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All Journal Techno.Com: Jurnal Teknologi Informasi Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik JAIS (Journal of Applied Intelligent System) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING CSRID (Computer Science Research and Its Development Journal) Abdimasku : Jurnal Pengabdian Masyarakat JoMMiT : Jurnal Multi Media dan IT Journal of Computing Theories and Applications Jurnal Informatika: Jurnal Pengembangan IT Journal of Multiscale Materials Informatics Journal of Future Artificial Intelligence and Technologies
Noor Ageng Setiyanto, Noor Ageng
Universitas Dian Nuswantoro
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences

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Artificial Intelligence Berbasis QSPR Dalam Kajian Inhibitor Korosi Muhamad Akrom; Usman Sudibyo; Achmad Wahid Kurniawan; Noor Ageng Setiyanto; Ayu Pertiwi; Aprilyani Nur Safitri; Novianto Hidayat; Harun Al Azies; Wise Herawati
JoMMiT Vol 7, No 1 (2023)
Publisher : Politeknik Negeri Media Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46961/jommit.v7i1.721

Abstract

Baja termasuk material yang memiliki ketahanan rendah terhadap serangan korosi Ketika berada pada lingkungan korosif. Inhibitor organik mampu menghambat korosi dengan efisiensi inhibisi yang tinggi. Tinjauan komparatif penting bagi pengembangan metode evaluasi kinerja inhibitor disajikan dalam karya ini. Kami mereview perkembangan artificial intelligence berbasis mesin learning dengan model QSPR dalam kajian penghambatan korosi. Makalah ini menjelaskan bagaimana metode pembelajaran mesin berbasis data dapat menghasilkan model yang menghubungkan sifat-aktivitas molekuler dengan penghambatan korosi oleh inhibitor berbasis bahan alam (green inhibitor). Teknik ini dapat digunakan untuk memprediksi kinerja senyawa yang belum disintesis atau diuji. Keberhasilan model ini memberikan paradigma untuk penemuan senyawa baru yang cepat, penghambat korosi yang efektif untuk berbagai logam dan paduan.
Optimasi Keamanan Watermarking pada Daubechies Transform Berbasis Arnold Cat Map Abdussalam Abdussalam; Eko Hari Rachmawanto; Noor Ageng Setiyanto; De Rosal Ignatius Moses Setiadi; Christy Atika Sari
Jurnal Informatika: Jurnal Pengembangan IT Vol 4, No 1 (2019): JPIT, Januari 2019
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v4i1.911

Abstract

Digital image security using Transform Domain algorithms such as Discrete Wavelet Transform (DWT) has been widely used. To improve the security of the DWT algorithm needs to randomize the pixel coefficient, namely Arnold Cat Map (ACM). Computing ACM as one of the chaos functions is known to be fast and fits with Transform Domain. DWT has been implemented in the Daubechies filter which is the development of the Haar filer. In this paper, we proposed the message insertion model using a combination of DWT and ACM on a 512x512 piskel grayscale image and a 64x64 pixel message on the LL subband. The experiments were performed on 2 different images to determine the ability produced by the combined algroithm. The ability test for message insertion process is done through Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and comparation between original image histogram and image insertion histogram. While in the process of message extraction, algorithmic capability test is done by calculating Normalized Cross Correlation (NCC) and its correlation. The highest MSE result is 2.9502 and the highest PSNR is 43.4323 dB, while the NCC value is 237.3584 with correlation 0.7181.
Artificial Intelligence Berbasis QSPR Dalam Kajian Inhibitor Korosi Akrom, Muhamad; Sudibyo, Usman; Kurniawan, Achmad Wahid; Setiyanto, Noor Ageng; Pertiwi, Ayu; Safitri, Aprilyani Nur; Hidayat, Novianto; Al Azies, Harun; Herawati, Wise
JoMMiT Vol 7 No 1 (2023): Artikel Jurnal Volume 7 Issue 1, Juni 2023
Publisher : Politeknik Negeri Media Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46961/jommit.v7i1.721

Abstract

Baja termasuk material yang memiliki ketahanan rendah terhadap serangan korosi Ketika berada pada lingkungan korosif. Inhibitor organik mampu menghambat korosi dengan efisiensi inhibisi yang tinggi. Tinjauan komparatif penting bagi pengembangan metode evaluasi kinerja inhibitor disajikan dalam karya ini. Kami mereview perkembangan artificial intelligence berbasis mesin learning dengan model QSPR dalam kajian penghambatan korosi. Makalah ini menjelaskan bagaimana metode pembelajaran mesin berbasis data dapat menghasilkan model yang menghubungkan sifat-aktivitas molekuler dengan penghambatan korosi oleh inhibitor berbasis bahan alam (green inhibitor). Teknik ini dapat digunakan untuk memprediksi kinerja senyawa yang belum disintesis atau diuji. Keberhasilan model ini memberikan paradigma untuk penemuan senyawa baru yang cepat, penghambat korosi yang efektif untuk berbagai logam dan paduan.
Web Phishing Classification using Combined Machine Learning Methods Waseso, Bambang Mahardhika Poerbo; Setiyanto, Noor Ageng
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.v1i1.8898

Abstract

Phishing is a crime that uses social engineering techniques, both in deceptive statements and technically, to steal consumers' personal identification data and financial account credentials. With the new Phishing machine learning approach, websites can be recognized in real-time. K-Nearest Neighbor(KNN) and Naïve Bayes (NB) are popular machine learning approaches. KNN and NB have their own strengths and weaknesses. By combining the two, deficiencies can be covered. So this study proposes to combine K-Nearest Neighbor with Naïve Bayes to classify phishing websites. Based on the results of the accuracy test of the combination of KNN with k=8 and Naïve Bayes, a maximum accuracy of 93.44% is produced. This result is 6.25% superior compared to using only one classifier.
Pelatihan Model Computational Thinking bagi Guru TK dan SD Gaussian Kamil School Semarang Trisnapradika, Gustina Alfa; Pertiwi, Ayu; Prabowo, Wahyu Eko Aji; Setiyanto, Noor Ageng; Putra Sumarjono, Cornellius Adryan
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 2 (2024): MEI 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i2.1888

Abstract

CT is the ability to think to solve problems whose solution is computing. CT abilities cannot possibly grow in an instant, CT knowledge and skills need time to grow and develop so that they produce as expected. CT training for kindergarten and elementary school teachers at Gaussian Kamil School will be carried out in several stages, starting with an introduction to the CT concept, practice questions, practicing CT using digital methods, and Unplugged. It is hoped that this training can improve teachers' CT abilities, so that teachers will infuse it with students. Students who receive CT from an early age are expected to be able to be independent and behave better because they are used to solving problems in the correct, fast and efficient way.
A Machine Learning Model for Evaluation of the Corrosion Inhibition Capacity of Quinoxaline Compounds Setiyanto, Noor Ageng; Azies, Harun Al; Sudibyo, Usman; Pertiwi, Ayu; Budi, Setyo; Akrom, Muhamad
Journal of Multiscale Materials Informatics Vol. 1 No. 1 (2024): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v1i1.10429

Abstract

Investigating potential corrosion inhibitors via empirical research is a labor- and resource-intensive process. In this work, we evaluated various linear and non-linear algorithms as predictive models for corrosion inhibition efficiency (CIE) values using a machine learning (ML) paradigm based on the quantitative structure-property relationship (QSPR) model. In the quinoxaline compound dataset, our analysis showed that the XGBoost model performed the best predictor of other ensemble-based models. The coefficient of determination (R2), mean absolute percentage error (MAPE), and root mean squared error (RMSE) metrics were used to objectively assess this superiority. To sum up, our study offers a fresh viewpoint on the effectiveness of machine learning algorithms in determining the ability of organic compounds like quinoxaline to suppress corrosion on iron surfaces.
Comprehensive Exploration of Machine and Deep Learning Classification Methods for Aspect-Based Sentiment Analysis with Latent Dirichlet Allocation Topic Modeling Setiadi, De Rosal Ignatius Moses; Marutho, Dhendra; Setiyanto, Noor Ageng
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.2024-3

Abstract

This research explores the effectiveness of machine learning (ML) and deep learning (DL) classification methods in Aspect-Based Sentiment Analysis (ABSA) on product reviews, incorporating Latent Dirichlet Allocation (LDA) for topic modeling. Using the Amazon reviews dataset, this research tests models such as Naive Bayes (NB), Support Vector Machines (SVM), Random Forest (RF), Long Short-Term Memory (LSTM), and Gated Recurrent Units(GRU). Important aspects such as the product's quality, practicality, and reliability are discussed. The results show that the RF and DL models provide competitive performance, with the RF achieving an accuracy of up to 94.50% and an F1 score of 95.45% for the reliability aspect. The study's conclusions emphasize the importance of selecting an appropriate model based on specifications and data requirements for ABSA, as well as recognizing the need to strike a balance between accuracy and computational efficiency.
Exploring Deep Q-Network for Autonomous Driving Simulation Across Different Driving Modes Setiawan, Marcell Adi; Setiadi, De Rosal Ignatius Moses; Astuti, Erna Zuni; Sutojo, T.; Setiyanto, Noor Ageng
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 3 (2024): December 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.3048-3719-31

Abstract

The rapid growth in vehicle ownership has led to increased traffic congestion, making the need for autonomous driving solutions more urgent. Autonomous Vehicles (AVs) offer a promising solution to improve road safety and reduce traffic accidents by adapting to various driving conditions without human intervention. This research focuses on implementing Deep Q-Network (DQN) to enhance AV performance in different driving modes: safe, normal, and aggressive. DQN was selected for its ability to handle complex, dynamic environments through experience replay, asynchronous training, and epsilon-greedy exploration. We designed a simulation environment using the Highway-env platform and evaluated the DQN model under varying traffic densities. The performance of the AV was assessed based on two key metrics: success rate and total reward. Our findings show that the DQN model achieved a success rate of 90.75%, 94.625%, and 95.875% in safe, normal, and aggressive modes, respectively. Although the success rate increased with traffic intensity, the total reward remained lower in aggressive driving scenarios, indicating room for optimization in decision-making processes under highly dynamic conditions. This study demonstrates that DQN can adapt effectively to different driving needs, but further optimization is needed to enhance performance in more challenging environments. Future work will focus on improving the DQN algorithm to maximize both success rate and reward in high-traffic scenarios and testing the model in more diverse and complex environments.
Penerapan Gamifikasi Materi Pembelajaran Tingkat SMA dengan Menggunakan Wordwall Setiyanto, Noor Ageng; Hidayat, Novianto Nur; Akrom, Muhamad; Pertiwi, Ayu; Aprihartha, Moch. Anjas; Safitri, Aprilyani Nur; Sudibyo, Usman; Prabowo, Wahyu Aji Eko; Al Azies, Harun; Naufal, Muhammad
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 1 (2025): JANUARI 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i1.2851

Abstract

Kegiatan Pengabdian Masyarakat ini dilaksanakan di SMA Negeri 2 Mranggen, Demak, dengan tujuan untuk menciptakan variasi materi pembelajaran melalui proses gamifikasi, sehingga pembelajaran menjadi lebih menarik dan interaktif bagi siswa tingkat menengah. Tema dari kegiatan ini adalah gamifikasi materi pembelajaran menggunakan alat bantu Wordwall, yang memungkinkan pengintegrasian elemen permainan dalam proses belajar-mengajar. Kegiatan ini melibatkan para guru di SMA Negeri 2 Mranggen, Demak. Metode yang digunakan meliputi observasi untuk memahami kebutuhan pembelajaran di sekolah, serta pelatihan langsung dalam bentuk seminar, demonstrasi, dan sesi diskusi interaktif. Teknik ini dirancang agar para guru dapat memahami konsep gamifikasi, mempraktikkan penggunaan Wordwall, dan mengembangkan materi ajar yang kreatif serta sesuai dengan kurikulum yang ada. Hasil kegiatan menunjukkan bahwa implementasi gamifikasi materi pembelajaran melalui Wordwall efektif dalam meningkatkan pemahaman guru terhadap konsep gamifikasi. Selain itu, para guru merasa terbantu dan termotivasi untuk menciptakan materi pembelajaran yang lebih kreatif, menarik, dan dinamis.
Prediction of Corrosion Inhibitor Efficiency Based on Quinoxaline Compounds Using Polynomial Regression Rana, Bastion Jader; Setiyanto, Noor Ageng; Akrom, Muhamad
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.v9i2.9031

Abstract

Corrosion is a natural process that leads to material degradation due to environmental factors. It significantly impacts financial and safety aspects, including structural weakening and economic losses in various industries such as oil, gas, and nuclear. Corrosion inhibitors, especially organic compounds like quinoxaline, are widely used to reduce corrosion by forming protective layers on metal surfaces. Quinoxaline compounds, characterized by their heterocyclic structure with nitrogen atoms, demonstrate promising inhibition efficiency in corrosive environments. In this study, machine learning (ML) approaches are utilized to predict the corrosion inhibition efficiency of quinoxaline compounds. Algorithms such as Gradient Boosting Regressor (GBR), Extreme Gradient Boosting Regressor (XGBR), and Automatic Relevance Determination (ARD) regression are compared. The implementation of polynomial functions significantly improves the prediction accuracy of these models. Among them, GBR achieved the best value with MSE, RMSE, MAE, MAPE, and R2 values of 0.0000001, 0.0003229, 0.0000029, 0.0002294, and 0.999999998, respectively. These findings highlight the potential of polynomial-enhanced ML models in accurately predicting corrosion inhibition efficiency. Moreover, the study demonstrates the viability of GBR as a reliable tool for analyzing and optimizing corrosion inhibitors for industrial applications.
Co-Authors Abdussalam Abdussalam Achmad Wahid Kurniawan Achmad Wahid Kurniawan Akrom, Muhamad Aprilyani Nur Safitri Ayu Pertiwi Ayu Pertiwi Budi, Setyo Cahaya Jatmoko Christy Atika Sari Daurat Sinaga De Rosal Ignatius Moses Setiadi Desi Purwanti Kusumaningrum Dhendra Marutho Dyarsa Singgih Pamungkas Eko Hari Rachmawanto Erlin Dolphina Erna Zuni Astuti Erwin Yudi Hidayat Feri Agustina Hanny Haryanto Harun Al Azies Herawati, Wise Hidayat, Novianto Hidayat, Novianto Nur Khafiizh Hastuti Kurnia, Lisa Lukman Sandjaya Moch Anjas Aprihartha Moch Arief Soeleman Muhamad Akrom Muhammad Naufal Novianto Hidayat Prabowo, Wahyu Aji Eko Prabowo, Wahyu Eko Aji Pujiono Pujiono Pulung Nurtantio Andono Putra Sumarjono, Cornellius Adryan Rana, Bastion Jader Safitri, Aprilyani Nur Setiawan, Marcell Adi Sinaga, Daurat Sudibyo, Usman Suprayogi Suprayogi T. Sutojo Tirta Indra Wibowo Trisnapradika, Gustina Alfa Usman Sudibyo Waseso, Bambang Mahardhika Poerbo Wise Herawati