Ahmad Zaenal Arifin
@unirow.ac.id
Mathematics
Universitas PGRI Ronggolawe
RESEARCH INTERESTS
mathematics
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Supiana Dian Nurtjahyani, Dwi oktafitria, Sriwulan, Ahmad Zaenal Arifin, Eko Purnomo, Aris Santoso, and Ali Mustofa
IOP Publishing
The limestone mining area is a karst area that has an important ecological function as a water conservation area. After the mining process, the ex-mining area becomes critical land that is poor in nutrients, decreases soil microbial diversity, increases soil pH and temperature. This study aimed to examine the use of conventional and block compost based on plant height parameters and stem diameter. Block compost was made using the bokashi method with the ingredient of teak leaf litter (Tectona grandis). The composition of leaf litter (30%), manure (40%), and sawdust (30%). Block compost is made by adding adhesive and it is made using a pressing device. Block compost application on plants is very effective compared without block compost. The average plant height with block compost is 163.2 cm, while without block compost is 27 cm. the average of stem growth diameter of plants with block compost of 1.61 cm, while without block compost was 0.71 cm. This shows that block compost is a solution in mining land reclamation.
D C R Novitasari, B D Supatmanto, M F Rozi, Hermansah, Y Farida, Rr D N Setyowati, Ilham, R Junaidi, A Z Arifin, and A R Fatoni
IOP Publishing
Abstract The wider sea area causes greater evaporation of water in Indonesia. In addition, these conditions have an impact on the season that Indonesia has. Indonesia’s high rainfall disrupts human activities. As a result, it is very important to detect cumulonimbus clouds using satellite imagery. The satellite image used is intended to be taken two values of the characteristics possessed. Characteristics taken are average cover and average cloud temperature. Previous studies predicting rain were only done using observational data taken at the height of 10 meters. This research predicts using satellite imagery that represents the cloud peak temperature value. Furthermore, the classification of data is done using backpropagation. The results of the classification process using backpropagation obtained the best results on the distribution of 80% training data and 20% testing data, with the activation function logging in the hidden layer and that the output layer. The results obtained indicate the accuration rate of 88,283%.
Dian Candra Rini Novitasari, Ahmad Hanif Asyhar, Muhammad Thohir, Ahmad Zaenal Arifin, Hanimatim Mu'jizah, and Ahmad Zoebad Foeady
IEEE
Early identification of cervical cancer is still being carried out intensively by the World Health Organization (WHO). Some programs for early identification of cervical cancer are carried out in several ways such as pap smears, IVA test, and colposcopy. Examinations such as pap smears require laboratories to identify cancer from a network of cervical cells. IVA test is done using acetic acid fluid, while colposcopy is done by identifying the condition of the vulva in the vagina and recorded into colposcopy photo data. From the colposcopy photos can be identified automatically using Computer Aided Diagnosis (CAD), by utilizing image processing and classifying them using artificial intelligence methods. In this research, early identification of cervical cancer based on cancer stage using texture information on colposcopy images looks at pixel neighbor information using the Gray Level Co-occurrence Matrix (GLCM) method and classifies it using the Kernel Extreme Learning Machine (KELM) method which is a development of the method ELM by adding a kernel to the system. The results showed that using a linear kernel resulted in an error of 78.5%, a polynomial kernel of 87.5% and the best accuracy achievement of 95% using a gaussian kernel with the best neighborhood angle was 45°. This shows that the data is more likely to have a Gaussian distribution with the best reading of the GLCM, using diagonal pixel readings.
Muhammad Thohir, Ahmad Zoebad Foeady, Dian Candra Rini Novitasari, Ahmad Zaenal Arifin, Bunga Yuwa Phiadelvira, and Ahmad Hanif Asyhar
IEEE
cervical cancer is the second deadliest disease for women. To reduce the number of deaths caused by this disease, it is necessary that there is prevention by early detection of cancer. The method used to identify the presence of cervical cancer is to make visual observations that produce image data. However, a visual observation also has weaknesses, so it needs to be done computer-based observation to facilitate early detection. In this study, the computer-based observation method used is preprocessing, followed by a feature extraction process using the Gray Level Co-occurrence Matrix (GLCM) and Support Vector Machine (SVM) as a classification method. The best SVM classification results are using the polynomial kernel and GLCM feature extraction with an angle of 450. The accuracy rate obtained is 90%.
Ahmad Hanif Asyhar, Ahmad Zoebad Foeady, Muhammad Thohir, Ahmad Zaenal Arifin, Dina Zatusiva Haq, and Dian Candra Rini Novitasari
IEEE
Cervical cancer ranks second highest cause of death in women in various worlds. This happens because most women are not aware of the symptoms of cervical cancer in the early stages. To reduce the number of deaths caused by cervical cancer by identifying the symptoms of cervical cancer in the early stages. Identification of early symptoms of cervical cancer can be made with colposcopy tests that produce colposcopy image data. Colposcopy test is a method to identify cervical cancer based on images of the cervix with an enlargement of up to 10 times and it gets accurate results. Accuracy results from colposcopy tests can be improved by using computational calculations. Besides being used to improve accuracy, computational calculations also make it easier for people to detect cervical cancer. In this study, computational calculations are performed by implementing the Long Short-Term Memory (LSTM) algorithm to identify cervical cancer using colposcopy data. The implementation of the LSTM algorithm in the classification process of colposcopy data with an optimal number of hidden layers of 150 hidden layers results in an accuracy rate of 66%.
Dian Candra Rini Novitasari, Ahmad Zoebad Foeady, Muhammad Thohir, Ahmad Zaenal Arifin, Khoirun Niam, and Ahmad Hanif Asyhar
IEEE
Cervical cancer is one of the diseases with the highest mortality rate. In the world, cervical cancer is ranked as the fourth most dangerous disease. Based on these problems, this paper can be an alternative to help medical authorities in detecting cervical cancer with the help of the Computer-Aided Diagnosis (CAD) System. CAD System used has two processes, such as preprocessing and classification. Preprocessing is useful to improve the image so that it is easier to do the process of identifying features. Preprocessing used is greyscale, histogram equalization, and median filter. Preprocessing results will be formed into a vector matrix using the reshaping process. The final step is the process of classifying data using the Deep Belief Network method. The best accuracy results obtained from the identification process of cervical cancer using the DBN method is 84%. Based on the results of accuracy, is expected to help reduce the number of deaths from cervical cancer with early detection.
F D Lestari, M Hafiyusholeh, A H Asyhar, W D Utami, and A Z Arifin
IOP Publishing
Ahmad Hanif Asyhar, Yuniar Farida, Nurissaidah Ulinnuha, Dian Candra Rini Novitasari, and Ahmad Zaenal Arifin
ASTES Journal
A R T I C L E I N F O A B S T R A C T Article history: Received: 08 January 2019 Accepted: 08 March 2019 Online: 20 March 2019 Mass media plays an important role in leading public opinion, including in the election of regional head candidates. The tendency of mass media coverage can be used as a parameter to measure the strength of each regional head candidate. To analyze the tendency of media opinion, sentiment analysis is needed. In this study, text mining techniques were used to analyze opinion sentiments of a regional head election in East Java from the national media perspective. The researcher used the Support Vector Machine algorithm to build a sentiment analysis model. News documents about candidates for the regional head in East Java 2018 were taken from national mass media samples, namely JPNN, Kompas, Detik and Republika. From the test results, the model built on Khoffifah's data as a candidate for regional head number one has a value of precision, recall and AUC of 0.927, 0.931 and 0.902, respectively. Furthermore, the model built on Gus Ipul's data as a candidate for regional head number two has a value of precision, recall, and AUC of 0.940, 0.948 and 0.890 respectively. The models built on both data shows good performance with accurate estimation results. Based on the data obtained, the national media tends to have alignments to the regional head candidate number two namely Gus Ipul.