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All Journal JURNAL SISTEM INFORMASI Jurnal Informatika dan Teknik Elektro Terapan Infotech Journal Proceeding SENDI_U JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Jurnal Penelitian Pendidikan IPA (JPPIPA) SMARTICS Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal SOLMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Informatika Universitas Pamulang JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) PROCEEDING STIMA MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Komputasi dan Teknologi Informasi IJISTECH (International Journal Of Information System & Technology) Jurnal Mantik BERNAS: Jurnal Pengabdian Kepada Masyarakat Tematik : Jurnal Teknologi Informasi Komunikasi IJISTECH Seminar Nasional Hasil Riset dan Pengabdian (SNHRP) Industrial Research Workshop and National Seminar J-ENSITEC (Journal of Engineering and Sustainable Technology) West Science Interdisciplinary Studies Jurnal Sistem Informasi West Science Interdisciplinary Studies Jurnal Informatika: Jurnal Pengembangan IT Science Information System and Technology SEMINAR TEKNOLOGI MAJALENGKA (STIMA) International Journal of Applied Information Systems and Informatics
Ade Bastian
Program Studi Informatika, Fakultas Teknik, Universitas Majalengka, Jln. KH. Abdul Halim. No.103 Majalengka 45416
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Pemberdayaan Perempuan Pesisir dengan Perangkat Digital: Dampak Pemantauan Kesehatan Anak dan Mitigasi Stunting Rahmawati, Ade; Bastian, Ade; Pangarsi Dyah Kusuma Wardani, Siti; Pauzan, Muh; Rifki, Muhammad
Jurnal SOLMA Vol. 14 No. 1 (2025)
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/solma.v14i1.16904

Abstract

Background: Stunting merupakan permasalahan kesehatan yang signifikan di Indramayu, terutama di kalangan masyarakat pesisir yang memiliki akses terbatas terhadap layanan kesehatan, pendidikan, dan gizi. Kondisi ekonomi yang kurang mendukung semakin memperburuk situasi, menghambat pemenuhan kebutuhan gizi anak-anak. Upaya pencegahan stunting di wilayah ini perlu dilakukan melalui pemberdayaan masyarakat dengan pendekatan yang inovatif dan berbasis teknologi. Metode: Program pengabdian masyarakat ini berfokus pada pemberdayaan perempuan pesisir melalui pemanfaatan alat digital guna meningkatkan pemantauan kesehatan anak dan mencegah stunting. Kegiatan yang dilakukan mencakup pelatihan intensif mengenai penggunaan teknologi digital untuk memantau tumbuh kembang anak secara mandiri. Selain itu, diberikan sesi edukasi mengenai pola asuh, gizi seimbang, dan kesehatan anak guna meningkatkan kesadaran masyarakat. Hasil: Program ini menunjukkan bahwa pemanfaatan perangkat digital secara efektif membantu perempuan pesisir dalam meningkatkan pemahaman dan perhatian terhadap kesehatan anak. Peserta menjadi lebih aktif dalam memantau pertumbuhan anak serta menerapkan pola asuh dan pemenuhan gizi yang lebih baik. Kesimpulan: Pemberdayaan perempuan pesisir melalui pemanfaatan teknologi digital berkontribusi dalam upaya pencegahan stunting di Indramayu. Pendekatan ini dapat diadopsi di wilayah lain dengan kondisi serupa sebagai upaya peningkatan kesejahteraan masyarakat pesisir.
DEVELOPMENT OF CNN-LSTM-BASED IMAGE CAPTIONING DATASET TO ENHANCE VISUAL ACCESSIBILITY FOR DISABILITIES Muhammad Rifki; Ade Bastian; Ardi Mardiana
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6657

Abstract

Visual accessibility in public spaces remains limited for individuals with visual impairments in Indonesia, despite technological advancements such as image captioning. This study aims to develop a custom dataset and a baseline CNN-LSTM image captioning model capable of describing sidewalk accessibility conditions in Indonesian language. The methodology includes collecting 748 annotated images from various Indonesian cities, with captions manually crafted to reflect accessibility features. The model employs DenseNet201 as the CNN encoder and LSTM as the decoder, with 70% of the data used for training and 30% for validation. Evaluation was conducted using BLEU and CIDEr metrics. Results show a BLEU-4 score of 0.27 and a CIDEr score of 0.56, indicating moderate alignment between model-generated and reference captions. While the absence of an attention mechanism and the limited dataset size constrain overall performance, the model demonstrates the ability to identify key elements such as tactile paving, signage, and pedestrian barriers. This study contributes to assistive technology development in a low-resource language context, providing foundational work for future research. Enhancements through data expansion, incorporation of attention mechanisms, and transformer-based models are recommended to improve descriptive richness and accuracy
Comparison of Machine Learning Algorithm for Enzyme Production Optimization from Industrial Waste Bastian, Ade; Fitriyani, Rofi; Susandi, Dony; Pangestu, Arki Aji; Mardiana, Ardi; Sujadi, Harun
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8212

Abstract

The manufacture of industrial enzymes from trash provides a sustainable remedy for environmental issues. This work investigates machine learning methods to enhance enzyme production from industrial waste by examining critical factors such as waste type and chemical makeup. Three algorithms—Linear Regression, Decision Tree, and Neural Network—were used to estimate and forecast enzyme production. Evaluation criteria, such as Mean Squared Error (MSE) and Coefficient of Determination (R²), were used to evaluate model performance. The results indicated that the Decision Tree method was the most effective, exhibiting lowest error and enhanced accuracy in selecting ideal production factors such as fermentation temperature and time. This method improves efficiency, lowers operating expenses, and encourages sustainable waste management practices. The results highlight the potential of machine learning to convert trash into useful industrial goods, providing a route to more sustainable biotechnology. Future study may enhance hybrid algorithms, include new waste factors, and facilitate real-time implementation for wider industrial applicability.  
PENCARIAN CERDAS ANTAR-MODA : EVOLUSI TEKNOLOGI VIDEO-TEXT RETRIEVAL Asyhari, Muhammad Fiddiana; Dimas, Fadli; Abu Bakar, Abib Maftuh; Bastian, Ade
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3.6607

Abstract

This study aims to examine scientific trends and current approaches in the field of video-text retrieval through a bibliometric analysis approach. The urgency of this topic is driven by the surge in the growth of audiovisual data and the increasing need for a retrieval system that is able to understand the semantic relationship between text and video. Bibliometric data were collected using Publish or Perish Harzing software with Google Scholar as the main source, then visualized and analyzed using VOSviewer to identify thematic clusters, keyword distributions, and author collaboration patterns. The InternVid dataset was used as an exploration reference because it provides millions of video clips with semantically rich text annotations. The analysis results show five main clusters that illustrate thematic directions in this field, including the development of cross-modal representation models, retrieval performance evaluation, and large-scale dataset construction. Human perception-based benchmarks such as VBench are also utilized to enrich the evaluative perspective beyond quantitative metrics. This study contributes to mapping the knowledge structure of the multimodal retrieval field and opens up opportunities for the development of more contextual, adaptive, and user-oriented intelligent retrieval systems.
Penerapan Convolutional Neural Network (CNN) untuk Klasifikasi Kualitas Beras sebagai Strategi Peningkatan Keamanan Pangan di Indonesia Ade Bastian; Priyadi, Deni; Zaliluddin, Dadan; Mardiana, Ardi; Wahid, Abrar; Rifki, Muhamamad; Fahmi Aziz, Muhamamad
TEMATIK Vol. 12 No. 1 (2025): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2025
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v12i1.2332

Abstract

Food fraud has emerged as a significant global issue, threatening public health, economic stability, and consumer trust across the food supply chain. In the context of rice—a staple consumed by more than half of the world’s population—the proliferation of counterfeit products poses a critical risk. This study aims to develop a deep learning-based classification model using Convolutional Neural Networks (CNN) to accurately distinguish between medium-grade, premium, and counterfeit rice. The research involved the systematic collection of 100 grain images per rice category, followed by preprocessing, data augmentation, and model training using an optimized CNN architecture for image-based classification. The dataset was split into training, validation, and testing subsets with a 60:20:20 ratio. The model was trained over 12 epochs, achieving a training accuracy of 95%. Evaluation using the test set yielded identical accuracy, with the confusion matrix confirming perfect classification across categories. External validation further demonstrated the model’s robustness and generalizability. The findings highlight CNN’s potential as an effective tool for enhancing food safety monitoring systems and combating rice fraud. This AI-driven approach contributes to agricultural quality control and emphasizes the role of machine learning in promoting food security and authenticity assurance. However, CNN models face limitations, including susceptibility to overfitting when trained on insufficiently diverse data and high computational demands during training. These challenges underscore the need for diversified datasets and the exploration of alternative architectures offering comparable performance with greater computational efficiency.
Mapping The Landscape of Speech Processing Research: : Trends, Insights, and Emerging Directions Mardiana, Ardi; Bastian, Ade; Rifki, Muhamamad; Tresna Irawan, Eka
Jurnal Informatika Universitas Pamulang Vol 10 No 1 (2025): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

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

Abstract

Speech processing has become a significant study domain within signal processing, artificial intelligence, and human-computer interaction. This work does a bibliometric analysis to ascertain research trends, notable problems, and prospective directions in voice processing. We assess significant research outputs, including publication growth, influential authors, renowned journals, and collaboration networks during the last two decades, using data sourced from credible scientific sources such as Scopus and Web of Science. The results underscore notable progress in automated voice recognition, speaker identification, and speech synthesis, while simultaneously confronting ongoing issues associated with multilingual datasets, noise resilience, and resource efficiency. Moreover, new technologies, such deep learning and neural architecture search, are recognized as catalysts for future developments. This bibliometric study seeks to provide scholars and practitioners with a thorough overview of the existing environment and strategic insights for the advancement of the voice processing domain.
The Evolution of Image Captioning Models: Trends, Techniques, and Future Challenges Bastian, Ade; Wahid, Abrar; Hafsari, Zacky; Mardiana, Ardi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2305

Abstract

This study provides a comprehensive systematic literature review (SLR) of the evolution of image captioning models from 2017 to 2025, with a particular emphasis on the impending problems, methodological enhancements, and significant architectural developments. The evaluation is guided by the increasing demand for precise and contextually aware image descriptions, and it adheres to the PRISMA methodology. It selects 36 relevant papers from reputable scientific databases. The results indicate a significant transition from traditional CNN-RNN models to Transformer-based architectures, which leads to enhanced semantic coherence and contextual comprehension. Current methodologies, such as prompt engineering and GAN-based augmentation, have further facilitated generalization and diversity, while multimodal fusion solutions, which incorporate attention mechanisms and knowledge integration, have improved caption quality. Additionally, significant areas of concern include data bias, equity in model assessment, and support for low-resource languages. The study underscores the fact that modern vision-language models, such as Flamingo, GIT, and LLaVA, offer robust domain generalization through cross-modal learning and joint embedding. Furthermore, the efficacy of computing in restricted environments is improved by the development of pretraining procedures and lightweight models. This study contributes by identifying future prospects, analyzing technical trade-offs, and delineating research trends, particularly in sectors such as healthcare, construction, and inclusive AI. According to the results, in order to optimize their efficacy in real-world applications, future picture captioning models must prioritize resource efficiency, impartiality, and multilingual capabilities.
A Bibliometric Analysis of Food Fraud Bastian, Ade
West Science Interdisciplinary Studies Vol. 1 No. 09 (2023): West Science Interdisciplinary Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsis.v1i09.79

Abstract

Food fraud threatens consumer health, food integrity, and global food supply systems, making it a global issue. Recently, bibliometric analysis has become a useful method for appraising studies in food science and fraud detection. This systematic review employs bibliometric tools to examine food fraud research trends, notable authors, and significant research issues. This investigation searches different scientific databases for relevant 1931–2023 papers. VOSviewer was used to show co-author networks, keyword co-occurrence, pattern quotations, and other bibliometric indicators. The bibliometric study shows a decade-long growth in food fraud studies. This food fraud detection bibliometric study covers the research landscape. These studies help food fraud researchers, policymakers, and other stakeholders discover research trends, prominent authors, significant work, and collaborative networks. These findings can inform future research, information exchange, and evidence-based food safety and consumer protection decisions.
Solution Search Simulation The Shortest Step On Chess Horse Using Breadth-First Search Algorithm Bastian, Ade; Nugraha, Rezha
International Journal of Artificial Intelligence Research Vol 2, No 2 (2018): December 2018
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (548.887 KB) | DOI: 10.29099/ijair.v2i2.58

Abstract

Horse seed in the chess board movement resembles the letter L. The chess pieces are one of a very hard-driven beans and seeds are often also the most dangerous if not carefully considered every movement. Simulation of this problem provides a chess board size n x n. Target (goal) of this problem is to move a horse beans of a certain position on a chess board position to the desired destination with the shortest movement simulates all possible solutions to get to the goal position. This problem is also one of the classic problems in artificial intelligence (AI). Settlement of this problem can use the help system and tree production tracking.Therefore, designed a simulation applications by utilizing several techniques of simulation programming and Breadth-First Search method. With this method, all nodes will be traced and the nodes at level n will be visited first before visiting the nodes at level n + 1. The purpose of this study is to design a software that is able to find all the solutions for the shortest movement toward the goal position by using the system of production and tracking tree.Results from this paper is that the software is able to find all solutions shortest movement a horse beans from the initial position to the goal position and displays the simulation of the movement of the horse in the chess board.
A Bibliometric Analysis of Food Fraud Bastian, Ade
West Science Interdisciplinary Studies Vol. 1 No. 09 (2023): West Science Interdisciplinary Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsis.v1i09.79

Abstract

Food fraud threatens consumer health, food integrity, and global food supply systems, making it a global issue. Recently, bibliometric analysis has become a useful method for appraising studies in food science and fraud detection. This systematic review employs bibliometric tools to examine food fraud research trends, notable authors, and significant research issues. This investigation searches different scientific databases for relevant 1931–2023 papers. VOSviewer was used to show co-author networks, keyword co-occurrence, pattern quotations, and other bibliometric indicators. The bibliometric study shows a decade-long growth in food fraud studies. This food fraud detection bibliometric study covers the research landscape. These studies help food fraud researchers, policymakers, and other stakeholders discover research trends, prominent authors, significant work, and collaborative networks. These findings can inform future research, information exchange, and evidence-based food safety and consumer protection decisions.
Co-Authors Abrar Wahid Abu Bakar, Abib Maftuh ade rahmawati Adnan Arshad Ai Komariah Alam, Muhammad Quthbul Aldri Frinaldi Ano Tarsono Ardi Mardiana Ardi Mardiana Ardi Mardiana Ardi Mardiana Arif Yusuf Budiman Aripin, Ali Maulana Hapid Arshad, Adnan Asep Rachmat Asyhari, Muhammad Fiddiana Azkiya, Muhammad Azkal Badhel, Yasser Gibran Berliani, Mega Billy Adrian Fernanda Budiman Budiman Cesoria, Yola Zerlinda Dadan Romadhoni Dadan Zaliluddin Dadan Zaliluddin Destiani, Putri Dety Sukmawati Devi Sukrisna Diana Surya Heriyana Didin Rudini Didin Rudini Dimas, Fadli Dinda Sri Wulansari Dony Susandi Erdiyanti, Yucky Putri Fahmi Aziz, Muhamamad Fernanda, Billy Adrian Firmansyah, Mochammad Bagasnanda Fitriani, Nadila Fitriyani, Rofi Hafsari, Zacky Haq, Rosdiana Harti, Adi Oksifa Rahma Harun Sujadi Hermawan, Dicky Ida Marina Ii Sopiandi, Ii Imas Naimah Hasnah Indra Permana, Indra Indradewa, Rhian Jabbar, Fathir Abdul Khoerunissa, Salsa Koswara, Engkos Kovertina Rakhmi Indriana Kusumadewi, Intan Latiful Abror Lia Milana Lidya Tresna Wahyuni Mega Berliani Miftahuddin Al-Aziz Mochammad Bagasnanda Firmansyah Mochammad Bagasnanda Firmansyah Muhammad Fahmi Ajiz Muhammad Iqbal Rizmaya Muhammad Iqbal Rizmaya Muhammad Rifki Muhammad Rifki Muhammad Syifa Al Maroghi Muhammad Taufiq Muhammad Taufiq Mukhlis Nadya Pratiwi Aisha Bakhtiar Nana Sutrisna Nana Sutrisna Nia Kurniati Nisa Brian Sulaeman Nugraha, Algi Nugraha, Faisol Nugraha, Rezha Nunu Nurdiana, Nunu Nurfajriah, Riska Nurhilda, Pebby Nurhimah, Enung Pangarsi Dyah Kusuma Wardani, Siti Pangestu, Arki Aji Pauzan, Muh Permana, Iip Indra Prahara, Ervin Gusti Dwi Priyadi, Deni Purnama, Crisda Putra, Agam Maulana Rahayu, Syifaa Puspita Riepah, Ipah Rifki, Muhamamad Riki Riyanto Riri Nurazizah Ristina Siti Sundari Rivki Anja Afrenda Rohmanudin, Wildan Rusmanto, Ayu Hafidzah Rusyn, Volodymyr Safari Yonasi Salwa, Alya Jihan Sandi Fajar Rodiansyah Sarmidi Sarmidi Sarmidi Sarmidi Satria Winata Sidik Zapar Sidik Sudjana, Muhammad Ridwan Shaleh Tantri Wahyuni Tika Sifana Tresna Irawan, Eka Tri Ferga Prasetyo Usup Suparma Vini Arifiani Rohmat Volodymyr Rusyn Wahid, Abrar Wahyuni, Kartika Sri Wahyuni, Lidya Tresna Whydiantoro Wildan Rohmanudin Wildan Zhilal Manafi Wiranagari, Relifa G Yofi Awwaluddin Yunus, Riza M ZAPAR SIDIK, SIDIK