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All Journal JURNAL ILMIAH INFORMATIKA Jurnal Telematika Jurnal INFOTEL
Angela, Dina
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Computer Science & IT Electrical & Electronics Engineering

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Rancang Bangun Antena Mikrostrip Bow-Tie 433 MHz Untuk Perangkat Jaringan Sensor Nirkabel Angela, Dina; Hartanto, Helmi Indra
Jurnal Telematika Vol. 13 No. 1 (2018)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v13i1.256

Abstract

A microstrip antenna that works at a frequency of 433 MHz and has dimensions of 225 x 105 mm, printed on inexpensive FR-4 materials, is proposed to be implemented in wireless sensor networks (WSN) in Indonesia. This microstrip antenna has a bow- tie patch that is directly supplied with a 50 Ohm coaxial cable. The measurement results of the antenna characteristics show -28.6 dB return loss of 154 MHz bandwidth. The wide bandwidth, 2.05 dBi gain, and omnidirectional radiation patterns support the antenna to overcome the propagation in the environment where the WSN system is placed.Sebuah antena mikrostrip yang bekerja pada frekuensi 433 MHz dan memiliki dimensi 225 x 105 mm, dicetak pada material FR-4 yang relatif murah, diusulkan untuk diimplementasikan dalam jaringan sensor nirkabel (JSN) di Indonesia, khususnya untuk perangkat pemantau cuaca, atau automatic weather station (AWS). Antena mikrostrip ini memiliki bentuk patch bow-tie yang dicatu langsung dengan 50 Ohm kabel koaksial. Hasil pengukuran terhadap karakteristik antena menunjukkan return loss -28,6 dB dari bandwidth 154 MHz. Bandwidth yang relatif lebar tersebut, gain 2,05 dBi, dan pola radiasi omnidirectional mendukung antena untuk dapat mengatasi propagasi yang terdapat pada lingkungan di mana sistem WSN ditempatkan.
Pengembangan Sistem Prediksi Harga Pasar Properti Menggunakan Big Data Platform Suakanto, Sinung; Christy, Aldi; Engel, Ventje Jeremias Lewi; Angela, Dina
Jurnal Telematika Vol. 13 No. 1 (2018)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v13i1.257

Abstract

Property is a industry that will grow. Some of the problems in property sale and purchase transactions are lack of information about fair sale prices. This price information may be above or below market prices. This study will develop a model for predicting the market price of property in an area. The premise of this research is that the selling price of a house is proportional to the land area and building area in a particular area. The datasets used from this study come from information on selling prices and building area prices from several websites. The approach used in research is using regression and modification. The results of this study are expected to produce a model of recommended property market prices in an area.Industri properti adalah salah satu industri yang diprediksi akan terus berkembang. Beberapa permasalahan yang muncul dalam transaksi jual-beli properti adalah sulitnya mendapatkan informasi mengenai harga jual properti yang wajar. Informasi harga tersebut bisa jadi berada di atas atau bawah harga pasaran. Penelitian ini akan mengembangkan model prediksi harga pasaran properti di suatu daerah. Premis dari penelitian ini adalah harga jual rumah sebanding dengan luas tanah dan luas bangunan di daerah tertentu. Dataset yang digunakan dari penelitian ini berasal dari informasi harga jual dan harga luas bangunan yang ada dari beberapa website. Pendekatan yang digunakan dalam penelitian adalah menggunakan regresi dan modifikasinya. Hasil dari penelitian ini diharapkan dapat menghasilkan sebuah model rekomendasi harga pasaran properti pada suatu daerah.
SISTEM PEMANTAUAN KUALITAS AIR IKAN NILA MEDIA BIOFLOK MENGGUNAKAN ALGORITMA LSTM DAN SVM Nugroho, T. Arief; Angela, Dina; Yoel Wijaya, Yoel Wijaya
JURNAL ILMIAH INFORMATIKA Vol 13 No 02 (2025): Jurnal Ilmiah Informatika (JIF)
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v13i02.10762

Abstract

Tilapia farming with biofloc systems faces challenges in maintaining water quality stability due to fluctuations in temperature and pH that can affect fish health. To overcome this, this research designs an Internet of Things (IoT)-based water quality monitoring system integrated with machine learning-based prediction and classification methods. The system is built using NodeMCU ESP8266, DS18B20 (temperature), and PH-4502C (pH) sensors. Historical data from the sensors is processed using the Long Short-Term Memory (LSTM) algorithm to predict the values of water quality parameters. Next, the predicted results are classified using a Support Vector Machine (SVM) into three categories: “Air Terlalu Asam”, “Kualitas Air Baik”, dan “Air Terlalu Basa”. The classification information is then automatically sent to the farmer via the Telegram application. Model testing showed very high performance, where the LSTM prediction model achieved an R-squared value of 0.8871 with a very low error rate (MAE: 0.0115; MSE: 0.0019; RMSE: 0.0436), while the SVM classification model managed to achieve an accuracy of 99.86%. The implementation of this system is expected to assist farmers in making quick and precise decisions, reduce the risk of fish mortality, and increase time and labor efficiency in biofloc pond operations..
Rancangan Dasar Sistem Aplikasi Pemantau Lalu Lintas dan Penghitung Kendaraan Berbasis Komputasi Tepi Heryanto, Hery; Hutagalung, Maclaurin; Gamaliel, Yoyok Yusman; Angela, Dina; Pratama, Dionisius; Martina, Inge; Nugroho, Tunggul Arief
JURNAL INFOTEL Vol 16 No 2 (2024): May 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i2.1105

Abstract

One of the main issues in Indonesia is congestion. The number of vehicles continues to increase and is less balanced by the development of transportation infrastructure, especially landlines, causing more complex problems. The Indonesian government needs an intelligent application system that can provide knowledge to unravel congestion. The problem is how to perform edge computing to reduce latency so that the highway monitoring application system runs in real time. This research proposes a basic design for a vehicle monitoring application system that can accurately recognize vehicles, count the number of vehicles, and propose an edge computation that brings computation directly to the data source. The dataset is a video of traffic in Bandung, Jakarta, and several other major cities. The images in the dataset consist of 4,890 training images, 467 validation images, and 231 testing images. In the proposed model, the YOLOv5 and YOLOv7 architectures accurately detect and count vehicles. The test results show a mAP value of 99.1% with an IoU threshold of 50%. Other results include a precision value of 96.2% and a recall of 97.7%. The proposed model can accurately monitor vehicles and reduce latency with an edge computing approach.
Co-Authors Christy, Aldi Gamaliel, Yoyok Yusman Gultom, Binsar Tonggo Parulian Hartanto, Helmi Indra Herry Imanta Sitepu Herry Sitepu Hery Heryanto Hutagalung, Maclaurin Hutahaean, Bernad Robinson Junaedi, Samuel Christian Martina, Inge Muchma, Faris Mazini Nugroho, T. Arief Nugroho, Tunggul A Nugroho, Tunggul Arief Panjaitan, Christian Porayauw, Tony Pratama, Dionisius Sinung Suakanto Sulingallo, Irwansa Ryan Tarigan, Claudia Ventje Jeremias Lewi Engel Yamato, David Yoel Wijaya, Yoel Wijaya Yonata, Yosi Yuyu Wahyu