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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) 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) IJOEM: Indonesian Journal of Elearning and Multimedia Blockchain Frontier Technology (BFRONT) Journal of Digital Market and Digital Currency

Henderi .

Raharja University

Author-ID : 2007989

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 Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Neuroscience Social Sciences Transportation Other

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PENGEMBANGAN PROTOTYPE SISTEM DIAGNOSA UNTUK PENANGANAN PENYAKIT IKAN PADA DINAS PERIKANAN KOTA TANGERANG SELATAN: 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

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

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

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

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

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.

Implementation of the K-Nearest Neighbor Algorithm for Classifying Immigration Residence Permit Applicants at the Class I Special Immigration Office TPI Soekarno-Hatta Azizah, Nur; Henderi, Henderi; Raja, Berisno Hendro Pardamean Manik
CCIT (Creative Communication and Innovative Technology) Journal Vol 18 No 2 (2025): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i2.3577

This study aims to apply the K-Nearest Neighbor (KNN) algorithm in classifying immigration residence permit applicants at the Class I Special Immigration Office TPI Soekarno-Hatta, focusing on the algorithm's effectiveness and accuracy in categorizing residence permit applicants based on the types of residence permits: Visit Stay Permit (ITK), Limited Stay Permit (ITAS), and Permanent Stay Permit (ITAP). This study employs a quantitative, experiment-based approach utilizing a dataset of 17,212 residence permit applicant records consisting of 11 key attributes, such as nationality, visa type, residence permit type, gender, and age group.The research process began with data preprocessing stages, including data cleaning, normalization, and dataset splitting into training and testing sets with 80:20 and 70:30 partitioning scenarios. The KNN algorithm was implemented using a parameter of k=5k = 5k=5, chosen based on experimentation to achieve optimal performance. The model's performance evaluation was conducted using accuracy, precision, and recall metrics derived from a confusion matrix. The findings reveal that the KNN algorithm successfully classifies data with the highest accuracy of 96.95% in the 80:20 dataset partition scenario and 96.84% in the 70:30 scenario. The Visit Stay Permit (ITK) class demonstrated the best performance with a precision of 97.46% and a recall of 99.97%, whereas the Permanent Stay Permit (ITAP) class showed the lowest performance with a recall of 59.79%, indicating challenges in recognizing patterns for this class. This study also identifies the advantages of the KNN algorithm, including its simplicity of implementation, flexibility in handling multiclass data, and effectiveness for low-dimensional datasets. However, the algorithm has limitations, such as sensitivity to imbalanced data distributions and high computational time for large datasets.

Comparative Study of Traditional and Modern Models in Time Series Forecasting for Inflation Prediction Henderi, Henderi; Sofiana, Sofa
International Journal for Applied Information Management Vol. 5 No. 3 (2025): Regular Issue: September 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v5i3.108

Time series forecasting plays a crucial role in economic analysis, particularly in anticipating inflation and policy planning. This study compares the performance of seven different time series forecasting models, namely ARIMA, SARIMA, ETS, Prophet, LSTM, XGBoost, and TCN, in predicting inflation rates. Each model was applied to four years of inflation data to test its accuracy and reliability. The evaluation was conducted using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) to measure the performance of each model. The results indicate that deep learning models, particularly LSTM and TCN, achieved the highest accuracy with the lowest MSE and RMSE values, specifically 0.0008 and 0.0015 for LSTM, and 0.0007 and 0.0013 for TCN, indicating their capability in capturing complex temporal patterns. Traditional models such as ARIMA and SARIMA, while effective in capturing trends and seasonality, showed limitations in handling non-linear patterns and sudden changes, with MSE and RMSE values of 0.0012 and 0.0024 for ARIMA, and 0.0011 and 0.0023 for SARIMA, respectively. ETS, with the highest MSE and RMSE values of 0.0013 and 0.0025, demonstrated limitations in dealing with the complexity of inflation data. XGBoost also showed good performance with MSE and RMSE values of 0.0009 and 0.0018, combining flexibility and robustness in handling complex data. Prophet achieved an MSE of 0.0010 and RMSE of 0.0020, indicating that while it effectively captures seasonal trends, there is room for improvement in handling rapid inflation increases. This research provides in-depth insights into the strengths and weaknesses of each model, as well as recommendations for practical applications in inflation forecasting. By presenting a comprehensive comparative analysis, this study aims to assist researchers and practitioners in selecting the most suitable forecasting model for their specific needs

E - LEADERSHIP : KONSEP DAN PENGARUHNYA TERHADAP EFEKTIVITAS KEPEMIMPINAN Henderi, Henderi; Maimunah, Maimunah; Nur Aisyah, Euis Siti
CCIT (Creative Communication and Innovative Technology) Journal Vol 100 No 2 (2008): CCIT JOURNAL
Publisher : Universitas Raharja

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Berbagai vendor software dan hardware telah mengeluarkan beberapa produk yang mendukung penerapan teknologi informasi untuk kepentingan para manajer organisasi. Namun pemanfaatan produk tersebut oleh para manajer belum optimal. Hal ini disebabkan para manajer belum memahami konsep penerapan dan cara kerja teknologi informasi untuk mendukung pelaksanaan fungsi-fungsi kepemimpinan yang disebut e-leadership. Sementara itu pemanfaatan teknologi informasi diberbagai organisasi dan perusahaan, dan penerapan konsep dan cara kerja e-leadership oleh para manajernya merupakan suatu keharusan. Organisasi yang dipimpin oleh manajer yang tidak berminat dan tidak menguasai penerapkan konsep dan cara kerja e-leadership senantiasa akan sulit bertahan diera persaingan yang semakin kompetitif. Untuk itu diperlukan penjabaran tentang konsep dan penerapan cara kerja e-leadership dan pengaruhnya terhadap efektivitas pelaksanaan fungsi manajer sebagai pemimpin, diantaranya dalam melakukan fungsi: perencanaan, pengelolaan, pendelegasian, motivasi, pengontrolan, dan evaluasi.

E - LEADERSHIP : KONSEP DAN PENGARUHNYA TERHADAP EFEKTIVITAS KEPEMIMPINAN Henderi, Henderi; Maimunah, Maimunah; Nur Aisyah, Euis Siti
CCIT (Creative Communication and Innovative Technology) Journal Vol 102 No 2 (2008): CCIT JOURNAL
Publisher : Universitas Raharja

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

E - LEADERSHIP : KONSEP DAN PENGARUHNYA TERHADAP EFEKTIVITAS KEPEMIMPINAN Henderi, Henderi; Maimunah, Maimunah; Nur Aisyah, Euis Siti
CCIT (Creative Communication and Innovative Technology) Journal Vol 102 No 2 (2008): CCIT JOURNAL
Publisher : Universitas Raharja

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Co-Authors Abas Sunarya Abas, Ashardi bin Achmad Badrianto Achmad Udin Zailani Adi Setiawan Aditya Prihantara Agung Yudo Ardianto Ahmad Sidik Ainiyatul Maghfiroh Al- Bahra Aldi Destaryana Alfiah, Fifit 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 Auliasari, Siti Risma 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 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 FAUZAN, AKMAL Fazlul Rahman Fitria Dewi, Alda Galuh Fitria Nursetianingsih Frama Yenti Giandari Maulani, Giandari Gugun Gunawan Gunawan, Deddy Gutama, Deden Hardan Hady, Hamdy Haekal Simangunsong, Fikri Muhammad Hamdani Hamdani Hamdani Hamdani 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 Kasim, Shahreen Binti 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 Misinem, Misinem 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 Pradana, Restu Adi Praditya Aliftiar Pramono, Galih Prih Diantono Abda`u Puspitasari, Novianti Putri, Cheetah Savana Putri, Dian Mustika 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 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 Ria 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 Tuah, Nooralisa Mohd 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

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