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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics SENSITEK Unri Conference Series: Community Engagement
Krisna Adiyarta
Universitas Budi Luhur
Humanities Computer Science & IT Electrical & Electronics Engineering

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Analisis Pengklasifikasian Dokumen dengan Pembobotan Frekuensi Kata Berbanding Logaritma Jumlah Kata Serta Fungsi Cosine Krisna Adiyarta; Yohana Yohana
Proceeding Seminar Nasional Sistem Informasi dan Teknologi Informasi 2018: Proceeding Seminar Nasional Sistem Informasi dan Teknologi Informasi (SENSITEK)
Publisher : STMIK Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/pss.v1i1.394

Abstract

Dokumen merupakan suatu kumpulan data yang berguna sebagai referensi dalam penulisan suatu karya tulis ilmiah maupun non ilmiah yang dapat dimanfaatkan sebagai referensi tulisan. Dengan dukungan referensi yang tepat maka suatu karya dapat dikatakan kredibel dan dapat dipercaya. Namun pertumbuhan yang pesat dari jumlah dokumen informasi maka diperlukan sebuah teknik pencarian yang tepat untuk dapat menemukan dokumen sesuai kebutuhan salah satunya dengan sistem temu kembali informasi (information retrieval system/IRS). Salah satu teknik IRS yang dapat digunakan untuk merepresentasikan profil dokumen adalah model ruang vektor (vector space model). Pembobotan yang  didasarkan pada term dengan teknik stemming untuk mendapatkan bentuk kata dasar dari term yang bersangkutan. Penelitian ini akan menguji aplikasi mesin klasifikasi teks bahasa Indonesia yang mengunakan algoritma stemming Nazief Adriani, algoritma K-Nearest Neighbor dan metode Vector Space Model berdasarkan pembobotan frekuensi kata berbanding logaritma jumlah kata serta fungsi cosine. Dengan penelitian ini dapat disimpulkan bahwa proses pengkategorian dokumen teks berbahasa Indonesia yang dilakukan melalui perbandingan antara dokumen uji dengan koleksi sampel pengujian  mendapatkan hasil yang sesuai dengan kategori yang telah ditentukan setelah diuji dan juga telah dilakukan pengukuran precision dan recall untuk mengetahui efektifitas proses klasifikasi.Kata kunci: klasifikasi dokumen, cosine, stemming, K-Nearest Neighbor
Classification of Physiological Signals for Emotion Recognition using IoT Sadhana Tiwari; Sonali Agarwal; Muhammad Syafrullah; Krisna Adiyarta
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1943

Abstract

Emotion recognition gains huge popularity now a days. Physiological signals provides an appropriate way to detect human emotion with the help of IoT. In this paper, a novel system is proposed which is capable of determining the emotional status using physiological parameters, including design specification and software implementation of the system. This system may have a vivid use in medicine (especially for emotionally challenged people), smart home etc. Various Physiological parameters to be measured includes, heart rate (HR), galvanic skin response (GSR), skin temperature etc. To construct the proposed system the measured physiological parameters were feed to the neural networks which further classify the data in various emotional states, mainly in anger, happy, sad, joy. This work recognized the correlation between human emotions and change in physiological parameters with respect to their emotion.
Testing Big Data Applications Narinder Punn; Sonali Agarwal; Muhammad Syafrullah; Krisna Adiyarta
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1952

Abstract

Today big data has become the basis of discussion for the organizations. The big task associated with big data stream is coping with its various challenges and performing the appropriate testing for the optimal analysis of the data which may benefit the processing of various activities, especially from a business perspective. Big data term follows the massive volume of data, (might be in units of petabytes or exabytes) exceeding the processing and analytical capacity of the conventional systems and thereby raising the need for analyzing and testing the big data before applications can be put into use. Testing such huge data coming from the various number of sources like the internet, smartphones, audios, videos, media, etc. is a challenge itself. The most favourable solution to test big data follows the automated/programmed approach. This paper outlines the big data characteristics, and various challenges associated with it followed by the approach, strategy, and proposed framework for testing big data applications.
A Third Order based Additional Regularization in Intrinsic Space of the Manifold Rakesh Kumar Yadav; Abhishek Singh; Shekhar Verma; S. Venkatesan; M. Syafrullah; Krisna Adiyarta
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1961

Abstract

Second order graph Laplacian regularization has the limitation that the solution remains biased towards a constant which restricts its extrapolationcapability. The lack of extrapolation results in poor generalization. An additional penalty factor is needed on the function to avoid its over-fitting on seen unlabeled training instances. The third order derivative based technique identifies the sharp variations in the function and accurately penalizes them to avoid overfitting. The resultant function leads to a more accurate and generic model that exploits the twist and curvature variations on the manifold. Extensive experiments on synthetic and real-world data set clearly shows thatthe additional regularization increases accuracy and generic nature of model.
Privacy Control In Social Networks By Trust Aware Link Prediction Syam Prasad Dhannuri; Sanjay Kumar Sonbhadra; Sonali Agarwal; P. Nagabhushan; M. Syafrullah; Krisna Adiyarta
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1972

Abstract

Social networks are exceedingly common in today’s society. A social network site is an online platform where people build social relations with others and share information. For the last two decades, rapid growth in the number of users and applications with these social networking sites, make the security as the most challenging issue. In this virtual environment, some greedy people intentionally perform illegal activities by accessing others’ private information. This paper proposes a novel approach to detect the illegal access of a particular’s information by using trustaware link prediction. The facebook dataset is used for experiments and the results justify the robustness andtrustworthiness of the proposed model.
Gesture recognition by learning local motion signatures using smartphones Prachi Agarwal; Sanjay Kumar Sonbhadra; Sonali Agarwal; P. Nagabhushan; Muhammad Syafrullah; Krisna Adiyarta
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1982

Abstract

In recent years, gesture or activity recognition is an important area of research for the modern health care system. An activity is recognized by learning from human body postures and signatures. Presently all smartphones are equipped with accelerometer and gyroscopes sensors, and the reading of these sensors can be utilized as an input to a classifier to predict the human activity. Although the human activity recognition gained a notable scientific interest in recent years, still accuracy, scalability and robustness need significant improvement to cater as a solution of most of the real world problems. This paper aims to fill the identified research gap and proposes Grid Search based Logistic Regression and Gradient Boosting Decision Tree multistage prediction model. UCI-HAR dataset has been used to perform Gesture recognition by learning local motion signatures. The proposed approach exhibits improved accuracy over preexisting techniques concerning to human activity recognition.
Client Side Channel State Information Estimation for MIMO Communication Sambhavi Tiwari; Abhishek Abhishek; Shkehar Verma; K Singh; M Syafrullah; Krisna Adiyarta
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1993

Abstract

Multiple-input multiple-output (MIMO) system relies on a feedback signal which holds channel state information (CSI) from receiver to the transmitter to do pre-coding for achieving better performance. However, sending CSI feedback at each time stamp for long duration is an overhead in the communication system. We introduce a deep reinforcement learning based channel estimation at receiver end for single user MIMO communication without CSI feedback. In this paper we propose to train the receiver with known pilot signals to analyse the stochastic behaviour of the wireless channel. The simulation on MIMO channel with additive white Gaussian noise (AWGN) shows that our proposed method can learn the different characteristics affecting the channel with limited number of pilot signals. Extensive experiments show that the proposed method was able to outperform the existing state-of-the-art end to end reinforcement learning method. The results demonstrate that the proposed method learns and predicts the stochastic time varying channel characteristic accurately at receiver’s end.
Implementation of Image Segmentation Techniques to Detect MRI Glioma Tumour Siti Rafidah Binti Kassim; Setyawan Widyartoh; Mohammad Syafrullah; Krisna Adiyarta; Widya Kumala Sari
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.2011

Abstract

Image identification to detect a tumour needs several stages of image processing along with identifying analysis. To get an accurate segmentation of the tumour contour and to identify brain tumour based on brain magnetic resonance imaging (MRI), a suitable techniques and stages of image processing are required to be applied. One technique of mid-level image processing became an objective this work. The objective of the study is to segment the boundary of tumour by applying the Modification of Region Fitting (MRF) method in term of data fitting. The performance of the Region Scalable Fitting (RSF) method and Modified Region Scalable Fitting (MRSF) is evaluated by comparing the number of iterations. As the result, the MRF method has successfully segmented the initial region of braintumour images.
Person tracking with non-overlapping multiple cameras Sanjay Kumar Sonbhadra; Sonali Agarwal; Muhammad Syafrullah; Krisna Adiyarta
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2049

Abstract

Monitoring and tracking of any target in a surveillance system is an important task. When these targets are human then this problem comes under person identification and tracking. At present, large scale smart video surveillance system is an essential component for any commercial or public campus. Since field of view (FOV) of a camera is limited; for large area monitoring, multiple cameras are needed at different locations. This paper proposes a novel model for tracking a person under multiple non-overlapping cameras. It builds the reference signature of the person at the beginning of the tracking system to match with the upcoming signatures captured by other cameras within the specified area of observation with the help of trained support vector machine (SVM) between two cameras. For experiments, wide area re-identification dataset (WARD) and a real-time scenario have been used with color, shape and texture features for person's re-identification.
Email classification via intention-based segmentation Sanjay Kumar Sonbhadra; Sonali Agarwal; Muhammad Syafrullah; Krisna Adiyarta
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2084

Abstract

Email is the most popular way of personal and official communication among people and organizations. Due to untrusted virtual environment, email systems may face frequent attacks like malware, spamming, social engineering, etc. Spamming is the most common malicious activity, where unsolicited emails are sent in bulk, and these spam emails can be the source of malware, waste resources, hence degrade the productivity. In spam filter development, the most important challenge is to find the correlation between the nature of spam and the interest of the users because the interests of users are dynamic. This paper proposes a novel dynamic spam filter model that considers the changes in the interests of users with time while handling the spam activities. It uses intention-based segmentation to compare different segments of text documents instead of comparing them as a whole. The proposed spam filter is a multi-tier approach where initially, the email content is divided into segments with the help of part of speech (POS) tagging based on voices and tenses. Further, the segments are clustered using hierarchical clustering and compared using the vector space model. In the third stage, concept drift is detected in the clusters to identify the change in the interest of the user. Later, the classification of ham emails into various categories is done in the last stage. For experiments Enron dataset is used and the obtained results are promising.
Co-Authors Abhishek Abhishek Abhishek Singh K Singh Mohammad Syafrullah Narinder Punn P. Nagabhushan Prachi Agarwal Rahayu, Sri Rakesh Kumar Yadav S. Venkatesan Sadhana Tiwari Sambhavi Tiwari Sanjay Kumar Sonbhadra Sanjay Kumar Sonbhadra Setyawan Widyartoh Shekhar Verma Shkehar Verma Siti Rafidah Binti Kassim Sonali Agarwal Sonali Agarwal Sonali Agarwal Swasti Broto Syam Prasad Dhannuri Widya Kumala Sari Wiwin Windihastuty Yohana Yohana