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All Journal Indonesian Journal of Electronics and Instrumentation Systems International Journal of Advances in Intelligent Informatics ELINVO (Electronics, Informatics, and Vocational Education) JOIV : International Journal on Informatics Visualization Journal of Applied Information, Communication and Technology (JAICT) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal ULTIMATICS JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Riset Rekayasa Elektro Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Teknik Informatika (JUTIF) Journal of Telecommunication, Electronics and Control Engineering (JTECE) Green Intelligent Systems and Applications JIM: Jurnal Ilmiah Mahasiswa Pendidikan Sejarah Jurnal INFOTEL Jurnal Indonesia : Manajemen Informatika dan Komunikasi
I Ketut Agung Enriko
Institut Teknologi Telkom Purwokerto
Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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Journal : JOIV : International Journal on Informatics Visualization

 1 
LoRaWAN for Smart Street Lighting Solution in Pangandaran Regency Enriko, I Ketut Agung; Gustiyana, Fikri Nizar; Kurnianingsih, Kurnianingsih; Puspita Sari, Erika Lety Istikhomah
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1198

Abstract

Smart street lighting is a key application in smart cities, enabling the monitoring and control of street lamps through internet connectivity. LoRa/LoRaWAN, an IoT technology, offers advantages such as low power consumption, cost-effectiveness, and a wide area network. With its extensive coverage of up to 15 kilometers and easy deployment, LoRa has become a favored connectivity option for IoT use cases. This study explores the utilization of LoRaWAN in Pangandaran, a regency in the West Java province of Indonesia. Implementing LoRaWAN in this context has resulted in several benefits, including the ability to monitor and control street lighting in specific areas of Pangandaran and real-time recording of energy consumption. The primary objective of this research is to estimate the number of LoRaWAN gateways required to support smart street lighting in Pangandaran. Two methods are employed: coverage calculation using the free space loss approach and capacity calculation. The coverage calculation suggests a requirement of 34 gateways, whereas the capacity calculation indicates that only two gateways are needed. Based on these findings, it can be inferred that, theoretically, a maximum of 34 gateways would be necessary for smart street lighting in the Pangandaran area. However, further research, including driving tests, is recommended to validate these results for future implementation. This study provides insights into the practical application of LoRaWAN technology in smart street lighting, specifically in Pangandaran. The findings contribute to optimizing infrastructure and resource allocation, ultimately enhancing the efficiency and effectiveness of urban lighting systems. 
Predicting Battery Storage of Residential PV Using Long Short-Term Memory Rakasiwi, Rizky Khaerul Maulana; Kurnianingsih, Kurnianingsih; Suharjono, Amin; Enriko, I Ketut Agung; Kubota, Naoyuki
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1603

Abstract

Solar power panels, or photovoltaic (PV), have recently grown rapidly as a renewable alternative energy source, especially since the increase in the basic electricity tariff. PV technology can be employed instead of the state electricity company to reduce the electricity used. Indonesia is one of the countries that have great potential in producing electricity from PV technology, considering that most of Indonesia's territory gets sunlight for most of the year and has a large land area. Considering the benefits of PV technology, it is necessary to carry out predictive monitoring and analysis of the energy generated by PV technology to maximize energy utilization in the future. The Internet of Things (IoT) and cloud computing system was developed in this research to monitor and collect data in real-time within 27 days and obtained 7831 data for each parameter that affects PV production. These data include data on the light intensity, temperature, and humidity at the location where the PV system is installed. The feature selection results using Pearson correlation revealed that the light intensity parameter significantly impacted the PV production system. This research used the Long Short-Term Memory (LSTM) method to predict future PV production. By tuning hyperparameters using 3000 epochs, the resulting RMSE value was 171.5720. The results indicated a significant change in the RMSE value compared to 100 epochs of 422.5780. This model can be applied as a forecasting system model at electric vehicle charging stations, given the increasing use of electric vehicles in the future.     Keywords— Forecasting; energy; Photovoltaic; LSTM; Internet of Thing. 
Classification of EEG Signal using Independent Component Analysis and Discrete Wavelet Transform based on Linear Discriminant Analysis Melinda, Melinda; Maulisa, Oktiana; Nabila, Nissa Hasna; Yunidar, Yunidar; Enriko, I Ketut Agung
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1219

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopment syndrome decreasing sufferers' social interaction, communication skills, and emotional expression. Autism syndrome can be detected using an electroencephalogram (EEG). This study utilized the EEG of autistic people to support the classification study of machine learning schemes to produce the best accuracy. One of the best approaches to classify the EEG signal is The Linear Discriminant Analysis (LDA), a machine learning technique to classify autism and normal EEG signals. LDA was chosen because it can maximize the distance between classes and minimize the number of scatters by utilizing between and within-class functions. This method was combined with other methods: Independent Components Analysis (ICA) and Discrete Wavelet Transform (DWT), to improve the accuracy system. ICA removes artifacts or signals other than brain signals that can cause noise in the EEG signal, so the analyzed signal was a complete EEG signal without other factors. DWT can help increase noise suppression in the EEG signal and provide signal information through frequency and time representation. The EEG dataset was collated from 16 children (eight autistic and eight normal). The signals from the dataset were filtered by artifacts using ICA, decomposed by three levels through DWT, and classified using the Linear Discriminant Analysis (LDA) technique. Using the Confusion Matrix, the results reveal the best accuracy of 99%.
Co-Authors Adi Wibowo Adian Fatchur Rochim Afandi, Mas Aly Agnesia Candra Sulyani Aisyatul Karima Alamsyah, Ahmad Tossin Amin Suharjono Angela Niarapika Nababan Anindya Wirasatriya Chandrayana Giri, Gede Chin, Wei Hong Danny Kurnianto Dewi, Mela Kartika Erika Lety Istikhomah Puspita Sari Erika Lety Istikhomah Puspita Sari* Estananto Fikri Nizar Gustiyana Fikri Nizar Gustiyana Fikri Nizar Gustiyana Fikri Nizar Gustiyana Filbert H. Juwono Gede Candrayana Giri Gilang Hijrian Fahreja Giri, Gede Chandrayana Gustiyana, Fikri Nizar Hedi Krishna Hedi Krishna Hedi Krishna Herryawan Pujiharsono I Gusti Bagus Astawa I Gusti Bagus Astawa Indriyanto, Slamet Kubota, Naoyuki Kurnianingsih Kurnianingsih, Kurnianingsih Kuswanda, Kuswanda Lutfan Lazuardi Maulisa, Oktiana Melinda Melinda Melinda Melinda Muhammad Aulia Baihaqy Muhammad Furqan Muhammad Irhamsyah Muhammad Irwan Yanwari Nabila, Nissa Hasna Nizar Gustiyana, Fikri Nuha, Muhammad Azza Ulin Nurlida Basir Puspita Sari, Erika Lety Istikhomah Rahmat Hardian Putra Rakasiwi, Rizky Khaerul Maulana Ramadhani, Eka Hero Ryan Anugrah Putra Sari, Erika Lety Istikhomah Puspita Silvi Nurandi Sri Kuntadi Sri Kuntadi Yunidar Yunidar Yunidar