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All Journal International Journal of Advances in Intelligent Informatics Journal of Applied Data Sciences Journal of Computer Scine and Information Technology Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika
Muhammad Habib Yuhandri
Universitas Putra Indonesia YPTK Padang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology

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Web-Based Design of Minangkabau Songket Motif Digital Catalog Muhammad Habib Yuhandri
Journal of Computer Scine and Information Technology Volume 8 Issue 1 (2022): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v8i1.25

Abstract

Indonesia's richness in culture requires special attention in terms of its preservation, therefore it is important for us to maintain and preserve Songket cloth, especially its motifs which are rich in the philosophy of local wisdom so that they can be passed down to the next generation. Information technology can be used to solve these problems, especially for digital catalogs that can be used to help preserve Songket cloth through digitizing the motifs on each cloth. Through digitization, it will be easy to get information about the shape of the motif, the name of the motif, the philosophy contained in the motif through an application. The purpose of this study was to build a digital catalog system on the WEB-based Minangkabau Songket motif. The development of this system is carried out by means of data collection, system analysis, system design, and system implementation. This system is built using the PHP programming language and MySQL database. The results of this study provide a tool in the form of a Minangkabau Songket Motif Digital Catalog system. The benefits of research provide the development of deep catalog technology to store information related to Minangkabau Songket motifsSongket
Hybrid Text Mining for Hate Speech Detection in Indonesia: A Naïve Bayes-Based Approach Muhammad Habib Yuhandri; Halifia Hendri; Richi Andrianto; Sarjon Defit
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 12 No. 1 (2026): April 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v12i1.5365

Abstract

Hate speech (HS) is defined as speech that conveys hateful meaning and intent. In contemporary times, the prevalence of hate speech has surged in the virtual realm, particularly on social media platforms. Among these platforms, Twitter, now renamed X, stands out as one of the most widely used and a significant medium for the dissemination of hate speech. Hate speech can be categorized into various levels of severity, including HS_Weak, HS_Moderate, and HS_Strong. This study utilizes a dataset comprising 13,169 tweets from the social media application X from Indonesia users in 2023 to investigate hate speech detection. The research employs a novel hybrid approach that integrates image input with five preprocessing techniques: data cleaning, case folding, tokenization, stop-words removal, and stemming. Following preprocessing, the study applies Natural Language Processing (NLP) techniques in conjunction with Naïve Bayes classification. The combination of these NLP methods proves to be highly effective for the classification of text data. The key findings of this research demonstrate that the hybrid method significantly enhances hate speech detection accuracy. The evaluation of the classification model, based on training and validation, reveals an accuracy rate of 80%, a precision value of 85%, a recall value of 75%, and an F1-score of 80%. These results indicate substantial improvement over previous research outcomes. The findings suggest that the hybrid method is robust and effective for hate speech detection on social media platforms. Future research should explore the comparison of this hybrid approach with other classification methods to further validate its efficacy and potential applications in various domains of text classification.
An enhanced MobileNetV2-191H architecture for accurate crescent moon detection using semantic segmentation Muhammad Habib Yuhandri; Yuhandri Yuhandri; Sumijan Sumijan
International Journal of Advances in Intelligent Informatics Vol 12, No 3 (2026): August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i3.2488

Abstract

This study proposes an enhanced lightweight semantic segmentation framework, called MobileNetV2-191H, for accurate crescent moon detection in astronomical observation images. Crescent moon detection is a challenging task because the crescent moon typically appears as a very thin and low-contrast object that is highly affected by atmospheric interference, cloud cover, image noise, and illumination variation near the horizon. The dataset used in this study was obtained from crescent moon observation videos provided by the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG), resulting in 6,283 extracted image frames that were divided into training and testing datasets. The proposed MobileNetV2-191H framework was developed by adapting the original MobileNetV2 architecture through the integration of additional segmentation-oriented layers, including transposed convolution, feature refinement, and pixel classification modules, to improve pixel-level segmentation capability for thin crescent moon objects. Experimental evaluation was conducted using semantic segmentation metrics, including precision, recall, F1-score, and Intersection over Union (IoU). The proposed model achieved 84.81% precision, 85.32% recall, 84.07% F1-score, and 72.64% IoU, outperforming several baseline segmentation models such as U-Net, DeepLabV3, SegNet, and EfficientNet. These results demonstrate that the proposed lightweight framework effectively improves crescent moon segmentation performance while maintaining computational efficiency for real-time astronomical observation applications.
Multimodal Deep Learning and IoT Sensor Fusion for Real-Time Beef Freshness Detection Bambang Kurniawan; Refni Wahyuni; Yulanda Yulanda; Yuda Irawan; Muhammad Habib Yuhandri
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.977

Abstract

Beef freshness quality is one of the important indicators in ensuring food safety and suitability. However, conventional methods such as manual visual inspection and laboratory testing cannot be widely applied in real-time and mass scale. To overcome these challenges, this study proposes a meat freshness detection system based on a multimodal approach that combines visual imagery and gas sensor data in a single IoT-based framework. This system is designed by utilizing the YOLOv11 architecture that has been optimized using the Adam optimizer. The dataset consisted of 540 original beef images, expanded into 1,296 images after augmentation. The model is trained on these augmented images and is able to achieve detection performance with a mAP@0.5 value of 99.4% and mAP@0.5:0.95 of 95.7%. As a further improvement, the cropped image features from the YOLOv11 model are processed through a combination of the ViT model and CNN to classify the level of meat freshness into three classes: Fresh, Medium, and Rotten with an accuracy of 99%. On the other hand, chemical data was obtained from the MQ136 and MQ137 gas sensors to detect H₂S and NH₃ levels which are indicators of meat spoilage. Data from visual and chemical data were then combined through a multimodal fusion method and classified using the Random Forest algorithm, producing a final prediction of Fit for Consumption, Need to Check, and Not Fit for Consumption. This multimodal model achieved a classification accuracy of 98% with a ROC-AUC score approaching 1.00 across all classes. While the proposed system achieved very high accuracy, further validation across diverse real-world environments is recommended to establish its generalizability.
Co-Authors Halifia Hendri Kurniawan, Bambang Refni Wahyuni Richi Andrianto Sarjon Defit Sumijan Yuda Irawan Yuhandri Yuhandri, Yuhandri Yulanda Yulanda