Pub Date : 2021-08-18DOI: 10.33096/ilkom.v13i3.870.244-251
M. K. Anam, Muhammad Fuad, Helda Yenni, Syarifah Eiva Fatda, Hadi Asnal, Hamdani Hamdani
{"title":"Application of usability testing for analyzing the quality of 'Keluarga' pharmacy system in Pekanbaru","authors":"M. K. Anam, Muhammad Fuad, Helda Yenni, Syarifah Eiva Fatda, Hadi Asnal, Hamdani Hamdani","doi":"10.33096/ilkom.v13i3.870.244-251","DOIUrl":"https://doi.org/10.33096/ilkom.v13i3.870.244-251","url":null,"abstract":"","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46974639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sentiment analysis is a technique to extract information of one’s perception, called sentiment, on an issue or event. This study employs sentiment analysis to classify society’s response on covid-19 virus posted at twitter into 4 polars, namely happy, sad, angry, and scared. Classification technique used is support vector machine (SVM) method which compares the classification performance figure of 2 linear kernel functions, linear and polynomial. There were 400 tweet data used where each sentiment class consists of 100 data. Using the testing method of k-fold cross validation, the result shows the accuracy value of linear kernel function is 0.28 for unigram feature and 0.36 for trigram feature. These figures are lower compared to accuracy value of kernel polynomial with 0.34 and 0.48 for unigram and trigram feature respectively. On the other hand, testing method of confusion matrix suggests the highest performance is obtained by using kernel polynomial with accuracy value of 0.51, precision of 0.43, recall of 0.45, and f-measure of 0.51.
{"title":"Performance comparison of support vector machine (SVM) with linear kernel and polynomial kernel for multiclass sentiment analysis on twitter","authors":"Rifqatul Mukarramah, Dedy Atmajaya, Lutfi Budi Ilmawan","doi":"10.33096/ilkom.v13i2.851.168-174","DOIUrl":"https://doi.org/10.33096/ilkom.v13i2.851.168-174","url":null,"abstract":"Sentiment analysis is a technique to extract information of one’s perception, called sentiment, on an issue or event. This study employs sentiment analysis to classify society’s response on covid-19 virus posted at twitter into 4 polars, namely happy, sad, angry, and scared. Classification technique used is support vector machine (SVM) method which compares the classification performance figure of 2 linear kernel functions, linear and polynomial. There were 400 tweet data used where each sentiment class consists of 100 data. Using the testing method of k-fold cross validation, the result shows the accuracy value of linear kernel function is 0.28 for unigram feature and 0.36 for trigram feature. These figures are lower compared to accuracy value of kernel polynomial with 0.34 and 0.48 for unigram and trigram feature respectively. On the other hand, testing method of confusion matrix suggests the highest performance is obtained by using kernel polynomial with accuracy value of 0.51, precision of 0.43, recall of 0.45, and f-measure of 0.51.","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41338214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-08DOI: 10.33096/ilkom.v13i3.856.206-215
A. Khumaidi, Y. Purwanto, Heru Sukoco, S. Wijaya
One of the challenges of exporting Arumanis mangoes is their accurate grading ability because the mangoes do not change color during ripening. Near-Infrared (NIR) spectroscopy is a non-destructive method for detecting the internal ripeness of fruit which is quite reliable. However, NIR absorbance bands are often nonspecific, extensive, and overlapping. Although SVM modeling is quite good in performance, it can still be improved by spectral transformation. In this study, 11 spectral transformation operations were compared with their combinations to find the best input model. Spectral transformation operations include SAVGOL, RNV, BASELINE, MSC, EMSC, NORML, CLIP, RESAMPLE, DETREND, SNV, and LSNV. In the 2 class classification model, the highest accuracy is obtained using RNV and SAVGOL. The prediction model for SSC content with the best MSE value uses 3 combinations of spectral transformation operations, namely DETREND, LSNV, and SAVGOL with parameter values: 'deriv_order': 0, 'filter_win': 31, 'poly_order': 6. As for the prediction model of mango hardness with The best MSE value uses 2 combinations of spectral transformation operations, namely LSNV and SAVGOL with parameter values: deriv_order ': 0,' filter_win ': 15,' poly_order ': 6.
{"title":"Effects of spectral transformations in support vector machine on predicting 'Arumanis' mango ripeness using near-infrared spectroscopy","authors":"A. Khumaidi, Y. Purwanto, Heru Sukoco, S. Wijaya","doi":"10.33096/ilkom.v13i3.856.206-215","DOIUrl":"https://doi.org/10.33096/ilkom.v13i3.856.206-215","url":null,"abstract":"One of the challenges of exporting Arumanis mangoes is their accurate grading ability because the mangoes do not change color during ripening. Near-Infrared (NIR) spectroscopy is a non-destructive method for detecting the internal ripeness of fruit which is quite reliable. However, NIR absorbance bands are often nonspecific, extensive, and overlapping. Although SVM modeling is quite good in performance, it can still be improved by spectral transformation. In this study, 11 spectral transformation operations were compared with their combinations to find the best input model. Spectral transformation operations include SAVGOL, RNV, BASELINE, MSC, EMSC, NORML, CLIP, RESAMPLE, DETREND, SNV, and LSNV. In the 2 class classification model, the highest accuracy is obtained using RNV and SAVGOL. The prediction model for SSC content with the best MSE value uses 3 combinations of spectral transformation operations, namely DETREND, LSNV, and SAVGOL with parameter values: 'deriv_order': 0, 'filter_win': 31, 'poly_order': 6. As for the prediction model of mango hardness with The best MSE value uses 2 combinations of spectral transformation operations, namely LSNV and SAVGOL with parameter values: deriv_order ': 0,' filter_win ': 15,' poly_order ': 6.","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42121788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-08DOI: 10.33096/ilkom.v13i2.822.140-147
B. Sugandi, Rahmi Mahdaliza
Nowadays, many children and adults do not know the type or name of fruits, especially if the fruit is a rare one. In this paper, a system was developed that can recognize fruit names in real time using a camera as a visual sensor. The camera captured the image and processed using image processing. This paper proposed a method using HSL color filters, RGB histograms and shapes of fruit objects to detect and recognize fruits. The proposed method was divided into two processes, namely the training and testing processes. The training process was carried out to obtain a database of each fruit. The first process of training was object detection using an HSL color filter. The calculation of the RGB histogram was conducted on the HSL color filtered object. After that, the object's roundness was measured. Meanwhile, the testing process was done by looking for the similarity of the histogram data of the test object with the reference object by using the histogram distance equation. The similarity of the object was determined by the distance value of the histogram of the tested fruit with the referenced fruit. Similar objects would have histogram distances less than the threshold values. Tests were implemented in several types of fruit. The test results showed the system could recognize fruit names accurately. E-ISSN 2548-7779 ILKOM Jurnal Ilmiah Vol. 13, No. 2, August 2021, pp. 140-147 141 Sugandi & Mahdaliza (Fruit recognition system using color filters and histograms) presented in section 4. Conclusions and some suggestions for future improvement of the system were presented in section 5. Method In this article, an algorithm was developed to perform automatic and real time recognition of fruit names. The fruit image was detected using an HSL color filter. The detected fruit was calculated based on its RGB histogram and shape (roundness). The matching process was done by comparing the test image with the reference image. The complete steps of the algorithm procedure used in this article were described in Figure 1. A. Hue, Saturation and Luminance (HSL) Color Filters The HSL color filter was a color filter used to distinguish one part of an object's color from another. HSL color filters are widely used to distinguish objects, especially if the background conditions are changing due to the influence of light. Compared to the original Red, Green and Blue (RGB) colors, HSL colors are easier to use to distinguish one object from another [13] – [16]. The conversion of the original RGB color to HSL was formulated in equation (1) r = R 255 ; g = G 255 ; b = B 255 d = max( r, g, b) − min( r, g, b) L = max(R, G, B) +min(R, G, B) 2 (1)
{"title":"Fruit recognition system using color filters and histograms","authors":"B. Sugandi, Rahmi Mahdaliza","doi":"10.33096/ilkom.v13i2.822.140-147","DOIUrl":"https://doi.org/10.33096/ilkom.v13i2.822.140-147","url":null,"abstract":"Nowadays, many children and adults do not know the type or name of fruits, especially if the fruit is a rare one. In this paper, a system was developed that can recognize fruit names in real time using a camera as a visual sensor. The camera captured the image and processed using image processing. This paper proposed a method using HSL color filters, RGB histograms and shapes of fruit objects to detect and recognize fruits. The proposed method was divided into two processes, namely the training and testing processes. The training process was carried out to obtain a database of each fruit. The first process of training was object detection using an HSL color filter. The calculation of the RGB histogram was conducted on the HSL color filtered object. After that, the object's roundness was measured. Meanwhile, the testing process was done by looking for the similarity of the histogram data of the test object with the reference object by using the histogram distance equation. The similarity of the object was determined by the distance value of the histogram of the tested fruit with the referenced fruit. Similar objects would have histogram distances less than the threshold values. Tests were implemented in several types of fruit. The test results showed the system could recognize fruit names accurately. E-ISSN 2548-7779 ILKOM Jurnal Ilmiah Vol. 13, No. 2, August 2021, pp. 140-147 141 Sugandi & Mahdaliza (Fruit recognition system using color filters and histograms) presented in section 4. Conclusions and some suggestions for future improvement of the system were presented in section 5. Method In this article, an algorithm was developed to perform automatic and real time recognition of fruit names. The fruit image was detected using an HSL color filter. The detected fruit was calculated based on its RGB histogram and shape (roundness). The matching process was done by comparing the test image with the reference image. The complete steps of the algorithm procedure used in this article were described in Figure 1. A. Hue, Saturation and Luminance (HSL) Color Filters The HSL color filter was a color filter used to distinguish one part of an object's color from another. HSL color filters are widely used to distinguish objects, especially if the background conditions are changing due to the influence of light. Compared to the original Red, Green and Blue (RGB) colors, HSL colors are easier to use to distinguish one object from another [13] – [16]. The conversion of the original RGB color to HSL was formulated in equation (1) r = R 255 ; g = G 255 ; b = B 255 d = max( r, g, b) − min( r, g, b) L = max(R, G, B) +min(R, G, B) 2 (1)","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46310599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-08DOI: 10.33096/ilkom.v13i2.827.148-154
A. T. Sumpala, M. Sutoyo, Huzain Azis, Fadhila Tangguh Admojo
The learning process has a correlation with learning achievement which can be shown through the marks given by a teacher to students from several fields of study. The ranking of student learning achievements performed by the school refers to the grades of the subject is important for the SNMPTN (National Selection for State Higher Education). To determine student achievements, the method used in the current study is the weighted product. If the results of student ranking using the WP method have the same value, then a portfolio assessment is used. Of the 127 student achievement ratings, there were seven people who had the same Vector value. Then, the seven people who have the same vector value were graded using portfolio assessment. The results showed that the implementation of the WP method and portfolio assessment could determine the ranking of student achievement. E-ISSN 2548-7779 ILKOM Jurnal Ilmiah Vol. 13, No. 2, August 2021, pp. 148-154 149 Sumpala, et. al. (The weighted product method and portfolio assessment in ranking student achievement) Several researchers have applied the WP method with different cases including the selection system for outstanding students with criteria for structure and grammar scores, vocabulary scores, reading scores, writing scores, listening scores, and speaking scores [6]. The application of the WP Method was also implemented in the Selection of the Best Graduates in Faculty of Engineering, University of Muhammadiyah Purwokerto with criteria for GPA, period of study, maximum C grade of 1 and no D grade [7]. Also, it was used for granting credit using the WP method at BMT Mu'amalah Sejahtera Kendari with the criteria used for completeness of files, guarantees, income, and type of business [8]. Portfolio evaluation is used if the results of ranking students with the WP approach have the same value. Portfolio evaluation is a collection of evidence of the success of a student or group of students, including the evidence of achievement, abilities, and attitudes [9]. Method The form of a decision support system flowchart using the WP method show in Figure 1. Figure 1. System flowchart A. Decision Support System (DSS) Decision support system is a tool designed to support managers in making decisions that require judgment and cannot be supported by algorithms [10]. The components of the DSS are presented as shown in Figure 2. Figure 2. The components of the DSS 150 ILKOM Jurnal Ilmiah Vol. 13, No. 2, August 2021, pp. 148-154 E-ISSN 2548-7779 Sumpala, et. al. (The weighted product method and portfolio assessment in ranking student achievement) Decision Support Systems have four interrelated subsystems: 1. Data Management consists of databases containing specific data managed by software (DMBS). 2. Model representation is a software that contains statistical models and quantitative models that has the ability to analyze. 3. Knowledge-Based Management is a subsystem that can help other subsystems and has the ability as a
{"title":"The weighted product method and portfolio assessment in ranking student achievement","authors":"A. T. Sumpala, M. Sutoyo, Huzain Azis, Fadhila Tangguh Admojo","doi":"10.33096/ilkom.v13i2.827.148-154","DOIUrl":"https://doi.org/10.33096/ilkom.v13i2.827.148-154","url":null,"abstract":"The learning process has a correlation with learning achievement which can be shown through the marks given by a teacher to students from several fields of study. The ranking of student learning achievements performed by the school refers to the grades of the subject is important for the SNMPTN (National Selection for State Higher Education). To determine student achievements, the method used in the current study is the weighted product. If the results of student ranking using the WP method have the same value, then a portfolio assessment is used. Of the 127 student achievement ratings, there were seven people who had the same Vector value. Then, the seven people who have the same vector value were graded using portfolio assessment. The results showed that the implementation of the WP method and portfolio assessment could determine the ranking of student achievement. E-ISSN 2548-7779 ILKOM Jurnal Ilmiah Vol. 13, No. 2, August 2021, pp. 148-154 149 Sumpala, et. al. (The weighted product method and portfolio assessment in ranking student achievement) Several researchers have applied the WP method with different cases including the selection system for outstanding students with criteria for structure and grammar scores, vocabulary scores, reading scores, writing scores, listening scores, and speaking scores [6]. The application of the WP Method was also implemented in the Selection of the Best Graduates in Faculty of Engineering, University of Muhammadiyah Purwokerto with criteria for GPA, period of study, maximum C grade of 1 and no D grade [7]. Also, it was used for granting credit using the WP method at BMT Mu'amalah Sejahtera Kendari with the criteria used for completeness of files, guarantees, income, and type of business [8]. Portfolio evaluation is used if the results of ranking students with the WP approach have the same value. Portfolio evaluation is a collection of evidence of the success of a student or group of students, including the evidence of achievement, abilities, and attitudes [9]. Method The form of a decision support system flowchart using the WP method show in Figure 1. Figure 1. System flowchart A. Decision Support System (DSS) Decision support system is a tool designed to support managers in making decisions that require judgment and cannot be supported by algorithms [10]. The components of the DSS are presented as shown in Figure 2. Figure 2. The components of the DSS 150 ILKOM Jurnal Ilmiah Vol. 13, No. 2, August 2021, pp. 148-154 E-ISSN 2548-7779 Sumpala, et. al. (The weighted product method and portfolio assessment in ranking student achievement) Decision Support Systems have four interrelated subsystems: 1. Data Management consists of databases containing specific data managed by software (DMBS). 2. Model representation is a software that contains statistical models and quantitative models that has the ability to analyze. 3. Knowledge-Based Management is a subsystem that can help other subsystems and has the ability as a","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45152266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-08DOI: 10.33096/ilkom.v13i2.847.155-162
Arif Budimansyah Purba, Ahmad Mubarok, Jajang Mulyana
The use of information technology in the field of education is currently a top priority for managing academic and supporting activities. Tri Putra Persada Horizon Education Foundation which manages two high schools, namely the College of Health Sciences and the College of Information and Computer Management should face a challenge to align business strategy with information technology, and how to integrate all the parts involved in the business and represent it in an information system. To find out the business strategy and governance of information technology used at the Tri Putra Persada Horizon Education Foundation, an Enterprises Architecture Framework is needed, one of which is TOGAF ADM. The Enterprises Architecture design contained in TOGAF ADM includes a vision architecture that defines the vision of the company or agency, a mapped business architecture in the form of value chain analysis, an information system architecture in which there is a data architecture and application architecture and the last is technology architecture. This research produced an enterprise architecture design blueprint consisting of artifacts, in the form of catalogues, matrices, and diagrams based on the phases of TOGAF ADM. The result of the Enterprise Architecture design was an integrated information system recommendation and the technology architecture. The design is expected to be a reference in improving the quality of business and is expected to make it easier to achieve the business goals of the Tri Putra Persada Horizon Education Foundation. 156 ILKOM Jurnal Ilmiah Vol. 13, No. 2, August 2021, pp. 155-162 E-ISSN 2548-7779 Purba, et. al. (Enterprise architecture design using TOGAF at foundation of triputra persada horizon education) of education. Another research is the Application of The Open Group Architectural Framework (TOGAF) Model for Enterprise Architecture Design at STMIK Widya Pratama Pekalongan conducted by Rusli et al. [9], with the aim of realizing a good governance university. And research on Application Architecture Planning at the University of Lampung Using the Zachman Framework conducted by Novianti et al. [10], aimed to find the fact that changes in application requirements were the most dominant thing to be immediately repaired in each work unit. Based on the problem formulation, the purpose of the research is to produce an Enterprise Architecture design by determining a business framework using the TOGAF ADM method and to produce an information system architecture design consisting of data architecture, application architecture, information architecture and technology architecture. This enterprise architecture design was later used as a reference in the development of an integrated information system between STMIK and STIKes Kharisma Karawang. Method A. Architecture Architecture is a structured working relationship of a system consisting of hardware, software and network [11]. Architecture is also known as the basis of organizational
{"title":"Enterprise architecture design using TOGAF at foundation of triputra persada horizon education","authors":"Arif Budimansyah Purba, Ahmad Mubarok, Jajang Mulyana","doi":"10.33096/ilkom.v13i2.847.155-162","DOIUrl":"https://doi.org/10.33096/ilkom.v13i2.847.155-162","url":null,"abstract":"The use of information technology in the field of education is currently a top priority for managing academic and supporting activities. Tri Putra Persada Horizon Education Foundation which manages two high schools, namely the College of Health Sciences and the College of Information and Computer Management should face a challenge to align business strategy with information technology, and how to integrate all the parts involved in the business and represent it in an information system. To find out the business strategy and governance of information technology used at the Tri Putra Persada Horizon Education Foundation, an Enterprises Architecture Framework is needed, one of which is TOGAF ADM. The Enterprises Architecture design contained in TOGAF ADM includes a vision architecture that defines the vision of the company or agency, a mapped business architecture in the form of value chain analysis, an information system architecture in which there is a data architecture and application architecture and the last is technology architecture. This research produced an enterprise architecture design blueprint consisting of artifacts, in the form of catalogues, matrices, and diagrams based on the phases of TOGAF ADM. The result of the Enterprise Architecture design was an integrated information system recommendation and the technology architecture. The design is expected to be a reference in improving the quality of business and is expected to make it easier to achieve the business goals of the Tri Putra Persada Horizon Education Foundation. 156 ILKOM Jurnal Ilmiah Vol. 13, No. 2, August 2021, pp. 155-162 E-ISSN 2548-7779 Purba, et. al. (Enterprise architecture design using TOGAF at foundation of triputra persada horizon education) of education. Another research is the Application of The Open Group Architectural Framework (TOGAF) Model for Enterprise Architecture Design at STMIK Widya Pratama Pekalongan conducted by Rusli et al. [9], with the aim of realizing a good governance university. And research on Application Architecture Planning at the University of Lampung Using the Zachman Framework conducted by Novianti et al. [10], aimed to find the fact that changes in application requirements were the most dominant thing to be immediately repaired in each work unit. Based on the problem formulation, the purpose of the research is to produce an Enterprise Architecture design by determining a business framework using the TOGAF ADM method and to produce an information system architecture design consisting of data architecture, application architecture, information architecture and technology architecture. This enterprise architecture design was later used as a reference in the development of an integrated information system between STMIK and STIKes Kharisma Karawang. Method A. Architecture Architecture is a structured working relationship of a system consisting of hardware, software and network [11]. Architecture is also known as the basis of organizational ","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44385936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-08DOI: 10.33096/ilkom.v13i3.863.235-243
Rozzi Kesuma Dinata, N. Hasdyna, Sujacka Retno, Muhammad Nurfahmi
The number of regions and types of plants in East Aceh Regency requires a data clustering process in order to easily find out which areas are most in-demand based on the type of plants. This study applies the k-means algorithm to classify the data. The data used in this study were obtained from the Department of Agriculture, Food Crops and Horticulture, East Aceh Regency. Based on the test results with k-means, the average number of iterations in the 2015-2019 data is 8,7,6,4,3 iterations for each commodity. The test results can show areas of interest for plant seeds with clusters of high demand, attractive, and less desirable. The system in this study was built based on the web using the PHP programming language.
{"title":"K-means algorithm for clustering system of plant seeds specialization areas in east Aceh","authors":"Rozzi Kesuma Dinata, N. Hasdyna, Sujacka Retno, Muhammad Nurfahmi","doi":"10.33096/ilkom.v13i3.863.235-243","DOIUrl":"https://doi.org/10.33096/ilkom.v13i3.863.235-243","url":null,"abstract":"The number of regions and types of plants in East Aceh Regency requires a data clustering process in order to easily find out which areas are most in-demand based on the type of plants. This study applies the k-means algorithm to classify the data. The data used in this study were obtained from the Department of Agriculture, Food Crops and Horticulture, East Aceh Regency. Based on the test results with k-means, the average number of iterations in the 2015-2019 data is 8,7,6,4,3 iterations for each commodity. The test results can show areas of interest for plant seeds with clusters of high demand, attractive, and less desirable. The system in this study was built based on the web using the PHP programming language.","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43799909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-08DOI: 10.33096/ilkom.v13i3.854.195-205
N. Wardhani, Hamdan Gani, Sitti Zuhriyah, Helmy Gani, Etika Vidyarini
This study aims to validate the correlation between meteorological factors and air pollution with the spread of Covid-19 in Jakarta, Indonesia. This study examined the Covid-19 cases of Jakarta and its five municipalities. The secondary data of Covid-19 cases, includes Daily Positive Cases (DPC) and Total Daily Positive Cases (TDPC), were retrieved from the Health Office of DKI Jakarta Province, while the meteorological and air pollution parameters were obtained from the online database archives. Kendall and Spearman rank correlation tests were used to analyze correlation between DPC and TDPC with meteorological and air pollution parameters. This study found that Air Quality Index and PM10 showed a significant positive correlation with DPC in municipalities of Jakarta. Also, the average air temperature was positively correlated to TDPC in all region of Jakarta. Average air temperature, Air Quality Index, and PM10 were the factors that take into account for the spread of Covid-19 pandemic in Jakarta, Indonesia. The warmer temperature associated to the higher number of case. Thus, there are no indications that the spread of Covid-19 in subtropical or temperate country may decrease when entering a warmer season that resembles the climatic characteristics in tropical region. Additionally, the significance of air pollutant factors implies that reducing air pollution should be promoted as it might reduce the spread of Covid-19. The findings of this study would be useful to support the strategy and policy in preventing the spread of Covid-19 in the country.
{"title":"A Correlation Method for Meteorological Factors and Air pollution in association to covid-19 pandemic in the most affected city in Indonesia","authors":"N. Wardhani, Hamdan Gani, Sitti Zuhriyah, Helmy Gani, Etika Vidyarini","doi":"10.33096/ilkom.v13i3.854.195-205","DOIUrl":"https://doi.org/10.33096/ilkom.v13i3.854.195-205","url":null,"abstract":"This study aims to validate the correlation between meteorological factors and air pollution with the spread of Covid-19 in Jakarta, Indonesia. This study examined the Covid-19 cases of Jakarta and its five municipalities. The secondary data of Covid-19 cases, includes Daily Positive Cases (DPC) and Total Daily Positive Cases (TDPC), were retrieved from the Health Office of DKI Jakarta Province, while the meteorological and air pollution parameters were obtained from the online database archives. Kendall and Spearman rank correlation tests were used to analyze correlation between DPC and TDPC with meteorological and air pollution parameters. This study found that Air Quality Index and PM10 showed a significant positive correlation with DPC in municipalities of Jakarta. Also, the average air temperature was positively correlated to TDPC in all region of Jakarta. Average air temperature, Air Quality Index, and PM10 were the factors that take into account for the spread of Covid-19 pandemic in Jakarta, Indonesia. The warmer temperature associated to the higher number of case. Thus, there are no indications that the spread of Covid-19 in subtropical or temperate country may decrease when entering a warmer season that resembles the climatic characteristics in tropical region. Additionally, the significance of air pollutant factors implies that reducing air pollution should be promoted as it might reduce the spread of Covid-19. The findings of this study would be useful to support the strategy and policy in preventing the spread of Covid-19 in the country.","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46561995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-08DOI: 10.33096/ilkom.v13i3.857.216-225
Sitti Zuhriyah, Billy Eden William Asrul, Suwatri Jura
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Pub Date : 2021-08-08DOI: 10.33096/ilkom.v13i2.852.175-186
Eka Purnama Harahap, Md. Asri Ngadi, U. Rahardja, F. Azhari, Kenita Zelina
This study proposes smart monitoring by utilizing IoT in agriculture which aims to assist farmers in monitoring crops in order to reduce the risk of failure. Quantitative method was employed to collect data from the Soil Moisture Sensor & DHT22 which are to read and write data that can be monitored on a cloud server or csv file to evaluate the risk. This monitoring system is created using the Python programming language by utilizing the Raspberry Pi as a microprocessor. The result of this study is data acquisition that is connected to the internet. Data can be accessed at Thingspeak to show indications and crop yields. Analogue form and indicators of water in soil moisture are indicated by colored marks. Proper monitoring shows more accurate crop data that enable the farmers to prevent crops from drying out. This system is expected to reduce the risk of crop failure as well as increase the agriculture productivity. 176 ILKOM Jurnal Ilmiah Vol. 13, No. 2, August 2021, pp. 175-186 E-ISSN 2548-7779 Harahap, et. al. (Internet of things based humidity control and monitoring system) that Banten has low rainfall compared to the average rainfall in Indonesia which reaches 2,000 – 3,000 mm per year. In the agricultural sector, rainfall is very influential on increasing crop yields [7]. The low rainfall frequently causes failure of chili harvest in Banten. This is exacerbated when the dry season begins which makes many farmers' plants wither or die due to lack of water. There are many elements that must be considered in planting chili such as soil moisture, lack of soil nutrients and lack of supervision. In its maintenance, chili must be continuously monitored. It should be watered 3 times a day to avoid drying out [8]. This process causes farmers to usually sacrifice a lot of time and energy to see the physical development of chili plants. This conventional method is not efficient because there is no accurate data that shows the level of soil moisture needed by chilies to grow completely and avoid drying out. Recently, there are many sophisticated tools and technologies used to increase crop yields and minimize losses. One of them is the incorporation of Smart IoT technology. In agriculture, the application of IoT technology aims to increase yields and harvest quality in order to reduce costs [9][10]. This technology can support farmers in obtaining better information so that it can help them in making decisions [11][12]. Extensive researches have been conducted to improve the IoT in agriculture. Research [13] proposes the importance of wireless sensors in agriculture to increase productivity as well as the significance of precision agriculture nowadays. In addition, this study shows the architecture that is applied to analyze and monitor environmental parameters. On the other hand, study [14] suggests an IoT framework in agriculture. This research also shows the various layers of the agricultural market and how IoT can be applied to each layer. Additiona
{"title":"Internet of things based humidity control and monitoring system","authors":"Eka Purnama Harahap, Md. Asri Ngadi, U. Rahardja, F. Azhari, Kenita Zelina","doi":"10.33096/ilkom.v13i2.852.175-186","DOIUrl":"https://doi.org/10.33096/ilkom.v13i2.852.175-186","url":null,"abstract":"This study proposes smart monitoring by utilizing IoT in agriculture which aims to assist farmers in monitoring crops in order to reduce the risk of failure. Quantitative method was employed to collect data from the Soil Moisture Sensor & DHT22 which are to read and write data that can be monitored on a cloud server or csv file to evaluate the risk. This monitoring system is created using the Python programming language by utilizing the Raspberry Pi as a microprocessor. The result of this study is data acquisition that is connected to the internet. Data can be accessed at Thingspeak to show indications and crop yields. Analogue form and indicators of water in soil moisture are indicated by colored marks. Proper monitoring shows more accurate crop data that enable the farmers to prevent crops from drying out. This system is expected to reduce the risk of crop failure as well as increase the agriculture productivity. 176 ILKOM Jurnal Ilmiah Vol. 13, No. 2, August 2021, pp. 175-186 E-ISSN 2548-7779 Harahap, et. al. (Internet of things based humidity control and monitoring system) that Banten has low rainfall compared to the average rainfall in Indonesia which reaches 2,000 – 3,000 mm per year. In the agricultural sector, rainfall is very influential on increasing crop yields [7]. The low rainfall frequently causes failure of chili harvest in Banten. This is exacerbated when the dry season begins which makes many farmers' plants wither or die due to lack of water. There are many elements that must be considered in planting chili such as soil moisture, lack of soil nutrients and lack of supervision. In its maintenance, chili must be continuously monitored. It should be watered 3 times a day to avoid drying out [8]. This process causes farmers to usually sacrifice a lot of time and energy to see the physical development of chili plants. This conventional method is not efficient because there is no accurate data that shows the level of soil moisture needed by chilies to grow completely and avoid drying out. Recently, there are many sophisticated tools and technologies used to increase crop yields and minimize losses. One of them is the incorporation of Smart IoT technology. In agriculture, the application of IoT technology aims to increase yields and harvest quality in order to reduce costs [9][10]. This technology can support farmers in obtaining better information so that it can help them in making decisions [11][12]. Extensive researches have been conducted to improve the IoT in agriculture. Research [13] proposes the importance of wireless sensors in agriculture to increase productivity as well as the significance of precision agriculture nowadays. In addition, this study shows the architecture that is applied to analyze and monitor environmental parameters. On the other hand, study [14] suggests an IoT framework in agriculture. This research also shows the various layers of the agricultural market and how IoT can be applied to each layer. Additiona","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69492455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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