Article Per Year (5 Year)
p-Index From 2021 - 2026
8.054
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Transmisi: Jurnal Ilmiah Teknik Elektro Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Majalah Ilmiah MOMENTUM Jurnal Informatika Upgris Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of New Media Technology ILKOM Jurnal Ilmiah Jurnal Teknologi Sistem Informasi dan Aplikasi Systemic: Information System and Informatics Journal Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Building of Informatics, Technology and Science Infotekmesin Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Informatika dan Rekayasa Perangkat Lunak Journal of Robotics and Control (JRC) Journal of Applied Engineering and Technological Science (JAETS) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Ilmiah Intech : Information Technology Journal of UMUS Komatika: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Informasi Cyberku Moneter : Jurnal Keuangan dan Perbankan Advance Sustainable Science, Engineering and Technology (ASSET)
Ricardus Anggi Pramunendar
Faculty Of Computer Science, Universitas Dian Nuswantoro
Aerospace Engineering Humanities Automotive Engineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Other

Published : 88 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 88 Documents
Search

 5   6   7   8   9 
Adaptive threshold for moving objects detection using gaussian mixture model Moch Arief Soeleman; Aris Nurhindarto; Muslih Muslih; Karis W.; Muljono Muljono; Farikh Al Zami; R. Anggi Pramunendar
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14878

Abstract

Moving object detection becomes the important task in the video surveilance system. Defining the threshold automatically is challenging to differentiate the moving object from the background within a video. This study proposes gaussian mixture model (GMM) as a threshold strategy in moving object detection. The performance of the proposed method is compared to the Otsu algorithm and gray threshold as the baseline method using mean square error (MSE) and Peak Signal Noise Ratio (PSNR). The performance comparison of the methods is evaluated on human video dataset. The average result of MSE value GMM is 257.18, Otsu is 595.36 and Gray is 645.39, so the MSE value is lower than Otsu and Gray threshold. The average result of PSNR value GMM is 24.71, Otsu is 20.66 and Gray is 19.35, so the PSNR value is higher than Otsu and Gray threshold. The performance of the proposed method outperforms the baseline method in term of error detection.
Implementasi AI sebagai Asisten Cerdas untuk Meningkatkan Kompetensi Guru dalam Penyusunan Instrumen Asesmen di SMA Negeri 1 Ngadiluwih Galuh Wilujeng Saraswati; Erba Lutfina; Affandy; Ricardus Anggi Pramunendar; Muhammad Syaifur Rohman
Komatika: Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 1 (2026): May 2026
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat, Institut Informatika Indonesia Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/komatika.v6i1.1482

Abstract

Perkembangan Artificial Intelligence (AI) yang pesat menuntut adaptasi kompetensi guru dalam menyusun instrumen evaluasi yang adaptif dan inovatif. Pengabdian masyarakat ini bertujuan untuk meningkatkan literasi digital tenaga pendidik di SMA Negeri 1 Ngadiluwih melalui pelatihan pemanfaatan AI sebagai asisten cerdas dalam penyusunan asesmen berbasis Higher Order Thinking Skills (HOTS) dan produksi media pembelajaran kreatif. Metode yang digunakan adalah Participatory Action Research (PAR) yang melibatkan 60 guru dari berbagai rumpun mata pelajaran. Pelatihan dilaksanakan selama 150 menit dengan alur kerja yang mencakup pemaparan teori, demonstrasi prompt engineering, dan praktik mandiri pembuatan video edukasi clay-motion. Hasil kegiatan menunjukkan adanya peningkatan kompetensi kognitif peserta secara signifikan, yang dibuktikan dengan kenaikan rata-rata nilai dari 5,63 pada pre-test menjadi 7,5 pada post-test. Selain itu, mitra berhasil memproduksi draf instrumen asesmen HOTS dan purwarupa media visual yang relevan dengan kebutuhan kurikulum. Meskipun terdapat kendala pada kesenjangan literasi digital antar generasi dan limitasi teknis perangkat, kegiatan ini terbukti efektif dalam mentransformasi peran AI sebagai asisten instruksional yang mampu mereduksi beban administrasi sekaligus meningkatkan kualitas konten edukasi di sekolah.
Enhancing Support Vector Machine Classification of Nutrient Deficiency in Rice Plants Through Particle Swarm Optimization-Based Feature Selection James Hartojo; Jessica Carmelita Bastiaans; Ricardus Anggi Pramunendar; Pulung Nurtantio Andono
IJNMT (International Journal of New Media Technology) Vol 11 No 2 (2024): Vol 11 No 2 (2024): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v11i2.3762

Abstract

The research focuses on the classification of nutrient deficiencies in rice plant leaves using a combination of Support Vector Machine (SVM) and Particle Swarm Optimization (PSO) methods for feature selection. Image features are extracted using Histogram of Oriented Gradients (HOG), which is then optimized with PSO to select the most relevant features in the classification process. Indonesia is one of the largest rice producers in the world, with food security as a major issue that requires sustainable solutions, especially in the agricultural sector. The growth and yield of rice plants are highly dependent on the availability of nutrients such as Nitrogen (N), Phosphorus (P), and Potassium (K). However, traditional observation methods to detect nutrient deficiencies in plants become inefficient as the scale of production increases. The dataset used includes images of rice leaves showing nitrogen (N), phosphorus (P), and potassium (K) deficiencies. Experiments show that the SVM model optimized with PSO provides a classification accuracy of 83.19% and a runtime of 129.63 seconds with 1150 best feature combinations out of 2303 extracted features, which is higher accuracy and faster runtime than the model that does not use PSO. These results show that the integration of PSO in the feature selection process not only improves the accuracy of the model, but also reduces the required computation time. This research makes an important contribution to the development of an automated system for the classification of nutrient deficiencies in crops, which can be implemented in large farms or other agricultural fields.
PENCAPAIAN KLASIFIKASI TERBAIK BERBASIS PERBAIKAN CITRA CLAHE DAN DARK CHANNEL PRIOR PADA SPESIES IKAN Dewi Pergiwati; Ricardus Anggi Pramunendar; Dwi Puji Prabowo; Farrikh Alzami; Rama Aria Megantara
Jurnal Teknik Informatika UMUS Vol 7 No 2 (2025): November
Publisher : Universitas Muhadi Setiabudi

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

Abstract

Ikan merupakan bahan pangan lauk-pauk utama yang dikonsumsi manusia untuk menunjang protein hewani dan zat-zat lain yang diperlukan tubuh. Ikan merupakan lauk-pauk pilihan utama yang memiliki harga relative murah dan mudah didapat. Namun pada nyatanya konsumsi ikan di Indonesia sangat rendah dibandingkan dengan negara-negara yang memiliki potensi sumberdaya perikanan yang jauh lebih rendah seperti negara Jepang, Korea Selatan, serta negara-negara di Asia lainnya. Di sisi lain, salah satu kekayaan Indonesia yang sangat berlimpah pada sector perairan adalah biota ikan. Dengan kondisi demikian, upaya peningkatan konsumsi ikan akan memberikan multiflier effect dalam lingkungan masyarakat. Selain meningkatkan tingkat kesehatan serta kecerdasan, juga semakin menggairahkan sektor perikanan untuk dapat mendorong peningkatan penyerapan tenaga kerja, meningkatkan pendapatan serta kesejahteraan pada masyarakat khususnya profesi nelayan, pembudidaya ikan, pengolah hasil ikan serta pihak terkait lainnya. Maka, perlu ditingkatkan kemampuan pengenalan ikan secara otomatis dengan bantuan computer untuk mengenali jenis-jenis ikan yang sangat beragam guna mempermudah proses pengelolaan dan distribusi ikan. Oleh karena itu dalam penelitian ini, peneliti ini mengusulkan untuk melakukan analisis dampak pre-processing dari kombinasi algoritma CLAHE dan DCP yang diterapkan dalam klasifikasi ikan dengan Random Forest.
Optimalisasi Identifikasi Sayuran Jenis Umbi berbasis Morfologi dan Jaringan Syaraf Tiruan untuk Mendukung Ketahanan Pangan Nasional Danang Kuswardono; Siti Aisyah; Henry Bastian; dwi Puji Prabowo; Ricardus Anggi Pramunendar
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 16 No. 2 (2025): JURNAL SIMETRIS VOLUME 16 NO 2 TAHUN 2025
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v16i2.15856

Abstract

Ketahanan pangan nasional menjadi tantangan strategis yang perlu dihadapisecara inovatif, terutama dalam konteks diversifikasi pangan dan pemanfaatansumber daya lokal seperti sayuran jenis umbi. Kentang, ubi jalar, dan talasmerupakan komoditas potensial sebagai sumber karbohidrat alternatif. Namun,identifikasi varietas umbi di lapangan masih bergantung pada metode manual yangbersifat subjektif, lambat, dan membutuhkan tenaga ahli.Penelitian ini selaras dengan target nasional seperti peningkatan skor GFSI dari 64 (2020) menjadi 69,8 (2024), serta skor Pola Pangan Harapan (PPH) dari 90,4 menjadi 95,2. Inovasi ini diharapkan menjadi kontribusi nyata dalam mendukung ketahanan pangan berkelanjutan melalui integrasi teknologi AI di sektor pertanian. Berbagai studi menunjukkan bahwa teknologi pengolahan citra digital berbasiskecerdasan buatan seperti Artificial Neural Network (ANN) dan Convolutional NeuralNetwork (CNN) efektif untuk klasifikasi morfologi tanaman secara otomatis danakurat. Dalam konteks tersebut, penelitian ini bertujuan mengembangkan sistemidentifikasi varietas sayuran jenis umbi berbasis morfologi visual dan jaringan syaraftiruan, yang dioptimalkan untuk kondisi lokal dan dapat diimplementasikan padaperangkat berspesifikasi rendah. Pengolahan data citra akan menggunakan percobaan pembagian holdout validation dan k-fold validation yang nantinya hasil dari percobaan tersebut akan digunakan dalam dalam menguji data mentah uji tunggal. Hasil dari percobaan tersebut diketahui bahwa Hasil dari beberapa percobaan diatas diketahui bahswa Backpropagation Neural Network berhasil digunakan dalam mengidentifikasi data citra hasil umbi. Backpropagation Neural Network yang dilakukan berhasil dengan Nilai rata-rata akurasi tertinggi untuk traingdx pada neuron 40 adalah 95,02%, sementara untuk trainlm akurasi terbaik tercapai pada neuron 20 dengan nilai 94,97%
Spatiotemporal Analysis of Peatland Fire Hotspots and Fire Intensity in Riau Province Using MODIS–VIIRS Multisensor Satellite Data Najwa Ratu Afi; Ramadhan Rakhmat Sani; Ricardus Anggi Pramunendar; Nurul Anisa Sri Winarsih; Ika Novita Dewi
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12686

Abstract

Peatland fires in Riau Province frequently occur during the dry season and contribute significantly to regional haze, environmental degradation and carbon emissions. Effective monitoring of these fires remains challenging due to their widespread distribution and varying intensity across peatland areas. This research aims to analyze the spatiotemporal characteristics of peatland fire hotspots in Riau Province using multisensor satellite observations from the NASA Fire Information for Resource Management System (FIRMS). The dataset integrates Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) data from the Suomi-NPP, NOAA-20 and NOAA-21 satellites. After applying filtering criteria of confidence ≥70% and Fire Radiative Power (FRP) ≥5 megawatts (MW), a total of 7,297 significant hotspots were identified during the July–October 2025 dry season. The results show that fire activity peaked in July with a maximum daily FRP of 25,611 MW and a monthly total of 65,120 MW, followed by a decline in September and a slight increase in October. The FRP distribution was highly right-skewed, with an average value of13.2 MW, while the most intense hotspots reached 189.4 MW. Estimated carbon dioxide (CO₂) emissions reached approximately 122,472 tons, indicating substantial environmental impacts. Spatial clustering and persistence analysis revealed several high-risk peatland zones with repeated fire occurrences. These findings demonstrate the importance of multisensor satellite monitoring for improving early fire detection, emission assessment and disaster mitigation strategies in peatland regions.
LDWFOX Optimization for Hyperparameter Tuning of Inception CNN in UAV-Based Vegetation Density Mapping Ricardus Anggi Pramunendar; Ashraf Alomoush; Dwi Puji Prabowo; Rama Aria Megantara; Farrikh Alzami; Nurul Anisa Sri Winarsih; Dewi Pergiwati; Guruh Fajar Shidik
Advance Sustainable Science Engineering and Technology Vol. 8 No. 4 (2026): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i4.3433

Abstract

Vegetation density classification from UAV imagery is a practical necessity in fire-prone landscapes, since fuel load on the ground directly informs risk management decisions. Convolutional neural networks handle this classification reasonably well, but good performance requires careful hyperparameter tuning, and manual trial and error produces results that are unstable and hard to reproduce. This study proposes LDW-FOX, a modified FOX metaheuristic using a Linearly Decreasing Weight mechanism to automate hyperparameter tuning for an Inception-based CNN. The original FOX algorithm applies fixed movement weights throughout optimization, causing search to stagnate early. LDW-FOX gradually reduces exploration intensity across iterations, pushing search toward exploitation as it converges. Five hyperparameters, namely learning rate, dropout rate, hidden layer size, activation function, and optimizer, were tuned on a balanced 3,000 image UAV dataset spanning three vegetation density classes. Manual tuning peaked at 61.00 percent test accuracy but varied considerably across epoch settings. LDW-FOX reached a peak test accuracy of 82.48 percent and a mean of 58.47 percent, outperforming the original FOX, whose mean was 55.30 percent. LDW-FOX showed a more consistent training-test gap than other swarm-based methods, with LDW variants beating unmodified counterparts under equal budgets. High variance across configurations indicates broader generalization needs testing.
Penentuan Metode Augmentasi pada CNN Berdasarkan Metode Optimasi Fox untuk Klasifikasi Citra Ikan Firman Wahyudi; Siti Hadiati Nugraini; Arief Soeleman; Ricardus Anggi Pramunendar; Pulung Nurtantio Andono
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

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

Abstract

Automatic identification of fish species plays an important role in various fields such as conservation biology, fisheries management, and biological research. The Convolutional Neural Network (CNN) method has become an effective solution for automating this process using digital images. However, achieving high accuracy requires careful consideration of factors such as the quantity and quality of data, image preprocessing methods, feature extraction techniques, classification algorithms, and optimization strategies. This study addresses these challenges by proposing a CNN model optimized using the FOX optimization algorithm to select the most suitable augmentation methods. The results show that selecting appropriate augmentation techniques, such as K-means Color Quantization, Horizontal Flip, Voronoi, Elastic Transformation, and Contrast Normalization, can significantly improve the accuracy of fish species recognition, reaching up to 98.75 percent during the training phase. The proposed model also demonstrates strong generalization capabilities with a validation accuracy of 96.90 percent, indicating minimal overfitting. Although the training process is computationally intensive, this approach has proven to be highly effective for applications that require high accuracy and strong generalization capabilities, thus contributing to a better understanding and management of marine ecosystems in support of sustainable fisheries practices..
Co-Authors Abdul Syukur Abu Salam Ade Yusupa Affandy Affandy Affandy Affandy Agus Winarno, Agus Agustina, Feri Ahmad Akrom Akrom, Ahmad Al-Azies, Harun ALI MUQODDAS Alvin, Fris Alzami, Farrikh Andi Kamaruddin Apriyanto Alhamad Arie Nugroho, Arie Arief Soeleman Arifin, Zaenal Ashraf Alomoush Aurelia Monica Sari Azzahra, Tarissa Aura Baroroh, Nurul Bastiaans, Jessica Carmelita Catur Supriyanto Catur Supriyanto Catur Supriyanto Catur Supriyanto D, Ishak Bintang Danang Kuswardono Danny Oka Ratmana Danny Oka Ratmana Darmawan, Aditya Aqil De Rosal Ignatius Moses Setiadi Dewi Nurdiyah Dewi Pergiwati Diana Aqmala Dibyo Adi Wibowo Dwi Puji Prabowo Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Dzuha Hening Yanuarsari, Dzuha Hening Edi Noersasongko Enrico Irawan Erba Lutfina Erlin Dolphina Etika Kartikadarma Evanita Evanita, Evanita F. Alzami Fafaza, Safira Alya Fajrian Nur Adnan Fakhrurrozi Fakhrurrozi, Fakhrurrozi Farikh Al Zami Fathorazi Nur Fajri Fatkhuroji Fatkhuroji Fauzi Adi Rafrastara Fikri Diva Sambasri Finki Dona Marleny Firman Wahyudi Firmansyah, Muhammad Ilham Go, Agnestia Agustine Djoenaidi Guruh Fajar Shidik Hamid, Maulana As’an Hartojo, James Harun Al Azies Hasan Asari Haydar, Muhammad Rifqi Fajrul Henry Bastian Henry Bastian, Henry I Ketut Eddy Purnama Ifan Rizqa Ika Novita Dewi Imran, Bahtiar Irham Ferdiansyah Katili Iswahyudi Iswahyudi James Hartojo Jessica Carmelita Bastiaans Karim, Muh Nasirudin Karis W. Kartika, Gita khoiriya latifah Khoirunnisa, Emila Khoirur Rizky, Muhammad Ivan Kristhina Evandari Kurnia Prayoga Wicaksono Kurniawan Aji Saputra Kurniawan, Defri Kusumawati, Yupie Lalang Erawan Lesmarna, Salsabila Putri M. Arif Soeleman M. Arif Soleman Mambang Maulana, Isa Iant Megantara, Rama Aria Moch Arief Soeleman Moch Arief Soeleman, Moch Arief Moch. Sjamsul Hidajat Mochamad Arief Soeleman Mochamad Hariadi Moh Adzka Fawaid Moh Yusuf, Moh Moh. Yusuf Mohammad Arif Muhammad Alkaff Muhammad Naufal Muhammad Nursandi Muhammad Syaifur Rohman Muhammad Syaifur Rohman Muhammad Zulfadhilah Muljono, - Muslih Muslih Muslih Muslih Nabila, Mira Najwa Ratu Afi Noor Wahyudi Nuanza Purinsyira Nugroho, Muhammad Bayu Nur Azise Nurhindarto, Aris Nurhindarto, Aris Pergiwati, Dewi Prabowo, D.P. Pulung Nurtantio Andono Pulung Nurtantyo Andono Puri Sulistiyawati Puri Sulistiyawati Puri Sulistiyawati Purwanto Purwanto Purwanto Purwanto Purwanto Purwanto Putu Samuel Prihatmajaya R.A. Megantara Rama Aria Megantara Rama Aria Megantara Ramadhan Rakhmat Sani Ramadhani, Irfan Wahyu Ramdan, Hendri Ratmana, Danny Oka Riadi, Muhammad Fatah Abiyyu Rifqi Mulya Kiswanto Ritzkal, Ritzkal Rohman, Muhammad Syaifur Rony Wijanarko Rozada, Akfi Ruri Suko Basuki Sambasri, Fikri Diva Santoso, Siane Saputra, Filmada Ocky Saputra, Resha Mahardhika Saraswati, Galuh Wilujeng Sasono Wibowo Sinaga, Daurat Siti Aisyah Siti Hadiati Nugraini Soeleman, M. Arief Soeleman, Moh. Arief Sri Winarno Stefanus Santosa Subhan Panji Cipta Sulistyowati, Tinuk Sunardi, Ph.D., Sunardi Sutini Dharma Oetomo Tamamy, Aries Jehan Teguh Tamrin Ullumudin, D.I.I Usman Sudibyo Vincent Suhartono Vincent Suhartono Vincent Suhartono Wibowo, Gentur Wahyu Nyipto Wijaya, Eka Setya Wildanil Ghozi Winarsih, Nurul Anisa Sri Yudha Tirto Pramonoaji Yuliman Purwanto Yuslena Sari, Yuslena Yuventius Tyas Catur Pramudi Zainal Arifin Hasibuan