Article Per Year (5 Year)
p-Index From 2020 - 2025
2.921
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Development Research Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Syntax Literate: Jurnal Ilmiah Indonesia Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Indonesia Sosial Teknologi
Moch Arief Soeleman
Dian Nuswantoro University
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

Published : 19 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 19 Documents
Search

 1   2 
Improving C4.5 Algorithm Accuracy With Adaptive Boosting Method For Predicting Students in Obtaining Education Funding Mohammad Ahmad Maidanul Abrori; Abdul Syukur; Affandy Affandy; Moch Arief Soeleman
Journal of Development Research Vol. 6 No. 2 (2022): Volume 6, Number 2, November 2022
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/jdr.v6i2.205

Abstract

The level of accuracy in determining the prediction of the provision of educational funding assistance is very important for the education agency. The large number of data on prospective beneficiaries can be processed into information that can be used as decision support in determining eligibility for education funding assistance. The data processing is included in the field of data mining. One method that can be applied in predicting the feasibility of receiving aid funds is classification. There are several classification algorithms, one of which is a decision tree. The famous decision tree algorithm is C4.5. The C4.5 algorithm can be applied in classifying prospective recipients of educational aid funds. This study uses datasets from student data of SMK Al Fattah Kertosono. The purpose of this study is to increase the accuracy of the C4.5 algorithm by applying adaboost in classifying students who deserve education funding and not, by comparing the results before and after applying adaboost. Validation in this study uses cross validation. While the measurement of accuracy is measured by the confusion matrix. The experimental results show that there is an increase in accuracy of 7.2%. The accuracy of the application of the C4.5 algorithm reaches 91.32%. While the accuracy of the application of the C4.5 algorithm with adaboost reached 98.55%.
Classification of Lombok Pearls using GLCM Feature Extraction and Artificial Neural Networks (ANN) Muh Nasirudin Karim; Ricardus Anggi Pramunendar; Moch Arief Soeleman; Purwanto Purwanto; Bahtiar Imran
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1317.209-217

Abstract

This study used the second-order Gray Level Co-occurrence Matrix (GLCM) and pearl image classification using the Artificial Neural Network (ANN). No previous research combines the GLCM method with artificial neural networks in pearl image classification. The number of images used in this study is 360 images with three labels, including 120 A images, 120 AA images, and 120 AAA images. The epochs used in this study were 10, 20, 30, 40, 50, 60, 70, and 80. The test results at epoch 10 got 80.00% accuracy, epoch 20 got 90.00% accuracy, epoch 30 got 93.33% accuracy, and epoch 40 got 94.44% accuracy. In comparison, epoch 50 got 95.55% accuracy, epoch 60 got 96.66% accuracy, epoch 70 got 96.66% accuracy, and epoch 80 got 95.55% accuracy. The combination of the proposed methods can produce accuracy in classifying pearl images, such as the classification test results.
Deteksi Dini Covid-19 Melalui Citra CT-Scan Paru-Paru Menggunakan K-Nearest Neighbor dengan Komparasi Jarak Lu'luul Maknun; Abdul Syukur; Affandy Affandy; Moch Arief Soeleman
Jurnal Indonesia Sosial Teknologi Vol. 3 No. 03 (2022): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1049.395 KB) | DOI: 10.59141/jist.v3i03.397

Abstract

Covid -19 yang telah mewabah dan menjadi pandemik secara global yang merupakan masalah utama yang perlu di perhatikan dan di tangani, beberapa cara yang harus di lakukan adalah dengan memutus mata rantai penyebaran virus salah satunya dengan melakukan deteksi dini dan melakukan karantina, dengan CT scan paru-paru. CT scan paru-paru dapat dijadikan jalan alternatif. Berdasarkan permasalahan di atas maka peneliti mengetahui kondisi paru-paru secara detail dan dalam mendiagnosis virus secara dini. Pada penelitian ini pendekatan yang di ajukan menggunakan metode K-NN dengan perhitungan jarak euclidean distance, manhattan distance, miskowski distance untuk deteksi dini Covid -19 melalui citra CT scan paru-paru yang di duga terinfeksi Covid -19 . dalam mendeteksi secara dini evaluasi yang di gunakan untuk mengetahui pervorma yang di usulkan menggunakan coufusion matrix dengan hasil eksperimen menunjukkan hasil dari tiga perhitungan jarak menunjukkan hasil akurasi yang baik dan menggunakan dataset secara publik yaitu euclidean distance berjumlah 83%, Manhattan distance berjumlah 87%, Minkowski berjumlah 76%, di harapkan metode ini dapat di gunakan dan di kembangkan untuk melengkapi dioglosa medis.
Improving the Accuracy of C4.5 Algorithm with Chi-Square Method on Pure Tea Classification Using Electronic Nose Mula Agung Barata; Edi Noersasongko; Purwanto; Moch Arief Soeleman
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 2 (2023): April 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i2.4687

Abstract

Tea is one of the plantation products within the Ministry of Agriculture of the Republic of Indonesia, which plays an essential role as a mainstay commodity that boosts the Indonesian economy. Each type of tea has different properties, and the aroma of each type of tea can measure the quality of the tea. The human sense of smell is still very limited in classifying pure types of tea. Therefore, a device is needed to help measure the aroma of tea from an electronic nose. The devices attached to several gas sensors help humans take data from the smell of pure tea and calculate the value of each type of tea to test datasets with data mining algorithms. This study uses the C4.5 algorithm as a classification method with advantages over noise data, missing values, and handling variables with discrete and continuous types. Meanwhile, Chi-square is used to perform attribute severing in the data preprocessing process to increase the accuracy of dataset testing. Testing a pure tea dataset with four whole attributes, namely CO2, CO, H2, and CH4, using the C4.5 algorithm resulted in an accuracy of 93.65% and an increase in the accuracy performance of the C4.5 algorithm by 94.27% with dataset testing using Chi-Square feature selection with the two highest value attributes.
BPNN Optimization With Genetic Algorithm For Classification of Tobacco Leaves With GLCM Extraction Features Kristhina Evandari; M. Arief Soeleman; Ricardus Anggi Pramunendar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 2 (2023): April 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i2.4743

Abstract

Tobacco leaves are one of the agricultural commodities cultivated by Indonesian farmers. In their application in the field, there are many obstacles in tobacco leaf cultivation, one of which is declining tobacco quality caused by weather factors. In this study, a technology-based analysis step was carried out to determine the classification in determining the quality of tobacco leaves. The research was carried out by applying the classification optimization of the Backpropagation Artificial Neural Network Method and genetic algorithms to determine the weights obtained from extracting GLCM features. You can get the weight value from the genetic algorithm on the homogeneity variable from this analysis step. The variable gets a weight value of 1. The results of this study obtained a classification value with the Backpropagation Artificial Neural Network Method model getting an accuracy value of 53.50% at a hidden layer value of 2,4,5,7. For classification with the Artificial Neural Network Method, Backpropagation, which is optimized with genetic algorithms, you get an accuracy value of 64.50% at the 4th hidden layer value. From this study, the value of optimization accuracy increased by 11% after being optimized with genetic algorithms.
The evaluation of convolutional neural network and genetic algorithm performance based on the number of hyperparameters for English handwritten recognition Muhammad Munsarif; Edi Noersasongko; Pulung Nurtantio Andono; Moch Arief Soeleman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1250-1259

Abstract

Convolutional neural network (CNN) has been widely applied to image recognition, especially handwritten English recognition. CNN's performance is good if the hyperparameter values are correct. However, the determination of precise hyperparameters is not a trivial task. This task is made more difficult when combined with a larger number of hyperparameters resulting in a high dimensionality of the search space. Usually, hyperparameter optimization uses a finite number. Previous studies have shown that a large number of hyperparameters can result in optimal CNN performance. However, the studies only apply to text mining datasets. This study offers two novelties. First, it applied 20 hyperparameters and their ranges to handwritten English. Second, this paper conducted seven experiments based on different hyperparameters and the number of hyperparameters. This paper also compares the existing methods, namely random and grid search. The experiment resulted in the proposed model being superior to the existing methods. EX3 is better than other experiments and a larger number of hyperparameters and layer-specific hyperparameter values are unimportant.
Customer Segmentation with RFM Model using Fuzzy C-Means and Genetic Programming Anas Syaifudin; Purwanto Purwanto; Heribertus Himawan; M. Arief Soeleman
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 2 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i2.2408

Abstract

One of the strategies a company uses to retain its customers is Customer Relationship Management (CRM). CRM manages interactions and supports business strategies to build mutually beneficial relationships between companies and customers. The utilization of information technology, such as data mining used to manage the data, is critical in order to be able to find out patterns made by customers when processing transactions. Clustering techniques are possible in data mining to find out the patterns generated from customer transaction data. Fuzzy C-Means (FCM) is one of the best-known and most widely used fuzzy grouping methods. The iteration process is carried out to determine which data is in the right cluster based on the objective function. The local minimum is the condition where the resulting value is not the lowest value from the solution set. This research aims to solve the minimum local problem in the FCM algorithm using Genetic Programming (GP), which is one of the evolution-based algorithms to produce better data clusters. The result of the research is to compare the application of fuzzy c-means (FCM) and genetic programming fuzzy c-means (GP-FCM) for customer segmentation applied to the Cahaya Estetika clinic dataset. The test results of the GP-FCM yielded an objective function of 20.3091, while for the FCM algorithm, it was 32.44741. Furthermore, evaluating cluster validity using Partition Coefficient (PC), Classification Entropy (CE), and Silhouette Index proves that the results of cluster quality from gp-fcm are more optimal than fcm. The results of this study indicate that the application of genetic programming in the fuzzy c-means algorithm produces more optimal cluster quality than the fuzzy c-means algorithm.
Comparison of Information Gain and Chi-Square Selection Features For Performance Improvement of Naive Bayes Algorithm On Determining Students With No PIP Recipients at SMKN 1 Brebes Magus Sarasnomo; Muljono Muljono; M. Arief Soeleman
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2224.578 KB) | DOI: 10.36418/syntax-literate.v7i4.6661

Abstract

All policies of the Smart Indonesia Program (PIP) through the form of the Smart Indonesia Card (KIP) are issued by the government under the auspices of the Ministry of Education and Culture (Kemendikbud) through the National Team for the Acceleration of Poverty Reduction (TNP2K). Helping to alleviate the poor category of students in order to obtain a proper education, prevent children dropping out of school, and fulfill their school needs are the goals of the program. This assistance can be used by students to meet all school needs such as transportation costs to go to school, the cost of buying school supplies, and school pocket money. This study aims to compare the Information Gain and Chi-Square selection features to improve the performance of the Naive Bayes algorithm in determining poor students who are recipients of the Smart Indonesia Program (PIP) at SMKN 1 Brebes, to determine the accuracy of the Naive Bayes, Information Gain and Chi-Square algorithms. and compare the level of accuracy and determine the attributes that affect the accuracy. At this stage, collecting relevant and useful research data, which is collected in the form of literature and data, and processed as research material. Sources of data used in this study in the form of primary data collection and secondary data. The primary data collection technique used in this study was a questionnaire or questionnaire, while the secondary data obtained in this study was through document files. At this stage, preliminary data processing is carried out, the data used is student data of SMKN 1 Brebes in 2021. The initial data collection obtained was 703 data, but not all records were used because they had to go through several stages of initial data processing (data preparation). The results of the Naive Bayes algorithm accuracy of 90.31% with an AUC of 0.967, after the addition of the Information Gain selection feature the accuracy becomes 90.88% with an AUC value of 0.970. The addition of the Information Gain selection feature can help improve the classification performance of the Naive Bayes algorithm even though the accuracy is not maximized. The accuracy of the Naive Bayes algorithm is 90.31% with an AUC of 0.967, after the addition of the Chi-Square selection feature the accuracy becomes 90.88% with an AUC value of 0.970. The accuracy results are not maximized but the addition of the Chi-Square selection feature can also improve the classification performance of the Naive Bayes algorithm. The accuracy of the Naive Bayes algorithm is 90.31% with an AUC of 0.967, after the addition of the Information Gain selection feature and the Chi-Square selection feature the accuracy becomes 90.88% with an AUC value of 0.970. The results of the same accuracy in the use of the Information Gain and Chi-Square selection features to increase the performance of the Naive Bayes algorithm by 0.57% although the accuracy results are still less than optimal.
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

Abstract

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.
Co-Authors Abdul Syukur Adhe Irham Thoriq Affandy Affandy Ahmad Zainul Fanani Ajib Susanto Ali Muqoddas Alzami, Farrikh Anas Syaifudin Ari Yahya A Arry Maulana Syarif Dwi Puji Prabowo, Dwi Puji Edi Noersasongko Edi Noersasongko Edi Sugiarto Elkaf Rahmawan Pramudya Esa Wahyu Andriansyah Farikh Al Zami Fikri Budiman Fikri Budiman Heribertus Himawan Imran, Bahtiar Karim, Muh Nasirudin Karis W. Khafiidz Hastuti Kristhina Evandari Lu'luul Maknun Magus Sarasnomo Mohammad Ahmad Maidanul Abrori Mohammad Faishol Zuhri Much Chafid Muhammad Haris Zuhri Muhammad Munsarif Mula Agung Barata Muljono, - Mursalim Mursalim Muslih Muslih Muslih Muslih Nanang Lestio Wibowo Noor Ageng Setiyanto, Noor Ageng Nurhindarto, Aris Pujiono Pujiono Pulung Nurtantio Andono Purwanto Purwanto Ricardus Anggi Pramunendar S. Kholidah Rahayu Maharani Saiful Ulya Widyatmoko Karis