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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Sistem Komputer Jurnal Media Infotama Scientific Journal of Informatics JOIN (Jurnal Online Informatika) JISA (Jurnal Informatika dan Sains) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Hasil Penelitian dan Pengembangan (JHPP)
Wiranto Herry Utomo
President University
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Other

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Analisis perbandingan Cloud DOCUMENT pada EYeOS dan Google docs Utomo, Wiranto Herry; Pormes, Rivort
Jurnal Sistem Komputer Vol 5, No 1 (2015)
Publisher : Jurnal Sistem Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jsk.v5i1.77

Abstract

Penelitian ini bertujuan menganalisis perbandingan antara eyeOS dengan Google Docs sebagai alternatif yang lebih baik yang harus digunakan untuk membantu pengguna dalam masalah pendokumntasian.  Dokumen berupa dokumen pengolah kata, spreadsheet, dan presentation. Perbandingan antara eyeOS dengan Google Docs ini nantinya akan diketahui manakah yang paling efektif serta efisien yang dibutuhkan untuk keperluan dokumentasi dan dokumen dapat dibagikan ke para user lainnya.
Student performance prediction using simple additive weighting method Harco Leslie Hendric Spits Warnars; Arif Fahrudin; Wiranto Herry Utomo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v9.i4.pp630-637

Abstract

In the world of student education is an important component where the role of students is as someone who is psychologically ready to receive lessons or other input from the school. However, each student has different performance and development, therefore it is important to do monitoring so that student performance will always be monitored by the school for improving student quality maintenance. Also, in the process of valuing education for students needs to be done by giving an appreciation in the form of giving gifts or just giving words and motivation so that students can perform better in learning and participating in other activities at school. In terms of selecting students with good performance or those who have a very declining development using the school method not only assess students by one criterion but with several criteria to produce a decision that can be accepted by many people. Performance Students must also be monitored by the school or the related rights. In this paper, the student performance prediction was assessed with 5 criteria components and the result shows there are 10 very satisfy students, 10 satisfying students, 10 well students, and 10 Enough students from sample 40 students.
Climate Prediction Using RNN LSTM to Estimate Agricultural Products Based on Koppen Classification Novia Andini; Wiranto Herry Utomo
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.911

Abstract

The yield of an agricultural process is very important and influential, where the harvest is used as a support for human life both as food and a source of income. Many factors can influence the success of agriculture, such as the climate that is going on around in the surrounding area. The wrong prediction in determining the future climate will cause crop failure due to incompatibility with the type of plant. In this era, many technologies have been able to predict climate, one of which is technology machine learning that has many types and techniques, which machine learning technology has been widely used in predicting many things. This study aims to predict the climate in an area which is intended to determine crop yields based on the Koppen classification, and also the prediction based on several parameters such as temperature, humidity, duration of sun exposure and rainfall. And the results of this study is have a loss of 0.006 and with the MAPE value as an indicator of the percentage error and as an indicator for determining the accuracy of the prediction results, which is 3.29%, which means that it is included in the very accurate category in predicting climate to estimate agricultural yields.
IMPLEMENTASI KONSEP INFORMATION RETRIEVAL DENGAN METODE CASE INSENTIVE SEARCH PADA MESIN PENCARI DOKUMEN QUALITY BROTOKUNCORO, AGUNG; FAHMI, HASANUL; UTOMO, WIRANTO HERRY
Jurnal Hasil Penelitian dan Pengembangan (JHPP) Vol. 2 No. 1 (2024): Januari
Publisher : Perkumpulan Cendekia Muda Kreatif Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61116/jhpp.v2i1.286

Abstract

Information Retrieval adalah bidang yang berkaitan dengan struktur, analisis, organisasi, penyimpanan, pencarian, dan pengambilan informasi. Mesin pencari dokumen yang menerapkan konsep information retrieval ini telah tersedia di PT Chemco Harapan Nusantara namun belum menunjukkan performa yang optimal. Mesin pencari dokumen yang tersedia belum dapat menentukan informasi mana yang terbaik relevansinya sesuai dengan permintaan pengguna dikarenakan masih terikat pada nama dokumen dengan penempatan huruf kapital dan huruf kecil yang sama persis dengan dokumen yang tersedia, sehingga saat pengguna berbeda menempatkan huruf capital dan huruf kecil saat memasukkan query, maka mesin pencari dokumen tidak menunjukkan hasil pencarian. Mesin pencari dokumen tersebut belum dapat memprediksi apa yang sebenarnya diinginkan pengguna sehingga memerlukan waktu yang lama untuk menemukan dokumen. Konsep Information Retrieval memiliki banyak teknik diantaranya adalah metode case insensitive search yaitu teknik memperlakukan huruf besar dan kecil sebagai identik saat melakukan pencarian teks. Tujuan penelitian ini adalah menerapkan metode case insensitive search pada mesin pencarian dokumen untuk mencapai efficient search dengan meningkatkan relevansi dan akurasihasil pencarian melalui penelitian eksperimen kuantitatif. Hasil penelitian menujukkan dengan penerapan metode case insensitive search tercapai efficient search yang meliputi nilai Recall 0.85, nilai Presisi 1.00, dan nilai F1-Score 0.91. Pada hasil tertera bahwa nilai F1-Score mendekati 1.0 maka model memiliki kinerja yang baik dalam mencapai keseimbangan antara Presisi dan Recall.
Deep learning utilization in Sundanese script recognition for cultural preservation Rosalina, Rosalina; Afriliana, Nunik; Utomo, Wiranto Herry; Sahuri, Genta
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1759-1768

Abstract

This study addresses the challenge of preserving the Sundanese script, a traditional writing system of the Sundanese community in Indonesia, which is at risk of being forgotten due to technological advancements. To tackle this problem, we propose a deep learning approach using the YOLOv8 model for the automatic recognition of Sundanese characters. Our methodology includes creating a comprehensive dataset, applying augmentation techniques, and annotating the characters. The trained model achieved a precision of 95% after 150 epochs, demonstrating its effectiveness in recognizing Sundanese characters. While some variability in accuracy was observed for certain characters and real-time applications, the results indicate the feasibility and promise of using deep learning for Sundanese script recognition. This research highlights the potential of technological solutions to digitize and preserve the Sundanese script, ensuring its continued legacy and accessibility for future generations. Thus, we contribute to cultural preservation by providing a method to safeguard the Sundanese script against obsolescence.
Privacy-preserving data mining optimization for big data analytics using deep reinforcement learning Utomo, Wiranto Herry; Rosalina, Rosalina; Afriyadi, Afriyadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1929-1937

Abstract

The rapid growth of big data analytics has heightened concerns about data privacy, necessitating the development of advanced privacy-preserving techniques. This research addresses the challenge of optimizing privacy-preserving data mining (PPDM) for big data analytics through the innovative application of deep reinforcement learning (DRL). We propose a novel framework that integrates DRL to dynamically balance privacy and utility, ensuring robust data protection while maintaining analytical accuracy. The framework employs a reinforcement learning agent to adaptively select optimal privacy-preserving strategies based on the evolving data environment and user requirements, while ensuring compliance with the latest security and privacy standards such as ISO/IEC 27001:2023. Experimental results demonstrate significant improvements in both privacy protection and data utility, surpassing traditional PPDM methods. Our findings highlight the potential of DRL in enhancing privacy-preserving mechanisms, offering a scalable and efficient solution for secure big data analytics.
CatBoost Optimization Using Recursive Feature Elimination Hadianto, Agus; Utomo, Wiranto Herry
JOIN (Jurnal Online Informatika) Vol 9 No 2 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i2.1324

Abstract

CatBoost is a powerful machine learning algorithm capable of classification and regression application. There are many studies focusing on its application but are still lacking on how to enhance its performance, especially when using RFE as a feature selection. This study examines the CatBoost optimization for regression tasks by using Recursive Feature Elimination (RFE) for feature selection in combination with several regression algorithm. Furthermore, an Isolation Forest algorithm is employed at preprocessing to identify and eliminate outliers from the dataset. The experiment is conducted by comparing the CatBoost regression model's performances with and without the use of RFE feature selection. The outcomes of the experiments indicate that CatBoost with RFE, which selects features using Random Forests, performs better than the baseline model without feature selection. CatBoost-RFE outperformed the baseline with notable gains of over 48.6% in training time, 8.2% in RMSE score, and 1.3% in R2 score. Furthermore, compared to AdaBoost, Gradient Boosting, XGBoost, and artificial neural networks (ANN), it demonstrated better prediction accuracy. The CatBoost improvement has a substantial implication for predicting the exhaust temperature in a coal-fired power plant.
Deblurring Photos with Lucy-Richardson and Wiener Filter Algorithm in RGBA Color Rustam, Michiavelly; Brotokuncoro, Agung; Utomo, Wiranto Herry; Fahmi, Hasanul
Jurnal Media Infotama Vol 21 No 1 (2025): April 2025
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v21i1.6709

Abstract

Photographers and social media influencers encounter challenges with hand tremors during photo capture, leading to unintended blurriness in their posts, reducing visual impact and audience engagement. To mitigate this problem, the authors aim to effectively reduce the blurring caused by instability in handling, producing sharper and noise-free photos. The methodology involves implementing the Lucy-Richardson and Wiener Filter algorithms into a Python-based web application optimized for RGBA photo processing. Data requirements include sample photos affected by hand tremors to validate the efficacy of the solution. The outcome successfully eliminates blur in captured photos affected by hand tremors in RGBA color format.
Arabica Coffee Price Prediction Using the Long Short Term Memory Network (LSTM) Algorithm Setiyani, Lila; Utomo, Wiranto Herry
Scientific Journal of Informatics Vol 10, No 3 (2023): August 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i3.44162

Abstract

Purpose:  Arabica coffee beans have been widely cultivated in various parts of the world. The need for coffee beans is estimated to increase every year. This was followed by the rapid growth of franchised coffee shops and cafes, therefore Arabica coffee beans have been traded legally in the world, thus making the price of these Arabica coffee beans a public concern. This prediction of the price of Arabica coffee beans can be input for business actors in the coffee shop, café franchises, and farmers in the decision-making process. This study aims to predict the price of Arabica coffee beans in 2023 and 2024 using the long short-term memory (LSTM) Algorithm.Methods:  The research procedure is carried out by collecting data, data analysis, and preprocessing, and building a forecasting model using the Long Short-Term Memory Network (LSTM) algorithm. Arabica coffee bean price datasets in this study were taken from The Pink Sheet World Bank Commodity Price Data, which presents Arabica coffee bean prices from 1960 to February 2023.Results:  The results of this study indicate the predicted price of Arabica coffee beans in 2023 and 2024 with Error (MAE), which is the average absolute difference between the actual value and the predicted value.Novelty:  What is most important and what differentiates it from previous research is in the preprocessing using two algorithms namely MinMaxScaler and Sliding Window. Meanwhile, for the training model, GridSearchCV is used. The model is evaluated using the lost function using Mean Squared Error (MSE) and Mean Absolute Error (MAE) thereby making it easy to evaluate the performance of the model. 
Co-Authors Afriliana, Nunik Afriyadi, Afriyadi Arif Fahrudin BROTOKUNCORO, AGUNG Fahmi, Hasanul Hadianto, Agus Lila Setiyani Novia Andini Pormes, Rivort Rosalina Rosalina Rustam, Michiavelly Sahuri, Genta Sahuri Warnars, Harco Leslie Hendric Spits