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Analisis Pola Radiasi dan Gain pada Antena Cassegrain dengan Frekuensi C-Band Munziah, Siti; Muhammadi, Imam; Praja, Muhammad Panji Kusuma
Journal of Telecommunication Electronics and Control Engineering (JTECE) Vol 6 No 2 (2024): Journal of Telecommunication, Electronics, and Control Engineering (JTECE)
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/jtece.v6i2.1212

Abstract

Teknologi komunikasi satelit merupakan teknologi telekomunikasi yang memiliki perangkat komunikasi dengan menempatkannya di ruang angkasa. Hal ini pasti membutuhkan alokasi spektrum frekuensi untuk layanan telekomunikasi. Frekuensi yang paling banyak digunakan untuk antena komunikasi satelit adalah frekuensi C-Band. Antena diperlukan untuk mengirim dan menerima gelombang elektromagnetik sebagai penghubung komunikasi antara stasiun bumi dan satelit. Antena yang banyak digunakan dalam komunikasi satelit adalah antena reflektor parabola karena memiliki nilai gain yang tinggi dan kemampuan pemfokusan yang baik. Pada penelitian ini dirancang antena jenis parabola Cassegrain pada frekuensi kerja 6,15 GHz menggunakan software CST Suite Studio 2019. Feed horn yang digunakan pada perancangan berbeda yaitu piramid dan conical horn, untuk membandingkan kinerja parameter antena. Maka, untuk mendapatkan hasil parameter antenna, dilakukan iterasi pada dimensi dan geometri antena. Hasil pengukuran pola radiasi dan gain antena cassegrain yang dihasilkan oleh feed horn berbeda menunjukkan conical horn menunjukkan hasil yang baik. Pola radiasi yang dihasilkan secara terarah pada arah main lobe 90deg. Nilai sidelobe yang rendah -28,2 dB, beamwidth 0,2°. Hasil pengukuran gain yang dihasilkan oleh feed horn berbeda menunjukkan conical horn menghasilkan gain yang besar pada frekuensi kerja 6,15 GHz sebesar 41 dBi. Nilai return loss yang dihasilkan sebesar -28,16 dB, VSWR sebesar 1,03 dengan polarisasi circular.
Performance Evaluation of Propagation Model in DVB-T2 Broadcasting: Case Study in Kebumen Cici Tri Eviana; Siti Zulaecha; Ganang Tulus Prananda; Solichah Larasati; Muhammad Panji Kusuma Praja
Jurnal Teknik Elektro Vol. 18 No. 1 (2026)
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v18i1.23531

Abstract

The widespread change from analog to digital television broadcasting in Indonesia has led to the emergence of blank spots in some areas. To overcome this problem, an optimal propagation model is needed to improve transmitter coverage, energy efficiency, and field strength through optimal transmit power for DVB-T2 broadcasts. This research addresses the selection and analysis of an appropriate propagation model, focusing on the application of the Longley-Rice and ITU-R.P 1546-6 models in the Kebumen region of Central Java, Indonesia. This study specifically compares the two models to determine the most effective model in reducing blank spots in the area. The results show that the Longley-Rice model produces greater field strength than the ITU-R.P 1546-6 model. In addition, this study also found that the farther the receiver is from the transmitter, the higher the free space loss value. This is because due to the hilly geographical conditions in the Kebumen area, it causes high loss. And the farther the receiver is from the transmitter and the greater the frequency value, the greater the field strength value. This research not only provides relevant empirical data for the Kebumen region but also offers insights that can be used in the optimization of digital broadcasting in other urban environments in the future.
Cerita Untuk Semua : Pelatihan Pembuatan Buku Digital Untuk Meningkatkan Literasi dan Akses Bacaan Muhammad Panji Kusuma Praja; Melinda Br Ginting; Slamet Indriyanto
El-Mujtama: Jurnal Pengabdian Masyarakat  Vol. 5 No. 6 (2025): El-Mujtama: Jurnal Pengabdian Masyarakat 
Publisher : Intitut Agama Islam Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/elmujtama.v5i4.8975

Abstract

The lack of children's reading media is a challenge faced by Kinder Club, an early childhood education institution focusing on literacy development. This community service aimed to improve the educators' ability to create digital storybooks independently and in accordance with children's developmental stages. The program included training on story idea development, visual narrative design using Canva, and publishing digital books through the Heyzine.com flipbook platform. As a result, the participants successfully produced age-appropriate digital storybooks in both content and visualization. The outputs include a collection of flipbook-format children's stories accessible online, enhancing reading access and empowering educators as content creators.
Performance Comparison of VGG16 and VGG19 Architectures for Corn Leaf Disease Classification Nofitasari Dwi Rezeki; Zein Hanni Pradana; Muhammad Panji Kusuma Praja
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.5956

Abstract

Corn (Zea Mays L.) faces challenges from leaf diseases, which become severe when farmers lack the expertise to recognize and manage them. This study presents a comparative analysis of VGG16 and VGG19 architectures for detecting corn leaf diseases, highlighting their performance under standardized conditions using transfer learning. The novelty of this study lies in the direct benchmarking of both models across multiple image resolutions and training epochs, which has not been comprehensively explored in previous studies. The system categorizes diseases based on images, thereby helping farmers manage corn leaf diseases more effectively. The VGG16 architecture was chosen for its balance of depth and computational efficiency, while VGG19 offers higher accuracy due to its increased layer depth and complexity. This system is expected to assist farmers in detecting corn leaf diseases more efficiently and accurately than previously possible. The dataset used in this study consists of 4198 images, divided into four categories: Healthy, Blight, Common Rust, and Gray Leaf Spot. The dataset was split into 80% for training and 20% for testing purposes. The classification results using 2 architectures, VGG16 and VGG19, with the use of the SGD optimiser, show that VGG19 outperforms VGG16. The VGG19 model demonstrated a performance level of 92.74% accuracy, alongside 91% for precision, recall, and F1-score. In comparison, VGG16 achieved a slightly lower accuracy of 92.62%, with precision at 91%, recall at 89%, and an F1-score of 90%. This performance variance is attributed to the architectural depth, as VGG19 utilizes 19 layers while VGG16 is limited to 16. Ultimately, this tool aims to provide farmers with a more precise and streamlined method for identifying corn foliage conditions.