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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Buana Informatika Jurnal Simantec IJIE (Indonesian Journal of Informatics Education) Jurnal ULTIMATICS Journal of Informatics and Vocational Education
Febri Liantoni
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Other

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Journal : Jurnal ULTIMATICS

 1 
Klasifikasi Daun Dengan Perbaikan Fitur Citra Menggunakan Metode K-Nearest Neighbor Febri Liantoni
Ultimatics : Jurnal Teknik Informatika Vol 7 No 2 (2015): Ultimatics: Jurnal Ilmu Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (671.182 KB) | DOI: 10.31937/ti.v7i2.356

Abstract

Plants are the most important part in life on earth as oxygen supplier to breathe, groceries, fuel, medicine and more. Plants can be classified based on its leaves shape. Classification process is required well data extraction feature, so it needs fixing feature process at pre-processing level. Combining median filter and image erosion is used for fixing feature process. Whereas for feature extraction is used invariant moment method. In this research, it is used leaves classification based on leaves edge shape. K-Nearest Neighbor Method (KNN) is used for leaves classification process. KNN method is chosen because this method is known rapid in training data, effective for large training data, simple and easy to learn. Testing the result of leaves classification from image which is on dataset has been built to get accuracy value about 86,67%. Index Terms—Classification, Median Filter, Invariant Moment, K-Nearest Neighbor.
Perbandingan Metode Single Exponential Smoothing dan Metode Holt untuk Prediksi Kasus COVID-19 di Indonesia Nur Hijrah As Salam Al Ihsan; Hanifah Hanun Dzakiyah; Febri Liantoni
Ultimatics : Jurnal Teknik Informatika Vol 12 No 2 (2020): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v12i2.1689

Abstract

Coronavirus disease (COVID-19) was first discovered in December 2019 in Wuhan, China, and spread so quickly into a pandemic. This outbreak has spread to 24 other countries, including Indonesia. Its spread is very fast, so a co-19 prediction study is needed to be able to make the right policy. To be able to predict the number of COVID-19 cases can be done with the Forecasting Technique. The purpose of this study is to forecast and compare Single Exponential Smoothing and Double Exponential Smoothing ¬ against the number of COVID-19 cases in Indonesia. The results of this study can be used as consideration for policymaking in dealing with the spread of COVID-19. Distribution predictions are based on data released by the Indonesian National Disaster Management Agency (BNPB) in the first 100 days of COVID-19 deployment. The results of this study are the Double Exponential Smoothing method is more accurate than the Single Exponential Smoothing method because the forecasting results show an increase from the previous data. And the percentage of errors (MAPE) obtained is significantly smaller.
Analisis Clustering Pengelompokan Penjualan Paket Data Menggunakan Metode K-Means Dimas Galang Ramadhan; Indri Prihatini; Febri Liantoni
Ultimatics : Jurnal Teknik Informatika Vol 13 No 1 (2021): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v13i1.1981

Abstract

At present with the COVID-19 pandemic situation that requires all activities based in the network, starting from work, college, school, everything is based on the network. Certain provider users will experience excessive data plan usage. This also has an effect on a counter that sells data packages, which must provide several data package services in accordance with current conditions. This research was conducted to analyze the grouping of sales of data packages that are often purchased by customers in a counter by using the K-Means method. The K-Means method is used because the K-Means algorithm is not influenced by the order of the objects used, this is proven when the writer tries to determine the initial cluster center randomly from one of the objects in the first calculation. sales of data packages at a counter. Variables used include Price, Active period, and number of data packages. The K-Means Cluster Analysis algorithm is basically applied to the problem of understanding consumer needs, identifying the types of data package products that are often purchased. The K-Means algorithm can be used to describe the characteristics of each group by summarizing a large number of objects so that it is easier.
Co-Authors Chastine Fatichah Dimas Galang Ramadhan Dwi Maryono Dwi Maryono Giovanni Anggiesta Putri Hanifah Hanun Dzakiyah Hendro Nugroho Indri Prihatini Kartika Candra Kirana Luky Agus Hermanto Muhammad Yusuf Farizi Nanik Suciati Nur Hijrah As Salam Al Ihsan Tri Hadiah Muliawati