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All Journal Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Journal of Electronics, Electromedical Engineering, and Medical Informatics International Journal of Advances in Data and Information Systems Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Internet of Things and Artificial Intelligence Journal International Journal of Research in Mathematics Education
Rozaq, Hasri Akbar Awal
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mathematics Mechanical Engineering Public Health

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Accurate Skin Tone Classification for Foundation Shade Matching using GLCM Features-K-Nearest Neighbor Algorithm Syahputra, Muhammad Reza; Mazdadi, Muhammad Itqan; Budiman, Irwan; Farmadi, Andi; Saputro, Setyo Wahyu; Rozaq, Hasri Akbar Awal; Sutaji, Deni
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4723

Abstract

Foundation shade matching remains a significant challenge in the beauty industry, particularly in Indonesia where consumers exhibit three distinct skin tone categories: ivory white, amber yellow, and tan. Manual foundation selection often results in mismatched shades, leading to customer dissatisfaction. This study presents a novel automated skin tone classification system combining Gray Level Co-Occurrence Matrix (GLCM) feature extraction with the K-Nearest Neighbor (KNN) algorithm. The GLCM method extracts four key texture features (contrast, homogeneity, energy, and entropy) from facial images, while KNN performs classification. A comprehensive dataset of 963 facial images was used, with 770 training and 193 test samples collected under controlled lighting conditions. After testing K values from 1 to 15, the optimal K=1 achieved 75.65% accuracy. Compared to baseline color histogram methods (60% accuracy), our GLCM-KNN approach demonstrates 15.65% improvement in classification performance. This research contributes to computer vision applications in beauty technology, enabling the development of mobile applications for virtual foundation try-on and personalized product recommendations. The findings have significant implications for the cosmetics industry, particularly for automated cosmetic shade matching systems and enhanced customer experience in online beauty retail. Further research is recommended to explore deep learning approaches and expand dataset diversity to improve accuracy.
KNN-MVO-SMOTE Algorithm for Air Quality Imbalanced Data Classification Rizky, Muhammad Miftahur; Mazdadi, Muhammad Itqan; Muliadi, Muliadi; Faisal, Mohammad Reza; Indriani, Fatma; Rozaq, Hasri Akbar Awal; Yildiz, Oktay
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1424

Abstract

This research addresses air pollution, a pressing global issue influenced by geographic and temporal factors, using advanced machine-learning techniques to enhance air quality classification. By integrating the K-Nearest Neighbors (KNN) algorithm with the Synthetic Minority Over-sampling Technique (SMOTE) and Multi-Verse Optimization (MVO), we tackle challenges like data imbalance and parameter optimization. Our novel approach, which combines SMOTE and MVO within the KNN framework, has significantly increased classification accuracy to 97%, substantially improving over previous methods. The dataset includes diverse geographic and temporal data, with potential biases acknowledged and addressed. This study highlights the efficacy of merging MVO and SMOTE to optimize classification models, making a substantial contribution to environmental analysis and the fight against air pollution. Future research will explore AutoML technology to improve algorithmic optimization, offering more efficient and adaptive solutions. This pioneering effort emphasizes the critical role of technological innovation in tackling environmental challenges and marks a significant advancement in combating global air pollution.
Deep Learning Approach for Earthquake Detection and Classification using MobileNet V2 Transfer Learning Siregar, Giel Utami Putri; Subekti, Agus; Rozaq, Hasri Akbar Awal; Yildiz, Oktay
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - 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.v7i1.1400

Abstract

Earthquake detection is a critical component of disaster management, as early identification of seismic events can help mitigate potential damage and support timely response efforts. This study evaluates the application of deep learning for binary earthquake detection using a lightweight Convolutional Neural Network (CNN) based on the MobileNetV2 architecture. The experiments were conducted using seismic waveform data from the Stanford Earthquake Dataset (STEAD), which were transformed into time–frequency representations through the Short-Time Fourier Transform (STFT). Spectrogram images derived from the seismic signals were used as input to the CNN models. Transfer learning was applied to MobileNetV2 to adapt the pretrained architecture to the earthquake detection task. The proposed approach achieved an accuracy of 99%, precision of 100%, recall of 97.96%, and an F1-score of 98.97% on the test dataset. In terms of model complexity, MobileNetV2 has 7,176,600 total parameters and 1,639,538 trainable parameters, indicating a favorable balance between performance and computational efficiency. For comparative evaluation, MobileNetV2 was benchmarked against several commonly used CNN architectures, including CNN Vanilla, MobileNetV1, VGG16, and ResNet, under the same experimental conditions. The results indicate that MobileNetV2 provides competitive detection performance while maintaining a significantly smaller model size. Although real-time deployment on mobile devices was not implemented in this study, the findings suggest that lightweight CNN architectures, such as MobileNetV2, hold promise for future earthquake detection systems operating in resource-constrained environments.
Co-Authors Abdilah, Muhammad Fariz Fata Agus Subekti Al Ghifari, Muhammad Akmal Ananda, Rona Sepri Andi Farmadi Anshari, Muhammad Ridha Dani Arifudin Deni Sutaji Dodon Turianto Nugrahadi Dwi Kartini, Dwi Erdi, Muhammad Fatatik, Isna Nurul Fatma Indriani Imam Tahyudin Irwan Budiman Mahmud Mahmud Maulida, Trisna Muhammad Itqan Mazdadi Muhammad Reza Faisal, Muhammad Reza Muliadi Mutmainah, Fatikhatun Prihasno, Izlah Nur Fadlila Herawati Putra, Feishal Azriel Arya Radityo Adi Nugroho Rizky, Muhammad Miftahur Rudy Herteno Saragih, Triando Hamonangan Setyo Wahyu Saputro Siregar, Giel Utami Putri Syafaat, Alif Yahya Syahputra, Muhammad Reza Wahyu Caesarendra Wardana, Muhammad Difha YILDIZ, Oktay Zuhdi, Shaifudin Zumaroh, Agnis Nur Afa