PENENTUAN DAERAH PRIORITAS PELAYANAN AKTA KELAHIRAN DENGAN METODE K-NN DAN K-MEANS

2020 ◽  
Vol 17 (1) ◽  
pp. 319-328
Author(s):  
Ade Muchlis Maulana Anwar ◽  
Prihastuti Harsani ◽  
Aries Maesya
Keyword(s):  
Nearest Neighbor ◽  
Information Gain ◽  
Birth Certificate ◽  
Population Data ◽  
Community Services ◽  
Birth Certificates ◽  
Similar Data ◽  
K Nearest Neighbor ◽  
Civil Registration ◽  
The Family

Population Data is individual data or aggregate data that is structured as a result of Population Registration and Civil Registration activities. Birth Certificate is a Civil Registration Deed as a result of recording the birth event of a baby whose birth is reported to be registered on the Family Card and given a Population Identification Number (NIK) as a basis for obtaining other community services. From the total number of integrated birth certificate reporting for the 2018 Population Administration Information System (SIAK) totaling 570,637 there were 503,946 reported late and only 66,691 were reported publicly. Clustering is a method used to classify data that is similar to others in one group or similar data to other groups. K-Nearest Neighbor is a method for classifying objects based on learning data that is the closest distance to the test data. k-means is a method used to divide a number of objects into groups based on existing categories by looking at the midpoint. In data mining preprocesses, data is cleaned by filling in the blank data with the most dominating data, and selecting attributes using the information gain method. Based on the k-nearest neighbor method to predict delays in reporting and the k-means method to classify priority areas of service with 10,000 birth certificate data on birth certificates in 2019 that have good enough performance to produce predictions with an accuracy of 74.00% and with K = 2 on k-means produces a index davies bouldin of 1,179.

scholarly journals PENENTUAN DAERAH PRIORITAS PELAYANAN AKTA KELAHIRAN DENGAN METODE K-NN DAN K-MEANS

2020 ◽  
Vol 17 (1) ◽  
pp. 319-328
Author(s):  
Ade Muchlis Maulana Anwar ◽  
Prihastuti Harsani ◽  
Aries Maesya
Keyword(s):  
Nearest Neighbor ◽  
Information Gain ◽  
Birth Certificate ◽  
Population Data ◽  
Community Services ◽  
Birth Certificates ◽  
Similar Data ◽  
K Nearest Neighbor ◽  
Civil Registration ◽  
The Family

Population Data is individual data or aggregate data that is structured as a result of Population Registration and Civil Registration activities. Birth Certificate is a Civil Registration Deed as a result of recording the birth event of a baby whose birth is reported to be registered on the Family Card and given a Population Identification Number (NIK) as a basis for obtaining other community services. From the total number of integrated birth certificate reporting for the 2018 Population Administration Information System (SIAK) totaling 570,637 there were 503,946 reported late and only 66,691 were reported publicly. Clustering is a method used to classify data that is similar to others in one group or similar data to other groups. K-Nearest Neighbor is a method for classifying objects based on learning data that is the closest distance to the test data. k-means is a method used to divide a number of objects into groups based on existing categories by looking at the midpoint. In data mining preprocesses, data is cleaned by filling in the blank data with the most dominating data, and selecting attributes using the information gain method. Based on the k-nearest neighbor method to predict delays in reporting and the k-means method to classify priority areas of service with 10,000 birth certificate data on birth certificates in 2019 that have good enough performance to produce predictions with an accuracy of 74.00% and with K = 2 on k-means produces a index davies bouldin of 1,179

PENENTUAN DAERAH PRIORITAS PELAYANAN AKTA KELAHIRAN DENGAN METODE K-NN DAN K-MEANS

2020 ◽  
Vol 17 (1) ◽  
pp. 319-328
Author(s):  
Ade Muchlis Maulana Anwar ◽  
Prihastuti Harsani ◽  
Aries Maesya
Keyword(s):  
Nearest Neighbor ◽  
Information Gain ◽  
Birth Certificate ◽  
Population Data ◽  
Community Services ◽  
Birth Certificates ◽  
Similar Data ◽  
K Nearest Neighbor ◽  
Civil Registration ◽  
The Family

Population Data is individual data or aggregate data that is structured as a result of Population Registration and Civil Registration activities. Birth Certificate is a Civil Registration Deed as a result of recording the birth event of a baby whose birth is reported to be registered on the Family Card and given a Population Identification Number (NIK) as a basis for obtaining other community services. From the total number of integrated birth certificate reporting for the 2018 Population Administration Information System (SIAK) totaling 570,637 there were 503,946 reported late and only 66,691 were reported publicly. Clustering is a method used to classify data that is similar to others in one group or similar data to other groups. K-Nearest Neighbor is a method for classifying objects based on learning data that is the closest distance to the test data. k-means is a method used to divide a number of objects into groups based on existing categories by looking at the midpoint. In data mining preprocesses, data is cleaned by filling in the blank data with the most dominating data, and selecting attributes using the information gain method. Based on the k-nearest neighbor method to predict delays in reporting and the k-means method to classify priority areas of service with 10,000 birth certificate data on birth certificates in 2019 that have good enough performance to produce predictions with an accuracy of 74.00% and with K = 2 on k-means produces a index davies bouldin of 1,179.

Classification of Muscle Fatigue in Dynamic Contraction Using Surface Electromyography Signals and Multifractal Singularity Spectral Analysis

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Kiran Marri ◽  
Ramakrishnan Swaminathan
Keyword(s):  
Muscle Fatigue ◽  
Surface Electromyography ◽  
Nearest Neighbor ◽  
Biceps Brachii ◽  
Information Gain ◽  
Multifractal Spectrum ◽  
Singularity Spectrum ◽  
K Nearest Neighbor ◽  
Dynamic Contraction ◽  
Semg Signals

Muscle fatigue is a neuromuscular condition experienced during daily activities. This phenomenon is generally characterized using surface electromyography (sEMG) signals and has gained a lot of interest in the fields of clinical rehabilitation, prosthetics control, and sports medicine. sEMG signals are complex, nonstationary and also exhibit self-similarity fractal characteristics. In this work, an attempt has been made to differentiate sEMG signals in nonfatigue and fatigue conditions during dynamic contraction using multifractal analysis. sEMG signals are recorded from biceps brachii muscles of 42 healthy adult volunteers while performing curl exercise. The signals are preprocessed and segmented into nonfatigue and fatigue conditions using the first and last curls, respectively. The multifractal detrended moving average algorithm (MFDMA) is applied to both segments, and multifractal singularity spectrum (SSM) function is derived. Five conventional features are extracted from the singularity spectrum. Twenty-five new features are proposed for analyzing muscle fatigue from the multifractal spectrum. These proposed features are adopted from analysis of sEMG signals and muscle fatigue studies performed in time and frequency domain. These proposed 25 feature sets are compared with conventional five features using feature selection methods such as Wilcoxon rank sum, information gain (IG) and genetic algorithm (GA) techniques. Two classification algorithms, namely, k-nearest neighbor (k-NN) and logistic regression (LR), are explored for differentiating muscle fatigue. The results show that about 60% of the proposed features are statistically highly significant and suitable for muscle fatigue analysis. The results also show that eight proposed features ranked among the top 10 features. The classification accuracy with conventional features in dynamic contraction is 75%. This accuracy improved to 88% with k-NN-GA combination with proposed new feature set. Based on the results, it appears that the multifractal spectrum analysis with new singularity features can be used for clinical evaluation in varied neuromuscular conditions, and the proposed features can also be useful in analyzing other physiological time series.

Feature Selection Algorithm for Hyperlipidemia Classification

2014 ◽  
Vol 701-702 ◽  
pp. 110-113
Author(s):  
Qi Rui Zhang ◽  
He Xian Wang ◽  
Jiang Wei Qin
Keyword(s):  
Feature Selection ◽  
Nearest Neighbor ◽  
Information Gain ◽  
Classification Systems ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Document Frequency ◽  
Selection Algorithms ◽  
Term Weights

This paper reports a comparative study of feature selection algorithms on a hyperlipimedia data set. Three methods of feature selection were evaluated, including document frequency (DF), information gain (IG) and aχ2 statistic (CHI). The classification systems use a vector to represent a document and use tfidfie (term frequency, inverted document frequency, and inverted entropy) to compute term weights. In order to compare the effectives of feature selection, we used three classification methods: Naïve Bayes (NB), k Nearest Neighbor (kNN) and Support Vector Machines (SVM). The experimental results show that IG and CHI outperform significantly DF, and SVM and NB is more effective than KNN when macro-averagingF1 measure is used. DF is suitable for the task of large text classification.

scholarly journals PELANGI: INOVASI PELAYANAN AKTA KELAHIRAN DI KOTA BANDA ACEH

2019 ◽  
Vol 5 (1) ◽  
pp. 11-20
Author(s):  
Cut Zamharira ◽  
Indah Rita Cahyani
Keyword(s):  
Data Analysis ◽  
Research Method ◽  
Birth Certificate ◽  
Birth Certificates ◽  
Data Presentation ◽  
Civil Registration ◽  
Analysis Technique ◽  
Bureaucratic Reform ◽  
The City ◽  
Banda Aceh

One form of bureaucratic reform in public services is to bring innovation. Since 2015 the city of Banda Aceh has implemented innovations in birth certificate services, by presenting PELANGI (direct servants). Pelangi is a mechanism for making birth certificates based on a mobile car with a proactive system. Department of Population and Civil Registration of the city of Banda Aceh visited villages in Banda Aceh City and provided birth certificate registration services on-site. The purpose of this study was to find out how the implementation of the "Pelangi" program and the extent to which the program was able to increase ownership of birth certificates in the city of Banda Aceh. The research method chosen is descriptive qualitative. With the hope of being able to answer in more detail related to Department of Population and Civil Registration's efforts in increasing the ownership of birth certificates for residents of the city of Banda Aceh through the innovation of the Pelangi. The key informants in this study came from officers and leadership elements of the Department of Population and Civil Registration of Banda Aceh and village officials who were partners. While data collection techniques are obtained through observations, interviews and documentations. Data analysis technique is done by reducing data, presentation and verification. The results of this study indicate that the implementation of the Pelangi innovation in the city of Banda Aceh based on a mobile car has become one of the innovations that is able to increase the number of birth certificate ownership in the city of Banda Aceh.

Effect of information gain on document classification using k-nearest neighbor

2022 ◽  
Vol 8 (1) ◽  
pp. 50
Author(s):  
Rifki Indra Perwira ◽  
Bambang Yuwono ◽  
Risya Ines Putri Siswoyo ◽  
Febri Liantoni ◽  
Hidayatulah Himawan
Keyword(s):  
Feature Selection ◽  
Test Data ◽  
Nearest Neighbor ◽  
Intelligent System ◽  
Information Gain ◽  
Training Data ◽  
State Universities ◽  
Features Selection ◽  
K Nearest Neighbor ◽  
Support Students

State universities have a library as a facility to support students’ education and science, which contains various books, journals, and final assignments. An intelligent system for classifying documents is needed to ease library visitors in higher education as a form of service to students. The documents that are in the library are generally the result of research. Various complaints related to the imbalance of data texts and categories based on irrelevant document titles and words that have the ambiguity of meaning when searching for documents are the main reasons for the need for a classification system. This research uses k-Nearest Neighbor (k-NN) to categorize documents based on study interests with information gain features selection to handle unbalanced data and cosine similarity to measure the distance between test and training data. Based on the results of tests conducted with 276 training data, the highest results using the information gain selection feature using 80% training data and 20% test data produce an accuracy of 87.5% with a parameter value of k=5. The highest accuracy results of 92.9% are achieved without information gain feature selection, with the proportion of training data of 90% and 10% test data and parameters k=5, 7, and 9. This paper concludes that without information gain feature selection, the system has better accuracy than using the feature selection because every word in the document title is considered to have an essential role in forming the classification.

scholarly journals MENGOPTIMALISASIKAN PELAYANAN PEMBUATAN AKTE KELAHIRAN

2017 ◽  
Vol 2 (1) ◽  
pp. 103-120
Author(s):  
Hayat Hayat ◽  
Laily Hidayah
Keyword(s):  
Public Services ◽  
Service Providers ◽  
Birth Certificate ◽  
Birth Certificates ◽  
The Public ◽  
Civil Registration ◽  
Qualified Personnel ◽  
Strategic Role ◽  
Survey Results

AbstrakTujuan penelitian ini adalah untuk mengoptimalkan pelayanan publik dalam pembuatanakta kelahiran. Untuk mengoptimalkan pelayanan pembuatan akte kelahiran,dibutuhkan konsep yang komprehenship dalam pelaksanaannya sehingga berimplikasisecara positif. Kebijakan pemimpin mempunyai dampak yang besar terhadapperubahan menjadi lebih baik untuk memaksimalkan dan mengoptimalkan kinerjapelayanan publik dalam pembuatan akte kelahiran. Pelayanan publik yang optimal,tentunya mempunyai indikasi pelayanan yang berkualitas dan prima. Pencapaian goodgovernance tidak lepas dari peran strategis aparatur yang berkualitas, professional danakuntabel dalam kinerja pelayanan publik. Aparatur pelayanan publik menjadi titiksentral dalam optimalisasi kinerja pelayanan publik. Metode penelitian ini dilakukandengan survey kepada obyek penelitian, kemudian dilakukan wawancara secaramendalam kepada penyedia pelayanan dan pengguna pelayanan. Disamping itu, datadokumentasi juga disiapkan sebagai data pendukung. Selanjutnya dilakukan analisisterhadap pelayanan pembuatan akta kelahiran berdasarkan hasil survey, keteranganketeranganpihak yang diwawancara serta dukungan data dokumen yang telah dipilahdan dipilih untuk melengkapi keakuratan data penelitian. Hasil penelitian menunjukkanbahwa pelayanan pembuatan akte kelahiran yang dilakukan di Dinas Kependudukandan Catatan Sipil Kabupaten sudah dilakukan secara berkesinambungan.Kata kunci: optimalisasi, kebijakan publik, pelayanan publik, akte kelahiranAbstractThe purpose of this study is to optimize the public service in the birth certificate. Tooptimize the service birth certificates, needed komprehenship concept in itsimplementation so that the implications are positive. Policy leaders have a majorimpact on the change for the better in order to maximize and optimize the performanceof public services in the manufacture of a birth certificate. Public services are optimal,of course, have an indication of quality service and excellence. Achievement of goodgovernance can not be separated from the strategic role of qualified personnel,professional and accountable in the performance of public services. Apparatus publicservices become a central point in optimizing the performance of public services. Thisresearch method survey conducted by the research object, and then conducted detailedinterviews to service providers and service users. In addition, the data also prepareddocumentation as supporting data. Furthermore, analyzing the birth certificate ofservice based on the survey results, informations the interviewee as well as support fordocument data that has been sorted and selected to complement the accuracy ofresearch data. The results showed that birth certificates services performed in theDepartment of Population and Civil Registration District has been done on an ongoingbasis.Keywords: optimization, public policy, public services, birth certificates

scholarly journals DETERMINING LECTURAL EVALUATION IN FACULTY OF ENGINEERING MALIKUSSALEH UNIVERSITY USING K-NN

2019 ◽  
Vol 11 (2) ◽  
pp. 307
Author(s):  
Asrianda Asrianda ◽  
Risawandi Risawandi ◽  
Gunarwan Gunarwan
Keyword(s):  
Nearest Neighbor ◽  
Predictor Variable ◽  
Training Data ◽  
Manhattan Distance ◽  
Similar Data ◽  
K Nearest Neighbor ◽  
Classification Result ◽  
Distance Method ◽  
Testing Data ◽  
Supervised Learning Algorithms

K-Nearest Neighbor is a method that can classify data based on the closest distance. In addition, K-NN is one of the supervised learning algorithms with learning processes based on the value of the target variable associated with the value of the predictor variable. In the K-NN algorithm, all data must have a label, so that when a new data is given, the data will be compared with the existing data, then the most similar data is taken by looking at the label of that data. Filling and processing many questionnaires to determining the results of lectural evaluation from the performance of lecturers certainly requires a lot of time and process. Therefore, it is necessary to apply the K-NN Manhattan Distance method. In this study, the testing data is taken from one of the training data and has a classification result that is "Very Good". After going through the K-NN Manhattan Distance method with k being the closest / smallest neighbor, then the following results are obtained: Distance 5.4, the classification result is "Very Good" and 74.03% of similarity value. Based on the results obtained, the result of the classification from K-NN Manhattan Distance method show similarities with the results of the pre-existing classification.

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