profile provides lower performance in detecting anomalies. To address these challenges, we propose using Transformer-based models, which leverage the self-attention mechanism to capture long-range dependencies in data, process sequences in parallel, and achieve superior performance in both accuracy and efficiency. Our experiments show that TFT outperforms the other models with an F1-score of 0.92 and a Precision-Recall AUC of 0.71, demonstrating significant improvements in anomaly detection. The TST model also shows competitive performance with an F1-score of 0.88 and Precision-Recall AUC of 0.68, offering a more efficient alternative to LSTMs. The results underscore that Transformer models, particularly TST and TFT, provide a robust solution for anomaly detection in real-time applications, offering improved performance, faster inference times, and lower memory usage than traditional models. In conclusion, Transformer-based models stand out as the most effective and scalable solution for large-scale, real-time anomaly detection in streaming time-series data, paving the way for their broader application across various industries. Future work will further focus on optimizing these models and exploring hybrid approaches to enhance detection capabilities and real-time performance.
Detecting Gender-Based Violence Discourse Using Deep Learning: A CNN-LSTM Hybrid Model Approach Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; Henderi, Henderi; Hasibuan, M. Said; Zakaria, Mohd Zaki; Ismail, Abdul Azim Bin
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.761

Abstract

Gender-Based Violence (GBV) is a critical social issue impacting millions worldwide. Social media discussions offer valuable insights into public awareness, sentiment, and advocacy, yet manually analyzing such vast textual data is highly challenging. Traditional text classification methods often struggle with contextual understanding and multi-class categorization, making it difficult to accurately identify discussions on Sexual Violence, Physical Violence, and other topics. To address this, the present study proposes a hybrid deep learning approach combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. CNN is utilized for extracting key linguistic features, while LSTM enhances the classification process by maintaining sequential dependencies. This hybrid CNN+LSTM model is evaluated against standalone CNN and LSTM models to assess its performance in classifying GBV-related tweets. The dataset was sourced from Kaggle, containing real-world Twitter discussions on GBV. Experimental results demonstrate that the hybrid model surpasses both CNN and LSTM models, achieving an accuracy of 89.6%, precision of 88.4%, recall of 89.1%, and F1-score of 88.7%. Confusion matrix and ROC curve analyses further confirm the hybrid model’s superior performance, correctly identifying Sexual Violence (82%), Physical Violence (15%), and Other (3%) cases with reduced misclassification rates. These results suggest that combining CNN’s feature extraction with LSTM’s contextual learning provides a more balanced and effective classification model for GBV-related text. This work supports the development of AI-based tools for social media monitoring, policy-making, and advocacy, helping stakeholders better understand and respond to GBV discussions. Future research could explore transformer-based models like BERT and real-time classification applications to further improve performance.
Co-Authors Abas Sunarya Achmad Badrianto Adi Setiawan Aditya Prihantara Agung Yudo Ardianto Ahmad Sidik Ainiyatul Maghfiroh Akmal Fauzan Al- Bahra Alda Galuh Fitria Dewi Aldi Destaryana Ali Djamhuri Alwan Hibatullah Andang Wijanarko Andrian Saputra Andrie Prajanueri Kristianto Anggrahini, Yunia Riska Anindita Septiarini, Anindita Ar Ridho Gusti Ari Ari Suhartanto Ari Suhartanto Arie Afriyoga Arief Setyanto Arif, Achmad Yusron Arifin, Rita Wahyu Aris Martono Ary Budi Warsito Asep Saefullah Asro, Asro B. Herawan Hayadi Badrianto, Achmad Bambang Soedijono W.A Bambang Soedijono, Bambang Bambang Soedjiono W.A Bangun Mukti Prasetyo Bin Ladjamudin, Al Bahra Bramantyo Yudi Wardhana Budiarto, Mukti Destyanto, Febrian Devi Rositawati Dewi, Deshinta Arrova Dian Mustika Putri Didi Rahmat Didik Setiyadi Dwinda Etika Profesi Efana Rahwanto Efana Rahwanto Ema Utami Euis Nurninawati Euis Siti Nur Aisyah Fahmie Al Khudhorie Fata Nidaul Khasanah Fazlul Rahman Fifit Alfiah Fitria Nursetianingsih Frama Yenti Giandari Maulani, Giandari Gugun Gunawan Gunawan, Deddy Gutama, Deden Hardan Haekal Simangunsong, Fikri Muhammad Hamdani Hamdani Hamdy Hady Hari Agustiyo Hatta, Heliza Rahmania Husein Muhammad Fahrezy Husni Teja Sukmana I Ketut Gunawan Ignatius Agus Supriyono Ilham Hizbuloh Ina Sholihah Widiati, Ina Sholihah Indri Handayani Indri Handayani Ira Tyas Ningrum Irwan Sembiring Ismail, Abdul Azim Bin Iwan Setyawan Jahiri, Muhamad Jahri, Muhamad Jamaludin, Dieng Asep Julia Kurniasih Junaidi Junaidi Junaidi Junaidi Kartawinata, Dea Karunia Suci Lestari Khairunnisak Nur Isnaini Khurotul Aeni, Khurotul Kurniawan, Tri Basuki Kusrini - Kusrini . Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Ladjamudin, Al-Bahra bin Ladjamudin, AlBahra Bin M Rizeki Yuda Saputra M Said Hasibuan M. Rizeki Yuda Saputra M. Suyanto, M. Maimunah Maimunah Maimunah, Maimunah Mashal Alqudah Maulidina, Muhammad Muflih Meta Amalia Dewi Moenawar Kholil Moh Muhtarom Mohammad Hairidzulhi Mohammad Santosa Mulyo Diningrat Muhamad Hendri Muhamad Yusuf Muhamad Yusup Mujianto, Ahmad Heru Mulyana, Muhamad Mulyati Mulyati Mulyati Mulyati Muntasir, Ibnu Nathan, Yogeswaran Neno, Friden Elefri Nia Kusniawati Novi Cholisoh Nugraha, Rizal Fitrah Nur Aisyah, Euis Siti Nur Azizah Padeli Padeli Periasamy, Jeyarani Pipin Romansyah Po Abas Sunarya Prabowo Pudjo Widodo Praditya Aliftiar Pramono, Galih Prih Diantono Abda`u Puspitasari, Novianti Putri, Cheetah Savana Qory Oktisa Aulia Rafika, Ageng Setiani Rahma Farah Ningrum Rahmat, Didi Rahwanto, Efana Raja, Berisno Hendro Pardamean Manik Randy Andrian Rani Putri Merliasari Rano Kurniawan Restu Adi Pradana Riki Mardiana Rita Wahyuni Arifin Ruli Supriati, Ruli Safar Dwi Kurniawan Saputra, M Rizeki Yuda Saputra, M. Rizeki Yuda Setianto, Yuni Ambar Shofiyul millah Singh, Harprith Kaur Rajinder Siti Khodijah Siti Ria Zuliana Siti Risma Auliasari Sofiana, Sofa Sri Rahayu Sudaryono Sudaryono Sudaryono Sudaryono Sugeng Santoso Suharto - Sulaiman, Agus Sutami, Sutami Suyatno Suyatno Swastika, Rulin Syahrial Shaddiq Taufik Hidayat Theopillus J. H. Wellem Toga Parlindungan Silaen Tri Wahyuningsih Tri Wahyuningsih Tri Wahyuningsih Tubagus Ahmad Harja Kusuma Umdatur Rosyidah Uning Lestari Untung Rahardja Viola Tashya Devana W, Bambang Soedijono Winarno Winarno Winarno Winarno Wing Wahyu Winarno Yeni Nuraeni Yulika Ayu Rantama Yuni Ambar S Yunia Riska Anggrahini Yusuf, Inayatul Izzati Diana Zakaria, Mohd Zaki Zcull, Harph