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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JURNAL DINAMIKA EKONOMI PEMBANGUNAN Jurnal Ilmu Dasar SAINSMAT IPTEK Journal of Proceedings Series KONSELI: Jurnal Bimbingan dan Konseling (E-Journal) Journal of Regional and City Planning Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi Journal of Applied Geospatial Information Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA JTERA (Jurnal Teknologi Rekayasa) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Unisda Journal of Mathematics and Computer Science (UJMC) Jurnal Penelitian dan Pengembangan Pelayanan Kesehatan International Journal of Pedagogy and Teacher Education J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika METIK JURNAL Building of Informatics, Technology and Science Jurnal Teknologi Informasi dan Terapan (J-TIT) Jurnal Perencanaan Pembangunan Journal of Education and Learning Mathematics Research (JELMaR) International Journal of Advances in Data and Information Systems Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Layanan Masyarakat (Journal of Public Service) TheJournalish: Social and Government The Journal of Indonesia Sustainable Development Planning (JISDeP) JoMMiT : Jurnal Multi Media dan IT Jurnal Riset Ilmu Ekonomi Jurnal Algoritma East Java Economic Journal SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam Jurnal Mahasiswa Ilmu Komputer PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Journal of Multiscale Materials Informatics Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial
Harun Al Azies
Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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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.
Deep learning for audio signal-based tempo classification scenarios Muljono, Muljono; Nurtantio Andono, Pulung; Ayu Wulandari, Sari; Al Azies, Harun; Naufal, Muhammad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1687-1701

Abstract

This article explains how to determine the tempo of the kendhang, an Indonesian traditional melodic instrument. This research presents novelty as technological research related to gamelan instruments, which has rarely been achieved thus far, through the introduction of kendhang tempo types through the sounds produced, with the hope of creating an automatic system that can recognize the kendhang tempo during a gamelan performance. The testing in this work will categorize the tempo of kendhang into three categories: slow, medium, and fast, utilizing one of the two scenario models proposed, mel frequency cepstral coefficients (MFCC) and convolutional neural network (CNN) in the first scenario, and mel spectrogram and CNN in the second. Kendhang's original audio data, which was captured in real time and later enhanced, makes up the data set. The model 1 scenario, which entails feature extraction using MFCC and classification using the CNN classification approach, is the best scenario in this research, based on the experimental results. When compared to the other suggested modeling scenarios, model 1 has a level of 97%, an average accuracy, and a gain value of 96.67%, making it a solid assistant in terms of kendhang's good tempo recognition accuracy.
Comparing Haar Cascade and YOLOFACE for Region of Interest Classification in Drowsiness Detection Andrean, Muhammad Niko; Shidik, Guruh Fajar; Naufal, Muhammad; Zami, Farrikh Al; Winarno, Sri; Azies, Harun Al; Putra, Permana Langgeng Wicaksono Ellwid
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7167

Abstract

Driver drowsiness poses a serious threat to road safety, potentially leading to fatal accidents. Current research often relies on facial features, specific eye components, and the mouth for drowsiness classification. This causes a potential bias in the classification results. Therefore, this study shifts its focus to both eyes to mitigate potential biases in drowsiness classification.This research aims to compare the accuracy of drowsiness detection in drivers using two different image segmentation methods, namely Haar Cascade and YOLO-face, followed by classification using a decision tree algorithm. The dataset consists of 22,348 images of drowsy driver faces and 19,445 images of non-drowsy driver faces. The segmentation results with YOLO-face prove capable of producing a higher-quality Region of Interest (ROI) and training data in the form of eye images compared to segmentation results using the Haar Cascade method. After undergoing grid search and 10-fold cross-validation processes, the decision tree model achieved the highest accuracy using the entropy parameter, reaching 98.54% for YOLO-face segmentation results and 98.03% for Haar Cascade segmentation results. Despite the slightly higher accuracy of the model utilizing YOLO-face data, the YOLO-face method requires significantly more data processing time compared to the Haar Cascade method. The overall research results indicate that implementing the ROI concept in input images can enhance the focus and accuracy of the system in recognizing signs of drowsiness in drivers.
Classification of Underdeveloped Areas in Indonesia Using the SVM and k-NN Algorithms Al Azies, Harun; Anuraga, Gangga
Jurnal ILMU DASAR Vol 22 No 1 (2021)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/jid.v22i1.16928

Abstract

The determination or classification of underdeveloped areas essentially consists of classifying several observations taking into account existing indicators. The classification method used is K-Nearest Neighbor (k-NN) and Support Vector Machines (SVM). This study aims to analyze the accuracy of the classification between SVM and k-NN algorithms in the classification of underdeveloped areas in Indonesia. The data source used in this study is secondary data obtained from the Central Bureau of Statistics (BPS). The data used are 514 districs and municipalities of Indonesia. After analysis, the conclusion is that there are 122 districs and municipalities that are left behind out of a total of 514 districs and municipalities in Indonesia. The most underdeveloped areas are on the island of Papua, followed by the areas of the islands of Bali and Nusa Tenggara, and Sulawesi. Based on the results of the classification of underdeveloped areas using the method SVM with the kernel RBF has the best results with the parameters C = 1 and γ = 0.05 while the results of the classification of underdeveloped areas using the method k-NN obtains the best results with k = 15 Based on the results of classification of underdeveloped areas using the SVM and the k-NN method, including the level of classification is very good. The two methods compared have the same precision value of 92.2% and can be used to determine the classification of underdeveloped areas. Keywords: classification, machine learning, supervised learning, underdeveloped areas.
Predicting Methanol Space-Time Yield from CO2 Hydrogenation Using Machine Learning: Statistical Evaluation of Penalized Regression Techniques Al Azies, Harun; Akrom, Muhamad; Budi, Setyo; Alfa Trisnapradika, Gustina; Nur Safitri, Aprilyani
International Journal of Advances in Data and Information Systems Vol. 5 No. 2 (2024): October 2024 - International Journal of Advances in Data and Information System
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v5i2.1341

Abstract

This study investigates the effectiveness of machine learning techniques, specifically penalized regression models Ridge Regression, Lasso Regression, and Elastic Net Regression in predicting methanol space-time yield (STY) from CO2 hydrogenation data. Using a dataset derived from Cu-based catalyst research, the study implemented a comprehensive preprocessing approach, including data cleaning, imputation, outlier removal, and normalization. The models were rigorously evaluated through 10-fold cross-validation and tested on unseen data. Ridge Regression outperformed the other models, achieving the lowest Root Mean Squared Error (RMSE) of 0.7706, Mean Absolute Error (MAE) of 0.5627, and Mean Squared Error (MSE) of 0.5938. In comparison, Lasso and Elastic Net Regression models exhibited higher error metrics. Feature importance analysis revealed that Gas Hourly Space Velocity (GHSV) and Molar Masses of Support significantly influence catalytic activity. These findings suggest that Ridge Regression is a promising tool for accurately predicting methanol production, providing valuable insights for optimizing catalytic processes and advancing sustainable practices in chemical engineering.
Ensemble Stacking of Machine Learning Approach for Predicting Corrosion Inhibitor Performance of Pyridazine Compounds Ariyanto, Noval; Azies, Harun Al; Akrom, Muhamad
International Journal of Advances in Data and Information Systems Vol. 5 No. 2 (2024): October 2024 - International Journal of Advances in Data and Information System
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v5i2.1346

Abstract

Corrosion is a major challenge affecting various industrial sectors, leading to increased operational costs and decreased equipment efficiency. The use of organic corrosion inhibitors is one of the promising solutions. This study applies an ensemble algorithm with a stacking method to estimate pyridazine-derived compounds corrosion inhibition efficiency. This study utilized various molecular characteristics of pyridazine compounds as inputs to predict inhibition efficiency values. After evaluating several boosting models, the stacking technique was chosen as it showed the best results. Stacking Model 6, which combines XGB, LGBM, and CatBoost as the base model with Random Forest as the meta-model, produced the most accurate prediction with an RMSE of 0.055. These findings indicate that machine learning approaches can effectively and efficiently predict corrosion inhibitor performance. This method offers a faster and more economical alternative to conventional experimental methods.
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.
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.
The Effectiveness of Continuous Formative Assessment in Hybrid Learning Models: An Empirical Analysis in Higher Education Institutions Sri Winarno; Harun Al Azies
International Journal of Pedagogy and Teacher Education Vol 8, No 1 (2024): International Journal of Pedagogy and Teacher Education - April
Publisher : The Faculty of Teacher Training and Education (FKIP), Universitas Sebelas Maret, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijpte.v8i1.89693

Abstract

This study evaluates the effectiveness of Continuous Formative Assessment (CFA) in enhancing student learning outcomes within hybrid learning environments. Data was collected through surveys and tests involving a sample of 120 students. The findings indicate that 85% of students agree or strongly agree that CFA is a beneficial evaluation model for improving learning outcomes. The average score for students' opinions on CFA, based on a 4-point Likert scale, is 3.35 (standard deviation = 0.697), reflecting a positive perception. Additionally, a high average confidence score of 3.78 indicates that students can achieve the necessary learning attainment levels when implementing CFA. The study emphasizes that adopting CFA as a learning evaluation model is beneficial, but it requires lecturers to be dedicated and attentive to its implementation. Lecturers should carefully analyze current educational system policies and their chosen learning strategies. Recommendations for lecturers include integrating CFA with existing educational policies, providing continuous feedback, and adapting teaching methods based on assessment results. This research significantly contributes to the advancement of learning evaluation techniques and highlights CFA's potential impact on hybrid learning models. It underscores the importance of lecturer involvement in effectively implementing CFA and provides insights into students' perceptions and confidence in their learning attainment. The findings suggest that CFA can enhance learning outcomes and student confidence, with implications for future research and practice in educational evaluation and hybrid learning environments.
AI-Based Models for Identifying Underdeveloped Villages in Indonesia's Rural Development Harun Al Azies
The Journal of Indonesia Sustainable Development Planning Vol 5 No 3 (2024): December
Publisher : Pusbindiklatren Bappenas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46456/jisdep.v5i3.611

Abstract

This study improves the prediction and classification of underdeveloped villages in Indonesia using Artificial Intelligence (AI) and machine learning. It identifies key factors driving underdevelopment to inform policy interventions that support Sustainable Development Goals (SDGs), particularly SDG 1 (No Poverty), SDG 10 (Reduced Inequality), and SDG 11 (Sustainable Communities). Using data from 75,261 villages based on Indonesia’s Village Development Index (IDM), the Decision Tree model achieved the highest classification accuracy at 99.5%. Analysis of feature importance revealed the Economic Resilience Index (IKE) as the most significant factor, followed by the Ecological Resilience Index (IKL) and the Social Resilience Index (IKS). These results align with the SDGs’ focus on economic, social, and environmental resilience. The research offers a data-driven approach to advancing rural development and guiding effective policy decisions in Indonesia.
Co-Authors Achmad Wahid Kurniawan Achmad Wahid Kurniawan Adhitya Nugraha Agus Suharsono Akrom, Muhamad Al zami, Farrikh Alfa Trisnapradika, Gustina Alzami, Farrikh Ananda, Imanuel Khrisna Andrean, Muhammad Niko Anwar Efendi Nasution Aprilyani Nur Safitri Ardytha Luthfiarta Ariyanto, Noval Ayu Febriana Dwi Rositawati Ayu Pertiwi Ayu Pertiwi Bambang Widjanarko Otok Brilianti Rochmanto, Hani Brilianto, Rivaldo Mersis Budi, Setyo Chasanah, Annisa Himatul Dea Trishnanti Dea Trishnanti Devi Putri Isnarwaty Dewi Agustini Santoso Dikaputra, Ishak Bintang Elvira Mustikawati P.H Fahmi Amiq Fawwaz Atha Rohmatullah Firmansyah, Gustian Angga Fitriani, Fenny Gangga Anuraga Ganiswari, Syuhra Putri Go, Agnestia Agustine Djoenaidi Guruh Fajar Shidik Gustina Alfa Trisnapradika Hani Brilianti Rochmanto Hani Brilianti Rochmanto Herawati, Wise Herowati, Wise Hidayat, Novianto Hidayat, Novianto Nur Ifan Rizqa Irnanda, Muhammad Diva Ishak Bintang Dikaputra Isnarwaty, Devi Putri ISWAHYUDI ISWAHYUDI Junta Zeniarja Kharisma, Ni Made Kirei Maulana, Isa Iant Megantara, Rama Aria Moch Anjas Aprihartha Mohammad Arif Muhamad Akrom Muhammad Naufal Muhammad Naufal, Muhammad Muljono Muljono Noor Ageng Setiyanto, Noor Ageng Noval Ariyanto Novianto Hidayat Nugraini, Siti Hadiati Nugroho, Dandy Prasetyo Nur Safitri, Aprilyani Prabowo, Wahyu Aji Eko Pratama, Ananta Surya Pravesti, Cindy Asli Pulung Nurtantio Andono Purhadi Purhadi Putra, Permana Langgeng Wicaksono Ellwid Rahman, Irfan Fauzia Rahmawati Erma Standsyah Ramadhan Rakhmat Sani Riadi, Muhammad Fatah Abiyyu Ricardus Anggi Pramunendar Rohmatullah, Fawwaz Atha Ruri Suko Basuki Safitri, Aprilyani Nur Sari Ayu Wulandari Setyo Budi Shafwah, Shifatush Sofiani, Hilda Ayu Sri Winarno Sri Winarno Sudibyo, Usman Supriadi Rustad Trishnanti, Dea Trisnapradika, Gustina Alfa Umam, Taufiqul Usman Sudibyo Vivi Mentari Dewi Wahyu Wisnu Wardana Wise Herawati Wise Herowati Zahro, Azzula Cerliana Zain, Affa Fahmi Zami, Farrikh Al