Article Per Year (5 Year)
p-Index From 2021 - 2026
5.32
P-Index
This Author published in this journals
All Journal Jurnal Media Infotama Pixel : Jurnal Ilmiah Komputer Grafis JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Pseudocode POSITIF Jurnal Informatika Upgris JURIKOM (Jurnal Riset Komputer) Jurnal Tekno Kompak JSAI (Journal Scientific and Applied Informatics) Journal of Technopreneurship and Information System (JTIS) Jusikom: Jurnal Sistem Informasi Ilmu Komputer Journal of Applied Engineering and Technological Science (JAETS) JISA (Jurnal Informatika dan Sains) Journal of Applied Data Sciences Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat ABDIMAS Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Jurnal Teknologi Sistem Informasi Jurnal Ilmu Komputer Dan Informatika eProceedings of Engineering Jurnal Rekayasa Sistem Informasi dan Teknologi Jurnal Informatika: Jurnal Pengembangan IT Pengabdian Kepada Masyarakat Bidang Teknologi dan Sistem Informasi JPMTT (Jurnal Pengabdian Masyarakat Teknologi Terbarukan) Applied Information Technology and Computer Science (AICOMS) Jurnal Teknokes
Dedy Agung Prabowo
Universitas Telkom
Religion Biochemistry, Genetics & Molecular Biology 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 Public Health Social Sciences Other

Published : 48 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 48 Documents
Search

 1   2   3   4   5 
Aplikasi Pencari Tempat Magang Berbasis Android Menggunakan Metode Agile Scrum Firdaus, Ammar Musthofa; Prabowo, Dedy Agung
Jurnal Informatika UPGRIS Vol 8, No 1: Juni 2022
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/jiu.v8i1.12029

Abstract

Internship is a process that is an obligation to be carried out in the academic process at the Telkom Institute of Technology Purwokerto, however with various media sources used in finding internships, students have problems in finding suitable internships. This study aims to apply Agile Scrum and Simple Additive Weighting in making applications to find a suitable internship place. Agile Scrum is used as a method of continuous application development, Simple Additive Weighting is used as a method in determining recommendations based on existing input. Application testing is carried out using Black Box Testing which tests the success of the function in carrying out its scenario and User Acceptance Test which is used as a benchmark in the suitability of the application with user needs. The results of this study are an android application in the form of an apk that was tested using Black Box Testing and got valid results in all scenarios and had a score of 87% on the User Acceptance Test
Analisis Sentimen Berbasis Aspek pada Layanan Hotel di Wilayah Kabupaten Banyumas dengan Word2Vec dan Random Forest Wijayanto, Sena; Prabowo, Dedy Agung; Kristiyanto, Daniel Yeri; Fathoni, M Yoka
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 1 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i1.4186

Abstract

Dalam industri pariwisata, hotel memiliki peran penting untuk membantu wisatawan karena menyediakan penginapan terutama bagi wisatawan dari luar kota. Kualitas layanan hotel dapat dilihat dari opini-opini yang diberikan ooleh pengunjung yang telah menginap di hotel tersebut. Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap ulasan yang diberikan oleh pengunjung hotel. Data ulasan tersebut diambil dari Traveloka menggunakan web scrapping. Metode yang digunakan untuk ekstraksi fitur adalah word2vec. Untuk klasifikasi sentimen, metode yang digunakan adalah random forest. Hasil percobaan terbaik didapatkan dari hasil percobaan dengan menggunakan jumlah tree 100, 200, dan 300 dengan hasil akurasi sebesar 82%-83%.
Analisis Kinerja Rantai Pasok Produk Kedelai Menggunakan Metode Supply Chain Operation Reference Fathoni, M Yoka; Prabowo, Dedy Agung; Wijayanto, Sena; Fernandez, Sandhy; Susanto, Ardi
Jurnal Informatika: Jurnal Pengembangan IT Vol 7, No 2 (2022)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v7i2.3740

Abstract

Indonesia is a country that has one of the advantages, namely as the largest agricultural country that has natural wealth, one of which is the agricultural sector. Soybean is one of the most widely grown crops in Indonesian agriculture and is included in the legumes group which has the highest vegetable protein content when compared to other types of beans such as red beans, green beans, and peanuts. The use of the SCOR method in this study is to measure good SCM performance, because SCOR divides supply chain processes into five 5 core processes, namely plan, source, make, deliver and return, where these processes have represented all supply chain activities. management from upstream to downstream in detail, so that it can define and categorize the measurement indicators needed in measuring Supply Chain Management performance. Based on the SCOR method, the results of the calculation of the final performance value of the soybean supply chain in the province of Central Java are 76.8 out of 100 which are in the "good" category.
Klasifikasi Kekeringan dan Penyakit pada Daun Padi Berdasarkan Ekstraksi Ciri Warna dan Tekstur Menggunakan CSPDarknet: Klasifikasi Kekeringan dan Penyakit pada Daun Padi Berdasarkan Ekstraksi Ciri Warna dan Tekstur Menggunakan CSPDarknet Abdul Jabbar Robbani; Dwi Putro Wicaksono, Aditya; Dedy Agung Prabowo
POSITIF : Jurnal Sistem dan Teknologi Informasi Vol 11 No 1 (2025): Positif : Jurnal Sistem dan Teknologi Informasi
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/positif.v11i1.14964

Abstract

The decline in rice productivity in Indonesia is often caused by drought and leaf diseases that are difficult to detect early. This condition requires a technology-based classification system that is able to provide fast and accurate diagnosis as support for decision making in the agricultural sector. This study aims to develop a rice leaf image classification model using the CSPDarknet architecture, with a color and texture feature extraction approach. The dataset used is the result of primary documentation that has gone through an augmentation process to increase the diversity of training data. The model architecture consists of a CSPDarknet backbone combined with a Cross-stage Partial Bottleneck with two Convolutions (C2f) block, Spatial Pyramid Pooling - Fast (SPPF), Global Average Pooling, and dropout to improve model generalization. Training was carried out using the Stratified 5-Fold Cross-Validation method and three optimizer variations, namely Stochastic Gradient Descent (SGD), Adam, and AdamW. The experimental results showed that the best model combination was achieved with the AdamW optimizer, with an average accuracy value of 99.72%, precision of 99.73%, recall of 99.72%, and F1-score of 99.72%. These findings indicate that the proposed classification approach is able to effectively distinguish healthy, diseased, and drought-affected leaves. In the future, this model has the potential to be further developed through the integration of Raspberry Pi-based Internet of Things (IoT) devices for real-time monitoring of plant conditions in the field.
Integrasi Edukasi Kesehatan Berbasis Multimedia dan Produksi Kombucha Lokal untuk Pemberdayaan Masyarakat Desa Muntang Paramadini, Adanti Wido; Aldo, Dasril; Faizah, Faizah; Sa'adah, Aminatus; Prabowo, Dedy Agung; An-Naayif, Hanief Taqiyuddien Adz-Dzaky; Oktavia, Laksmi Dwi; Briliana, Carlita Wahyu; Salsabila, Luciana; Fiqrian, Muhammad Nafal
Jurnal Pengabdian Masyarakat: Pemberdayaan, Inovasi dan Perubahan Vol 5, No 6 (2025): JPM: Pemberdayaan, Inovasi dan Perubahan
Publisher : Penerbit Widina, Widina Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59818/jpm.v5i6.2540

Abstract

This community-based program aimed to improve health literacy among residents of Muntang Village, Kemangkon District, Purbalingga Regency through the integration of multimedia-based health education and the production of locally sourced kombucha as a probiotic product based on village potential. The activities were implemented through several main stages, including community needs analysis, program socialization, development and utilization of multimedia educational media, training on hygienic kombucha production using local ingredients, implementation of digital promotion, mentoring, and sustainability evaluation. The results showed a 70 percent increase in community health literacy based on pre-test and post-test comparisons involving 50 training participants. The program also successfully produced 200 bottles of local kombucha with various flavor variants and improved hygienic production skills, with all participants able to independently apply standard operating procedures. The involvement of village youth in digital promotion contributed to a 50 percent increase in social media activity within three months, while the use of digital educational media engaged 50 active users during the program period. Participant satisfaction surveys yielded an average score of 4.6 on a 5-point scale, categorized as very satisfied. Overall, the program successfully established a community empowerment model based on health literacy and local product innovation that has the potential to be sustainably replicated in other rural communities.ABSTRAKProgram pengabdian ini bertujuan untuk meningkatkan literasi kesehatan masyarakat Desa Muntang, Kecamatan Kemangkon, Kabupaten Purbalingga melalui integrasi edukasi kesehatan berbasis multimedia dan produksi kombucha lokal sebagai produk probiotik berbasis potensi desa. Kegiatan dilaksanakan melalui beberapa tahapan utama, meliputi analisis kebutuhan masyarakat, sosialisasi program, pengembangan dan pemanfaatan media edukasi multimedia, pelatihan produksi kombucha higienis berbahan lokal, penerapan promosi digital, pendampingan, serta evaluasi keberlanjutan. Hasil pelaksanaan menunjukkan peningkatan literasi kesehatan masyarakat sebesar 70 persen berdasarkan perbandingan hasil pre-test dan post-test pada 50 peserta pelatihan. Program ini juga berhasil menghasilkan 200 botol kombucha lokal dengan berbagai varian rasa, serta meningkatkan keterampilan produksi higienis masyarakat hingga seluruh peserta mampu menerapkan SOP produksi secara mandiri. Keterlibatan remaja desa dalam promosi digital berdampak pada peningkatan aktivitas media sosial sebesar 50 persen dalam tiga bulan, sementara penggunaan media edukasi digital melibatkan 50 pengguna aktif selama periode program. Survei kepuasan peserta menunjukkan skor rata-rata 4,6 pada skala 5 yang termasuk kategori sangat puas. Program ini berhasil membangun model pemberdayaan masyarakat berbasis literasi kesehatan dan inovasi produk lokal yang berpotensi direplikasi pada komunitas pedesaan lainnya secara berkelanjutan.
Hybrid Optimization Model for Integrated Image Data Extraction Expert System in Rice Plant Disease Classification Aldo, Dasril; Kurniawati, Ajeng Dyah; Prabowo, Dedy Agung; Fauzi, Ahmad; Saputra , Wahyu Andi; Sudianto, Sudianto; Yasin, Feri; Agustianto, Satya Helfi; Pangestu, Farhan Aryo; Sulaeman, Gilang
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i1.6633

Abstract

The purpose of this study is to increase the accuracy for rice plant disease classification by developing a hybrid optimization model using Convolutional Neural Network (CNN) in combination with Extreme Learning Machine (ELM), followed by Support Vector Machine. A key issue is to overcome with traditional expert systems that difficult, due the variation differences and complex among rice plant image data set. For feature extraction, plant images are passed through CNN and for classification ELM & SVM used. Experimental results show the best accuracy of 98.63% is attained using CNN+ELM model on images resized to 100x100 pixels and has precision, recall, F1-Score all at value=0.99 By comparison, the CNN+SVM model achieves an accuracy of 91.92% using that same image size. Top AbstractIntroductionMethodsResultsDiscussionConclusionReferencesOverall, the proposed CNN+ELM combination can classify rice plant diseases better than using only a conventional approach (CNN) through various results from devices with limited computing power. The study presents a novel plant disease detection system that can be utilized for the development of precise tools to help improve agricultural management practices.
Food Detection to Estimate Calories Using Detection Transformer Kristanto, Joshua Putra Fesha; Prabowo, Dedy Agung; Yohani Setiya Rafika Nur
Jurnal Teknokes Vol. 18 No. 4 (2025): Desember
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jteknokes.v18i4.132

Abstract

Accurately estimating calorie intake remains a common challenge, as many individuals have limited understanding of portion sizes and the caloric content of foods. This lack of nutritional knowledge is a major cause of both over- and under-calorie consumption and contributes to significant public health problems, including obesity, cardiovascular disease, and chronic metabolic disorders. Although computer vision–based approaches for dietary assessment have advanced, many methods still rely on handcrafted features, anchor-based CNN detectors, or controlled geometric assumptions. This indicates a practical gap in developing a fully functional system that operates on basic RGB images captured under everyday conditions. This study aims to develop an end-to-end food detection and calorie estimation system using the Detection Transformer (DETR) to predict calorie values directly from food images. The main contributions of this study include: (1) employing DETR to address non-maximum suppression limitations and improve the stability of multi-food recognition; (2) using a bounding box area-to-weight ratio as a low-complexity alternative to segmentation-based food portion estimation; and (3) developing a user-friendly interface for output visualization that displays detected food items and their estimated calorie values in real-world scenarios involving irregular food shapes and varying focal lengths. A DETR-based detector was trained using 2,228 COCO-formatted images across six distinct food classes. Calorie values were estimated by predicting food weight based on bounding box measurements, followed by calorie calculation using standardized reference weights. The method assessed robustness by evaluation on both controlled and real-life food images. Experimental results demonstrated moderate performance, with 0.617 mean Average Precision (mAP) and 0.656 mean Average Recall (mAR). The weight prediction module served as the primary estimation component, achieving a mean absolute residual of 8.7. These findings suggest that bounding box area is a reliable estimator of serving size. This study serves as a proof of concept for monitoring individual food intake and provides a foundation for further improvement in sub-item recognition, three-dimensional volume estimation, and the inclusion of broader food classes.
The Classification of Anxiety, Depression, and Stress on Facebook Users Using the Support Vector Machine Wijiasih, Tsania Maulidia; Amriza, Rona Nisa Sofia; Prabowo, Dedy Agung
JISA(Jurnal Informatika dan Sains) Vol 5, No 1 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i1.1273

Abstract

Social media remains an essential platform for connecting people with friends, family, and the world around them. However, when events spread on social media are primarily negative, it will cause depression, anxiety, and stress that tend to increase. This study aims to classify depression, anxiety, and stress using the Support Vector Machine. The data in this study were obtained from active Facebook users using the Depression Anxiety Stress Scale (DASS 21) questionnaire. This study adopted the Knowledge Discover Database process. The result of this study is an evaluation of the performance of the Support Vector Machine classification of depression, anxiety, and stress. The accuracy of the Support Vector Machine in this study is 98.96%.
Co-Authors 12.5202.0161 Daniel Yeri Kristiyanto Abdul Jabbar Robbani Adanti Wido Paramadini Agustianto, Satya Helfi Agustomi, Endri Ajeng Dyah Kurniawati Akhdan Syarif Hidayatullah, Dias Akhmad, Fajar Kamaludin Alon Jala Tirta Segara An-Naayif, Hanief Taqiyuddien Adz-Dzaky Andri Sarpiadi Ardi Susanto Ardi Susanto Aritonang, Sudarsono Azzahra, Fathya Yuanita Briliana, Carlita Wahyu Cahyo Prihantoro Dandi Sunardi Dasril Aldo Dedy Abdullah Dedy Abdullah Dernata, Jaka Dimas Fanny Hebrasianto Permadi Dofiyer, Fernaldo Christofer Dwi Putro Wicaksono, Aditya Eki Agustiawan Faizah Faizah Fauzi Ahmad Muda Fauzian Setiawan, Kelvin Fernaldo Christofer Dofiyer Fiqrian, Muhammad Nafal Firdaus, Ammar Musthofa Gunawan Gunawan Hengki Putra Irawan Jaka Dernata Kirman Kirman, Kirman Kristanto, Joshua Putra Fesha M Yoka Fathoni marhalim, marhalim Marsally , Silvia Van Marsally, Silvia Van Muflih Haura Muhamad Azrino Gustalika Muhammad Husni Rifqo Muna, Bunga Laelatul Nicolaus Euclides Wahyu Nugroho Novian Adi Prasetyo Oktavia, Laksmi Dwi Pangestu, Farhan Aryo Paradise Perdi Leo Ade Candra Pratama, Rendra Agung Putra, Erwin Dwika Rachman, Ari Rakhma, Nazwa Aulia Ramdani, Cepi Rendra Agung Pratama Rendra Agung Pratama Resad Setyadi Rifqo, Muhammad Husni Rona Nisa Sofia Amriza Sa'adah, Aminatus Salsabila, Luciana Sandhy Fernandes Sandhy Fernandez Saputra , Wahyu Andi Sarah Astiti S.Kom., M.MT Silvia Van Marsally Sonita, Anisya Sudianto Sudianto, Sudianto Sulaeman, Gilang Sundari Sundari Syaputri, Yopita Syarif Hidayatullah, Dias Akhdan Tarigan, Emya Ninta Teguh Ramadhani, Dimas Toyib, Rozali Triaji Morgana Kaban Usman, Muhammad Lulu Latif Utami, Annisaa Wicaksono, Apri Pandu Wijiasih, Tsania Maulidia Yasin, Feri Yohani Setiya Rafika Nur Yuza Reswan