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Indrawanti, Annisaa Sri; Wibisono, Waskitho

doi:10.21609/jiki.v8i2.307

Cited by 3 publications

(6 citation statements)

References 6 publications

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“…We use one-way Manova [23] to analyze and find the significant difference between interval configurations involved in this research. The congestion control research can leverage the statistical method such as Analysis of Variance (ANOVA) [15,24] to analyze the measurement data [25]. Anova is an approach to determine the data group's differences by inspecting the similarity between them [24].…”

Section: Methodsmentioning

confidence: 99%

Fine Tuning of Interval Configuration for Deep Reinforcement Learning Based Congestion Control

Naqvi,

Hilman,

Anggorojati

2023

Jurnal Ilmu Komputer dan Informasi

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It is apparent that various internet services in today’s digital ecosystem effectuate different types of networks’ quality of services (QoS) requirements. This condition, in fact, adds another level of complexity to the current network congestion control protocols. Therefore, it drives the adoption of deep reinforcement learning to improve the protocols’ adaptability to the dynamic networks’ QoS requirements. In this case, the state-of-the-art works on congestion control protocols, formulate the markov decision process (MDP) by transforming the congestion control pattern from the saw tooth congestion window to the staircase sending rate per-interval cycles. This approach treats congestion control as a sequential decision-making process that fits reinforcement learning. However, the interval configuration parameter that gives the optimum QoS has not been empirically studied. In this work, we present an extensive study on various interval configuration parameters for the deep reinforcement learning-based congestion control agent. Our work shows that various interval configuration, which consists of the RTT estimator and the n parameter, results in different QoS. The experiment shows that the RTTjk has significantly higher throughput than RTTewma and RTTmin−filtered in various network conditions. Furthermore, we found that the RTTjk with n = 2.0 is superior to other configurations in almost all networking scenarios. Whereas the RTTjk with n = 1.0 is optimal for a network environment with fixed bandwidth scenario.

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Section: Methodsmentioning

confidence: 99%

Fine Tuning of Interval Configuration for Deep Reinforcement Learning Based Congestion Control

Naqvi,

Hilman,

Anggorojati

2023

Jurnal Ilmu Komputer dan Informasi

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“…Ada pre-processing dari data accelerometer yang diambil sebelum proses klasifikasi untuk mengekstraksi data accelerometer. Untuk mendapatkan detail aktivitas pasien, data accelerometer disegmentasikan ke dalam fixed window seperti yang ditunjukkan dalam studi penelitian [4] - [6]. Perubahan satu aktivitas ke aktivitas yang lain dari pasien dapat diperoleh dengan menggeser window tersebut.…”

Section: Pendahuluanunclassified

“…Perubahan aktivitas pasien, dari satu aktivitas ke aktivitas yang lain dapat diprediksi dengan melihat kemiripan sampel data sebelum terjadi perubahan dengan sampel data setelah terjadi perubahan aktivitas. Beberapa penelitian [6] memprediksi perubahan aktivitas dengan melihat kemiripan data sebelum perubahan dengan sampel data setelah perubahan menggunakan analysis of variance. Namun, menurut [7] variasi jumlah sampel yang digunakan untuk memprediksi perubahan aktivitas dan pergeseran sampel data menentukan besar akurasi prediksi perubahan aktivitas.…”

Section: Pendahuluanunclassified

“…This cellular-based monitoring system uses an accelerometer sensor to retrieve data on changes in patient activity and analyzes the effect of several variations in the number of data samples and sliding-windows on the accuracy of the system in predicting changes in patient activity. Variations in the number of N samples tested were 4,6,8,10,20,30,40,50,60,70,80,90 and 100 samples, while the sliding-window N variation tested was 1 ,2,3,4,5,6,7,8,9 and 10 samples where there is a change in activity every 30 seconds for 330 seconds (10 changes in activity) for each number of N samples and N sliding windows. The results shown are N sample data = 6 providing the highest activity change prediction accuracy, amounting to 90.15%, while N sliding window data = 6 providing the highest activity change prediction accuracy, amounting to 92.72%.…”

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confidence: 99%

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Analisis Akurasi Prediksi Perubahan Aktivitas Pada Sistem Monitoring Aktivitas Jarak Jauh Pasien Isolasi Mandiri Berbasis IOT

Indrawanti

Husni

Ghozali

2022

Jurnal.ilmiah.informatika

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Patients who contract the disease should avoid contact with other people. One way to do this is to self-isolate at home. The family of the patient who cares for the activities that are carried out in self-isolation to find out the condition of the patient's condition, his condition is improving or deteriorating. To avoid direct contact, the patient's activity, independently, can be monitored by remotely predicting changes in patient activity using an Internet of Things-based remote monitoring system for self-isolating patient activities. This cellular-based monitoring system uses an accelerometer sensor to retrieve data on changes in patient activity and analyzes the effect of several variations in the number of data samples and sliding-windows on the accuracy of the system in predicting changes in patient activity. Variations in the number of N samples tested were 4,6,8,10,20,30,40,50,60,70,80,90 and 100 samples, while the sliding-window N variation tested was 1 ,2,3,4,5,6,7,8,9 and 10 samples where there is a change in activity every 30 seconds for 330 seconds (10 changes in activity) for each number of N samples and N sliding windows. The results shown are N sample data = 6 providing the highest activity change prediction accuracy, amounting to 90.15%, while N sliding window data = 6 providing the highest activity change prediction accuracy, amounting to 92.72%.

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“…There is a pre-processing of the retrieved raw data accelerometer before classification process for the feature extraction and classification purpose. To get the detailed activity from the raw data, it segments the raw data into a fixed window as shown as the research studies [4]- [6]. The sliding windows is widely used because of its simplicity.…”

Section: Introductionmentioning

confidence: 99%

Mobile-based Activity Monitoring System for the Self-quarantine Patient

Indrawanti

Mandyartha

2021

ATCSJ

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Nowadays, not all the patient can be hospitalized because of the COVID-19 pandemics. So, the self-quarantine for the patient with the various diseases will be the given solution by the hospital. It would make the hospital needs a system that can monitor the activity and the position of the patient from a distance. Nowadays, mobile phone is equipped by the sensor that can detect the user movement. Not only the user’s position, but also the user’s activity. In this paper, it will be developed an activity and position monitoring system for the self-quarantine patient that can be used in their home. The mobile activity monitoring can be achieved by activity recognition using classification method. For the needs of performance testing, we evaluate some classification method for activity recognition to compare the among classification method for the activity recognition. Some tested classification methods are Naïve Bayes, KNN, KStar and TreeJ48. Furthermore, we tested the impact of sliding windows per N samples taken to the accuracy of the activity recognition. We choose the best N sample that could give the best accuracy for activity recognition. The system not only monitor the patient’s activity, but also the patient’s position. The position monitoring can be achieved using Google Maps API. The result is Naive bayes has the accuracy of 81.25%, KNN has the accuracy of 78.125%, KStar has the accuracy of 78.125% and TreeJ48 has the accuracy of 75%. The N sample that could give the best accuracy is 6 with the accuracy of 90.15%.

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Cited by 3 publications

References 6 publications

Fine Tuning of Interval Configuration for Deep Reinforcement Learning Based Congestion Control

Fine Tuning of Interval Configuration for Deep Reinforcement Learning Based Congestion Control

Analisis Akurasi Prediksi Perubahan Aktivitas Pada Sistem Monitoring Aktivitas Jarak Jauh Pasien Isolasi Mandiri Berbasis IOT

Mobile-based Activity Monitoring System for the Self-quarantine Patient

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