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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Perfecting a Video Game with Game Metrics 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

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

 1 
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.
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 Abdul Syukur Adhe Irham Thoriq Affandy Affandy Ahmad Zainul Fanani Ajib Susanto Ali Muqoddas Alzami, Farrikh Anas Syaifudin Ari Yahya A Arry Maulana Syarif Barata, Mula Agung Dwi Puji Prabowo, Dwi Puji Edi Noersasongko Edi Noersasongko Edi Sugiarto Elkaf Rahmawan P Elkaf Rahmawan Pramudya Esa Wahyu Andriansyah Farikh Al Zami Fikri Budiman Fikri Budiman Heribertus Himawan Imran, Bahtiar Karis W. Khafiidz Hastuti Kristhina Evandari Lu'luul Maknun Magus Sarasnomo Mohammad Ahmad Maidanul Abrori Mohammad Faishol Zuhri Much Chafid Muh Nasirudin Karim Muhammad Haris Zuhri Muhammad Munsarif Muljono Muljono Muljono Muljono Mursalim Mursalim Muslih Muslih Muslih Muslih Nanang Lestio Wibowo Noor Ageng Setiyanto Nurhindarto, Aris Pujiono Pujiono Pulung Nurtantio Andono Pulung Nurtantio Andono Purwanto Purwanto R. Anggi Pramunendar Ricardus Anggi Pramunendar S. Kholidah Rahayu Maharani Saiful Ulya Tutik Khotimah Widyatmoko Karis