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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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Optimization of Application Deployment Architecture in Container Orchestration Fachrudin, Mochamad Rizal; Muslikh, Ahmad Rofiqul
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.8972

Abstract

Container orchestration has become a widely adopted standard for application deployment among medium to large-scale organizations. Docker Swarm is one of the popular container orchestration tools due to its relatively simple configuration. However, if the Docker Swarm cluster architecture is not properly designed, the goal of container orchestration, which is availability, cannot be achieved optimally. Challenges such as centralized traffic on a single node and service dependency on a single node are critical issues that need to be addressed. This study proposes solutions through an experimental approach involving the design, implementation, testing, and evaluation of a Docker Swarm cluster architecture to address these challenges. The results of this study demonstrate that the proposed architecture successfully resolves these issues. Traffic can be distributed more evenly across all nodes. When only one node is available, 5 out of 10 requests can be handled with a response latency of 197.4 ms. With two nodes available, the number of requests handled increases to 7 out of 10, with a response latency of 534.86 ms. The greater the number of available nodes, the more requests can be successfully processed. Services also become more flexible, and capable of running on any node, while offering additional benefits such as dual load balancing through DNS-based load balancing and the default load balancing provided by Docker Swarm's routing mesh. However, limitations such as the need for more complex adjustments and configurations should be considered, especially when implementing this architecture in on-premise environments, to ensure the best adoption and results.
Perancangan Desain UI/UX Berbasis Aplikasi pada Perumda Air Minum Tirta Komodo Kabupaten Manggarai Menggunakan Metode Design Thinking Jehadu, Yustina Vania Ghaisani; Muslikh, Ahmad Rofiqul
Jurnal Vokasi Teknik Informatika Vol 4 No 1 (2024)
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/javit.v4i1.166

Abstract

Perkembangan teknologi informasi dan komunikasi telah mengubah cara manusia berinteraksi dengan informasi, termasuk dalam sektor pelayanan publik seperti Perusahaan Daerah Air Minum (PDAM). PDAM adalah singkatan dari Perusahaan Daerah Air Minum, yang merupakan badan usaha milik pemerintah daerah yang menyediakan air bersih kepada warga di Indonesia. Tugas utama PDAM adalah menyediakan air bersih, membangun infrastruktur dan menyediakan saluran air bagi masyarakat. Penelitian ini bertujuan untuk meningkatkan kualitas pelayanan pada Perumda Air Minum Tirta Komodo Kabupaten Manggarai dalam penyediaan air bersih kepada masyarakat. Dalam hal ini, peneliti akan menganalisis kebutuhan pelanggan terkait layanan yang diberikan oleh Perumda Air Minum Tirta Komodo Kabupaten Manggarai. Metode yang digunakan peneliti yaitu Metode Design Thinking. Peneliti menggunakan kuisioner untuk mencapai tujuan dari penelitian ini. Dan dari penelitian ini, peneliti menemukan permasalahan yaitu, masyarakat masih kesulitan dalam melakukan pembayaran secara manual, pihak PDAM sendiri juga masih sulit untuk pemantauan konsumsi, pemberitahuan dan peringatan, pelaporan masalah maupun informasi dan edukasi. Jadi, solusi dari permasalahan ini yaitu, peneliti menghasilkan sebuah rancangan desain UI/UX yang diharapkan dapat mengatasi masalah yang ada yang dihadapi oleh masyarakat maupun pihak Perumda Air Minum Tirta Komodo Kabupaten Manggarai.
Komparasi Metode Mean dan KNN Imputation dalam Mengatasi Missing Value pada Dataset Kecil Yulian Pamuji, Fandi; Ahmad Rofiqul Muslikh; Rizza Muhammad Arief; Delviana Muti
Jurnal Informatika Polinema Vol. 10 No. 2 (2024): Vol 10 No 2 (2024)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v10i2.5031

Abstract

Missing value pada dataset yang kecil akan mengakibatkan berkurangnya data yang dapat digunakan untuk pembelajaran sehingga prediksi hasil klasifikasi dari data tersebut akan berkurang. Metode Imputasi sebagai solusi metode yang paling umum digunakan untuk menangani masalah dataset yang tidak lengkap. Metode Imputasi proses di mana beberapa teknik statistik digunakan untuk mengganti data yang hilang dengan nilai pengganti. Tujuan penelitian ini dengan kinerja klasifikasi yang dapat dipertahankan dengan metode imputasi missing value, karena metode ini dapat menghindari berkurangnya jumlah dataset yang digunakan dalam proses klasifikasi pada dataset dan meningkatkan kinerja klasifikasi pada dataset yang tidak ideal terutama untuk jumlah dataset yang kecil. Berdasarkan hasil eksperimen yang telah dilakukan dari penelitian ini yaitu bahwa pengujian metode imputasi Mean dan KNN Imputation dengan metode klasifikasi mampu menangani data kosong dengan jumlah missing value sedikit maupun banyak dengan menghasilkan nilai accuracy mencapai kinerja prediksi yang lebih besar dibandingkan dengan menggunakan missing value nilai 0. Kemudian untuk dataset Hepatitis nilai Accuracy tinggi menggunakan metode imputasi KNN Imputasi dengan nilai 0,79 menggunakan metode Logistic Regression dan dataset Ginjal Kronis nilai Accuracy tinggi menggunakan metode imputasi Mean dengan nilai 0,97 dengan menggunakan metode Naïve Bayes. Hal tersebut menunjukkan bahwa proses metode imputasi terhadap nilai kosong disetiap column dataset kecil pada tahap data preprocessing memberikan pengaruh terhadap nilai Accuracy metode Mean dan KNN Imputation pada metode klasifikasi.
PERAMALAN JUMLAH PENDUDUK KOTA PASURUAN DENGAN MENGGUNAKAN METODE SINGLE DAN DOUBLE EXPONENTIAL SMOOTHING Pravisya, Raihan Ihza; Muslikh, Ahmad Rofiqul
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.
RICE DISEASE RECOGNITION USING TRANSFER LEARNING XCEPTION CONVOLUTIONAL NEURAL NETWORK Muslikh, Ahmad Rofiqul; Setiadi, De Rosal Ignatius Moses; Ojugo, Arnold Adimabua
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.
Plant Diseases Classification based Leaves Image using Convolutional Neural Network Satrio Bagus Imanulloh; Ahmad Rofiqul Muslikh; De Rosal Ignatius Moses Setiadi
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 Fachrul Mustofa; Achmad Nuruddin Safriandono; Ahmad Rofiqul Muslikh; De Rosal Ignatius Moses Setiadi
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.
Outlier Detection Using Gaussian Mixture Model Clustering to Optimize XGBoost for Credit Approval Prediction De Rosal Ignatius Moses Setiadi; Ahmad Rofiqul Muslikh; Syahroni Wahyu Iriananda; Warto Warto; Jutono Gondohanindijo; Arnold Adimabua Ojugo
Journal of Computing Theories and Applications Vol. 2 No. 2 (2024): JCTA 2(2) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11638

Abstract

Credit approval prediction is one of the critical challenges in the financial industry, where the accuracy and efficiency of credit decision-making can significantly affect business risk. This study proposes an outlier detection method using the Gaussian Mixture Model (GMM) combined with Extreme Gradient Boosting (XGBoost) to improve prediction accuracy. GMM is used to detect outliers with a probabilistic approach, allowing for finer-grained anomaly identification compared to distance- or density-based methods. Furthermore, the data cleaned through GMM is processed using XGBoost, a decision tree-based boosting algorithm that efficiently handles complex datasets. This study compares the performance of XGBoost with various outlier detection methods, such as LOF, CBLOF, DBSCAN, IF, and K-Means, as well as various other classification algorithms based on machine learning and deep learning. Experimental results show that the combination of GMM and XGBoost provides the best performance with an accuracy of 95.493%, a recall of 91.650%, and an AUC of 95.145%, outperforming other models in the context of credit approval prediction on an imbalanced dataset. The proposed method has been proven to reduce prediction errors and improve the model's reliability in detecting eligible credit applications.
Aspect-Based Sentiment Analysis on E-commerce Reviews using BiGRU and Bi-Directional Attention Flow De Rosal Ignatius Moses Setiadi; Warto Warto; Ahmad Rofiqul Muslikh; Kristiawan Nugroho; Achmad Nuruddin Safriandono
Journal of Computing Theories and Applications Vol. 2 No. 4 (2025): JCTA 2(4) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.12376

Abstract

Aspect-based sentiment Analysis (ABSA) is vital in capturing customer opinions on specific e-commerce products and service attributes. This study proposes a hybrid deep learning model integrating Bi-Directional Gated Recurrent Units (BiGRU) and Bi-Directional Attention Flow (BiDAF) to perform aspect-level sentiment classification. BiGRU captures sequential dependencies, while BiDAF enhances attention by focusing on sentiment-relevant segments. The model is trained on an Amazon review dataset with preprocessing steps, including emoji handling, slang normalization, and lemmatization. It achieves a peak training accuracy of 99.78% at epoch 138 with early stopping. The model delivers a strong performance on the Amazon test set across four key aspects: price, quality, service, and delivery, with F1 scores ranging from 0.90 to 0.92. The model was also evaluated on the SemEval 2014 ABSA dataset to assess generalizability. Results on the restaurant domain achieved an F1-score of 88.78% and 83.66% on the laptop domain, outperforming several state-of-the-art baselines. These findings confirm the effectiveness of the BiGRU-BiDAF architecture in modeling aspect-specific sentiment across diverse domains.
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 Yulian Pamuji, Fandi