Article Per Year (5 Year)
p-Index From 2021 - 2026
7.548
P-Index
This Author published in this journals
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 RABIT: Jurnal Teknologi dan Sistem Informasi Univrab 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 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 BERNAS: Jurnal Pengabdian Kepada Masyarakat 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
Religion 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 Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 62 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 62 Documents
Search

 3   4   5   6   7 
Evaluasi Dampak Pelatihan Portofolio Digital Berbasis Google Sites pada Siswa SMKN 9 Semarang Al Azies, Harun; Pertiwi, Ayu; Sutojo, T.; Setiadi, De Rosal Ignatius Moses; Pratama, Ananta Surya; Irnanda, Muhammad Diva; Umam, Taufiqul
BERNAS: Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 2 (2026)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jb.v7i2.17536

Abstract

Perkembangan teknologi digital menuntut siswa sekolah menengah kejuruan memiliki kemampuan mendokumentasikan pengalaman dan kompetensi secara terstruktur melalui portofolio digital. Namun, pemanfaatan portofolio digital sebagai media representasi diri dan personal branding siswa masih belum optimal. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk mengevaluasi dampak pelatihan portofolio digital berbasis platform Google Sites terhadap pemahaman siswa Organisasi Siswa Intra Sekolah di SMK Negeri 9 Semarang. Metode yang digunakan adalah pendekatan evaluatif dengan desain one-group pretest–posttest. Data dikumpulkan melalui instrumen tes pemahaman yang mencakup klaster personal branding dan konsep portofolio digital, serta dianalisis menggunakan uji Wilcoxon Signed-Rank Test. Hasil kegiatan menunjukkan adanya peningkatan skor pemahaman siswa setelah pelaksanaan pelatihan, yang didukung oleh perbedaan skor pretest dan posttest yang signifikan secara statistik. Analisis berdasarkan klaster pemahaman juga menunjukkan peningkatan ketepatan jawaban pada kedua klaster yang diukur. Kegiatan ini menunjukkan bahwa pelatihan portofolio digital berbasis Google Sites memberikan dampak positif terhadap penguatan pemahaman siswa. Kegiatan pengabdian ini berpotensi dikembangkan melalui pendampingan berkelanjutan agar portofolio digital dapat dimanfaatkan secara optimal sebagai media dokumentasi dan pengembangan diri siswa.
Machine Learning-Assisted Discovery and Optimization of Sodium-Ion Batteries: A Review Gustina Alfa Trisnapradika; Harun Al Azies; Muhamad Akrom; Usman Sudibyo; Noor Ageng Setiyanto
Journal of Multiscale Materials Informatics Vol. 3 No. 1 (2026): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v3i1.15954

Abstract

Sodium-ion batteries (SIBs) have emerged as a promising alternative to lithium-ion batteries due to the natural abundance, low cost, and wide geographic availability of sodium resources. However, their practical implementation is hindered by challenges such as lower energy density, slower ion diffusion, and limited cycle stability. In recent years, machine learning (ML) has been increasingly applied to accelerate the discovery, design, and optimization of SIB materials and systems. This review provides a comprehensive overview of ML applications in sodium-ion battery research, including electrode material discovery, electrolyte optimization, performance prediction, and degradation analysis. Various ML techniques, such as supervised learning, unsupervised learning, and deep learning, are discussed in relation to their roles in materials informatics. Additionally, challenges such as data scarcity, model interpretability, and transferability are critically analyzed. Finally, future perspectives on integrating ML with high-throughput experiments and quantum computing are highlighted to guide next-generation sodium-ion battery research.
Optimasi Hyperparameter Model Ensemble untuk Klasifikasi Sentimen Ulasan OVO Annisa Himatul Chasanah; Harun Al Azies
JTERA (Jurnal Teknologi Rekayasa) Vol 10 No 2: December 2025
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31544/jtera.v10.i2.2025.95-104

Abstract

Pertumbuhan layanan dompet digital di Indonesia mendorong meningkatnya jumlah ulasan pengguna yang mengandung opini penting terkait kualitas layanan. Analisis sentimen menjadi penting untuk memahami persepsi pengguna terhadap aplikasi OVO. Penelitian ini menganalisis 10.644 ulasan dari Google Playstore yang dikumpulkan melalui teknik web scraping. Ulasan tersebut diproses melalui tahapan text preprocessing, representasi fitur menggunakan Word2Vec, serta penyeimbangan kelas menggunakan Synthetic Minority Oversampling Technique (SMOTE). Tiga algoritma ensemble learning yaitu Random Forest, XGBoost, dan LightGBM diterapkan dan dioptimasi melalui Grid Search dan Randomized Search, dengan evaluasi menggunakan 10-Fold Cross-Validation serta uji statistik paired t-test. Hasil menunjukkan bahwa meskipun XGBoost dan LightGBM memperoleh nilai cross-validation yang lebih tinggi, performa terbaik pada data uji dicapai oleh Random Forest. Model tersebut mencapai akurasi 89,90% dan ROC-AUC Macro 91,11% pada skema Grid Search, serta akurasi 89,76% dan ROC-AUC Macro 91,19% pada skema Randomized Search. Temuan ini menunjukkan bahwa Random Forest memiliki kemampuan generalisasi paling stabil terhadap data ulasan OVO dibandingkan dua model boosting. Penelitian ini memberikan kontribusi pada pengembangan analisis sentimen berbahasa Indonesia melalui integrasi Word2Vec, SMOTE, dan optimasi hyperparameter, serta membuka peluang eksplorasi lanjutan menggunakan contextual embedding dan teknik penyeimbangan data yang lebih adaptif.
Komparasi SVM dan IndoBERT dalam Klasifikasi Sentimen Program Makanan Bergizi Gratis Shifatush Shafwah; Harun Al Azies
JTERA (Jurnal Teknologi Rekayasa) Vol 10 No 2: December 2025
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31544/jtera.v10.i2.2025.105-112

Abstract

Program Makanan Bergizi Gratis (MBG) memunculkan beragam respons masyarakat di media sosial, khususnya pada platform X (Twitter). Analisis sentimen diperlukan untuk memahami kecenderungan opini publik terhadap program tersebut. Penelitian ini membandingkan kinerja Support Vector Machine (SVM) dan IndoBERT dalam mengklasifikasikan sentimen positif dan negatif pada 2.674 tweet terkait MBG. Data diperoleh melalui web scraping dan diproses melalui tahapan cleaning, normalisasi teks, tokenisasi, serta pelabelan menjadi dua kelas sentimen. Ketidakseimbangan data ditangani menggunakan Synthetic Minority Oversampling Technique (SMOTE). Model SVM dilatih menggunakan representasi fitur TF-IDF, sedangkan IndoBERT dilatih melalui fine-tuning sebagai model transformer. Evaluasi performa dilakukan menggunakan 10-Fold Cross-Validation, confusion matrix, ROC-AUC, dan uji statistik paired t-test. Hasil penelitian menunjukkan bahwa SVM memperoleh akurasi 94,64% dan F1-Score 94,63%, sedangkan IndoBERT mencapai akurasi 90,11% dan F1-Score 89,92%. Meskipun IndoBERT mencatat nilai AUC sedikit lebih tinggi, kinerja keseluruhan SVM lebih unggul secara konsisten pada data yang telah diseimbangkan dengan SMOTE. Uji paired t-test menghasilkan nilai p < 0,05, yang menunjukkan bahwa perbedaan performa kedua model bersifat signifikan. SVM lebih efektif digunakan untuk klasifikasi sentimen dua kelas pada dataset MBG yang relatif kecil dan bersifat informal.
Prediksi Aksebilitas Molekul Tamu pada Metal-Organic Framework dengan SMOTE dan AdaBoost-Machine Learning Moch Anjas Aprihartha; Harun Al Azies; Wahyu Aji Eko Prabowo; Usman Sudibyo; Ika Puspitasari; Indah Putianik; Fatma Ahardika Nurfaizal
METIK Jurnal Vol. 10 No. 1 (2026): METIK Jurnal Issue Published
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/45crx119

Abstract

Metal-Organic Frameworks (MOFs) are a special class of organic-inorganic hybrid materials widely known for their regular and periodic crystal structures. MOFs are composed of metal ions or clusters connected by organic linkers that form a three-dimensional lattice-shaped series. The advantage of MOFs is their ability to capture guest molecules in their pores. Based on these capabilities, MOFs can be utilized in various applications such as gas absorption and separation processes, catalysts, and therapeutic compound delivery systems. Currently, in creating new materials, the MOFs synthesis process still applies a conventional trial-and-error approach that has the potential for high failure rates. The purpose of this study is to develop a machine learning model as an efficient tool design in creating new MOFs materials before the experimental process is carried out. This study implements the SMOTE and AdaBoost methods integrated with machine learning algorithms in classifying MOFs pores based on the pore limiting diameter (PLD) size. The results obtained from the CART-Gentle AdaBoost model provide the best performance with an accuracy of 72.82%; precision 71.32%; recall 73.53%; specificity 72.88%; and f1 score 72.39%. This model is quite suitable for use in identifying MOF structures that are accessible to guest molecules compared to other classification models.
TIME SERIES FORECASTING SAHAM PT ASTRA MENGGUNAKAN ALGORITMA AUTOREGRESSIVE INTEGRATED MOVING AVERAGE DAN PROPHET Naufal Malik Herlambang; Harun Al Azies
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7231

Abstract

Stock price movements are highly volatile, requiring reliable forecasting models to support investment decision-making. This study compares the performance of time-series models, namely the Autoregressive Integrated Moving Average (ARIMA) and the Prophet, in predicting the closing price of PT Astra International Tbk (ASII.JK). The study employs secondary data obtained from Yahoo Finance covering the period from October 2020 to October 2025. Model evaluation is conducted using an out-of-sample backtesting scheme with RMSE, MAE, MAPE, and directional accuracy (DA) as performance metrics. The results indicate that the ARIMA(2,1,2) model provides superior numerical accuracy, achieving a MAPE of 6.26%, while the Prophet model with a changepoint prior scale of 0.5 yields a MAPE of 7.33%. In terms of price movement direction, Prophet demonstrates a higher DA value of 57.26%. Visual analysis shows that ARIMA predictions closely track actual price movements, with relatively small deviations, whereas Prophet produces increasingly wide uncertainty intervals at longer forecasting horizons. Based on these findings, ARIMA is more suitable for precise price forecasting, while Prophet is better suited for analyzing price direction and trend dynamics.
EVALUASI KINERJA ARSITEKTUR CNN BERBASIS TRANSFER LEARNING XCEPTION DAN MOBILENETV2 UNTUK KLASIFIKASI CITRA LIMBAH Wachid Zufar Ramadhan; Harun Al Azies
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7235

Abstract

Waste management is an increasingly crucial environmental issue, particularly given the growing volume of waste and the lack of an effective sorting system. Reliance on manual sorting is considered inefficient, difficult to scale, and prone to errors, necessitating an automated approach based on visual intelligence. This study analyses the performance of two transfer learning-based Convolutional Neural Network (CNN) architectures, namely Xception and MobileNetV2, for image sorting of organic and inorganic waste. The Garbage Classification dataset, consisting of 15,515 images, was used, with preprocessing stages including normalisation, augmentation, handling class imbalance via class weights, and training using K-Fold Cross Validation and hyperparameter tuning. Validation results show that MobileNetV2 achieves the highest accuracy of 98.03%, but its performance decreases on the test data to 85.50%. In contrast, Xception demonstrates better generalisation with a test accuracy of 92.50%, an AUC of 0.918, and stable precision, recall, and F1-score metrics. A t-test also confirmed a statistically significant difference in the performance of the two models. Xception was deemed more feasible for implementation in an automated waste image sorting system under operational conditions. These results provide a basis for recommendations to developers and stakeholders to strengthen innovative waste management strategies and mitigate environmental impacts.
A Machine Learning Model for Evaluation of the Corrosion Inhibition Capacity of Quinoxaline Compounds Noor Ageng Setiyanto; Harun Al Azies; Usman Sudibyo; Ayu Pertiwi; Setyo Budi; Muhamad Akrom
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.
Layerwise Quantum Training: A Progressive Strategy for Mitigating Barren Plateaus in Quantum Neural Networks Harun Al Azies; Muhamad Akrom
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.
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.
Co-Authors Achmad Wahid Kurniawan Achmad Wahid Kurniawan Adhitya Nugraha Agus Suharsono Alfa Trisnapradika, Gustina Alzami, Farrikh Ananda, Imanuel Khrisna Ananta Surya Pratama Andrean, Muhammad Niko Annisa Himatul Chasanah 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 De Rosal Ignatius Moses Setiadi Dea Trishnanti Dea Trishnanti Devi Putri Isnarwaty Dewi Agustini Santoso Dikaputra, Ishak Bintang Elvira Mustikawati P.H Fahmi Amiq Farrikh Al Zami Fatma Ahardika Nurfaizal 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 Herawati, Wise Herowati, Wise Hidayat, Novianto Hidayat, Novianto Nur Ifan Rizqa Ika Puspitasari Indah Putianik 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 Diva Irnanda Muhammad Naufal Muhammad Naufal, Muhammad Muljono Muljono Naufal Malik Herlambang 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 Rivaldo Mersis Brilianto Rohmatullah, Fawwaz Atha Ruri Suko Basuki Safitri, Aprilyani Nur Sari Ayu Wulandari Setyo Budi Shafwah, Shifatush Shifatush Shafwah Sofiani, Hilda Ayu Sri Winarno Sri Winarno Sudibyo, Usman Supriadi Rustad T. Sutojo Taufiqul Umam Trishnanti, Dea Trisnapradika, Gustina Alfa Umam, Taufiqul Usman Sudibyo Vivi Mentari Dewi Wachid Zufar Ramadhan Wahyu Aji Eko Prabowo Wahyu Wisnu Wardana Wise Herawati Wise Herowati Zahro, Azzula Cerliana Zain, Affa Fahmi Zami, Farrikh Al