Pub Date : 2021-11-28DOI: 10.31603/komtika.v5i2.6280
Herlawati Herlawati, Rahmadya Trias Handayanto, Prima Dina Atika, Fata Nidaul Khasanah, Ajif Yunizar Pratama Yusuf, Dwi Yoga Septia
Tourism is the sources of income which is influenced by customer satisfaction. One way to know customer satisfaction is feedback, one of which is a review using an application. One of the feedback applications is Google Review. Such applications are have been widely used, for example in this study in this case study, Summarecon Mal Bekasi, can reach 60,000 comments. To find out the sentiment of the large number of comments, it is necessary to use computational tools. The current research applies sentiment analysis using the Naïve Bayes method and the Support Vector Machine. Data retrieval is done by web scrapping technique. Furthermore, the comment data is processed by pre-processing and labelling using the Lexicon dictionary. The process of applying sentiment analysis is carried out to determine whether the comments are positive or negative. In this study, the accuracy of the Naïve Bayes and Support Vector Machine methods in conducting sentiment analysis on the Summarecon Mal Bekasi review with a data of 2,143 comments with an accuracy for Naïve Bayes and Support Vector Machine 80.95% and 100% respectively. A Jason-style application is built to show the implementation in Flask framework. Keywords:
旅游是收入的来源，受顾客满意度的影响。了解客户满意度的一种方法是反馈，其中之一是使用应用程序进行审查。其中一个反馈应用是谷歌评论。这样的应用已经得到了广泛的应用，例如在本研究中，在本案例研究中，Summarecon Mal Bekasi，可以达到6万条评论。为了找出大量评论的情绪，有必要使用计算工具。目前的研究使用Naïve贝叶斯方法和支持向量机进行情感分析。数据检索是通过网页抓取技术完成的。此外，通过使用Lexicon字典进行预处理和标记来处理评论数据。应用情绪分析的过程是进行，以确定评论是积极的还是消极的。在本研究中，Naïve贝叶斯和支持向量机方法对2,143条评论的Summarecon Mal Bekasi评论进行情感分析的准确率为Naïve，贝叶斯和支持向量机的准确率分别为80.95%和100%。构建了一个jason风格的应用程序来展示Flask框架中的实现。关键词:
{"title":"Analisis Sentimen Pada Situs Google Review dengan Naïve Bayes dan Support Vector Machine","authors":"Herlawati Herlawati, Rahmadya Trias Handayanto, Prima Dina Atika, Fata Nidaul Khasanah, Ajif Yunizar Pratama Yusuf, Dwi Yoga Septia","doi":"10.31603/komtika.v5i2.6280","DOIUrl":"https://doi.org/10.31603/komtika.v5i2.6280","url":null,"abstract":"\u0000 \u0000 \u0000 \u0000 Tourism is the sources of income which is influenced by customer satisfaction. One way to know customer satisfaction is feedback, one of which is a review using an application. One of the feedback applications is Google Review. Such applications are have been widely used, for example in this study in this case study, Summarecon Mal Bekasi, can reach 60,000 comments. To find out the sentiment of the large number of comments, it is necessary to use computational tools. The current research applies sentiment analysis using the Naïve Bayes method and the Support Vector Machine. Data retrieval is done by web scrapping technique. Furthermore, the comment data is processed by pre-processing and labelling using the Lexicon dictionary. The process of applying sentiment analysis is carried out to determine whether the comments are positive or negative. In this study, the accuracy of the Naïve Bayes and Support Vector Machine methods in conducting sentiment analysis on the Summarecon Mal Bekasi review with a data of 2,143 comments with an accuracy for Naïve Bayes and Support Vector Machine 80.95% and 100% respectively. A Jason-style application is built to show the implementation in Flask framework. \u0000 \u0000 \u0000 \u0000 \u0000 \u0000Keywords: \u0000 \u0000 \u0000 \u0000","PeriodicalId":292404,"journal":{"name":"Jurnal Komtika (Komputasi dan Informatika)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122240998","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 : 2021-11-28DOI: 10.31603/komtika.v5i2.5901
Zahrina Aulia Adriani, Irma Palupi
In order to increase student performance, several universities use machine learning to analyze and evaluate their data so that it enables to improve the quality of education in the university. To get a new insight from the tracer study dataset as the relevance between university performance and student capability with business and industries work, the author will develop a model to predict student performance based on the tracer study dataset using Artificial Neural Network (ANN). For obtaining attributes that correspond to labels, Phi Coefficient Correlation will be used to select the attributes with high correlation as Feature Selection. The author is also performing the oversampling method using Synthetic Minority Oversampling Technique (SMOTE) because this dataset is imbalanced and evaluates the model using K-Fold Cross-Validation. According to K-Fold Cross Validation, the result shows that K = 3 has a low standard deviation of evaluation score and it's the best candidate of K to split the dataset. The average standard deviation is 0.038 for all score evaluations (Accuracy, Precision, Recall, and F-1 Score). After applied SMOTE to treating the imbalanced dataset with the data splitting 65 training data and 35 testing data, the accuracy value increases by 10% from 0.77 to 0.87.
{"title":"Prediction of University Student Performance Based on Tracer Study Dataset Using Artificial Neural Network","authors":"Zahrina Aulia Adriani, Irma Palupi","doi":"10.31603/komtika.v5i2.5901","DOIUrl":"https://doi.org/10.31603/komtika.v5i2.5901","url":null,"abstract":"In order to increase student performance, several universities use machine learning to analyze and evaluate their data so that it enables to improve the quality of education in the university. To get a new insight from the tracer study dataset as the relevance between university performance and student capability with business and industries work, the author will develop a model to predict student performance based on the tracer study dataset using Artificial Neural Network (ANN). For obtaining attributes that correspond to labels, Phi Coefficient Correlation will be used to select the attributes with high correlation as Feature Selection. The author is also performing the oversampling method using Synthetic Minority Oversampling Technique (SMOTE) because this dataset is imbalanced and evaluates the model using K-Fold Cross-Validation. According to K-Fold Cross Validation, the result shows that K = 3 has a low standard deviation of evaluation score and it's the best candidate of K to split the dataset. The average standard deviation is 0.038 for all score evaluations (Accuracy, Precision, Recall, and F-1 Score). After applied SMOTE to treating the imbalanced dataset with the data splitting 65 training data and 35 testing data, the accuracy value increases by 10% from 0.77 to 0.87.","PeriodicalId":292404,"journal":{"name":"Jurnal Komtika (Komputasi dan Informatika)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127117330","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 : 2021-11-28DOI: 10.31603/komtika.v5i2.5995
S. Nurajizah, Ita Yulianti, Elin Panca Saputra, Rani Kurnia Dewi
Dental and oral disease is one of the diseases that has been felt by most of the people. Insufficient information and the limited level of public awareness of the prevention of dental and oral diseases make the impact quite dangerous if not handled properly. An appropriate information system is needed in overcoming and providing solutions for handling a disease as early as possible. Expert systems can be used as a means of information on the treatment of dental and oral diseases. The manufacture of the expert system in this study initially used the forward chaining method, which is a method that searches based on information that is made into a set of rules so as to get a conclusion. However, after re-analysis, two other methods, namely certainty factor and dempster shafer, were also applied in this study with the aim of overcoming the shortcomings of the forward chaining method, one of which is uncertainty in producing a conclusion or diagnosis of disease. Determining the type of dental and oral disease can be known by looking at the symptoms experienced by the patient. The use of an expert system for diagnosing dental and oral diseases can be used as an initial solution in helping someone to treat the disease. The existence of this expert system can be used as consideration in making decisions to determine the type of dental and oral disease quickly, precisely and accurately.
{"title":"Implementasi Metode Forward Chaining, Certainty Factor dan Dempster Shafer pada Sistem Pakar Diagnosis Penyakit Gigi dan Mulut","authors":"S. Nurajizah, Ita Yulianti, Elin Panca Saputra, Rani Kurnia Dewi","doi":"10.31603/komtika.v5i2.5995","DOIUrl":"https://doi.org/10.31603/komtika.v5i2.5995","url":null,"abstract":"\u0000 \u0000 \u0000 \u0000Dental and oral disease is one of the diseases that has been felt by most of the people. Insufficient information and the limited level of public awareness of the prevention of dental and oral diseases make the impact quite dangerous if not handled properly. An appropriate information system is needed in overcoming and providing solutions for handling a disease as early as possible. Expert systems can be used as a means of information on the treatment of dental and oral diseases. The manufacture of the expert system in this study initially used the forward chaining method, which is a method that searches based on information that is made into a set of rules so as to get a conclusion. However, after re-analysis, two other methods, namely certainty factor and dempster shafer, were also applied in this study with the aim of overcoming the shortcomings of the forward chaining method, one of which is uncertainty in producing a conclusion or diagnosis of disease. Determining the type of dental and oral disease can be known by looking at the symptoms experienced by the patient. The use of an expert system for diagnosing dental and oral diseases can be used as an initial solution in helping someone to treat the disease. The existence of this expert system can be used as consideration in making decisions to determine the type of dental and oral disease quickly, precisely and accurately. \u0000 \u0000 \u0000 \u0000","PeriodicalId":292404,"journal":{"name":"Jurnal Komtika (Komputasi dan Informatika)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114568279","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 : 2021-07-15DOI: 10.31603/KOMTIKA.V5I1.5189
H. Setiawan, Ema Utami, Sudarmawan Sudarmawan
The World Health Organization (WHO) COVID-19 is an infectious disease caused by the Coronavirus which originally came from an outbreak in the city of Wuhan, China in December 2019 which later became a pandemic that occurred in many countries around the world. This disease has caused the government to give a regional lockdown status to give students the status of "at home" for students to enforce online or online lectures, this has caused various sentiments given by students in responding to online lectures via social media twitter. For sentiment analysis, the researcher applies the nave Bayes algorithm and support vector machine (SVM) with the performance results obtained on the Bayes algorithm with an accuracy of 81.20%, time 9.00 seconds, recall 79.60% and precision 79.40% while for the SVM algorithm get an accuracy value of 85%, time 31.60 seconds, recall 84% and precision 83.60%, the performance results are obtained in the 1st iteration for nave Bayes and the 423th iteration for the SVM algorithm
{"title":"Analisis Sentimen Twitter Kuliah Online Pasca Covid-19 Menggunakan Algoritma Support Vector Machine dan Naive Bayes","authors":"H. Setiawan, Ema Utami, Sudarmawan Sudarmawan","doi":"10.31603/KOMTIKA.V5I1.5189","DOIUrl":"https://doi.org/10.31603/KOMTIKA.V5I1.5189","url":null,"abstract":"\u0000 \u0000 \u0000The World Health Organization (WHO) COVID-19 is an infectious disease caused by the Coronavirus which originally came from an outbreak in the city of Wuhan, China in December 2019 which later became a pandemic that occurred in many countries around the world. This disease has caused the government to give a regional lockdown status to give students the status of \"at home\" for students to enforce online or online lectures, this has caused various sentiments given by students in responding to online lectures via social media twitter. For sentiment analysis, the researcher applies the nave Bayes algorithm and support vector machine (SVM) with the performance results obtained on the Bayes algorithm with an accuracy of 81.20%, time 9.00 seconds, recall 79.60% and precision 79.40% while for the SVM algorithm get an accuracy value of 85%, time 31.60 seconds, recall 84% and precision 83.60%, the performance results are obtained in the 1st iteration for nave Bayes and the 423th iteration for the SVM algorithm \u0000 \u0000 \u0000 ","PeriodicalId":292404,"journal":{"name":"Jurnal Komtika (Komputasi dan Informatika)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130380100","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 : 2021-07-15DOI: 10.31603/komtika.v5i1.5134
Dedy Hariyadi, Faulinda Ely Nastiti
Peretas saat ini tidak hanya menyerang instansi pemerintah seperti pada tahun 2019 melainkan sudah melakukan serangan ke instansi pendidikan. Hal ini sesuai dengan pantauan dan identifikasi Badan Siber dan Sandi Negara bahwa instansi pendidikan telah diserang sebanyak 38% pada tahun 2020. Sebagai wujud tindakan preventif terkait dengan serangan siber pada instansi pendidikan perlu dilakukan sebuah tindakan analisis keamanan informasi terhadap sistem-sistem yang terpasang. Pada artikel ini diusulkan tahapan teknis melakukan analisis keamanan informasi menggunakan perangkat lunak dengan lisensi Free Open Source Software, yaitu Sudomy dan OWASP ZAP. Menggunakan kedua perangkat lunak tersebut didapatkan hasil analisis potensi-potensi celah keamanan pada sistem informasi yang terpasang pada Universitas Duta Bangsa.
{"title":"Analisis Keamanan Sistem Informasi Menggunakan Sudomy dan OWASP ZAP di Universitas Duta Bangsa Surakarta","authors":"Dedy Hariyadi, Faulinda Ely Nastiti","doi":"10.31603/komtika.v5i1.5134","DOIUrl":"https://doi.org/10.31603/komtika.v5i1.5134","url":null,"abstract":"Peretas saat ini tidak hanya menyerang instansi pemerintah seperti pada tahun 2019 melainkan sudah melakukan serangan ke instansi pendidikan. Hal ini sesuai dengan pantauan dan identifikasi Badan Siber dan Sandi Negara bahwa instansi pendidikan telah diserang sebanyak 38% pada tahun 2020. Sebagai wujud tindakan preventif terkait dengan serangan siber pada instansi pendidikan perlu dilakukan sebuah tindakan analisis keamanan informasi terhadap sistem-sistem yang terpasang. Pada artikel ini diusulkan tahapan teknis melakukan analisis keamanan informasi menggunakan perangkat lunak dengan lisensi Free Open Source Software, yaitu Sudomy dan OWASP ZAP. Menggunakan kedua perangkat lunak tersebut didapatkan hasil analisis potensi-potensi celah keamanan pada sistem informasi yang terpasang pada Universitas Duta Bangsa.","PeriodicalId":292404,"journal":{"name":"Jurnal Komtika (Komputasi dan Informatika)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124445783","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 : 2021-07-15DOI: 10.31603/komtika.v5i1.5188
Arif Rahman, Ema Utami, S. Sudarmawan
Sentiment analysis is a science to extract text to get someone's emotions for that. The benefits of sentiment analysis have many benefits, one of which is to see whether or not customers have a good response to the product and this can be an input for the development of the product's business in the future. The weakness of previous studies in research sentiment analysis is that the authors conduct research to improve the results of previous studies using the naïve Bayes algorithm that is optimized with a genetic algorithm. From the results of the research that has been done, the average value in this study is on average better than previous studies, no applications have been identified as underfitting or overfitting and finally the naïve Bayes algorithm that has been optimized by the genetic algorithm can be a classification solution for sentiment analysis.
{"title":"Sentimen Analisis Terhadap Aplikasi pada Google Playstore Menggunakan Algoritma Naïve Bayes dan Algoritma Genetika","authors":"Arif Rahman, Ema Utami, S. Sudarmawan","doi":"10.31603/komtika.v5i1.5188","DOIUrl":"https://doi.org/10.31603/komtika.v5i1.5188","url":null,"abstract":"Sentiment analysis is a science to extract text to get someone's emotions for that. The benefits of sentiment analysis have many benefits, one of which is to see whether or not customers have a good response to the product and this can be an input for the development of the product's business in the future. The weakness of previous studies in research sentiment analysis is that the authors conduct research to improve the results of previous studies using the naïve Bayes algorithm that is optimized with a genetic algorithm. From the results of the research that has been done, the average value in this study is on average better than previous studies, no applications have been identified as underfitting or overfitting and finally the naïve Bayes algorithm that has been optimized by the genetic algorithm can be a classification solution for sentiment analysis.","PeriodicalId":292404,"journal":{"name":"Jurnal Komtika (Komputasi dan Informatika)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115505185","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 : 2021-07-15DOI: 10.31603/komtika.v5i1.5139
Herlawati Herlawati, Fata Nidaul Khasanah, Prima Dina Atika, Rafika Sari, Rahmadya Trias Handayanto
Land use/cover greatly affect the quality of an area. Therefore, many regional planners need assistance byother fields, such as geoinformatics, computer science, environment, and others. Although prediction and forecasting have been widely studied, in regardto real conditions (geospatial)itstill needmoredevelopment, especially thoseinvolving a combination of regional types, such as urban and suburban areas. This study uses a remote sensing base and geographic information system in predicting land in the city and district of Bekasi, West Java, Indonesia. With two scenarios compared (business as usual and vegetation conservation), the model that has been created and validated (with an AUC accuracy result of 0.828) is used to predict land use change until 2030. Scenarios with vegetation conservation are able to keep green areas to switch to land types others, such as buildings and industry
{"title":"Prediksi Perubahan Penggunaan Lahan dan Pola Berdasarkan Citra Landsat Multi Waktu dengan Land Change Modeler (LCM)","authors":"Herlawati Herlawati, Fata Nidaul Khasanah, Prima Dina Atika, Rafika Sari, Rahmadya Trias Handayanto","doi":"10.31603/komtika.v5i1.5139","DOIUrl":"https://doi.org/10.31603/komtika.v5i1.5139","url":null,"abstract":"Land use/cover greatly affect the quality of an area. Therefore, many regional planners need assistance byother fields, such as geoinformatics, computer science, environment, and others. Although prediction and forecasting have been widely studied, in regardto real conditions (geospatial)itstill needmoredevelopment, especially thoseinvolving a combination of regional types, such as urban and suburban areas. This study uses a remote sensing base and geographic information system in predicting land in the city and district of Bekasi, West Java, Indonesia. With two scenarios compared (business as usual and vegetation conservation), the model that has been created and validated (with an AUC accuracy result of 0.828) is used to predict land use change until 2030. Scenarios with vegetation conservation are able to keep green areas to switch to land types others, such as buildings and industry","PeriodicalId":292404,"journal":{"name":"Jurnal Komtika (Komputasi dan Informatika)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115527594","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 : 2021-07-15DOI: 10.31603/KOMTIKA.V5I1.5140
Maulidya Dwi Nurmalasari, K. Kusrini, S. Sudarmawan
Diabetes is caused by a deficiency of the hormone insulin, which is secreted by the pancreas to lower blood sugar levels. The factors that trigger the occurrence of diabetes are derived from various factors such as a combination of genetic and environmental factors. The phenomenon of the emergence of various beverage brand outlets can be one of the triggers for blood sugar levels in humans. Normal blood sugar levels in the body range from 70-130 mg/dL before eating, less than 180 mg/dL two hours after eating, less than 100 mg/dL after not eating or surviving for eight hours, and 100-140 mg/dL at bedtime. This research aims to determine which algorithm is suitable for building knowledge about diabetes using the Naïve Bayes and K-Nearest Neighbor (KNN) algorithm. The accuracy results from Naïve Bayes are 85.60% and K- Nearest Neighbor of 91.61%. The results showed that K-Nearest Neighbor proved to have the best accuracy.
{"title":"Komparasi Algoritma Naive Bayes dan K-Nearest Neighbor untuk Membangun Pengetahuan Diagnosa Penyakit Diabetes","authors":"Maulidya Dwi Nurmalasari, K. Kusrini, S. Sudarmawan","doi":"10.31603/KOMTIKA.V5I1.5140","DOIUrl":"https://doi.org/10.31603/KOMTIKA.V5I1.5140","url":null,"abstract":"Diabetes is caused by a deficiency of the hormone insulin, which is secreted by the pancreas to lower blood sugar levels. The factors that trigger the occurrence of diabetes are derived from various factors such as a combination of genetic and environmental factors. The phenomenon of the emergence of various beverage brand outlets can be one of the triggers for blood sugar levels in humans. Normal blood sugar levels in the body range from 70-130 mg/dL before eating, less than 180 mg/dL two hours after eating, less than 100 mg/dL after not eating or surviving for eight hours, and 100-140 mg/dL at bedtime. This research aims to determine which algorithm is suitable for building knowledge about diabetes using the Naïve Bayes and K-Nearest Neighbor (KNN) algorithm. The accuracy results from Naïve Bayes are 85.60% and K- Nearest Neighbor of 91.61%. The results showed that K-Nearest Neighbor proved to have the best accuracy.","PeriodicalId":292404,"journal":{"name":"Jurnal Komtika (Komputasi dan Informatika)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127285905","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}
The number of smartphone and internet users in Indonesia is currently very large. This is become the basis for the use and development of mobile-based applications for the advantage of education. However, not all High Schools in Indonesia have a mobile-based system. Another problem is that most of them still use conventional methods in implementing teaching and learning activities, and reporting learning outcomes to parents. This is also still the case at SMA Negeri 1 Pangkalanbaru, Bangka Tengah. This problem is what we want to raise and solve by developing applications that can solve these problems. The research was developed using the Prototype method which consists of stages of Data Collection, Rapid Planning, Prototype Design, and Prototype Testing. By using the UML tool, results are obtained in the form of parents who can monitor grades, school information, and school announcements. In addition, students can take attendance online, register for extracurricular activities, and view announcements. From the testing results it is known that the system performance is running well as it should.
目前，印尼的智能手机和互联网用户数量非常庞大。这就成为了使用和开发基于移动的教育应用程序的基础。然而，并不是印尼所有的高中都有手机系统。另一个问题是，大多数学校在实施教学活动和向家长报告学习成果方面仍然使用传统方法。邦加登加的SMA Negeri 1 Pangkalanbaru也是如此。我们希望通过开发能够解决这些问题的应用程序来提出并解决这个问题。本研究采用原型方法进行，包括数据收集、快速规划、原型设计和原型测试四个阶段。通过使用UML工具，可以以家长的形式获得结果，家长可以监视成绩、学校信息和学校通知。此外，学生可以在线考勤，注册课外活动，并查看公告。从测试结果可知，系统性能运行良好。
{"title":"Implementasi Aplikasi Monitoring Nilai dan Kegiatan Siswa Berbasis Android dengan Metode Prototype","authors":"Fransiskus Panca Juniawan, Dwi Yuny Sylfania, Rendy Rian Chrisna Putra, Rahmat Sulaiman","doi":"10.31603/KOMTIKA.V5I1.5119","DOIUrl":"https://doi.org/10.31603/KOMTIKA.V5I1.5119","url":null,"abstract":"The number of smartphone and internet users in Indonesia is currently very large. This is become the basis for the use and development of mobile-based applications for the advantage of education. However, not all High Schools in Indonesia have a mobile-based system. Another problem is that most of them still use conventional methods in implementing teaching and learning activities, and reporting learning outcomes to parents. This is also still the case at SMA Negeri 1 Pangkalanbaru, Bangka Tengah. This problem is what we want to raise and solve by developing applications that can solve these problems. The research was developed using the Prototype method which consists of stages of Data Collection, Rapid Planning, Prototype Design, and Prototype Testing. By using the UML tool, results are obtained in the form of parents who can monitor grades, school information, and school announcements. In addition, students can take attendance online, register for extracurricular activities, and view announcements. From the testing results it is known that the system performance is running well as it should.","PeriodicalId":292404,"journal":{"name":"Jurnal Komtika (Komputasi dan Informatika)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123715723","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 : 2021-07-15DOI: 10.31603/komtika.v5i1.4605
Yuslena Sari, Muhammad Alkaff, M. Arif Rahman
Cassava or better known as cassava is one of the staples of rice which is popular in Indonesia. Cassava plants can flourish in almost all regions of Indonesia. However, cassava is a plant that is susceptible to plant disease, which attacks the disease resulting in a decrease in the amount of productivity of tubers produced by cassava plants. The application of identifying cassava disease based on leaf image is expected to be useful as a support for cassava farming in easily detecting cassava disease, so that it can be dealt with more quickly. This study uses the Gray Level Co-occurrence Matrix (GLCM) method as an extraction feature and the Probabilistic Neural Network (PNN) method for identification processes. Based on the results of tests on 6 types of cassava leaf images, obtained an accuracy of 83.33%.
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