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Deep Learning Jaringan Saraf Tiruan Untuk Pemecahan Masalah Deteksi Penyakit Daun Apel Sutriawan Sutriawan; Ahmad Zainul Fanani; Farrikh Alzami; Ruri Suko Basuki
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) Vol 11, No 1 (2023): Jurnal TIKomSiN, Vol. 11, No. 1, April 2023
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/tikomsin.v11i1.729

Diseases on apple leaves are becoming a major issue for apple growers since they can cause the crop to fail. Due to the diversity of diseases that can affect apple leaves, it can be challenging for farmers to determine the cause of leaf damage. The purpose of this research is to evaluate a convolutional neural network (CNN) method for its potential use in solving the problem of apple leaf disease identification. Four types of illness are dealt with: normal, multi-illness, rusty, and scabby. Many methods, such as data preparation and a preset VGG-16 artificial neural network (CNN) architecture, are recommended for use in the deep artificial neural network processing method. The most precise outcomes occurred when the beta parameter value was set to 2 = 0.999 at Ephoch to 85/100 with an accuracy of 0.7582, and when the epsilon parameter value was set to 1e-07 at Ephoch to 32/100 with an accuracy of 0.7582 with the best accuracy.

COMPARISON OF SIFT AND ORB METHODS IN IDENTIFYING THE FACE OF BUDDHA STATUE Linda Marlinda; Fikri Budiman; Ruri Suko Basuki; Ahmad Zainul Fanani
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 8 No. 2 (2023): JITK Issue February 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v8i2.4086

The statue is part of the heritage facial recognition process which is immobile and artistically stylized. Identifying the similarities between the statues can help provide an important reference for tourism in recognizing the faces of the statues which are different and have almost the same characteristics in every country, especially in Indonesia, among the facial recognition of the statues based on the condition, color, and shape of the face. The purpose of this study is to apply the original images that have characteristics, partially done manually to various types of transformations and calculate matching evaluation parameters such as the number of key points in the image, the level of matching, and the required execution time for each algorithm. To confirm the efficiency of the proposed method, experiments were carried out on private data sets obtained from statues under low light conditions and in different poses. The data was taken based on the image of the Buddha's face and matched with the facial image of the Buddha statue available in the database using comparisons resulting from data processing using the Sift and ORB methods with various types of transformations. The result will be seen in the image that is matched with the best algorithm for each type of distortion. The faces tested are images of the faces of the Buddha statues that are recognized, and photos of some of the original statues that were not saved due to unclear lighting and camera distance factors. The results show that the number of key points generated is the number of key points, the ORB method gives fewer results compared to the SIFT method and the average SIFT recognition and processing time shows better performance for an average of 100% at a SIFT matching rate of 2% with time 0.400285 and the ORB method is 1% for the time 0.400961

Antlion Optimizer Algorithm Modification for Initial Centroid Determination in K-means Algorithm Nanang Lestio Wibowo; Moch Arief Soeleman; Ahmad Zainul Fanani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.4997

Clustering is a grouping of data used in data mining processing. K-means is one of the popular clustering algorithms, is easy to use, and is fast in clustering data. The K-means method groups the data based on k distances and randomly determines the initial centroid as a reference for processing. Careless selection of centroids can result in poor clustering processes and local optima. One of the improvements in determining the initial centroid on the k-means method is to use the optimization method to determine the initial centroid. The modified Antlion Optimizer (ALO) method is used to improve poor clustering in the initial centroid determination and as an alternative to determining the initial centroid in the k-means method for better clustering results. The results of the research on the use of the proposed method for determining the initial centroid provide an increase in clustering compared to the usual k-means and k-means++ methods. This is evidenced by the evaluation of the sum of intragroup distance (SICD) with UCI datasets, namely iris, wine, glass, ecoli, and cancer, in each method, the best SICD value was obtained in the proposed method. Then measuring the best SICD value for each method and dataset is measured by providing a ranking proving that the proposed method on the iris, wine, and cancer datasets gets the first rank, and on the ecoli and glass datasets the proposed method and the k-means++ method both get the first rank. From the average ranking value, the proposed method is ranked first, which provides evidence that the proposed method can improve the clustering results and can be an alternative method for determining the initial center of a cluster using the k-means method.

Peningkatan Algoritma C4.5 Berbasis PSO Pada Penyakit Kanker Payudara Rudi Nurcahyo; Ahmad Zainul Fanani; Affandy Affandy; Mochammad Ilham Aziz
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6841

Onenof the diseases innthe world that causes deathnin women isncancer. Cancernis a diseasencaused by uncontrolled enlargement of abnormal organs in the body. Cancer diagnosis is made using anthropometric data from routine blood analysis. The data used is the Breast Cancer Coimbra Data Set obtained from the UCI Machine Learning Repository. The C4.5 method is andecision treenalgorithm that is often used in the classification process. The selection of the right features, as well as the selectionnof the right method to overcome the class imbalance in the classification process cannimprove the performancenof the C4.5 algorithm. confusion matrix can benused in the Test to determine Classification accuracy. In this research, the application of PSO as a feature organization.

Optimasi K-means Clustering Dengan Menggunakan Particle Swarm Optimization Untuk Menentukan Jumlah Cluster Pada Kanker Serviks Indrawan Setiaji; Affandy Affandy; Ahmad Zainul Fanani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6292

Cervical cancer is one of the most common cancers among women in the world. It is most common in developing countries. Cervical cancer develops slowly in the body. Clustering is needed so that cervical cancer can be treated quickly. The K-means method was chosen because of its ability to group large amounts of data and fast computation time. The K-means method is also very easy to implement, flexible, and uses simple principles, which can be explained non-statistically. The many advantages that K-means has, also has weaknesses because it uses random clustering numbers and the results are not optimal. The difficulty in accurately determining the amount of clustering in the dataset. The K-means method cannot provide an optimal solution for determining the number of clustering, so it needs to be improved in order to obtain an optimal solution. PSO was chosen because it has several advantages, namely requiring few parameters, easy to implement, fast convergence, more efficient because it requires little computation and is simple. The results showed that the PSO - K-means method can prove to provide a significant contribution by directly obtaining optimum clustering results without having to do repeated experiments with a Silhouette Coefficient value of 0.83 and a Davies Bouldien Index value of 1.91.

Analisis Metode Smoote pada Klasifikasi Penyakit Jantung Berbasis Random Forest Tree Yulianto, Satria Pradana Rizki; Fanani, Ahmad Zainul; Affandy, Affandy; Aziz, Mochammad Ilham
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7712

Cardiovascular disease is the number one cause of death globally. Cardiovascular disease is a disease caused by impaired function of the heart and blood vessels. At present, there are many predictive tools that use machine learning as a basis, including predictions on heart disease in particular. There are many methods in machine learning to predict heart disease, as well as many parameters to look for to find the highest level of accuracy. This study, aims to obtain the best methods and parameters for the classification of heart disease.

Exploiting Silhouette Principle Component For Dimension Reduction In Breast Ultrasound Images Classification Kartikadarma, Etika; Fanani, Ahmad Zainul; Pujiono, Pujiono; Affandy, Affandy; Wulandari, Sari Ayu
International Journal of Artificial Intelligence Research Vol 8, No 1 (2024): June 2024
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1165

This paper proposes the use of the Dimensional Reduction method with the Silhouette Principle Component (SPC) Approach to improve the classification of breast ultrasound images. The PCA method is used to reduce the dimensions of medical images to improve classification, with MobileNet-v2 and DenseNet-121 as the optimal classification algorithm choices. The results show that the SPC method succeeded in producing efficient feature representation with data sizes that are almost the same as the original data, while PCA produces greater dimensionality reduction. The SPC model also shows the best performance in terms of accuracy and loss. This research makes a significant contribution to the development of more sophisticated and efficient medical image analysis techniques to support the diagnosis and treatment of breast cancer.

Penerapan Random Oversampling dan Algoritma Boosting untuk Memprediksi Kualitas Buah Jeruk Ananda, Imanuel Khrisna; Fanani, Ahmad Zainul; Setiawan, Dicky; Wicaksono , Duta Firdaus
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 1 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i1.25836

According to the 2019 data, global orange production has increased significantly, reaching 79 million tons. However, despite the availability of various types of oranges in Indonesian markets, many vendors still sell low-quality oranges. To address this issue, researchers have applied random oversampling and boosting algorithms to predict orange quality, using the public Orange Quality Analysis Dataset. This study uses random oversampling to address data imbalance and combines it with boosting algorithms like Adaboost, Gradient Boosting, Light GBM, and CatBoost. The data features considered include size, weight, sweetness level, acidity level, and others. The accuracy of the boosting algorithms used varied, with CatBoost showing the highest accuracy rate of 91.42%. The hope is that this research can help orange producers create high-quality products and reduce the occurrence of low-quality oranges, ultimately providing consumers with better oranges. Additionally, this can help producers market their oranges both domestically and internationally.

Pengembangan Aplikasi Dewan Masjid Indonesia (DMI) Berbasis Ekonomi Umat Dengan Metode Waterfall Winarsih, Nurul Anisa Sri; Fanani, Ahmad Zainul; Saraswati, Galuh Wilujeng; Rohman, Muhammad Syaifur
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.379

The conventional economic system has been developing for a long time, followed by the Islamic economic system which is currently developing rapidly in Indonesia. Sharia economic activities are based on the rules of the Koran regarding right and wrong, good and bad, and halal and haram rules. The background of sharia business ethics is the Prophet Muhammad SAW which is based on the Al-Quran and Hadith. The sharia economy can be seen with the emergence of Islamic banks, almost all major banks in Indonesia have sharia branches. Not only that, e-money and sharia payment gateways already exist. This is based on the large number of Indonesian people who adhere to Islam and the increasing awareness of Muslims in Indonesia in implementing the Islamic economic system. Since March 2, 2020, the Corona virus or Covid-19 pandemic has entered Indonesia. Many employees become unemployed. 50% of MSMEs could go bankrupt in the next few months. Whereas small businesses make a big contribution to the absorption of jobs in Indonesia which creates income for the population. The management of the Semarang City DMI organization took the initiative to ease the burden on the people, especially in the city of Semarang by making the DMI application based on the people's economy. Waterfall is the method used in this research. Waterfall is suitable for application development with a complete needs analysis. After the PSBB period ended, there were many unemployed who tried their luck by selling small businesses and MSMEs opened slowly. It is hoped that the DMI application can introduce the business of the community around the mosque and other people can buy the business easily through the application.

Pengembangan Aplikasi Dewan Masjid Indonesia (DMI) Berbasis Ekonomi Umat Dengan Metode Waterfall Winarsih, Nurul Anisa Sri; Fanani, Ahmad Zainul; Saraswati, Galuh Wilujeng; Rohman, Muhammad Syaifur
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.379

The conventional economic system has been developing for a long time, followed by the Islamic economic system which is currently developing rapidly in Indonesia. Sharia economic activities are based on the rules of the Koran regarding right and wrong, good and bad, and halal and haram rules. The background of sharia business ethics is the Prophet Muhammad SAW which is based on the Al-Quran and Hadith. The sharia economy can be seen with the emergence of Islamic banks, almost all major banks in Indonesia have sharia branches. Not only that, e-money and sharia payment gateways already exist. This is based on the large number of Indonesian people who adhere to Islam and the increasing awareness of Muslims in Indonesia in implementing the Islamic economic system. Since March 2, 2020, the Corona virus or Covid-19 pandemic has entered Indonesia. Many employees become unemployed. 50% of MSMEs could go bankrupt in the next few months. Whereas small businesses make a big contribution to the absorption of jobs in Indonesia which creates income for the population. The management of the Semarang City DMI organization took the initiative to ease the burden on the people, especially in the city of Semarang by making the DMI application based on the people's economy. Waterfall is the method used in this research. Waterfall is suitable for application development with a complete needs analysis. After the PSBB period ended, there were many unemployed who tried their luck by selling small businesses and MSMEs opened slowly. It is hoped that the DMI application can introduce the business of the community around the mosque and other people can buy the business easily through the application.

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