Jurnal Infotel (Sekolah Tinggi Teknologi Telematika Telkom Purwokerto)
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Accuracy Analysis of K-Nearest Neighbor and Naïve Bayes Algorithm in the Diagnosis of Breast Cancer
Full text linkIn the medical field, there are many records of disease sufferers, one of which is data on breast cancer. An extraction process to fine information in previously unknown data is known as data mining. Data mining uses pattern recognition techniques such as statistics and mathematics to find patterns from old data or cases. One of the main roles of data mining is classification. In the classification dataset, there is one objective attribute or it can be called the label attribute. This attribute will be searched from new data on the basis of other attributes in the past. The number of attributes can affect the performance of an algorithm. This results in if the classification process is inaccurate, the researcher needs to double-check at each previous stage to look for errors. The best algorithm for one data type is not necessarily good for another data type. For this reason, the K-Nearest Neighbor and Naïve Bayes algorithms will be used as a solution to this problem. The research method used was to prepare data from the breast cancer dataset, conduct training and test the data, then perform a comparative analysis. The research target is to produce the best algorithm in classifying breast cancer, so that patients with existing parameters can be predicted which ones are malignant and benign breast cancer. This pattern can be used as a diagnostic measure so that it can be detected earlier and is expected to reduce the mortality rate from breast cancer. By making comparisons, this method produces 95.79% for K-Nearest Neighbor and 93.39% for Naïve BayesIn the medical field, there are many records of disease sufferers, one of which is data on breast cancer. An extraction process to fine information in previously unknown data is known as data mining. Data mining uses pattern recognition techniques such as statistics and mathematics to find patterns from old data or cases. One of the main roles of data mining is classification. In the classification dataset, there is one objective attribute or it can be called the label attribute. This attribute will be searched from new data on the basis of other attributes in the past. The number of attributes can affect the performance of an algorithm. This results in if the classification process is inaccurate, the researcher needs to double-check at each previous stage to look for errors. The best algorithm for one data type is not necessarily good for another data type. For this reason, the K-Nearest Neighbor and Naïve Bayes algorithms will be used as a solution to this problem. The research method used was to prepare data from the breast cancer dataset, conduct training and test the data, then perform a comparative analysis. The research target is to produce the best algorithm in classifying breast cancer, so that patients with existing parameters can be predicted which ones are malignant and benign breast cancer. This pattern can be used as a diagnostic measure so that it can be detected earlier and is expected to reduce the mortality rate from breast cancer. By making comparisons, this method produces 95.79% for K-Nearest Neighbor and 93.39% for Naïve Baye
Performance Analysis of CRC-Polar Concatenated Codes
Full text linkPolar code has been proven to obtain Shannon capacity for Binary Input Discrete Memoryless Channel (BIDMC) and its use has been proposed as the channel coding in 5G technology. However, its performance is limited in finite block length, compared to Turbo or LDPC codes. This research proposes the use of various CRC codes to complement Polar codes with finite block length and analyses the performance based on Block Error Rate (BLER) to Es/N0 (dB). The CRC codes used are of degrees 11 and 24, with 3 different polynomial generators for each degree. The number of bits in the information sequence is 32. The list sizes used are 1, 2, 4, and 8. Simulation results show that the concatenation of CRC and Polar codes will yield good BLER vs Es/N0 performance for short blocks of codeword, with rates 32/864 and 54/864. Concatenating CRC codes with Polar codes will yield a BLER performance of 10-2 with Es/N0 values of -9.1 to -7.5 dB when CRC codes of degree 11 is used, depending on the SC list used. The use of CRC codes of degree 24 enables a BLER performance of 10-2 with Es/N0 values of -7 to -6 dB when the SC list used is 1 or 2. The use of CRC codes of degree 24 combined with SC list with sizes 4 or 8 will improve the BLER performance to 10-2 with Es/N0 values of -8 to -7.5 dBPolar code has been proven to obtain Shannon capacity for Binary Input Discrete Memoryless Channel (BIDMC) and its use has been proposed as the channel coding in 5G technology. However, its performance is limited in finite block length, compared to Turbo or LDPC codes. This research proposes the use of various CRC codes to complement Polar codes with finite block length and analyses the performance based on Block Error Rate (BLER) to Es/N0 (dB). The CRC codes used are of degrees 11 and 24, with 3 different polynomial generators for each degree. The number of bits in the information sequence is 32. The list sizes used are 1, 2, 4, and 8. Simulation results show that the concatenation of CRC and Polar codes will yield good BLER vs Es/N0 performance for short blocks of codeword, with rates 32/864 and 54/864. Concatenating CRC codes with Polar codes will yield a BLER performance of 10-2 with Es/N0 values of -9.1 to -7.5 dB when CRC codes of degree 11 is used, depending on the SC list used. The use of CRC codes of degree 24 enables a BLER performance of 10-2 with Es/N0 values of -7 to -6 dB when the SC list used is 1 or 2. The use of CRC codes of degree 24 combined with SC list with sizes 4 or 8 will improve the BLER performance to 10-2 with Es/N0 values of -8 to -7.5 d
Development Grouping of Synonym Set Thesaurus Vocabulary The Qur’an in English Using Hierarchical Clustering Algorithm
Full text linkResearch in the field of text mining to process entries or words from the Qur'an is very beneficial for Muslims. This study aims to establish a set of synonyms for the thesaurus in the words of the Qur'an. This research is used because the source of knowledge about the science of the Qur'an is still lacking. The dataset in this study uses the Corpus Qur'an and English Translation. This research is a research development of an article that has been published, namely "The Development of Al-Qur'an Vocabulary Set Synonyms with WordNet Approach" by Laras Gupitasari. Input from this research system uses nouns from the translation of English words in the Quran. The output of the system produces several groups that have the same level of closeness of meaning displayed, the first group means the word in the group has a close meaning. To produce output, this study uses word grouping with a hierarchical grouping method and calculates distances using common paths, then groups results according to the closeness of meaning from word entries. The evaluation in this study produced an F-Measure value of 76%, F-Measure Value is an evaluation to measure the accuracy of predictions issued by the system.Research in the field of text mining to process entries or words from the Qur'an is very beneficial for Muslims. This study aims to establish a set of synonyms for the thesaurus in the words of the Qur'an. This research is used because the source of knowledge about the science of the Qur'an is still lacking. The dataset in this study uses the Corpus Qur'an and English Translation. This research is a research development of an article that has been published, namely "The Development of Al-Qur'an Vocabulary Set Synonyms with WordNet Approach" by Laras Gupitasari. Input from this research system uses nouns from the translation of English words in the Quran. The output of the system produces several groups that have the same level of closeness of meaning displayed, the first group means the word in the group has a close meaning. To produce output, this study uses word grouping with a hierarchical grouping method and calculates distances using common paths, then groups results according to the closeness of meaning from word entries. The evaluation in this study produced an F-Measure value of 76%, F-Measure Value is an evaluation to measure the accuracy of predictions issued by the system
Analysis And Performance Comparison of Microwave And WiFi 802.11ac Based Backhaul For Long Term Evolution Network In Urban Area
Full text linkIncreasing user requirements for LTE networks, data traffic from eNodeB to core network is also increases, therefore, the recommended solution for meeting this high data traffic is to use a backhaul network design. Backhaul is the path or network used to connect eNodeB with the core network. In this research, backhaul technology used is wi-fi 802.11ac backhaul and microwave backhaul. In this study begins by collecting existing data, then perform capacity calculations to find out the number of eNodeB needed and to find out the capacity of the backhaul links to be designed, then determine the antenna height to achieve LOS conditions, then calculate the desired performance standards and calculate the backhaul network link budget on microwave and wi-fi technologies. Based on the calculation results in terms of capacity, the total user target is 90,167 users and has a throughput capacity per eNodeB of 61 Mbps. In the link-capacity calculation, the total link capacity is 427 Mbps. From the simulation results that using microwave technology, the average RSL value is -30.90 dBm, the value meets the -57 dBm threshold standard and the value of availability does not meet the standard of 99.999% because the average value obtained is 99.998095%. Whereas for wi-fi technology, the average RSL value is -39.24 dBm and meet the -72 dBm threshold standard, for the average availability value meets 99.999% standard, with a value of 100%. From the results of the two technologies, can be conclude that the wi-fi technology is more suitable for the use of backhaul network design in Ciputat Sub-district.Increasing user requirements for LTE networks, data traffic from eNodeB to core network is also increases, therefore, the recommended solution for meeting this high data traffic is to use a backhaul network design. Backhaul is the path or network used to connect eNodeB with the core network. In this research, backhaul technology used is wi-fi 802.11ac backhaul and microwave backhaul. In this study begins by collecting existing data, then perform capacity calculations to find out the number of eNodeB needed and to find out the capacity of the backhaul links to be designed, then determine the antenna height to achieve LOS conditions, then calculate the desired performance standards and calculate the backhaul network link budget on microwave and wi-fi technologies. Based on the calculation results in terms of capacity, the total user target is 90,167 users and has a throughput capacity per eNodeB of 61 Mbps. In the link-capacity calculation, the total link capacity is 427 Mbps. From the simulation results that using microwave technology, the average RSL value is -30.90 dBm, the value meets the -57 dBm threshold standard and the value of availability does not meet the standard of 99.999% because the average value obtained is 99.998095%. Whereas for wi-fi technology, the average RSL value is -39.24 dBm and meet the -72 dBm threshold standard, for the average availability value meets 99.999% standard, with a value of 100%. From the results of the two technologies, can be conclude that the wi-fi technology is more suitable for the use of backhaul network design in Ciputat Sub-district
Trilateration Method for Estimating Location in RSSI-Based Indoor Positioning System Using Zigbee Protocol
Full text linkWireless network technology that is used today is developing rapidly because of the increase needed for location information of an object with high accuracy. Global Positioning System (GPS) is a technology to estimate the current location. Unfortunately, GPS has a lack of accuracy around 10 meters when used indoors. Therefore, it began to be developed with the concept of an indoor positioning system. This is a technology used to estimate the location of objects in a building by utilizing WSN (Wireless Sensor Network). The purpose of this study is to estimate the location of the unknown nodes in the lecturer room as objects and obtain the accuracy of the system being tested. The positioning process is based on the received signal strength (RSSI) on the unknown node using the ZigBee module. The trilateration method is used to estimate the unknown nodes located at the observation area based on the signal strength received at the time of testing. The result shows that the path loss coefficient value at the observation area was 0.9836 and the Mean Square Error of the test was 1.54 meters, which implies that the system can be a solution to the indoor GPS problem.Wireless network technology that is used today is developing rapidly because of the increase needed for location information of an object with high accuracy. Global Positioning System (GPS) is a technology to estimate the current location. Unfortunately, GPS has a lack of accuracy around 10 meters when used indoors. Therefore, it began to be developed with the concept of an indoor positioning system. This is a technology used to estimate the location of objects in a building by utilizing WSN (Wireless Sensor Network). The purpose of this study is to estimate the location of the unknown nodes in the lecturer room as objects and obtain the accuracy of the system being tested. The positioning process is based on the received signal strength (RSSI) on the unknown node using the ZigBee module. The trilateration method is used to estimate the unknown nodes located at the observation area based on the signal strength received at the time of testing. The result shows that the path loss coefficient value at the observation area was 0.9836 and the Mean Square Error of the test was 1.54 meters, which implies that the system can be a solution to the indoor GPS problem
Outage Performances of 5G Channel Model Influenced by Barometric Pressure Effects in Yogyakarta
Full text linkAbstract — The fifth-generation cellular technology (5G) is predicted to adopt a high-frequency channel, which could lead to a new challenge, namely, wave propagation attenuation. This attenuation is affected by natural conditions, such as barometric pressure, rain rate, humidity, and vegetation density. This paper proposes a 5G channel model under the barometric pressure effect to address the issue. The channel model is obtained from series computer simulations by operating frequency of 28 GHz and real-field parameters of Yogyakarta environments. The 5G channel model frameworks consist of two steps. First, generate the instantaneous Power Delay Profile (PDP) using NYU Wireless Simulator with real-field parameters of the environment. Second, the instantaneous PDP is then used to calculate the representative PDP. PDP differs from one country to another, especially on 5G technology, because of the high-frequency band, which is sensitive to nature. To observe the barometric pressure effect, we need to generate the instantaneous PDP with minimum and maximum barometric effects. PDP value used to calculate the outage probability of channel capacity (C) is smaller than the coding rate (R), indicating a failure of detection at the receiver based on the Shannon theory. Outage probability is obtained by the cumulative distribution function of the capacity evaluated against the coding rate. Outage probability results in both scenarios can reach a point of 10-4, for coding rate ½ needs 17.649883 dB, coding rate ¾ needs 20.020953 dB, and coding rate 1 needs 22 dB. This shows that barometric does not significantly influence the performance of the 5G communication system.Abstract — The fifth-generation cellular technology (5G) is predicted to adopt a high-frequency channel, which could lead to a new challenge, namely, wave propagation attenuation. This attenuation is affected by natural conditions, such as barometric pressure, rain rate, humidity, and vegetation density. This paper proposes a 5G channel model under the barometric pressure effect to address the issue. The channel model is obtained from series computer simulations by operating frequency of 28 GHz and real-field parameters of Yogyakarta environments. The 5G channel model frameworks consist of two steps. First, generate the instantaneous Power Delay Profile (PDP) using NYU Wireless Simulator with real-field parameters of the environment. Second, the instantaneous PDP is then used to calculate the representative PDP. PDP differs from one country to another, especially on 5G technology, because of the high-frequency band, which is sensitive to nature. To observe the barometric pressure effect, we need to generate the instantaneous PDP with minimum and maximum barometric effects. PDP value used to calculate the outage probability of channel capacity (C) is smaller than the coding rate (R), indicating a failure of detection at the receiver based on the Shannon theory. Outage probability is obtained by the cumulative distribution function of the capacity evaluated against the coding rate. Outage probability results in both scenarios can reach a point of 10-4, for coding rate ½ needs 17.649883 dB, coding rate ¾ needs 20.020953 dB, and coding rate 1 needs 22 dB. This shows that barometric does not significantly influence the performance of the 5G communication system