Rekaman live Sabtu, 4 Juli 2020

Pada Roundtable ini akan membahas mengenai Digital Signal Processing dan Deep Learning dengan narasumber:

  • Dr. Agfianto Eko Putra (Dosen DIKE, Fak. MIPA, Universitas Gadjah Mada, Ketua Aptikom Daerah Istimewa Yogyakarta) — Pembahasan: digital signal processing untuk kasus speech signal dan seismic dengan MATLAB;
  • M. Octaviano Pratama, M.Kom (Chief Scientist BISA AI) — Pembahasan: klasifikasi low level dan high level speech feature dengan Deep Learning;
klik disini untuk link YOUTUBE-nya atau langsung PLAY saja video-nya. Terima kasih dan semoga bermanfaat.

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In this study, we use Lora-based data communication for communication between boats and database management systems for vessel. The maximum distance from the Lora is 10 km, and in order to be able to monitor fishermen along the fisherman’s coast in fishing, it is necessary to have a multi-gateway Lora to increase fishermen’s tracking coverage. The system is built using multi gateway Lora installed on the coast. Devices built with a Lora client are installed on a boat with GPS input, the technique of sending data on the device to the gateway uses a time base with a 1-minute period. To minimize data on the gateway that intersects the range of other gateways, a time flag is made to determine the data at a certain time to determine the trajectory of the boat. The result this research is system-monitoring vessel, which is successfully built and can provide fishing boats along the coast with long-range coverage. Based on experiment, multi gateway can be implemented by creating an identical ID gateway, the Lora client will broadcast to the nearest gateway. The maximum distance obtained is 1.35Km NLOS and to cover 20 Km of beach length is needed 15 gateways.

[http://doi.org/10.1088/1757-899x/850/1/012035]

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Implementation of LPWAN very large application, such as remote location, agriculture, mining, and transportation. This paper discusses for data format on implementation small vessel tracking for traditional fisherman. data format is very important for tracking system can be monitoring vessel simple. data format can be optimized perform on IoT network, such as delay, bandwidth, range and power consumption energy. Data format with 68 Bytes Strings stream to gateway Lora every 3 seconds. Data format for gateway to Broker server utilize MQTT format with 4-way connection, such as connect, connect ack, publish data and disconnect. Based on experiment, data format can use robust for small vessel and can implemented on this application.

[http://doi.org/10.1088/1742-6596/1450/1/012074]

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by Kurniasari, E., Putra, A.E., and Agoestien, N.G.

Lifting and modulus operations are the main operations on the RSA algorithm which require long computation time. Implementation of the operation of the lift and modulus on hardware devices requiring more resources than other arithmetic operations. Montgomery modular multiplication, the method which can be used to simplify the operation of lift and modulus, is implemented on FPGA, to speed up the computing process. The implementation results in this study, which is done using VHDL on the Xilinx Artix 7 series FPGA, were able to work at a maximum frequency of 133.76 MHz, requiring 17.66% LUT (11,195 of 63,400) and 7.14% of IOBs (15 of 210).

(DOI: 10.1109/ICST47872.2019.9166353)

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by Afianah, N., Putra, A.E., and Dharmawan, A.

The studies related to the synthesis of backpropagation artificial neural network algorithms are still based on the direct synthesis, so it requires an effort to modify the algorithm into hardware language so it can be optimized, synthesized and implemented into the FPGA. The High-Level Synthesis (HLS) is a software compiler which able to convert C specifications into Register Transfer Level (RTL) form, which can be synthesized into FPGAs. So the designer can focus on the optimization of the algorithm itself, including speed and resource optimization. This paper discus the results of the synthesis of backpropagation artificial neural network algorithms using HLS (High-Level Synthesis) software. The C-synthesis results based on the Zynq7000 FPGA showed an accuracy of 96.56%, were able to be clocked with a period of around 9.37 ns, with resource usage of 18% for BRAM_18K, 67% for DSP48E, 25% for FF and 71% for LUT. While the utilization difference is not significant compare to the previous studies, the optimization effort using an HLS tools need to be taken into account.

(DOI: 10.1109/ICST47872.2019.9166209)

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Rangkuti, A.H., Harjoko, A., and Putra, A.E.

This research evolves feature extraction algorithms in overcoming difficulties in classifying batik images that encounter changes in scale and rotation. the algorithm is multiscale and multilevel extended center symmetric local binary pattern (M2ECS-LBP). In utilizing this algorithm using several types of windows to obtain optimal feature extraction results, ranging from the size of 6×6, 9×9, 12 x 12 and 15×15 or a combination of several windows. However, for the use of algorithm carried out sequentially, it also requires a special strategy to obtain optimal image feature extraction results to support performance accuracy in the classification. The results of classification accuracy using K-Nearest neighborhood had reached up until the percentage to 78,4 – 81.7 percent of the image undergoing changes in scale and rotation. However, if the batik image undergoes a change in scale but the rotation is the same then the accuracy percentage can reach 98-99 percent. This algorithm is a very powerful breakthrough with lighter computing techniques than other algorithms. This research can be continued to recognize moving images, expected with maximum accuracy.

[click here]

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Random Forest (RF) is known as one of the best classifiers in many fields. They are parallelizable, fast to train and to predict, robust to outlier, handle unbalanced data, have low bias, and moderate variance. Apart from these advantages, there are still opportunities to increase RF efficiency. The absence of recommendations regarding the number of trees involved in RF ensembles could make the number of trees very large. This can increase the computational complexity of RF. Recommendations for not pruning the decision tree further aggravates the condition. This research attempts to build an efficient RF ensemble while maintaining its accuracy, especially in problem activity. Data collection is performed using an accelerometer sensor on a smartphone device. The data used in this research are collected from five peoples who perform 11 different activities. Each activity is carried out five times to enrich the data. This study uses two steps to improve the efficiency of the classification of the activity: 1) Optimal splitting criteria for activity classification, 2) Measured pruning to limit the tree depth in RF ensemble. The first method in this study can be applied to determine the splitting criteria that are most suitable for the classification problem of activities using Random Forest. In this case, the decision model built using the Gini Index can produce the highest accuracy. The second method proposed in this research successfully builds less complex pruned-tree without reducing its classification accuracy. The research results showed that the method applied to the Random Forest in this study was able to produce a decision model that was simple but yet accurate to classify activity.

[https://dx.doi.org/10.14569/IJACSA.2019.0100658]

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Heriyanto, Hartati and Putra, 2018

The recitation of the Qur’an has its own uniqueness, among others having a special rule in reading and pronunciation, which is called tajwid science. At the time of the Qur’an is recited, there are often mistakes due to the limitations of knowledge of Tajwid. Therefore, the availability of tools to facilitate in checking the appropriateness of recitation is very much needed by those who recite the Qur’an and face limitations in understanding the science of tajwid. Checking the Qur’an reading is a problem that must be solved according to the rules. So far, voice identification studies have problems with feature extraction, compatibility or suitability testing, and accuracy. The issue of feature extraction, suitability, and impermanence testing have been improved in this study, which consists of two stages. The first stage is the extraction of the sound character of the Qur’an reading and the second stage is the testing of the conformity of the Qur’anic recitation and accuracy. In the first stage feature extraction is handled using MFCC and Normalization of Dominant Weight (NDW). Characteristics of reading the Qur’an as reference table is taken from one reader of Al-Qur’an who has competence in the field of science tajwid, for sampling 5-7 people as a source for testing. The process of the second stage of conformity testing of Qur’an reading is done starting from filtering, sequential multiplication of reference table and Conformity Uniformity Pattern (CUP). The sample of reading conformity test is taken from 11 Qur’anic letters containing 8 reading laws and 886 records. The test is performed on the dominant frame, the number of cepstral coefficient and the number of frames. The reading conformance test provides an average accuracy of 91.37% on the nine dominant frames. The test for the number of cepstral coefficients in the c-23 can be an average of 96.65%, while the number of frames on the F-10 is the best average of 96.65%.

[https://doi.org/10.14738/aivp.62.4268]

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Oleh Agfianto Eko Putra, Unggul Adhi Nugroho, Bakhtiar Alldino Ardi Sumbada dan Catur Atmaji

Abstrak

Telah dirancang-bangun penerima  paket APRS  berbasis  Raspberry  Pi  2  untuk  stasiun bumi.Tuner  TV  digunakan  sebagai  penerima  sinyal,  alat  penerima  diakses  melalui  laptop secara nirkabel. Antena Yagi dengan sebuah pengendali digunakan agar dapat secara otomatis mengarahkanke  satelit.Ujicoba  dilakukan  dengan  menerima  paket  APRS  yang  dipancarkan digipeater  satelit  International  Space  Station  (ISS)  dan  satelit  LAPAN-A2.  Hasil  penelitian menunjukkan  bahwa alat ini mampu  mendapatkan paket  APRS  satelit  ISS  dengan  jumlah  6 paket dari 10 paket yang dipancarkan. Paket yang diterima memiliki rata-rata amplitudo pada frekuensi 1.200 Hz dan 2.200 Hz yang bernilai jauh lebih kecil dibandingkan amplitudo audio keseluruhan.  Hal  ini  menunjukkan  bahwa  terdapat  derau  yang  tinggi  pada  sinyal.  Sedangkan paket APRS dari satelit LAPAN-A2 belum berhasil diperoleh.

(informasi lebih lanjut klik https://doi.org/10.22146/ijeis.44299)

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by Endang Anggiratih and Agfianto Eko Putra

Abstract

Ship identification on satellite imagery can be used for fisheries management, monitoring of smuggling activities, ship traffic services, and naval warfare. However, high-resolution satellite imagery also makes the segmentation of the ship difficult in the background, so that to handle it requires reliable features so that it can be identified adequately between large vessels, small vessels and not ships. The Convolution Neural Network (CNN) method, which has the advantage of being able to extract features automatically and produce reliable features that facilitate ship identification. This study combines CNN ZFNet architecture with the Random Forest method. The training was conducted with the aim of knowing the accuracy of the ZFNet layers to produce the best features, which are characterized by high accuracy, combined with the Random Forest method. Testing the combination of this method is done with two parameters, namely batch size and a number of trees. The test results identify large vessels with an accuracy of 87.5% and small vessels with an accuracy of not up to 50%.

(for more information please click https://doi.org/10.22146/ijccs.37461)

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