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3rd International Conference on Smart City Innovation, ICSCI 2020

作者： Wiradnyana, I.G.N.P. Suthanaya, P.A. Wedagama, D.M.P. Yana, A.A.G.A. Dissanayake, D. Engineering Study Program Faculty of Engineering Udayana University Indonesia Department of Civil Engineering Faculty of Engineering Udayana University Indonesia School of Engineering Newcastle University Newcastle upon Tyne United Kingdom

Travel behaviour of domestic tourists concerning their choices of transportation mode has a significant role in managing traffic in the tourist destination. This study analyses the domestic tourists' choice and their pattern of needs of transportation mode in Bali as the case study. The developed models are based on utility maximisation of underlying preferences for each of the available modes of transport. Domestic tourists are randomly selected and interviewed using the questionnaire to record their daily trips. The study result indicates that income, number of visits and travel companions significantly influence the choice of transportation mode for domestic tourists. Meanwhile, income, number of visits, length of stays, trip purpose and main issues in selecting local transport mode significantly affect the pattern of needs of transportation mode for domestic tourists. For example, two accompanying persons will be three times more than that of seven persons or more to choose motorcycle and bus than a car. Domestic tourists who have visited Bali twice will be three times more than those who visited Bali once to use travel agents and rented vehicles than public and online transport and others. Further studies on short duration trips and sustainable mode of transport are suggested. © Published under licence by IOP Publishing Ltd.

关键词： Surveys

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An Examination of the Possible Application of Artificial Intelligence Audit Process in Organizations 6

An Examination of the Possible Application of Artificial Int...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Sardjono, Wahyu Pranata, Dewi Sagita Isamesal, Muhammad Information Systems Management Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Cyber Security Studi Program Department of Informatics Faculty of Natural Mathematical & Engineering Sciences King’s College London West Minster WC2R 2LS United Kingdom Management Department Faculty of Economics and Busines Universitas of Airlangga Surabaya10430 Indonesia

ISBN: (纸本)9798331539603

This study investigates how artificial intelligence (AI) can transform audit processes, including how AI can be used in businesses to execute non-technical audit tasks and what effect this will have on those tasks. A qualitative case study methodology is used in the research design to give detailed insights into how AI is being adopted by businesses. This study aims to analyze the aspects that impact AI implementation from the viewpoints of managers, auditors, and other stakeholders. The findings show that artificial intelligence (AI) has a great deal of promise for non-technical audit procedures in businesses. AI can automate tedious jobs, evaluate large data sets to identify hazards and enhance teamwork and communication. These developments may lead to increased productivity, maybe better audit quality, and more effective processes. But there are still difficulties. Data quality is essential to the success of AI integration, and auditors may exhibit bias in favor of human professionals. Careful thought must also be given to ethical issues about transparency and possible job displacement. All things considered, this study adds significant knowledge to both the academic literature and real-world auditing applications. It improves our comprehension of the function of AI in the industry and provides useful information for people involved in information technology development, audit, and legislation. © 2024 IEEE.

关键词： Data quality

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Early Detection of Heart Rate Conditions in Autistic Children Using IoT-Based Systems

Early Detection of Heart Rate Conditions in Autistic Childre...

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International Conference on Electrical engineering and Computer Science (ICECOS)

作者： Nizam Albar Melinda Melinda Yuwaldi Away dept. Electrical Engineering Master of Electrical Engineering Study Program Universitas Syiah Kuala Banda Aceh Indonesia dept. Electrical Engineering and Computer Engineering Faculty Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (数字)9798350368253

ISBN: (纸本)9798350368260

Early detection of heart rate conditions in autistic children is crucial for anticipating tantrum reactions and providing prompt medical intervention. Previous research designed a prototype for acquiring Electrocardiogram (ECG) signals using the AD8232 sensor, but it required a long time to identify heart rate conditions. This study aims to develop the previous prototype using Internet of Things (IoT) implementation to acquire and identify the heart rate conditions of autistic children in real time. The ECG signal data is analyzed using Butterworth Filter and Artificial Neural Network (ANN) integrated into Raspberry Pi and results are transmitted to the Blynk App. Testing over 20 minutes showed that in the first 10 minutes, the system identified a normal heart rate at 105.19 BPM with 67% accuracy, while in the second 10 minutes, the heart rate rose to 117.19 BPM with tantrum symptoms and 75% accuracy. This system helps medical personnel provide immediate intervention during tantrums.

关键词： Heart rate Pediatrics Accuracy Prototypes Artificial neural networks Electrocardiography Real-time systems Internet of Things Servers Testing

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Security Door Lock Using Multi-Sensor System Based on RFID, Fingerprint, and Keypad

Security Door Lock Using Multi-Sensor System Based on RFID, ...

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Green Energy, Computing and Sustainable Technology (GECOST), International Conference on

作者： Joni Welman Simatupang Ramses Wanto Tambunan Electrical Engineering Study Program Faculty of Engineering President University Cikarang Indonesia

ISBN: (纸本)9781665486644

Thefts problem in household needs to be anticipated with home security system. One of simple methods is using automatic solenoid door lock system, so that it is difficult to be duplicated and will reduce the chance of theft action when the house is empty. Therefore, a home security system prototype that can be accessed by utilizing biometric fingerprint, Radio Frequency Identification (RFID), and keypad sensors was designed and tested. Arduino Uno works to turn on the door lock solenoid, so door access will be given when authentication is successful. Experimental results show that fingerprint sensor works well by being able to read fingerprints perfectly and the average time required to scan a fingerprint was 3.7 seconds. Meanwhile, Radio Frequency Identification (RFID) sensor detects Electronic-Kartu Tanda Penduduk (E-KTP) and the average time required for Radio Frequency Identification (RFID) to scan the card is about 2.4 seconds. Keypad functions to store password to unlock the door which produces the average time of 3.7 seconds after 10 trials. Average time to open with multi-sensor is 9.8 seconds. However, its drawback is no notification or SMS which directly be accessed by a cellphone or website with Wi-Fi or Telegram applications allow homeowners to monitor their doors from afar as to minimize the number of house thefts.

关键词： Prototypes Thumb Passwords Fingerprint recognition Sensor systems Sensors Security

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Implications for fault locking south of Jakarta from an investigation of seismic activity along the Baribis fault, northwestern Java, Indonesia

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SCIENTIFIC REPORTS 2022年第1期12卷 1-10页

作者： Widiyantoro, S. Supendi, P. Ardianto, A. Baskara, A. W. Bacon, C. A. Damanik, R. Rawlinson, N. Gunawan, E. Sahara, D. P. Zulfakriza, Z. Husni, Y. M. Lesmana, A. Global Geophysics Research Group Faculty of Mining and Petroleum Engineering Institut Teknologi Bandung Bandung 40132 Indonesia Faculty of Engineering Maranatha Christian University Bandung 40164 Indonesia Agency for Meteorology Climatology and Geophysics (BMKG) Jakarta 10720 Indonesia Department of Earth Sciences Bullard Labs University of Cambridge Cambridge CB3 0EZ UK Geophysical Engineering Study Program Faculty of Mining and Petroleum Engineering Institut Teknologi Bandung Bandung 40132 Indonesia PT. Reasuransi Maipark Multivision Tower Menteng Atas Jakarta 12960 Indonesia

Recent borehole seismic deployments conducted along the Baribis Fault in northwestern Java reveal that it may be active. In this study, we exploit these data to locate proximal earthquakes using a relative relocation technique, estimate their moment magnitudes using a spectral fitting method and compute their focal mechanisms via waveform inversion. We observe that seismicity in the eastern part of the fault is significantly higher than in the west, where a previous GPS study of the region south of Jakarta demonstrated the existence of high compression rates. These observations imply that the western Baribis Fault is locked, and that neighbouring areas, including southern Jakarta and its surroundings, may be highly vulnerable to future sizeable earthquakes when accumulated elastic strain energy is eventually released during fault rupture. Significantly, the current generation of Indonesia's national hazard maps have not considered seismicity along the Baribis Fault. Our new results therefore call for an urgent reappraisal of the seismic hazard in northwestern Java that carefully takes into account the Baribis Fault and its earthquake potential, particularly in light of its proximity to Jakarta, a megacity that lies at the heart of one of the most densely populated islands in the world.

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Ensemble Deep Learning NARX for Estimating Time Series of Earthquake Occurrence

Ensemble Deep Learning NARX for Estimating Time Series of Ea...

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Robotics, Automation and Artificial Intelligence (RAAI), IEEE International Conference on

作者： Hapsoro Agung Nugroho Eka Yuni Astuty Aries Subiantoro Benyamin Kusumoputro Department of Electrical Engineering Research Center for Artificial Intelligence and Data Engineering Faculty of Engineering University of Indonesia Depok Indonesia Information Systems Study Program Faculty of Engineering Darma Persada University Jakarta Indonesia

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

Predicting earthquake occurrences in time series data remains a challenging task in seismology. NARX (Nonlinear Autoregressive with eXogenous input) neural networks have recently shown promise for accurate predictions. While previous research has demonstrated NARX's effectiveness in estimating earthquake frequencies, the architecture still relies on several pre-determined parameters. These parameters include the number of hidden layers, number of neurons, and delay time. This study aims to investigate the effectiveness of Ensemble Deep Learning NARX neural networks in predicting time series of earthquakes. The proposed ensemble model integrates multiple NARX neural networks, each trained on a distinct subset of earthquake data. This approach aims to enhance prediction accuracy and bolster model robustness. The dataset consists of time series records detailing earthquake occurrence frequencies and magnitudes. The results show performance evaluation metrics in terms of Mean Square Error (MSE) values. For training frequency data, the MSE is 4.10e-26, and for testing, it is 6.05e-22. Regarding training magnitude data, the MSE is 2.86e-21 for training and 3.17e-19 for testing. This study makes a valuable contribution to the advancement of earthquake prediction techniques, underscoring the potential of Ensemble Deep Learning NARX neural networks for precise time series estimation in seismology.

关键词： Deep learning Training Time series analysis Earthquakes Seismology Estimation Predictive models

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Performance Analysis of YOLO-DeepSORT on Thermal Video-Based Online Multi-Object Tracking

Performance Analysis of YOLO-DeepSORT on Thermal Video-Based...

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Robotics, Automation and Artificial Intelligence (RAAI), IEEE International Conference on

作者： Nur Ibrahim Arsyad Ramadhan Darlis Herianto Benyamin Kusumoputro Department of Electrical Engineering Research Center for Artificial Intelligence and Data Engineering Faculty of Engineering University of Indonesia Depok Indonesia Information Systems Study Program Faculty of Engineering Darma Persada University Jakarta Indonesia

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

Recently, thermal cameras have been used in various fields, including surveillance systems and advanced driver assistance systems (ADAS), as they perform better in low light than visible-light cameras. Some challenges in the surveillance system or ADAS field related to thermal cameras are occlusion and thermal crossover between objects with similar appearances during object detection or object tracking tasks, which can lead to misdetection, false positives, and lost tracking. In this paper, performance analysis of you-only-look-once (YOLO) combined with deep online real-time tracking (DeepSORT) on thermal video-based online multi-object tracking (MOT) in occlusion and thermal crossover scene is presented. YOLO, as one of state-of-the-art method for detection task, is used for detection system. Then, the detected object from YOLO is tracked using DeepSORT. The results demonstrate that the online MOT of sequential thermal images using YOLO-DeepSORT achieved a MOTA score of 44.2% and IDF1 of 45.3%. Thus, negative example was added in YOLO training process to reduce false detection, and it gives improvement with MOTA score of 63.8% and IDF1 score of 54.6%.

关键词： YOLO Training Surveillance Cameras Real-time systems Thermal analysis Performance analysis

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LoRa Network Design for Monitoring Landslides on 150 KV Delivery Tower

LoRa Network Design for Monitoring Landslides on 150 KV Deli...

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International Conference on Smart Computing & Communications (ICSCC)

作者： I Wayan Dikse Pancane I Putu Gelgel Astika Putra I Wayan Gede Suarjana Agus Supranartha Electrical Engineering Study Program Universitas Pendidikan Nasional Denpasar Indonesia Department of Public Health Faculty of Sport Science and Public Health Universitas Negeri Manado Sulawesi Indonesia Electro Enginering Elektrical Engineering Program Study Politechnic Negeri Bali Badung Indonesia

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

The objective of this research is to design and develop an integrated landslide early detection system at Tower Number Eight, 150 kV Gianyar. The research is expected to result in the construction of a communication system based on LoRa technology that will serve to reduce the potential damage from landslides. In order to achieve this, several parameters must be considered as benchmarks. These include RSSI, SNR, BER and PDR. The device operates by monitoring changes in position, soil moisture and rainfall, and then transmitting the data via LoRa technology. At the sink, the data is recorded and monitored for RSSI, SNR, PDR and BER values. In the test, the source 1 node, situated 300 metres away, exhibited an RSSI value of −100 to −109 dBm and an SNR value of 6.75 to −6.7 in both sunny and rainy weather, with a PDR value of 100%. The source 2 node, situated 600 metres away, exhibited an RSSI value of −97 to −109 dBm and an SNR value of 8.5 to −11.75 dB, with a PDR value of 70 to 100%. It should be noted that the number of bits in the sensor data sent is always complete. This is evidenced by the bit error ratio (BER) value, which is always 0 on both source nodes.

关键词： Rain Electricity Poles and towers Soil moisture LoRa Terrain factors Reliability Monitoring Signal to noise ratio Meteorology

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Evaluation Electrical System of Mini Plant Waste Energy on Institut Teknologi PLN

Evaluation Electrical System of Mini Plant Waste Energy on I...

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Converging Technology in Electrical and Information engineering (ICCTEIE), International Conference on

作者： Pawenary Dwi Listiawati Hendri Electrical Engineering Study Program Faculty of Electricity and Renewable Energy Insitut Teknologi PLN Jakarta Indonesia

Waste is a problem in almost every region, especially related to how the processing of waste can be converted into other forms as a form of energy that can be utilized, one of which is waste processing by pyrolysis is one of the technologies to convert waste into useful energy or fuel. At this time waste processing requires waste processing machines ranging from organic and un-organic waste shredding machines, briquette processing machines, pellets, using star-delta 3-phase motors, generally still conventional/manually. Current research, the author began to develop a waste and flue gas particle handling machine, a non-incinerator pyrolysis machine with the wet method, a high efficiency cyclone system which originally used a machine manually/ conventionally, replaced with a Delta series Star - Delta series with interlock and interface) automatically. Garbage processing The machine in addition to aiming to reduce waste waste, the machine must be made automatically and friendly to the operator who runs it.

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Spatial Hotspot Data and Weather for Forest Fire Data Clustering 5

Spatial Hotspot Data and Weather for Forest Fire Data Cluste...

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5th International Conference on Information and Communications Technology, ICOIACT 2022

作者： Mimboro, Prasetyo Kusrini, Kusrini Laksito, Arif Dwi Postgraduate Program Universitas Amikom Yogyakarta Yogyakarta Informatics Engineering Departement Amikom Yogyakarta Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Amikom Yogyakarta Indonesia

ISBN: (数字)9781665451406

ISBN: (纸本)9781665451406

Prediction of fire forest will be needing several parameters that located on the same location and on the same time frame. This is important to have data on the same location and same time periods, since forest fire mostly triggered by weather or temperature condition on certain area on the certain time. Since there are many parameters involved, the preprocessing will need to made the data have standard structure. The clustering process needed to give a label to the data with five class label, very low risk, low risk, medium risk, high risk, very high risk. Based on the data set, we measurement the best cluster number related with DB index, the results shows that 8 cluster is the best number of the data with DB Index score are 0.8856. © 2022 IEEE.

关键词： Fires

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