Abstrak - Wajah adalah bagian tubuh terpenting yang harus selalu dijaga dan dirawat. Sehingga banyak cara merawat wajah yang dilakukan untuk mendapatkan wajah putih, bersih, dan terbebas dari jerawat. Pengetahuan analisa kulit wajah sangat diperlukan untuk menentukan jenis kulit serta produk perawatan yang sesuai dengan jenis kulit. Sistem pakar merupakan sistem yang mengadopsi pengetahuan dari seorang pakar dan dapat berperan layaknya seorang pakar dalam menangani proses konsultasi. Tujuan dari penelitian ini yaitu merancang sebuah sistem pakar yang dapat menentukan jenis kulit wajah berbasis android dengan menerapkan metode certainty factor dalam proses penghitungan derajat tingkat keyakinan. Dengan aplikasi sistem pakar berbasis android ini konsultan dapat melakukan pemeriksaan dengan mudah, dan pendiagnosaan sekaligus solusi dapat terselesaikan secara cepat dan tepat berdasarkan data yang diinputkan.Katakunci: Android, Certainty Factor, Perancangan Sistem PakarAbstract - Face is the most important body part that should always be kept and cared for. So many ways of taking care of the face are done to get the face of white, clean, and free of acne. The knowledge of facial skin analysis is necessary to determine the skin type as well as care products that suit the skin type. The expert system is a system that adopts the knowledge of an expert and can play like an expert in handling the consultation process. The purpose of this research is to design an expert system that can determine the type of Android-based facial skin by applying the certainty factor method in the process of counting degrees of confidence. With this Android-based expert System Application consultants can perform inspections with ease, and the solution can be easily resolved quickly and precisely based on the data being inputed.Keywords: Android, Certainty Factor, Expert System Design
Seiring dengan perkembangan zaman, teknologi berkembang dengan pesat saat ini. Dengan perkembangan teknologi sekarang ini memudahkan semua orang mengakses apa saja. Banyak teknologi yang sudah ditemukan salah satunya adalah pengolahan citra digital, Identifikasi pada sebuah citra sudah lama dikembangkan salah satunya dengan membedakan tekstur pada citra tersebut. Tekstur citra dapat dibedakan oleh kerapatan, keseragaman, kekasaran dan keteraturan dari citra yang diteliti. Agriculture saat ini sedang ramai di bahas khususnya di indonesia, banyak sekali penelitian yang di lakukan dalam sektor pertanian guna memajukan sektor pertanian itu sendiri. Dalam penelitian kali ini yaitu ekstraksi fitur menggunakan Hu-moment, Haralick dan Histogram dan klasifikasi menggunakan algoritma Random Forest. Peneliti mencoba mengklasifikasi buah-buahan segar atau busuk, dengan algoritma yang digunakan yaitu algoritma Random Forest, penelitian ini mendapatkan akurasi yang sangat tinggi yakni 99.6% sangat baik sekali. Namun guna memperbaharui penelitian bisa mencoba beberapa fitur dan algorithma yang berbeda agar mendapatkan perbandingan atau hasil yang lebih maksimal.Kata kunci: ekstraksi fitur, Hu-moment Haralick dan Histogram, Random Forest.
The pandemic that occurred in Indonesia has not yet subsided and far from under control. Indonesian Ministry of Health is most appropriate person to responsible for providing an explanation of actual situation and extent to which state has handled it. However, he has rarely appeared in public lately to explain about handling of Covid-19 pandemic. In response, many people are pros and cons come to give their opinions and feedback. The increasing use of internet during pandemic, especially on social media, where one of them is Twitter, which is a means of expressing opinions. Posting tweets is a community habit to assess or respond to events, as well as represent public's response to an event, especially Ministry of Health steps and policies in handling and breaking chain of Covid-19 pandemic.The tweet posts were taken only in Indonesian-language and also related to performance of Government, especially Ministry of Health. After that, a label is given so that sentiment of tweets is known. To test results of these sentiments, an algorithm is used by comparing two methods of Support Vector Machine (SVM) and Naïve Bayes (NB). Validation was carried out using k-Fold Cross Validation to obtain an accuracy value. The results show that accuracy value for NB algorithm is 66.45% and SVM algorithm has a greater accuracy value of 72.57%. So it can be seen that SVM algorithm managed to get the best accuracy value in classifying positive comments and negative comments related to sentiment analysis towards Ministry of Health. Keywords—Support Vector Machine, Naïve Bayes, Analisis sentimen, K-Fold Cross Validation
Twitter is one of the social media with the number of users who reach millions of users. The number of Twitter users in 2019 increased by 17 percent in 2018 to 145 million users with a variety of good both positive and bad. The negative impacts that occur such as the spread of status, images, and videos that affect pornography especially among freedom groups. Homosexuals are sexually oriented people who like the same sex that occurs in men, the rejection often experienced by men makes one of the reasons intellectuals use Twitter social media to show their personal relationships, open to each other, socializing with same sex, looking for conversation, to become a place to find a partner. The purpose of this study is to determine the positive and negative sentiments to determine the level of accuracy of intellectual pornography tweets in Indonesia from data taken from Twitter tweets by using the TF-IDF and k-NN methods. The results of this study get an accuracy value of 88.25% containing pornography and the remaining 11.75% not containing pornography will contain news, news, and other information.Keywords: homosexual, sentiment analysis, twitterAbstrak: Twitter merupakan salah satu media sosial dengan jumlah pengguna mencapai jutaan pengguna. Jumlah pengguna Twit-ter pada tahun 2019 dicatat meningkat 17 persendari tahun 2018 menjadi 145 juta pengguna dengan berbagai dampak baik dampak positif maupun dampak negatif. Dampak negatif yang ditimbulkannya seperti penyebaran status, gambar, dan video yang bersifat pornografi khsusunya di kalangan kaum homoseksual. Homoseksual merupakan orang yang berorientasi seksual sebagai penyuka sesama jenis yang terjadi pada kaum pria, Penolakan yang sering dialami kaum homoseksual men-jadikan salah satu alasan kaum homoseksual menggunakan media sosial Twitter untuk menunjukkan identitas diri mereka, saling terbuka, bersosialisasi dengan sesama jenis, mencari penghasilan, hingga menjadi ajang pencarian pasangan. Tujuan dari penelitian ini adalah untuk mengetahui sentimen positif dan negatif untuk mengetahui tingkat akurasi terhadap tweet pornografi kaum homoseksual di Indonesia dari data yang diambil dari tweet Twitter dengan menggunakan metode TF-IDF dan k-NN. Hasil penelitian ini mendapatkan nilai accuracy sebesar 88,25% mengandung unsur pornografi dan sisanya sebesar 11,75 tidak mengandung unsur pornografi akan tetapi berisi iklan, berita, dan informasi lainnya.Kata kunci: homoseksual, sentimen analisis, twitter
In the competition of the business world today, we are required to always develop business in order to always be successful in competition. Fachry PropertyLand is one of the business fields engaged in the sale of homes. Everywhere this shop must meet the needs of customers who are currently trending. On Land Fachry Property Around the issue that always appears regarding sales. Where many employees do not meet their sales targets. Based on this, it is expected to facilitate the Land Fachry Property in assessing the appropriateness of its employees in determining employees who have met the target, has not met the target and does not meet the target, in the grouping process, the grouping method will be used using the K-Me Clustering Algorithm as a method of manual replacement and in its implementation the Data Mining software uses RapidMiner Studio version 9.2. With the application of Rapid Miner Studio, it is expected that the owner of Fachry Propertyland can see the results of the grouping that meets the target, does not meet the target and does not meet the target. It is expected that the owner of Fachry Propertyland can take action on these employees.
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