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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi dan Manajemen Informatika Briliant: Jurnal Riset dan Konseptual JOURNAL OF APPLIED INFORMATICS AND COMPUTING JSI (Jurnal sistem Informasi) Universitas Suryadarma Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Vokasi Informatika (JAVIT) J-Intech (Journal of Information and Technology) Jurnal Teknologi Informasi Cyberku Journal of Computing Theories and Applications Jurnal Informatika Polinema (JIP) Journal of Information System and Application Development Journal of Future Artificial Intelligence and Technologies
Muslikh, Ahmad Rofiqul
Faculty Of Information Technology, Universitas Merdeka, Malang, East Java, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science

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RETRACTED: Klasifikasi Data Time Series Arus Lalu Lintas Jangka Pendek Menggunakan Algoritma Adaboost dengan Random Forest Ahmad Rofiqul Muslikh; Heru Agus Santoso; Aris Marjuni
BRILIANT: Jurnal Riset dan Konseptual Vol 4, No 1 (2019): Volume 4 Nomor 1, Februari 2019
Publisher : Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (302.293 KB) | DOI: 10.28926/briliant.v4i1.272

Abstract

RETRACTEDFollowing a rigorous, carefully concerns and considered review of the article published in BRILIANT: Jurnal Riset dan Konseptual to article entitled Klasifikasi Data Time Series Arus Lalu Lintas Jangka Pendek Menggunakan Algoritma Adaboost Dengan Random Forest Vol 4, No 1, pp. 78-96, February 2019, DOI: http://dx.doi.org/10.28926/briliant.v3i3.272.This article has been found to be in violation of the BRILIANT: Jurnal Riset dan Konseptual Publication principles and has been retracted.The editor investigated and found that the article published in Jurnal Teknologi Informasi CyberKU Vol. 14 no 1 January 2018, pp. 24-38.The document and its content has been removed from BRILIANT: Jurnal Riset dan Konseptual, and reasonable effort should be made to remove all references to this article.
KLASIFIKASI DATA TIME SERIES ARUS LALU LINTAS JANGKA PENDEK MENGGUNAKAN ALGORITMA ADABOOST DENGAN RANDOM FOREST Ahmad Rofiqul Muslikh; Heru Agus Santoso; Aris Marjuni
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 14 No 1 (2018): Jurnal Teknologi Informasi CyberKU Vol.14 no 1 2018
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (584.878 KB)

Abstract

Data traffic in Indonesia is used for management control traffic flow, while the data on get results from the survey will be undertaken directly localized, the survey will be undertaken are less effective, and the data obtained from the survey results were used as a reference in control traffic flow, and therefore to obtain the data traffic flow more effective in need of a new approach that can classified and predict the data in the can with higher accuracy, so that density and congestion can be predicted earlier. In this study used the approach of using Adaboost and Random Forest algorithms to classification and predict the survey data that are time series, the results of testing for prediction using Adaboost with Random Forest With Confusion Matrix as a measuring accuracy rate of 87,8%, and the rate of error in getting at 0 , 0629. On the results using Adaboost with a Random Forest approach proved to be more efficient in predicting the survey data rather than simply relying on the original data to predict traffic flow
RICE DISEASE RECOGNITION USING TRANSFER LEARNING XCEPTION CONVOLUTIONAL NEURAL NETWORK Ahmad Rofiqul Muslikh; De Rosal Ignatius Moses Setiadi; Arnold Adimabua Ojugo
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.1529

Abstract

As one of the major rice producers, Indonesia faces significant challenges related to plant diseases such as blast, brown spot, tugro, leaf smut, and blight. These diseases threaten food security and result in economic losses, underscoring the importance of early detection and management of rice diseases. Convolutional Neural Network (CNN) has proven effective in detecting diseases in rice plants. Specifically, transfer learning with CNN, particularly the Xception model, has the advantage of efficiently extracting automatic features and performing well even with limited datasets. This study aims to develop the Xception model for rice disease recognition based on leaf images. Through the fine-tuning process, the Xception model achieved accuracies, precisions, recalls, and F1-scores of 0.89, 0.90, 0.89, and 0.89, respectively, on a dataset with a total of 320 images. Additionally, the Xception model outperformed VGG16, MobileNetV2, and EfficientNetV2.
PERAMALAN JUMLAH PENDUDUK KOTA PASURUAN DENGAN MENGGUNAKAN METODE SINGLE DAN DOUBLE EXPONENTIAL SMOOTHING Raihan Ihza Pravisya; Ahmad Rofiqul Muslikh
JSI (Jurnal sistem Informasi) Universitas Suryadarma Vol 11, No 2 (2024): JSI (Jurnal sistem Informasi) Universitas Suryadarma
Publisher : Universitas Dirgantara Marsekal Suryadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35968/jsi.v11i2.1253

Abstract

This study aims to forecast the population of Pasuruan City using Single and Double Exponential Smoothing methods. Population forecasting is crucial for urban planning, economic development, and public services. Data from 2010 to 2022 provided by the Central Bureau of Statistics of Pasuruan City was utilized for this research. The Single Exponential Smoothing method demonstrated higher accuracy with lower Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) values compared to the Double Exponential Smoothing method. Specifically, the MSE for the Single Exponential Smoothing method was 623,986,327,670, while the Double Exponential Smoothing method recorded an MSE of 1,269,743,472,543. The corresponding RMSE values were 7,899,281 and 11,268,289, respectively. The results indicate that the Single Exponential Smoothing method is more reliable for predicting the population trends in Pasuruan City.The findings of this research can aid local governments and policymakers in making informed decisions regarding resource allocation, infrastructure development, and social services. Future studies could consider incorporating external factors such as migration, birth rates, and government policies to enhance the accuracy of population forecasts.
Plant Diseases Classification based Leaves Image using Convolutional Neural Network Imanulloh, Satrio Bagus; Muslikh, Ahmad Rofiqul; Setiadi, De Rosal Ignatius Moses
Journal of Computing Theories and Applications Vol. 1 No. 1 (2023): JCTA 1(1) 2023
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i1.8877

Abstract

Plant disease is one of the problems in the world of agriculture. Early identification of plant diseases can reduce the risk of loss, so automation is needed to speed up identification. This study proposes a custom-designed convolutional neural network (CNN) model for plant disease recognition. The proposed CNN model is not complex and lightweight, so it can be implemented in model applications. The proposed CNN model consists of 12 CNN layers, which consist of eight layers for feature extraction and four layers as classifiers. Based on the experimental results of a plant disease dataset consisting of 38 classes with a total of 87,867 image records. The proposed model can get high performance and not overfitting, with 97%, 98%, 97% and 97%, respectively, for accuracy, precision, recall and f1-score. The performance of the proposed model is also better than some popular pre-trained models, such as InceptionV3 and MobileNetV2. The proposed model can also work well when implemented in mobile applications.
Dataset and Feature Analysis for Diabetes Mellitus Classification using Random Forest Mustofa, Fachrul; Safriandono, Achmad Nuruddin; Muslikh, Ahmad Rofiqul; Setiadi, De Rosal Ignatius Moses
Journal of Computing Theories and Applications Vol. 1 No. 1 (2023): JCTA 1(1) 2023
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i1.9190

Abstract

Diabetes Mellitus is a hazardous disease, and according to the World Health Organization (WHO), diabetes will be one of the main causes of death by 2030. One of the most popular diabetes datasets is PIMA Indians, and this dataset has been widely tested on various machine learning (ML) methods, even deep learning (DL). But on average, ML methods are not able to produce good accuracy. The quality of the dataset and features is the most influential thing in this case, so deeper investment is needed to examine this dataset. This research will analyze and compare the PIMA Indians and Abelvikas datasets using the Random Forest (RF) method. The two datasets are imbalanced, in fact, the Abelvikas dataset is more imbalanced and has a larger number of classes so it is be more complex. The RF was chosen because it is one of the ML methods that has the best results on various diabetes datasets. Based on the test results, very contrasting results were obtained on the two datasets. Abelvikas had accuracy, precision, and recall, reaching 100%, and PIMA Indians only achieved 75% for accuracy, 87% for precision, and 80% for the best recall. Testing was done with 3, 5, 7, 10, and 15 tree number parameters. Apart from that, it was also tested with k-fold validation to get valid results. This determines that the features in the Abelvikas dataset are much better because more complete glucose features support them.
Music-Genre Classification using Bidirectional Long Short-Term Memory and Mel-Frequency Cepstral Coefficients Wijaya, Nantalira Niar; Setiadi, De Rosal Ignatius Moses; Muslikh, Ahmad Rofiqul
Journal of Computing Theories and Applications Vol. 1 No. 3 (2024): JCTA 1(3) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.9655

Abstract

Music genre classification is one part of the music recommendation process, which is a challenging job. This research proposes the classification of music genres using Bidirectional Long Short-Term Memory (BiLSTM) and Mel-Frequency Cepstral Coefficients (MFCC) extraction features. This method was tested on the GTZAN and ISMIR2004 datasets, specifically on the IS-MIR2004 dataset, a duration cutting operation was carried out, which was only taken from seconds 31 to 60 so that it had the same duration as GTZAN, namely 30 seconds. Preprocessing operations by removing silent parts and stretching are also performed at the preprocessing stage to obtain normalized input. Based on the test results, the performance of the proposed method is able to produce accuracy on testing data of 93.10% for GTZAN and 93.69% for the ISMIR2004 dataset.
Multi-label Classification of Indonesian Al-Quran Translation based CNN, BiLSTM, and FastText Muslikh, Ahmad Rofiqul; Akbar, Ismail; Setiadi, De Rosal Ignatius Moses; Islam, Hussain Md Mehedul
Techno.Com Vol. 23 No. 1 (2024): Februari 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i1.9925

Abstract

Studying the Qur'an is a pivotal act of worship in Islam, which necessitates a structured understanding of its verses to facilitate learning and referencing. Reflecting this complexity, each Quranic verse is rich with unique thematic elements and can be classified into a range of distinct categories. This study explores the enhancement of a multi-label classification model through the integration of FastText. Employing a CNN+Bi-LSTM architecture, the research undertakes the classification of Quranic translations across categories such as Tauhid, Ibadah, Akhlak, and Sejarah. Based on model evaluation using F1-Score, it shows significant differences between the CNN+Bi-LSTM model without FastText, with the highest result being 68.70% in the 80:20 testing configuration. Conversely, the CNN+Bi-LSTM+FastText model, combining embedding size and epoch parameters, achieves a result of 73.30% with an embedding size of 200, epoch of 100, and a 90:10 testing configuration. These findings underscore the significant impact of FastText on model optimization, with an enhancement margin of 4.6% over the base model.
Fine tuning model Convolutional Neural Network EfficientNet-B4 dengan augmentasi data untuk klasifikasi penyakit kakao Pradana, Akbar Ganang; Setiadi, De Rosal Ignatius Moses; Muslikh, Ahmad Rofiqul
Journal of Information System and Application Development Vol. 2 No. 1 (2024): March 2024
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jisad.v2i1.11899

Abstract

Cocoa is an important agricultural commodity in Indonesia which contributes to the economy with a production share of 15.68%. Cocoa diseases, such as Black Pod Rot and Pod Borer, are very detrimental to farmers. So it is necessary to build a recognition model that can classify automatically with high performance. Unfortunately the collected dataset is very unbalanced, and this is an additional challenge as it can reduce recognition performance. This study proposes disease recognition in cocoa images using the EfficientNet-B4 Convolutional Neural Network (CNN) model with fine-tuning. In this study also used seven kinds of data augmentation. The result is that the proposed CNN model has a high accuracy of 97.3% which is an increase of about 7.4% compared to the original model, at relatively few epochs. In addition, the proposed model is compared with other CNN models such as Xception, InceptionV3, ResNet, DenseNet, and EfficientNet, using the same approach, namely fine-tuning and epoch. The result is that the proposed method is superior to other models. This confirms that the proposed CNN model can also work better on unbalanced data.
Analyzing InceptionV3 and InceptionResNetV2 with Data Augmentation for Rice Leaf Disease Classification Firnando, Fadel Muhamad; Setiadi, De Rosal Ignatius Moses; Muslikh, Ahmad Rofiqul; Iriananda, Syahroni Wahyu
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 1 (2024): June 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-4

Abstract

This research aims to evaluate and compare the performance of several deep learning architectures, especially InceptionV3 and InceptionResNetV2, with other models, such as EfficientNetB3, ResNet50, and VGG19, in classifying rice leaf diseases. In addition, this research also evaluates the impact of using data augmentation on model performance. Three different datasets were used in this experiment, varying the number of images and class distribution. The results show that InceptionV3 and InceptionResNetV2 consistently perform excellently and accurately on most datasets. Data augmentation has varying effects, providing slight advantages on datasets with lower variation. The findings from this research are that the InceptionV3 model is the best model for classifying rice diseases based on leaf images. The InceptionV3 model produces accuracies of 99.53, 58.94, and 90.00 for datasets 1, 2, and 3, respectively. It is also necessary to be wise in carrying out data augmentation by considering the dataset's characteristics to ensure the resulting model can generalize well.
Co-Authors Achmad Nuruddin Safriandono Akbar, Ismail Amelisa, Eka Puspita Roro Arif Saivul Affandi Arnold Adimabua Ojugo De Rosal Ignatius Moses Setiadi Delviana Muti Dian Kristiawan Nugroho Edi Subiyantoro Elan, Melania Seindang Fachrudin, Mochamad Rizal Fandi Yulian Pamuji Firnando, Fadel Muhamad Heru Agus Santoso Imanulloh, Satrio Bagus Islam, Hussain Md Mehedul Jehadu, Yustina Vania Ghaisani Jutono Gondohanindijo, Jutono Khamdani, Ahmad Rizal Luh Putu Ratna Sundari Marcus, Ronald David Mardiana Andarwati Mustofa, Fachrul Ojugo, Arnold Adimabua Pradana, Akbar Ganang Raihan Ihza Pravisya Rizza Muhammad Arief Swalaganata, Galandaru Syahroni Wahyu Iriananda, Syahroni Wahyu Warto - Wijaya, Nantalira Niar Yulian Pamuji, Fandi