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All Journal Jurnal Informatika dan Teknik Elektro Terapan Indonesian Journal of Artificial Intelligence and Data Mining ILKOM Jurnal Ilmiah Jambura Journal of Electrical and Electronics Engineering Jurnal Teknik Elektro dan Komputasi (ELKOM) JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Pengabdian kepada Masyarakat Nusantara COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat International Journal of Advanced Multidisciplinary Time in Physics: Journal for Theoretical, Instrumentation, Material-Molecular, and Education Physics Jurnal Riset Multidisiplin dan Inovasi Teknologi
Ramza, Harry
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Social Sciences

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Journal : Jurnal Informatika dan Teknik Elektro Terapan

 1 
ANALISIS KINERJA JARINGAN 4G LTE MENGGUNAKAN METODE DRIVE TEST DI KELURAHAN KAMPUNG RAMBUTAN, JAKARTA TIMUR Akram, Ar'rafi; Melvandino, Figo Hafidz; Bragaswara, Wildan Yuda; Ramza, Harry
Jurnal Informatika dan Teknik Elektro Terapan Vol. 11 No. 3 (2023)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v11i3.3140

Abstract

Saat ini, sistem komunikasi seluler telah menjadi suatu kebutuhan penting bagi masyarakat. Penelitian ini bertujuan untuk menganalisis kinerja jaringan 4G LTE melalui drive test dengan parameter RSRP (Reference Signal Received Power), RSSI (Received Signal Strength Indicator), RSRQ (Reference Signal Received Quality), dan SNR (Signal-to-Noise Ratio). Nilai data dari hasil drive test pada provider XL Axiata, nilai kategori sangat baik berada di kawasan SMPN 257 Jakarta dengan RSSI -75 dBm, RSRP -68 dBm, RSRQ -12 dB, dan SNR 12 dB, nilai kategori sangat buruk di kawasan SMA Teladan 1 dengan RSSI -91 dBm, RSRP -98 dBm, RSRQ -16 dan SNR -2 dB. Pada provider Telkomsel, nilai kategori sangat baik berada di kawasan SDN Susukan 09 dengan RSSI -75 dBm, RSRP - 69 dBm, RSRQ -8 dB, dan SNR 20 dB, nilai kategori sangat buruk di kawasan SMA Teladan 1 dengan RSSI -95 dBm, RSRP -107 dBm, RSRQ -15 dan SNR -3 dB. Pada provider Indosat Ooredoo, nilai kategori sangat baik berada di kawasan SMPN 257 Jakarta dengan RSSI -51 dBm, RSRP -78 dBm, RSRQ -13 dB, dan SNR 10,4 dB, nilai kategori sangat buruk di kawasan SDIT Al-Kahfi dengan RSSI -59 dBm, RSRP -99 dBm, RSRQ -20 dan SNR 6 dB.
KLASIFIKASI AKTIVITAS OLAHRAGA BERDASARKAN CITRA FOTO DENGAN MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK Akram, Ar'rafi; Rachmadinasya, Safira Adinda; Melvandino, Figo Hafidz; Ramza, Harry
Jurnal Informatika dan Teknik Elektro Terapan Vol. 11 No. 3s1 (2023)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v11i3s1.3496

Abstract

In an era of advancing technology and information, sports are also receiving increasing attention from various sectors, including enthusiasts and participants in the sports industry. However, to better understand and manage the sports world, a thorough analysis and understanding of various aspects of sports are necessary, including classification and recognition of different types of sports. One potent and effective approach to image pattern recognition is the Convolutional Neural Network (CNN). CNN is a classification method particularly suitable for classifying digital images. The architecture of CNN is designed effectively to recognize objects within images. The dataset employed comprises 2348 samples for training, 294 samples for testing, and 294 samples for validation. The training process of the CNN model using DenseNet121 architecture yields an accuracy rate of 99%, with a validation accuracy rate of 88.78%. Through this research, it is expected that the application of CNN will create a system capable of automatically and accurately identifying the types of sports being performed by individuals or groups based on images or captured visuals of sporting activities.
Co-Authors Adianto, Alfret Agustini Rodiah Machdi Akram, Ar'rafi Aprianto, Rizqi Naufal Ariyansyah, Riyan Assaidi, Humaid Badron, Khairayu Bragaswara, Wildan Yuda Davy Wiranata, Ade Eko Kurniawan Emilia Roza Farhan Alif Fayakun, Kun Gunadi, Reza Ismail, Ahmad Fadzil Januar Akbar Khomarudin, Muhammad Rokhis Kun Fayakun M. Asep Rizkiawan Maryani Hartuti, Maryani Md Khir, Mohd Haris Melvandino, Figo Hafidz Miftahuddin Miftahuddin Mohammad Syuhaimi Ab-Rahman Mujirudin, Mohammad Naufal, Daffa Dean Nuroji Nuroji Pinardi, Sofia Pratiwi, Nunik Rachmadinasya, Safira Adinda Rifaldi Rifaldi, Rifaldi Rosalina Rosalina Saepurohman, Asep Sahar, Fahmi Saksono, Tono Salam Maulana, Salam Saumi, Farhan Sinduningrum, Estu Siregar, Masbah Rotuanta Tagore Sofwan, Agus Sugiharto, Dedy Ulum, As’ad Syaifudin Widodo, Muh. Adnan Yudhanto, Tossa Hario