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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) Building of Informatics, Technology and Science 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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QOS ANALYSIS OF LEO SATELLITE BROADBAND NETWORK FOR IOT IN SMART FARMING Ramadhani, Eka Hero; Enriko, I Ketut Agung; Sari, Erika Lety Istikhomah Puspita; Alamsyah, Ahmad Tossin; Nuha, Muhammad Azza Ulin
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.7824

Abstract

The need for food in the form of agricultural products is currently increasing along with the growth of the world's population. However, the workforce in the agricultural sector in the modern era is decreasing because many young people are reluctant to become farmers. Therefore, the concept of Smart Farming emerged to overcome this problem by helping farmers manage and run agriculture efficiently using modern technology that can work automatically or be monitored or operated remotely using the internet network, for example, the Internet of Things (IoT) Smart Farming. However, agricultural areas located in remote or isolated villages are difficult to reach by terrestrial internet network infrastructure. Therefore, Low Earth Orbit (LEO) satellite broadband network infrastructure can be a new solution, so it needs to be researched. This research analyzes the Quality of Service (QoS) of LEO satellite broadband networks in IoT Smart Farming. The methods used consist of prototyping, experimentation, and analysis. QoS analysis based on throughput, packet loss, delay, and jitter parameters. The results of the experiment and analysis of this study indicate that the throughput value is 1243 bps. The speed test results show an average download speed of 88,89 Mbps and an upload speed of 14,08 Mbps. The packet loss value is 0%, which means that all packets were successfully sent. The average delay value is 97 ms. The jitter value is 26 ms. The results of this study can be further studied and developed for other use cases that are constrained by terrestrial internet network infrastructure.
Big data analytics for relative humidity time series forecasting based on the LSTM network and ELM Kurnianingsih, Kurnianingsih; Wirasatriya, Anindya; Lazuardi, Lutfan; Wibowo, Adi; Enriko, I Ketut Agung; Chin, Wei Hong; Kubota, Naoyuki
International Journal of Advances in Intelligent Informatics Vol 9, No 3 (2023): November 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i3.905

Abstract

Accurate and reliable relative humidity forecasting is important when evaluating the impacts of climate change on humans and ecosystems. However, the complex interactions among geophysical parameters are challenging and may result in inaccurate weather forecasting. This study combines long short-term memory (LSTM) and extreme learning machines (ELM) to create a hybrid model-based forecasting technique to predict relative humidity to improve the accuracy of forecasts. Detailed experiments with univariate and multivariate problems were conducted, and the results show that LSTM-ELM and ELM-LSTM have the lowest MAE and RMSE results compared to stand-alone LSTM and ELM for the univariate problem. In addition, LSTM-ELM and ELM-LSTM result in lower computation time than stand-alone LSTM. The experiment results demonstrate that the proposed hybrid models outperform the comparative methods in relative humidity forecasting. We employed the recursive feature elimination (RFE) method and showed that dewpoint temperature, temperature, and wind speed are the factors that most affect relative humidity. A higher dewpoint temperature indicates more air moisture, equating to high relative humidity. Humidity levels also rise as the temperature rises.
Implementation of LoRaWAN on Energy Monitoring System on the Onion Leaf Pest Light Trap device Enriko, I Ketut Agung; Kuswanda, Kuswanda; Kurnianto, Danny; Gustiyana, Fikri Nizar
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.v6i2.1370

Abstract

Shallot cultivation is a significant livelihood for farmers due to the high selling value of the harvest. However, most farmers still control shallot pests by excessively spraying pesticides, leading to concerns about excess residue on the plants. Physical control methods, such as installing light traps on plantations, have been attempted, but their manual operation is inefficient for farmers' working time and prone to wasting electrical energy due to negligence. In this research, the light trap will be optimized by implementing an automatic light monitoring and control system, allowing farmers to estimate the cost of electricity used and adjust usage according to their needs. The LYNX32 BOARD LoRa plays an important role as a data processor, connected to the LDR sensor to enable automatic light activation, and the PZEM-004T sensor to monitor the voltage and current of the light trap, transmitting this data via LoRa communication. In this research, automatic control operates based on the light intensity, and LoRa can transmit data up to a distance of 250 meters under line-of-sight (LOS) conditions. The PZEM-004T sensor has good accuracy for voltage, with an error of 0.08%, while the error percentage for current and power is 13.26% and 5%, respectively.
LORAWAN GATEWAY PLANNING USING AS923-2 FREQUENCY IN TASIKMALAYA FOR MONITORING ODC Enriko, I Ketut Agung; Nizar Gustiyana, Fikri; Chandrayana Giri, Gede
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 5 (2023): JUTIF Volume 4, Number 5, October 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.5.955

Abstract

This study aims to design a LoRaWAN network and find out how many gateways are needed to cover the research area and to design an IoT-based monitoring system for ODC devices on the FTTH network based on data at PT. Telkom Witel Tasikmalaya. The method used is a simulation using Atoll software version 3.40 and several calculation stages to predict the parameters of RSSI (Received Signal Strength Indicator) and SINR (Signal to Interference Noise Ratio) in a planning area of ​​358.66 km2. This study using AS923 frequency with a bandwidth of 125 kHz and a Spreading factor of 10. The results obtained are signal strength (RSSI) and signal quality (SINR) parameters. Based on the results of calculations and planning simulations, it produces 20 gateways using SF 10 with an RSSI parameter of -69.53 dBm and a SINR parameter of 20.21 dBm, each gateway can cover 4-5 km2 in the planning area.
Implementation Control And Monitoring System Water Quality of Koi Fish Ponds Based On the Internet Of Things Enriko, I Ketut Agung
JAICT Vol. 9 No. 1 (2024)
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/jaict.v9i1.5379

Abstract

Koi fish is one of the ornamental fish that is in great demand and has a fairly high price. Water quality plays an important role in the success of keeping koi fish. The quality of koi fish water must be at an ideal temperature of 25-30 °C and an acidity level or pH of 7-8 pH. The level of salt contained in water for koi fish must also be considered. A pond with a size of 200 x 50 x 100 cm requires a salt content of 1 to 2 ppm. Giving this salt is done to prevent the growth of bacteria in the koi pond which can come at any time. Ignorance of pond owners about the value and condition of water quality can disrupt the health of koi fish which can cause death. Based on these problems, the authors created a water quality control and monitoring system in koi fish ponds. The system created consists of a pH sensor, temperature sensor, and salinity sensor, and uses the Message Queuing Telemetry Transport (MQTT) protocol. The process of sending data to the IoT platform using a WiFi network. Based on the temperature sensor test, there is an average error of 1.4% with a sensor accuracy level of 98.6%. Testing the pH sensor and salinity sensor using the linear regression method. As for the pH sensor, the average error is 2% with an accuracy rate of 98%. The results of the salinity sensor test obtained an average error value of 7.6% with an accuracy rate of 92.3%. Then in the MQTT protocol, the parameters for delay and jitter have a bad category, while throughput has a moderate category, and packet loss has a very good category according to the TIPHON standard.  
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%.
A Comparative Study of LSTM and BiLSTM Performance in Predicting XAU/USD Prices Enriko, I Ketut Agung; Gustiyana, Fikri Nizar
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9414

Abstract

Gold price forecasting in the XAU/USD market is challenging due to nonlinear dynamics, high volatility, and sensitivity to global macroeconomic factors. This study compares the performance of Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM) architectures in forecasting XAU/USD closing prices using historical data from 2023–2026. Data preprocessing includes cleaning, chronological ordering, normalization, and transformation using a sliding window approach. A window size of 60 time steps is selected to represent approximately three months of daily trading activity, enabling the models to capture short- to medium-term temporal dependencies while limiting excessive noise and computational burden. The dataset is divided chronologically into training and out-of-sample testing sets to ensure proper generalization assessment. Both models employ identical architectures with two recurrent layers (50 hidden units each) and are trained using the Adam optimizer with epoch variations (20–100). Evaluation on unseen test data uses MAE, MSE, RMSE, MAPE, and R² metrics. LSTM achieves its lowest MAE of 21.26 at 40 epochs, while BiLSTM attains its best performance at 80 epochs with an MAE of 20.86 and R² of 0.9981. However, extending training to 100 epochs leads to performance degradation in BiLSTM, indicating sensitivity to overtraining. Overall, optimal performance is achieved through balanced training duration rather than increased architectural complexity.
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