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INDONESIA
Jurnal Informatika
Published by Universitas Ahmad Dahlan
ISSN : 19780524     EISSN : 25286374     DOI : 10.26555
Core Subject : Science,
Computer Science & IT
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol 16, No 2 (2022): May 2022" : 5 Documents clear
masked face identification using the convolutional neural network method Daru Thobrani Furqon; Murinto Murinto
Jurnal Informatika Vol 16, No 2 (2022): May 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v16i2.a25381

Abstract

            In times of a pandemic like this, masks are part of the main needs in daily activities when outside the home. Because masks can help us avoid the Covid-19 virus, it often happens among people when doing activities outside the home that they forget to wear masks, therefore the level of public awareness of the importance of wearing masks is decreasing.  This study aims to create a system that can classify people who wear masks and do not wear masks as an evaluation material for the level of public awareness of the importance of wearing masks. The total data used in this study were 600 data samples which were divided into two, namely 300 data samples wearing masks and 300 data samples not wearing masks. The CNN architecture in this study is the same as the CNN architecture in general, the  difference is the depth level of the convolution layer and pooling which consists of 5  convolution layers, 5 max pooling, and finally, 2 layers dense In the training process, it gets  the highest accuracy rate of 98%, while in the validation process it gets the highest level of  accuracy at 95%. Therefore, the results of these two processes show that the application of deep earning by utilizing the convolutional neural network can classify objects that wear masks and do not wear masks properly. The results of testing the research dataset are quite maximal by using 40 new dataset testing data to test the convolutional neural network that has been created by the researchers to get an overall accuracy result of 97.5%.
Predictive Analytics on Product Sales at Heva Inc. Using K – Means Method Qurrota Nastiti Rizqita Aura Syifa; Murein Miksa Mardhia
Jurnal Informatika Vol 16, No 2 (2022): May 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v16i2.a24754

Abstract

Prediction is the process of estimating something that is most likely to happen in the future based on previous and current knowledge that is owned, with the goal of minimizing the error. Prediction allows people to recognize and then solve difficulties that are occurring or are expected to arise.This study began with preparation, literature review, data collection, and knowledge discovery in databases (KDD). One of the processes is data mining using the K – Means method, which is critical for obtaining the research's results and conclusions. This research also uses the RapidMiner application as a comparison of the results with the results obtained by python coding.By using 4 clusters, products were categorized into 4 labels, namely very good products, good products, bad products, and very bad products.  The research resulted in 11 products in the bad product category, 12 products in the good product category, 10 products in the very good category, and 18 products in the very good product category.  The very good product label was further clarified with visualization to show the best time to restock each recommended product.
Time series clustering of Malaysia Air quality time series data Mohd Aftar Abu Bakar; Fatin Nur Afiqah Suris; Noratiqah Mohd Ariff; Kamarulzaman Ibrahim; Tan Zhen Jie
Jurnal Informatika Vol 16, No 2 (2022): May 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v16i2.a25421

Abstract

Air quality is often associated with the area location and activities where air quality in cities is usually more polluted than in rural areas. This study aims to study the pattern of time series data from air quality stations by performing cluster analysis of air quality station based on the particulate matter 10 micrometres or less in diameter (PM10) and particulate matter 2.5 micrometres or less in diameter (PM2.5) time series data. The clusters obtained from the cluster analysis were compared with the station area category and station location. This study which uses air quality data obtained from the Department of Environment, Malaysia from 5 July 2017 until 30 June 2019, shows five types of air quality patterns in Malaysia. The results also show that none of the clusters is dominated by any station's category. Therefore, it is less appropriate to relate the air quality patterns and the station area category. However, the results show that air quality patterns were related to the station's location, where nearby stations have similar air quality patterns.
Rainfall prediction using artificial neural network with historical weather data as supporting parameters A H Pratomo; Budi Santosa; S P Tahalea; E T Paripurno; J D Peasetyo; Herlina Jayadianti; M F Pitayandanu
Jurnal Informatika Vol 16, No 2 (2022): May 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v16i2.a25422

Abstract

Changing climatic patterns are caused by changes in variables, such as rainfalland air temperature that occur continuously in the long term. Rainfall itself isinfluenced by several weather factors such as air humidity, wind speed, airpressure, and temperature. This study experimented to test a combination of9 additional weather parameters such as dew point, wind gusts, cloud cover,humidity, rainfall, air pressure, air temperature, wind direction, and windspeed to predict daily rainfall for one year using the main parameters of therainfall time series. Prediction is done using Artificial Neural Network(ANN). The ANN architecture used is to use 3 to 11 input parameters, 1hidden layer totaling 60 neurons with the ReLu activation function, and 1neuron in the output layer without an activation function. ANN withoutadditional weather parameters obtained an MSE of 0.01654, while predictionusing additional weather parameters obtained an MSE of 0.00884. So thecombination of rainfall time series parameters with additional weatherparameters is proven to provide a smaller MSE value
Modelling for changing transitive active imperative sentences to passive imperative sentences with algebraic structure approach Yuliana Shinta; Bahri Susila; Arnawa Imade
Jurnal Informatika Vol 16, No 2 (2022): May 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v16i2.a25423

Abstract

The active imperative sentences often tend to sound harsh. The sentence has a commanding meaning and ends with an exclamation mark. In the Indonesian language, to be more polite, the sentence uses the word politeness and a different sentence structure. These more polite imperative sentences are called passive imperative sentences. Changing an active imperative sentence to a passive imperative sentence can be done mathematically through several stages. These stages are determining the set of word, and the set of word types, using binary operations to obtain the rules for changing the pronoun as an object to subject, determining the rules for substituting active verbs into passive verbs, determining algebraic structures for an active imperative sentence, specifying a set of politeness words, specifying rules for passive imperative sentence, transformation an active imperative sentence into a passive imperative sentence. The change method produces a mathematical model p to construct the more polite imperative sentence.

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