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All Journal International Journal of Advances in Intelligent Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Journal of Information Systems and Informatics Journal of Innovation Information Technology and Application (JINITA) Abdimas Altruis: Jurnal Pengabdian Kepada Masyarakat Jurnal Algoritma
Imam Tahyudin
Universitas Amikom Purwokerto
Humanities Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Environmental Science Mechanical Engineering Public Health Social Sciences Other

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Pendampingan Digital Marketing untuk Anggota dan Pengurus PCINU Jepang melalui Platform nusamart.id Imam Tahyudin; Andi Dwi Riyanto; Leni Setiani
Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Vol 4, No 3 (2021): Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstormin
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/japhb.v4i3.2707

Abstract

Pengurus Cabang Istimewa Nahdlatul Ulama (PCINU) Jepang merupakan cabang organisasi terbesar di Indonesia Nahdlatul Ulama (NU). Keberadaannya di Jepang telah resmi menjadi salah satu organisasi nirlaba tercatat di Kementrian Hukum Jepang. Saat ini terdapat 300 anggota aktif di PCINU Jepang. Salah satu program kerja unggulan PCINU Jepang adalah peningkatan kesejahteraan dan keterampilan warga NU. Untuk lebih mengoptimalkan program kerja tersebut maka diperlukan adanya peran serta dari pihak lain seperti dari Universitas Amikom Purwokerto. Universitas Amikom Purwokerto sangat kredibel untuk berperan aktif dalam membekali keterampilan tentang kemampuan digital marketing kepada anggota PCINU Jepang sebagai bentuk implementasi kerjasama yang telah dijalin. Kemampuan tentang digital marketing dirasa sangat diperlukan saat ini terlebih pada saat pandemi seperti. Beberapa kompetensi yang bisa dioptimalkan pada program ini adalah bagaimana melihat potensi sebuah produk, riset kata kunci yang bayak digunakan orang untuk mencari produk, serta bagaimana memaksimalkan media sosial seperti Facebook dan Instagram untuk promosi. Selain itu, dikenalkan juga platform nusamart.id untuk mendukung praktek digital marketing. Kegiatan ini diadakan dengan metode pelatihan secara daring menggunakan zoom meeting sebanyak lima kali pertemuan selama lima bulan. Hasil yang diharapkan dari kegiatan ini adalah meningkatnya keterampilan tentang kompetensi digital marketing sehingga dapat meningkatkan kesejahteraan anggota PCINU Jepang.
The mortality modeling of covid-19 patients using a combined time series model and evolutionary algorithm Imam Tahyudin; Rizki Wahyudi; Wiga Maulana; Hidetaka Nambo
International Journal of Advances in Intelligent Informatics Vol 8, No 1 (2022): March 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i1.669

Abstract

COVID-19 pandemics for as long as two years ago since 2019 gives many insights into various aspects, including scientific development. One of them is the fundamental research of computer science. This research aimed to construct the best model of COVID-19 patients’ mortality and obtain less prediction errors. We performed the combination methods of time series, SARIMA, and Evolutionary algorithm, PARCD, to predict male patients who died because of COVID-19 in the USA, containing 1.008 data. So, this research proposed that SARIMA-PARCD has a powerful combination for addressing the complex problem in a dataset. The prediction error of SARIMA-PARCD was compared with other methods, i.e., SARIMA, LSTM, and the combination of SARIMA-LSTM. The result showed that the SARIMA-PARCD has the smallest MSE value of 0.0049. Therefore, the proposed method is competitive to implement in other cases with similar characteristics. This combination is robust for solving linear and non-linear problems.
Pengembangan Aplikasi Tiga-Tingkat Menggunakan Metode Scrum pada Aplikasi Presensi Karyawan Glints Academy imam tahyudin; Zidni Iman Sholihati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 1 (2022): Februari 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (347.835 KB) | DOI: 10.29207/resti.v6i1.3793

Abstract

The rapid development of technology requires a software development management system that can be adaptive in rapidly changing circumstances. Scrum is an agile method that has the advantage of being agile and adaptive. Glints Academy holds an Industry Project Exploration as the program to prepare students for the rapid development of technology and reduce the gap between the education field and industrial field by MBKM program from the Ministry of Education and Culture. This study aims to apply the Scrum method in a heterogeneous developer team and divergent ability backgrounds to build an application with three-level architecture. The developer team is college students who come from different regions spread across Indonesia with full online implementation. Scrum is used because it is advantageous to other methods in a relatively fast-changing environment and also provides good quality control. The sprints were carried out in two sprints with two weeks of development in each sprint. The application built is an employee attendance application with a three-tier architecture: client, server, and data. The client-tier application is a front-end server built using the React.js framework while the server-tier and data-tier are built-in back-end servers with the Node.js and Express.js frameworks. JWT (JSON Web Token) authentication determines access role to functions and resources available on the back-end server. The result is a web application that fulfills the entire product backlog determined by the product owner. The results of this research are this method can used to develop features enhancement in the middle of the application development process without affecting the main feature development and this method is effectively used for different team developer backgrounds and during its online development
INOVASI PROMOSI KERAJINAN “SANDAL BANDOL” SEBAGAI OLEH-OLEH UNIK UNTUK PENGUNJUNG HOTEL DI KABUPATEN BANYUMAS MELALUI E-KATALOG Imam Tahyudin; Bayu Ibnu Syafiq; Bagus Budi Santoso
ABDIMAS ALTRUIS: Jurnal Pengabdian Kepada Masyarakat Vol 4, No 1 (2021): April 2021
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (549.205 KB) | DOI: 10.24071/aa.v4i1.2329

Abstract

Promotional innovation using e-catalogs is one of the efforts to increase the number of bandol sandal handicraft sale. The bandol sandal e-catalog is used to focus on promotions for hotel visitors in Banyumas district. The steps taken include the selection of quality sandal products and attractive designs through a design competition. Then the selected product is made a catalog and e-catalog. Through cooperation with hotel management, it is expected to be able to target hotel customers in Banyumas district. This service activity succeeded in selecting attractive and quality bandol sandals, catalogs and e-catalogs.
Visual Content Captioning and Audio Conversion using CNN-RNN with Attention Model Aldy Agil Hermanto; Giat Karyono; Imam Tahyudin; Boby Sandityas Prahasto
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2788

Abstract

The primary objective of this research is to develop an image captioning and audio conversion system based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) with the integration of an Attention Mechanism, aimed at improving accessibility for visually impaired individuals. The research design follows a systematic approach involving data collection, preprocessing, model development, training, evaluation, and implementation. The methodology utilizes CNN for visual feature extraction, RNN for language modeling, and an Attention Mechanism to enhance contextual relevance in caption generation. Google Text-to-Speech (gTTS) is also integrated to convert generated captions into audio format. The main outcomes demonstrate that the model is capable of generating coherent and contextually relevant captions, as validated through qualitative assessment and quantitative measurement using the BLEU score. Experimental results show decreasing training and validation loss over 8 epochs without signs of overfitting, indicating stable model performance. The attention visualization confirms the model’s ability to focus on relevant image regions during caption generation. In conclusion, the proposed CNN-RNN architecture with Attention effectively generates descriptive captions and converts them into speech, showing strong potential for real-world accessibility applications.
A Hybrid Feature-Enriched IndoBERT Framework for Sentiment Analysis of Ride-Hailing Service Reviews in Indonesia Puas Triawan; Imam Tahyudin; Purwadi
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1587

Abstract

This study examines sentiment classification for Indonesian ride-hailing user reviews, which often contain informal expressions, ambiguity, and strong contextual dependency. Existing studies commonly rely on either traditional machine learning or transformer-based models, while limited attention has been given to integrating heterogeneous feature representations. To address this gap, this study proposes a feature-level hybrid integration strategy combining TF-IDF and IndoBERT embeddings. This approach enables the model to capture statistical term importance and contextual semantic meaning within a unified representation. A quantitative experimental design was applied to approximately 20,000 reviews collected from Gojek, Grab, and Maxim. Sentiment labels were generated through rating-based mapping and manually validated for consistency. The dataset, which was relatively balanced across positive, neutral, and negative classes, was divided into training and testing sets using an 80:20 split. Model performance was evaluated on the test set using accuracy, precision, recall, and F1-score. The proposed hybrid model achieved the highest accuracy of 93.5%, outperforming IndoBERT (91.8%) and traditional machine learning models (78.4%–87.6%). The results show that feature-level integration improves sentiment classification performance, although neutral sentiment remains challenging due to contextual ambiguity.
Peningkatan Akurasi Sistem Rekomendasi E-Commerce Collaborative Filtering dan Negative Sampling untuk Mengatasi Masalah Sparsity Gilang Miftkahul Fahmi Fahmi; Imam Tahyudin; Fandy Setyo Utomo
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3198

Abstract

The rapid growth of e-commerce presents challenges in delivering relevant product recommendations to users. This study develops a deep learning–based recommendation system by comparing the performance of Neural Collaborative Filtering (NCF) and Autoencoder models with the classical User-Based Collaborative Filtering approach using the RetailRocket dataset, which contains 2,756,101 user–product interactions. The research focuses on the application of negative sampling techniques to address the extremely high level of data sparsity. The experimental results show that NCF achieves the best performance, outperforming both the Autoencoder and the classical method in terms of Precision@10, Recall@10, and F1@10 metrics. The main contribution of this study lies in the application of NCF to a large-scale and highly sparse e-commerce dataset, demonstrating its superiority in handling extreme sparsity and producing more relevant and accurate recommendations. In addition, the study confirms the effectiveness of negative sampling techniques in improving recommendation prediction quality. These findings have theoretical implications by reinforcing the role of neural architectures in modern recommendation systems and practical implications for deploying more efficient and accurate models in real-world e-commerce platforms, potentially enhancing user experience and customer satisfaction.
Penerapan Algoritma XGBoost dengan SMOTE untuk Klasifikasi Kanker Payudara pada Dataset Wisconsin Adrianus Anggoro; Imam Tahyudin; Ades Tikaningsih
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3423

Abstract

Penelitian ini bertujuan untuk mengembangkan model deteksi kanker payudara menggunakan algoritma Extreme Gradient Boosting (XGBoost) pada Breast Cancer Wisconsin Diagnostic Dataset. Dataset terdiri dari 569 sampel dengan 30 fitur medis yang merepresentasikan karakteristik morfologi tumor, dengan dua kelas target yaitu benign (jinak) dan malignant (ganas). Tahapan penelitian meliputi pembersihan data, imputasi nilai hilang, normalisasi fitur, serta penerapan teknik Synthetic Minority Over-sampling Technique (SMOTE) untuk menangani ketidakseimbangan kelas. Model XGBoost dievaluasi menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil pengujian menunjukkan bahwa model mencapai akurasi sebesar 94,55%, dengan nilai recall kelas malignant sebesar 95,24%, yang mengindikasikan kemampuan tinggi dalam mendeteksi kanker ganas. Confusion matrix menunjukkan hanya 2 kasus false negative, menandakan sensitivitas model yang sangat baik terhadap kelas minoritas. Dibandingkan dengan model tanpa SMOTE, penerapan SMOTE terbukti meningkatkan recall pada kelas malignant secara signifikan. Hasil penelitian ini menunjukkan bahwa algoritma XGBoost dengan penanganan imbalance class efektif digunakan sebagai sistem pendukung keputusan dalam diagnosis kanker payudara dan berpotensi membantu deteksi dini secara lebih akurat.
Transfer Learning VGG16 untuk Deteksi Kanker Otak MRI: Analisis Komparatif CNN, FNN, LSTM Nesa Puspitasari; Imam Tahyudin
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3431

Abstract

Kanker otak memiliki tingkat mortalitas tinggi akibat keterlambatan diagnosis, sehingga sistem deteksi dini yang akurat menjadi kebutuhan mendesak. Penelitian ini mengusulkan pendekatan transfer learning dua tahap (initial training dan fine-tuning) menggunakan VGG16 sebagai ekstraktor fitur, dikombinasikan dengan tiga arsitektur klasifikasi CNN, FNN, dan LSTM untuk deteksi kanker otak pada citra MRI. Kebaruan penelitian terletak pada perbandingan sistematis ketiga arsitektur tersebut dalam kerangka transfer learning pada dataset MRI berskala kecil (818 citra, rasio 80:20) dengan augmentasi data. VGG16+LSTM mencapai akurasi tertinggi (96,38%), diikuti VGG16+FNN (96,21%) dan VGG16+CNN (94,74%). Model terbaik diintegrasikan ke dalam aplikasi web sebagai sistem pendukung keputusan klinis untuk skrining awal. Hasil ini mengonfirmasi efektivitas transfer learning dua tahap dalam mengatasi keterbatasan data sekaligus meningkatkan performa klasifikasi berbasis MRI.
Analisis Komparatif Image-to-Video Artificial Intelligence Pada Animasi 2D Menggunakan PSNR Dan SSIM Rizki Pamuji; Imam Tahyudin
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3438

Abstract

The development of generative Artificial Intelligence (AI) technology has had a significant impact on the multimedia sector, particularly in image-to-video techniques that are capable of automatically transforming static images into videos. This study aims to analyze and compare the video quality produced by four AI platforms, namely Kling, Runway, PixVerse, and Pika, in the context of 2D animation. The method used is a comparative experimental approach combining quantitative and qualitative methods. The data consist of three rendered 2D animation images from Blender that were converted into 5-second videos using identical prompts on each platform. Quantitative evaluation was conducted through measurements of processing time, Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). Meanwhile, qualitative evaluation involved panelists using a Likert scale to assess nine visual aspects. The results indicate that Pika and Runway excelled in processing time efficiency, with average times of 34.4 seconds and 36.3 seconds, respectively. Kling achieved the highest PSNR and SSIM values, with an average PSNR of 14.62 dB and an SSIM of 0.41, indicating the best technical quality. On the other hand, Runway received the highest ratings in terms of visual and aesthetic aspects based on respondent evaluations. Overall, no single platform outperformed the others across all aspects of the study. Therefore, the selection of a platform should be adjusted according to user needs, whether in terms of efficiency, technical quality, or visual aesthetics. This study highlights the importance of an integrated evaluation approach to produce a more comprehensive assessment of video quality.
Co-Authors Ades Tikaningsih Adrianus Anggoro Aldy Agil Hermanto Andi Dwi Riyanto Bagus Budi Santoso Bayu Ibnu Syafiq Boby Sandityas Prahasto Fandy Setyo Utomo Giat Karyono Gilang Miftkahul Fahmi Fahmi Hidetaka Nambo Leni Setiani Nesa Puspitasari Puas Triawan Purwadi Rizki Pamuji Rizki Wahyudi Wiga Maulana Zidni Iman Sholihati