Article Per Year (5 Year)
p-Index From 2021 - 2026
2.534
P-Index
This Author published in this journals
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

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

Found 19 Documents
Search

 1   2 
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article has been found to be in violation of the BRILIANT: Jurnal Riset dan Konseptual Publication principles and has been retracted.The editor investigated and found that the article published in Jurnal Teknologi Informasi CyberKU Vol. 14 no 1 January 2018, pp. 24-38.The document and its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
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.
Integrating SMOTE-Tomek and Fusion Learning with XGBoost Meta-Learner for Robust Diabetes Recognition Setiadi, De Rosal Ignatius Moses; Nugroho, Kristiawan; Muslikh, Ahmad Rofiqul; Iriananda, Syahroni Wahyu; Ojugo, Arnold Adimabua
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.2024-11

Abstract

This research aims to develop a robust diabetes classification method by integrating the Synthetic Minority Over-sampling Technique (SMOTE)-Tomek technique for data balancing and using a machine learning ensemble led by eXtreme Gradient Boosting (XGB) as a meta-learner. We propose an ensemble model that combines deep learning techniques such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU) with XGB classifier as the base learner. The data used included the Pima Indians Diabetes and Iraqi Society Diabetes datasets, which were processed by missing value handling, duplication, normalization, and the application of SMOTE-Tomek to resolve data imbalances. XGB, as a meta-learner, successfully improves the model's predictive ability by reducing bias and variance, resulting in more accurate and robust classification. The proposed ensemble model achieves perfect accuracy, precision, recall, specificity, and F1 score of 100% on all tested datasets. This method shows that combining ensemble learning techniques with a rigorous preprocessing approach can significantly improve diabetes classification performance.
Evaluasi Tata Kelola Sistem Informasi Arsip Digital (SIAD) Menggunakan Framework COBIT 2019 di Disdukcapil Kabupaten Malang Amelisa, Eka Puspita Roro; Marcus, Ronald David; Muslikh, Ahmad Rofiqul
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 2 (2025): JPTI - Februari 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.583

Abstract

Kemajuan teknologi informasi (TI) memberikan peluang besar bagi instansi pemerintah untuk meningkatkan kualitas layanan publik. Namun, Dinas Kependudukan dan Pencatatan Sipil Kabupaten Malang masih menghadapi tantangan dalam mengoptimalkan tata kelola Sistem Informasi Arsip Digital (SIAD). Hal ini memengaruhi efektivitas dan efisiensi pengelolaan arsip, serta layanan administrasi kependudukan. Penelitian ini menggunakan framework COBIT 2019 untuk mengevaluasi tingkat kapabilitas tata kelola pada SIAD. Fokus penelitian mencakup lima domain utama, yaitu APO12 - Manajemen Risiko, BAI09 - Manajemen Aset, DSS03 - Manajemen Masalah, DSS05 - Manajemen Keamanan Layanan, dan MEA03 - Kepatuhan terhadap Regulasi. Data dikumpulkan melalui kuesioner berbasis skala Guttman dan dianalisis menggunakan model capability level ISACA. Hasil penelitian menunjukkan bahwa seluruh domain telah mencapai Level 4 (Predictable) dengan kategori Largely Achieved, yang berarti proses telah terdokumentasi dan berjalan sistematis. Namun, ditemukan kesenjangan pada pemantauan real-time, analisis prediktif, dan otomatisasi proses. Penelitian ini merekomendasikan penerapan teknologi pendukung, pelatihan sumber daya manusia, serta evaluasi dan audit berkala untuk meningkatkan tata kelola TI. Penelitian ini memberikan panduan strategis bagi Disdukcapil untuk mencapai tata kelola TI yang lebih efisien dan berkelanjutan.
Analisis Pemilihan Media Promosi UMKM untuk Meningkatkan Volume Penjualan Menggunakan Metode Analytical Hierarchy Process (AHP) Subiyantoro, Edi; Muslikh, Ahmad Rofiqul; Andarwati, Mardiana; Swalaganata, Galandaru; Pamuji, Fandi Yulian
Jurnal Teknologi dan Manajemen Informatika Vol. 8 No. 1 (2022): Juni 2022
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v8i1.6760

Abstract

The increase in the number of creative industry entrepreneurs on the scale of UMKMs in Indonesia must be supported by several factors so that these businesses can develop. These factors range from business conditions, environment, facilities, and infrastructure, to technology. In terms of the use of technology, UMKM business actors can use it in various fields including the procurement of raw materials, the production process to the marketing and promotion stages of the products produced. This analysis aims to determine the weight the importance of the criteria to create an element of UMKM sales volume. In addition, it also helps UMKM actors in making decisions in choosing and using which alternative best suits their needs. Based on the results of the analysis of this study, it can be concluded that alternative social media is a priority criterion in increasing the sales volume of UMKM actors. Based on the overall average weight value, the alternative for social media is to expand the market by increasing the intensity of promotions with various social media. Such as WhatsApp Business, Instagram, Facebook, YouTube, and others to increase product sales for UMKM actors.
COMPARATIVE ANALYSIS OF PERFORMANCE AND EFFICIENCY OF LOAD BALANCING ALGORITHMS ON INGRESS CONTROLLER Khamdani, Ahmad Rizal; Muslikh, Ahmad Rofiqul; Affandi, Arif Saivul
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4040

Abstract

Kubernetes has become the dominant container orchestration platform in production environments, with the ingress controller playing a critical role in managing external traffic to services within the cluster. This study aims to provide recommendations for optimal load balancing algorithms for Kubernetes production environments by analyzing and comparing the performance of four algorithms namely round robin, static-rr, least connection, and random on the HAProxy ingress controller. The research method is conducted through observation using k6 and Grafana performance test tools, as well as literature studies, with measurements including total requests, throughput, latency, CPU usage, and memory at various levels of user load. The data was analyzed using descriptive statistical techniques, normality test, homogeneity test, and tests for group differences using one-way ANOVA or Kruskal-Wallis H. The results show that static-rr excels in throughput, total requests, and CPU and memory efficiency at high load, while least connection is more effective for latency at low load. Round robin and random showed stable performance at low load but less optimal at high load. The conclusion of this study is that choosing the right load balancing algorithm depends on the load characteristics and desired performance metrics, to ensure optimal Kubernetes performance under various load scenarios in production environments.
Analisis Kepuasaan Pengguna Aplikasi GRAB Sebagai Transportasi Online Dengan Metode TAM Elan, Melania Seindang; Muslikh, Ahmad Rofiqul
J-INTECH (Journal of Information and Technology) Vol 12 No 02 (2024): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v12i02.1304

Abstract

Grab is a company that provides digital-based transportation services that continues to improve the quality of its services. Grab offers a variety of service options, from transportation, goods delivery, to food delivery. Problems with the Grab application, such as usage problems, inaccurate location points, and fictitious orders, have an impact on consumer satisfaction. Analysis of aspects that impact satisfaction is needed to assess application performance and consumer satisfaction levels. The goal of this observation is to measure user satisfaction which can ultimately provide an overview of the success of implementing the system based on user perceptions using the TAM method. This research method uses TAM. The use of TAM in this research uses 5 variables in TAM including Perceived Ease of Use, Perceived Usefulness, Behavioral Intention, Actual Use, and Attitude Toward Using. The results obtained from this research in the form of independent variables have a contribution of 74.8% to user satisfaction, while the remaining 25.2% has an impact from other variables that are not present in this form of linear regression. This means that the independent variable has a contribution of 74.8% to user satisfaction, while the remaining 25.2% has an impact from other variables that are not present in this form of linear regression.
Co-Authors Achmad Nuruddin Safriandono 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 Fachrul Mustofa Fandi Yulian Pamuji Firnando, Fadel Muhamad Heru Agus Santoso Islam, Hussain Md Mehedul Jehadu, Yustina Vania Ghaisani Jutono Gondohanindijo Khamdani, Ahmad Rizal Luh Putu Ratna Sundari Marcus, Ronald David Mardiana Andarwati Ojugo, Arnold Adimabua Pradana, Akbar Ganang Pravisya, Raihan Ihza Rizza Muhammad Arief Satrio Bagus Imanulloh Swalaganata, Galandaru Syahroni Wahyu Iriananda Syahroni Wahyu Iriananda, Syahroni Wahyu Warto Warto Warto Warto Yulian Pamuji, Fandi