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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 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
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Optimasi Hyperparameter Model Ensemble untuk Klasifikasi Sentimen Ulasan OVO Chasanah, Annisa Himatul; Al Azies, Harun
JTERA (Jurnal Teknologi Rekayasa) Vol 10, No 2: Desember 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 Shafwah, Shifatush; Al Azies, Harun
JTERA (Jurnal Teknologi Rekayasa) Vol 10, No 2: Desember 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.
Klasifikasi Opini Publik terhadap Kenaikan PPN 12% di Platform X menggunakan Multinomial Naïve Bayes Hani Brilianti Rochmanto; Harun Al Azies
UJMC (Unisda Journal of Mathematics and Computer Science) Vol 10 No 2 (2024): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department, Faculty of Sciences and Technology Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v10i2.9120

Abstract

The increase in Value-Added Tax to 12% in 2025 has sparked diverse public opinions on the social media platform X (Twitter). This study aims to classify public sentiment toward the policy using Multinomial Naïve Bayes with a Term Frequency-Inverse Document Frequency (TF-IDF) approach. Multinomial Naïve Bayes is a probabilistic classification algorithm that assumes feature independence. Data were collected through web crawling using the keyword "ppn 12%" and underwent pre-processing, including text normalization, stopword removal, and stemming. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The best-performing model was obtained by tuning the alpha hyperparameter to 0.01, achieving an average accuracy of 83.37%, precision of 83.32%, recall of 83.38%, and an F1-score of 82.99% using 10-fold cross-validation. The findings indicate that Multinomial Naïve Bayes, combined with SMOTE and hyperparameter tuning, effectively classifies public sentiment and provides insights into public responses regarding the Value-Added Tax policy.
Evaluation of Histogram-Based Image Enhancement Methods for Facial Images in Drowsy Driver Using No-Reference Metrics Naufal, Muhammad; Al Azies, Harun; Alzami, Farrikh; Brilianto, Rivaldo Mersis
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12055

Abstract

Low-light facial images suffer significant quality degradation, leading to performance degradation in surveillance and face recognition systems, where conventional enhancement methods often produce over-enhancement or unnatural noise artifacts. This study compares three histogram equalization methods, namely HE, AHE, and CLAHE, for low-light facial image enhancement, with evaluation using no-reference quality assessment metrics, including NIQE, LOE, and Entropy, as well as visual analysis and histogram distribution. The results showed that AHE produced the lowest NIQE (4.96 ± 1.38) and the highest entropy (7.86 ± 0.11) but had significant noise artifacts, HE produced an overly even distribution with NIQE of 6.34 ± 1.41, while CLAHE showed the most balanced performance with the lowest LOE (0.07 ± 0.02) and the best visual quality when using the optimal clip limit in the range of 1.2-2.0, providing an optimal trade-off between contrast enhancement, naturalness preservation, and artifact minimization with computational efficiency below 1 ms.
A Stacking Approach to Enhance K-Nearest Neighbors Performance for Autism Screening Al Azies, Harun; Naufal, Muhammad
Jurnal Teknologi Informasi dan Terapan Vol 11 No 2 (2024): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v11i2.432

Abstract

The increasing prevalence of autism spectrum disorders necessitates improved early screening methods for children to ensure timely intervention and support. While existing screening techniques play a vital role, they often face challenges regarding accuracy, accessibility, and scalability. This research addresses these gaps by enhancing the K-Nearest Neighbors (K-NN) algorithm by implementing a stacking model that integrates multiple distance metrics—Manhattan and Minkowski—to improve predictive performance. Utilizing a public dataset, the study employed K-Fold Cross-Validation with K=5 to ensure a robust evaluation of the models. The results demonstrated that the stacking model achieved an average accuracy of 86.67%, significantly surpassing the traditional K-NN approaches, which reported accuracies of 82.67% for Manhattan and 81.33% for Minkowski. A user-friendly web interface was also developed to facilitate real-world application, allowing users to input data and receive immediate predictive outcomes regarding autism risk. These findings confirm the effectiveness of the stacking method in enhancing K-NN performance and highlight its potential for practical use in autism screening. Future research may explore alternative machine learning algorithms and additional features to refine the predictive capabilities and user experience further.
Optimizing Driver Drowsiness Detection: Evaluating CLAHE and AHE Enhancement Techniques Naufal, Muhammad; Al Azies, Harun; Al Zami, Farrikh; Brilianto, Rivaldo Mersis
Sistemasi: Jurnal Sistem Informasi Vol 15, No 2 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i2.5206

Abstract

Driver drowsiness is a critical factor in road safety, and early detection can be key to preventing accidents. This research focuses on improving the accuracy of drowsiness detection by enhancing the contrast of driver facial images using image processing techniques. Specifically, the study explores the effectiveness of Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) in this context. The research utilizes the Drowsy Driver Detection (DDD) dataset, which includes facial images categorized into Drowsy and Non-Drowsy classes. AHE and CLAHE techniques are applied to preprocess these images, aiming to improve contrast and subsequently enhance drowsiness detection accuracy. Evaluation metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Signal-to-Noise Ratio (SNR) are employed to assess the quality of the processed images. The findings indicate that CLAHE performs better than AHE in terms of image enhancement. CLAHE achieves significantly lower MSE (93.90) compared to AHE (103.92), along with higher PSNR (28.41 for CLAHE vs. 27.97 for AHE) and SNR (0.49 for CLAHE vs. 0.04 for AHE) values. These results suggest that CLAHE effectively enhances contrast and improves image clarity. The success of CLAHE as a contrast enhancement technique highlights its potential application in real-time driver monitoring systems. In conclusion, this research underscores the importance of image preprocessing techniques like CLAHE in advancing driver safety technologies, emphasizing their potential to enhance the performance of drowsiness detection systems in practical driving scenarios.
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 Trisnapradika, Gustina Alfa; Al Azies, Harun; Akrom, Muhamad; Sudibyo, Usman; Setiyanto, Noor Ageng
Journal of Multiscale Materials Informatics Vol. 3 No. 1 (2026): April (In Progress)
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.
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 De Rosal Ignatius Moses Setiadi 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 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 T. Sutojo 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