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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) 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 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 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 Physics Public Health Social Sciences Transportation Other

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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.
Machine Learning and Density Functional Theory Investigation of Corrosion Inhibition Capability of Ionic Liquid Safitri, Aprilyani Nur; Akrom, Muhamad; Al Azies, Harun; Pertiwi, Ayu; Kurniawan, Achmad Wahid; Herowati, Wise; Rustad, Supriadi
International Journal of Advances in Data and Information Systems Vol. 6 No. 1 (2025): April 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i1.1372

Abstract

This study investigated the corrosion inhibition potential of ionic liquid compounds using a QSPR-based machine learning predictive model combined with DFT calculations. The Gradient Boosting (GB) model was identified as the most effective predictor, demonstrating excellent accuracy with a high R² value of 0.98. Additionally, the model exhibited low RMSE (0.95), MAE (0.84), and MAD (0.94) values. The predicted corrosion inhibition efficiencies (CIE) for three new ionic liquid compounds (IL1, IL2, and IL3) were 88.95, 90.82, and 93.16, respectively, which aligned well with experimental data. By integrating DFT simulations into the data updating process, facilitated by machine learning, the approach proved invaluable for identifying new corrosion inhibitors. This work highlighted the continuous refinement of data related to the corrosion inhibition effects of ionic liquid compounds.
Layerwise Quantum Training: A Progressive Strategy for Mitigating Barren Plateaus in Quantum Neural Networks Al Azies, Harun; Akrom, Muhamad
Journal of Multiscale Materials Informatics Vol. 2 No. 1 (2025): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v2i1.12948

Abstract

Barren plateaus (BP) remain a core challenge in training quantum neural networks (QNN), where gradient vanishing hinders convergence. This paper proposes a layerwise quantum training (LQT) strategy, which trains parameterized quantum circuits (PQC) incrementally by optimizing each layer separately. Our approach avoids deep circuit initialization by gradually constructing the QNN. Experimental results demonstrate that LQT mitigates the onset of barren plateaus and enhances convergence rates compared to conventional and residual-based QNN, rendering it a scalable alternative for Noisy Intermediate-Scale Quantum (NISQ)-era quantum devices.
Analisis Sentimen Ulasan Pengguna iPhone dengan Pendekatan Hibrida RoBERTa dan XGBoost Zain, Affa Fahmi; Azies, Harun Al; Ananda, Imanuel Khrisna
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2277

Abstract

User reviews play an important role in shaping perceptions of products, including the iPhone. Sentiment analysis of these reviews can provide valuable insights for companies to improve product and service quality. This study explores sentiment analysis of iPhone user reviews using a hybrid approach that combines RoBERTa and XGBoost to improve classification accuracy. The model was built and tested on a public dataset containing 2,960 reviews obtained from the Kaggle platform, following data cleaning processes. Preprocessing steps included handling missing values, encoding, and class balancing using the Synthetic Minority Over-sampling Technique (SMOTE). RoBERTa was used to extract text features and understand contextual meaning, while XGBoost served as the classification algorithm. The evaluation showed an accuracy of 99.74%, with an increase in the F1-score from 0.99 to 1.00 after applying SMOTE, particularly in the minority class. These findings demonstrate the superiority of the RoBERTa-XGBoost approach over traditional methods and contribute to the development of more balanced and adaptive classification models for imbalanced data.
IMPROVING AWARENESS OF INTERNET SECURITY AND ETHICS AMONG STUDENTS AT SMA NEGERI 2 MRANGGEN Naufal, Muhammad; Hidayat, Novianto Nur; Trisnapradika, Gustina Alfa; Al Azies, Harun
Jurnal Layanan Masyarakat (Journal of Public Services) Vol. 9 No. 2 (2025): JURNAL LAYANAN MASYARAKAT
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/.v9i2.2025.204-214

Abstract

This community service initiative aimed to enhance the awareness of internet security and ethics among high school students at SMA Negeri 2 Mranggen, Demak Regency, Central Java. The program utilized a structured methodology consisting of outreach, training, and evaluation stages conducted in a hands-on environment within the school's computer laboratory. The training covered key topics such as data security, phishing attacks, malware, and ethical internet use. The sessions were held in the school’s computer laboratory to provide hands-on experience. Each training session had a duration of 3×45 minutes, attended by 31 students, allowing comprehensive exploration of the material. Pre- and post-tests were administered to assess the effectiveness of the training. The results demonstrated a significant improvement in students' knowledge, with average scores increasing from 49 in the pre-test to 72.67 in the post-test. A paired t-test analysis confirmed this improvement as statistically significant, with a T-statistic of -13.971 and a P-value of 2.07 × 10-14. The findings highlight the program's success in raising awareness and equipping students with the skills to navigate the digital world safely and responsibly. This initiative underscores the importance of educational programs in fostering internet literacy and security awareness among young users. To build on these findings, it is recommended that similar training sessions to be conducted regularly to reinforce the concepts learned. Additionally, a long-term plan is proposed as a form of sustainability of this community service program, namely by expanding the training targets not only to students but also to teachers, housewives and children who are already accustomed to gadgets.
Deteksi Struktur Material Perovskit ABO3 Berbasis Machine Learning Rahman, Irfan Fauzia; Al Azies, Harun; Akrom, Muhamad
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 1 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v9i1.1036

Abstract

This study proposes a machine learning-based classification approach to identify perovskite and non-perovskite structures in ABO? compounds. Perovskites have garnered significant attention as a source of functional materials, including solar cells and catalysts. Yet, discovering new materials remains a considerable challenge in terms of efficiency and exploration speed. This research addresses this gap by offering a data-driven method that automatically classifies compound structures based on crystallographic and chemical descriptors. The dataset comprises various structural and chemical features, which are analyzed using descriptive statistics, boxplot visualization, and multivariate correlation to understand the data distribution and inter-feature relationships. Four machine learning algorithms, LightGBM, XGBoost, CatBoost, and K-Nearest Neighbors (KNN), were tested and evaluated based on accuracy, precision, recall, and F1 score. Results show that LightGBM achieved the best performance with 97% accuracy, a 98% F1 score, and a confusion matrix indicating minimal classification errors. Feature importance analysis identified the tolerance factor (t), the B to O atomic radii ratio, and the AO and BO bond lengths as the most influential features. These findings highlight that tree-based boosting models effectively capture complex structural patterns, and this approach can accelerate the discovery of new materials.
Integrating counseling with technology: An evaluation of the Bicarakan.id application through user review analysis with machine learning Al Azies, Harun; Rochmanto, Hani Brilianti; Pravesti, Cindy Asli; Fitriani, Fenny
KONSELI: Jurnal Bimbingan dan Konseling (E-Journal) Vol 11 No 2 (2024): KONSELI : Jurnal Bimbingan dan Konseling (E-journal)
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/kons.v11i2.24357

Abstract

Online counseling has transformed mental health services by offering a convenient and cost-effective alternative to traditional in-person therapy. This study investigates the role of technology in counseling by analyzing user reviews of the Bicarakan.id app from the Google Play Store. A machine learning approach was employed to identify critical patterns and themes within the reviews. Text pre-processing methods such as tokenization, stop-word removal, and TF-IDF vectorization were applied to a dataset of 125 user reviews. The Elbow method helped determine the optimal number of clusters, which was three. Clustering performance was assessed using the Silhouette score, with three clusters yielding the highest average score of 0.4939, indicating a moderate level of clustering effectiveness. Cluster 1 primarily contained positive reviews, emphasizing user satisfaction with the app's services. Cluster 2 included more specific feedback on users' experiences with counselors and app features. Cluster 3 focused on the app's accessibility and ease of use while raising concerns about data privacy and the lack of offline consultation options. The study underscores the significance of using user feedback to enhance and improve technology-driven mental health solutions.
SPATIAL MODELING OF SCHOOL DROPOUT RATES IN UNDERDEVELOPED AREAS OF PAPUA USING GEOGRAPHICALLY WEIGHTED REGRESSION Al Azies, Harun; Brilianti Rochmanto, Hani; Fitriani, Fenny
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3.6784

Abstract

This study examines the factors hypothesized to contribute to school dropout rates in disadvantaged regions of Papua Province and explores potential geographical influences. The primary aims are to derive parameter estimates and statistical tests for the model of underdeveloped regions in Papua using Geographically Weighted Regression (GWR) and to determine the factors influencing school dropout rates in these areas, providing a basis for governmental policy development to mitigate school dropout issues in disadvantaged regions. Findings reveal that the highest dropout rates occur at the junior high school level, with indications of spatial clustering in dropout cases due to heterogeneity among observation sites. This suggests that regions with elevated dropout rates, or conversely low rates, are likely to have neighboring areas with comparable patterns, necessitating the use of spatial regression modeling with a Fixed Gaussian Kernel function. GWR analysis resulted in two clusters based on significant variables, which include the student-teacher ratio at the junior high school level, the student-classroom ratio at the junior high school level, and the elementary school dropout rate (APTs).
Fairer Public Complaint Classification on LaporGub: Integrating XLM-RoBERTa with Focal Loss for Imbalance Data Zahro, Azzula Cerliana; Alzami, Farrikh; Sani, Ramadhan Rakhmat; Fahmi, Amiq; Megantara, Rama Aria; Naufal, Muhammad; Azies, Harun Al; Iswahyudi, Iswahyudi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15260

Abstract

The advancement of digital technology has provided opportunities for governments to improve the quality of public services through citizen complaint channels. One example of this implementation in Indonesia is Lapor Gub, managed by the Dinas Komunikasi dan Informasi Provinsi Jawa Tengah (Communication and Information Agency of Central Java Province). This platform receives thousands of complaints daily, ranging from infrastructure, social issues, to illegal levies. However, the large volume of data and the imbalanced distribution of categories pose significant challenges for both manual and automated processing. This study aims to classify citizen complaint texts using XLM-RoBERTa combined with Focal Loss as an approach to handle data imbalance. The dataset consists of 53,774 complaints after data cleaning and text preprocessing. The training process applied a stratified split (78% training, 18% validation, 10% testing) and fine-tuning for 10 epochs. Model performance was evaluated using accuracy, precision, recall, and macro F1-score. The results show that the model without Focal Loss achieved 78.1% accuracy with a macro F1-score of 0.606, while the model with Focal Loss improved the macro F1-score to 0.625 with 78.5% accuracy. These findings demonstrate that the application of Focal Loss enhances the model’s ability to recognize minority categories without reducing performance on majority classes. Therefore, the combination of RoBERTa and Focal Loss offers an effective solution to support faster, fairer, and more transparent public complaint management.
Stacking Machine Learning Approach for Predicting Thermal Stability of Zinc–Metal Organic Frameworks (Zn-MOF) Pratama, Ananta Surya; Irnanda, Muhammad Diva; Umam, Taufiqul; Nugroho, Dandy Prasetyo; Azies, Harun Al
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8329

Abstract

Thermal stability is a fundamental parameter that determines the feasibility of Metal Organic Frameworks (MOF) for high-temperature industrial applications, including catalysis, gas purification, and energy storage. Experimental evaluation of thermal stability, while accurate, is often costly and time-consuming, highlighting the need for computational prediction models that are both efficient and dependable. This study develops a Quantitative Structure Property Relationship (QSPR) model using a stacking ensemble regression framework to predict the thermal stability of Zn-MOFs. The stacking approach combines Linear Regression, Lasso Regression, and Huber Regression as base learners, with Linear Regression serving as the meta-model, thereby leveraging the complementary strengths of individual algorithms. Results demonstrate that the stacking ensemble consistently outperformed all single models, delivering highly reliable predictions that remained stable across multiple validation scenarios. Furthermore, external validation with experimental data confirmed the model’s robustness and its ability to generalize beyond the training dataset. These findings underline the reliability of stacking as not only a tool for improving accuracy but also for ensuring predictive stability and reproducibility. The study highlights the potential of machine learning, particularly ensemble methods, as a powerful and trustworthy predictive framework for the rational design of thermally stable MOFs, offering both scientific and industrial significance in sustainable energy applications.
Co-Authors Achmad Wahid Kurniawan Achmad Wahid Kurniawan Adhitya Nugraha Agus Suharsono Akrom, Muhamad 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 Dea Trishnanti Dea Trishnanti Devi Putri Isnarwaty Dikaputra, Ishak Bintang Elvira Mustikawati P.H Fahmi Amiq Fawwaz Atha Rohmatullah Firmansyah, Gustian Angga Fitriani, Fenny Gangga Anuraga Ganiswari, Syuhra Putri Guruh Fajar Shidik Gustina Alfa Trisnapradika Hani Brilianti Rochmanto Herawati, Wise Herowati, Wise Hidayat, Novianto Hidayat, Novianto Nur Irnanda, Muhammad Diva Ishak Bintang Dikaputra Isnarwaty, Devi Putri ISWAHYUDI ISWAHYUDI Junta Zeniarja Kharisma, Ni Made Kirei Megantara, Rama Aria Moch Anjas Aprihartha Muhamad Akrom Muhammad Naufal Muhammad Naufal, Muhammad Muljono Muljono Noor Ageng Setiyanto, Noor Ageng Noval Ariyanto Novianto Hidayat 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 Rohmatullah, Fawwaz Atha Safitri, Aprilyani Nur Sari Ayu Wulandari Setyo Budi 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