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All Journal JMM (Jurnal Masyarakat Mandiri) Jurnal Teknologi Dan Sistem Informasi Bisnis Indonesian Journal of Electrical Engineering and Computer Science Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Applied Data Sciences Journal of Computer Scine and Information Technology Jurnal Komtekinfo Jurnal Teknik Mesin, Industri, Elektro dan Informatika Jurnal Pengabdian Masyarakat Dharma Andalas CSRID Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Culture education and technology research
Halifia Hendri
Universitas Putra Indonesia YPTK Padang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering 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 Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Public Health Social Sciences Other

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Segmentation and Classification of Vitamin C Content in Red Chili Pepper Images Using the Linear Discriminant Analysis (LDA) Method: Segmentation and Classification of Vitamin C Content in Red Chili Pepper Images Using the Linear Discriminant Analysis (LDA) Method Ramadhanu, Agung; Chan, Fajri Rinaldi; Yasmin, Nabilla; Negoro, Wahyu Saptha; Mardison, Mardison; Hendri, Halifia
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 2 (2025): Juni 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.2.2025.149-162

Abstract

The vitamin C content in red chili peppers plays a crucial role in meeting nutritional needs, particularly in free nutritious lunch programs. Red chili peppers are one of the essential sources of vitamin C in daily consumption. However, vitamin C content in chilies can degrade due to storage and drying processes. This study develops a segmentation and classification method for vitamin C content in red chili pepper images using Linear Discriminant Analysis (LDA) as a faster and more efficient alternative to conventional laboratory methods. The dataset consists of 100 red chili images categorized into fresh and dried chilies. The analysis process includes preprocessing, feature extraction of color and texture (RGB, HSV, GLCM), dimensionality reduction, and classification using LDA. Experimental results show that this method achieves 99% accuracy on training data and 97% on test data, demonstrating that digital image processing can serve as a non-destructive approach for food quality estimation. This approach has the potential to be applied in food quality monitoring within the food industry and public nutrition programs.
Rancang Bangun Prototipe Pengukuran dan Pemantauan Suhu, Kelembapan Serta Cahaya Secara Otomatis Berbasis IOT Pada Rumah Jamur Merang Ifvan Yurisvi; Okta Andrica Putra; Halifia Hendri
Culture education and technology research (Cetera) Vol. 3 No. 1 (2026): Vol.3 No.1 2026
Publisher : FKIP - Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/ctr.v2i4.218

Abstract

Rancang bangun prototipe ini bertujuan menghasilkan sistem pengukuran dan pemantauan kondisi lingkungan rumah jamur merang yang bekerja otomatis dan dapat dipantau jarak jauh berbasis Internet of Things (IoT). Sistem memanfaatkan ESP8266 sebagai penghubung perangkat ke internet sehingga seluruh data sensor dapat dikirim dan ditampilkan secara real-time pada dashboard aplikasi Blynk. Untuk aspek keamanan akses, prototipe dilengkapi RFID sehingga hanya pengguna berwenang yang dapat mengakses sistem serta data hasil pembacaan sensor. Parameter yang dipantau meliputi suhu dan kelembapan udara menggunakan sensor DHT11, kelembapan tanah menggunakan sensor soil moisture, tingkat keasaman tanah menggunakan sensor pH tanah, serta intensitas cahaya menggunakan sensor LDR, di mana seluruh nilai pengukuran ditampilkan pada antarmuka Blynk. Mekanisme kendali otomatis diterapkan pada dua kondisi utama: ketika suhu/kelembapan tanah mendekati nilai yang tidak mendukung pertumbuhan jamur merang, waterpump akan mengalirkan air untuk menjaga kelembapan tanah tetap sesuai; dan ketika intensitas cahaya terlalu rendah atau terlalu tinggi, motor servo akan mengatur pencahayaan melalui bilik pintu rumah jamur. Dengan integrasi pemantauan dan aktuasi tersebut, prototipe diharapkan meningkatkan efisiensi pengelolaan lingkungan budidaya, mempercepat respons terhadap perubahan kondisi, serta mempermudah pengawasan rumah jamur merang secara berkelanjutan.
Perancangan Gazebo Sehat dengan Atap Otomatis Berbasis IoT dan Notifikasi Blynk Haniva Budiana Deswary; Retno Devita; Halifia Hendri
Culture education and technology research (Cetera) Vol. 3 No. 1 (2026): Vol.3 No.1 2026
Publisher : FKIP - Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/ctr.v2i4.222

Abstract

Perancangan gazebo sehat dengan atap otomatis berbasis Internet of Things (IoT) dan notifikasi Blynk bertujuan untuk meningkatkan kenyamanan serta mendukung aspek kesehatan pengguna di ruang terbuka melalui sistem yang adaptif terhadap perubahan cuaca. Di wilayah tropis seperti Indonesia, cuaca yang tidak menentu dan paparan sinar matahari berlebihan dapat memengaruhi aktivitas serta kondisi fisik masyarakat. Paparan sinar matahari pagi pada pukul 06.00 hingga 10.00 diketahui membantu proses pembentukan vitamin D yang berperan penting bagi kesehatan tulang dan daya tahan tubuh. Oleh karena itu, sistem dirancang mempertahankan atap tetap terbuka pada rentang waktu optimal tersebut. Sistem ini menggunakan sensor hujan, sensor suhu (DHT), sensor cahaya (LDR), dan sensor ultrasonik yang terhubung dengan mikrokontroler ESP32 untuk membaca kondisi lingkungan secara real-time serta mengontrol mekanisme buka tutup atap secara otomatis sesuai parameter yang ditetapkan. Ketika terdeteksi hujan, peningkatan suhu, atau perubahan intensitas cahaya di luar batas aman, atap akan menutup sebagai bentuk perlindungan terhadap cuaca ekstrem dan menjaga ketahanan bangunan. Aplikasi Blynk diintegrasikan sebagai media pengontrolan jarak jauh dan sistem notifikasi berbasis internet. Hasil pengujian menunjukkan sistem bekerja responsif, stabil, dan sesuai perancangan.
Development of New Identification Formula to Extract Organic Fertilizer Content Based on Organic Fertilizer Image Agung Ramadhanu; Mardison Mardison; Halifia Hendri; Febri Hadi; Larissa Navia Rani; Yuhandri Yuhandri
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1300

Abstract

Traditional laboratory techniques for examining the nutrient content of organic fertilizers, specifically nitrogen (N), phosphorus (P), and potassium (K), are expensive, time-intensive, and pose environmental hazards. To address these issues, this paper presents a novel, non-destructive, image-based classification algorithm to identify fertilizer nutrient content. The proposed technique integrates color space conversion, unsupervised clustering, texture extraction, and an adapted New Identification Weighting (NIW) method. The NIW is derived from prior probability-based distance measurements and optimized with a balancing weighting factor to improve analytical stability across heterogeneous agricultural images. First, RGB images of fertilizers are converted into the perceptually uniform CIE L*a*b color space, which enhances color distinction under varying lighting conditions. Next, the images are segmented using K-Means clustering, followed by Gray-Level Co-occurrence Matrix (GLCM) extraction to capture textural and structural features. A key innovation of this research is the NIW method, functioning as an adaptive feature prioritization tool that assesses each features contribution to nutrient classification, effectively overcoming the limitations of previous a priori approaches. The system was tested on a dataset of 500 organic fertilizer images, achieving an overall classification accuracy of 97%, demonstrating its effectiveness and robustness. This approach offers a highly accurate and interpretable alternative to conventional chemical testing, making it a feasible, scalable, and affordable field tool for smart farming. By enabling on-site nutrient analysis, it strongly supports sustainable agricultural practices. Future work will focus on enhancing the systems flexibility to varying environmental conditions and integrating this approach into mobile-based diagnostic devices to facilitate real-time decision-making in agriculture.
Automated Pixel-Level Concrete Defect Detection using U-Net Architecture: A Comparative Study with Clustering-Based Segmentation Halifia Hendri; Larissa Navia Rani; Sofika Enggari; Agung Ramadhanu; Febri Hadi
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1298

Abstract

Concrete surface defect detection is a critical aspect of maintaining the integrity and safety of infrastructure in civil engineering. Traditional manual inspection methods are time-consuming, prone to human subjectivity, and often limited by physical accessibility, necessitating the development of robust automated solutions. This paper presents an automated pixel-level concrete surface defect detection system utilizing the U-Net deep learning architecture. The primary contribution and novelty of our approach lie in optimizing the network's encoder-decoder structure with skip connections to effectively capture both broad contextual features and precise spatial localization. This overcomes the critical limitations of existing traditional methods, which frequently struggle with complex concrete background textures, inherent noise, and uneven illumination. To validate our approach, the proposed U-Net model is systematically compared against a widely used baseline method, K-Means clustering combined with Gray-Level Co-occurrence Matrix (GLCM) texture analysis. The evaluation was conducted using a comprehensive dataset consisting of 1000 high-resolution concrete images. Experimental results reveal that the deep learning architecture vastly outperforms the traditional baseline. Specifically, the U-Net model achieved an outstanding F1-Score of 92.47%, a precision of 93.18%, and a mean Intersection over Union (mIoU) of 86.55%. In stark contrast, the K-Means and GLCM approach only yielded an F1-Score of 69.83% and an mIoU of 54.21%. These quantitative findings demonstrate that the proposed U-Net-based system not only successfully minimizes false segmentations but also provides a highly reliable, efficient, and scalable computational framework. Ultimately, this research delivers a practical solution that can be seamlessly integrated into continuous automated structural health monitoring systems, paving the way for safer and more proactive civil infrastructure management.
Hybrid Decision Support System and Image Processing for Classifying Priority Applications in the Padang Government Agung Ramadhanu; Mardison; Halifia Hendri; Febri Hadi; Dodi Guswandi; Deri Marse Putra; Romi Hardianto; Syafrika Deni Rizki
CSRID (Computer Science Research and Its Development Journal) Vol. 18 No. 1 (2026): Februari 2026
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.18.1.2026.178-191

Abstract

The development of e-government has encouraged every Regional Apparatus Organization (OPD) within the Padang City Government to submit various digital applications to improve the quality of public services. However, the large number of applications often creates challenges in determining priorities, primarily due to limited resources and budgets. This research aims to design a Hybrid Decision Support System (DSS) that combines the WASPAS (Weighted Aggregated Sum Product Assessment) method and the development of the K-Means Clustering method to provide a more objective and measurable priority classification. The WASPAS method is used to provide a ranking of alternatives based on predetermined criteria, such as urgency of need, service impact, funding availability, and alignment with the regional strategic plan. Next, the K-Means algorithm is applied to group the calculation results into several priority classes, ranging from the most urgent to the least urgent. As an innovation, this research also utilizes image processing techniques to visualize the K-Means classification results, allowing for a more intuitive and easily understood presentation of priority grouping patterns for decision-makers. In this research, data were collected from 52 OPDs within the Padang City Government as a case study. The test results show that the hybrid DSS approach combining WASPAS and K-Means successfully produces priority scale classification with an accuracy level of 94.75%, which demonstrates consistency and accelerates the application evaluation process at OPDs. Integration with image processing for visualization of clustering results also successfully helps clarify data interpretation and facilitates analysis. Thus, this system is expected to support more effective, transparent decision-making in accordance with the principles of electronic-based governance in Padang City.
Automated Fruit Image Classification Based on HSV Features, Morphological Segmentation, and Extreme Learning Machine Agung Ramadhanu; Halifia Hendri; Wahyu Saptha Negoro; Mardison Mardison; Larissa Navia Rani; Sofika Enggari; Muhammad Reza Putra
CSRID (Computer Science Research and Its Development Journal) Vol. 18 No. 1 (2026): Februari 2026
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.18.1.2026.135-147

Abstract

Fruit image classification plays a crucial role in smart agriculture, particularly in automating sorting and quality control processes. This study proposes a fruit classification system by integrating HSV color space conversion, adaptive thresholding, morphological segmentation, and the Extreme Learning Machine (ELM) algorithm. The dataset consists of three fruit classes—apple, pineapple, and watermelon—with a total of 480 images, divided into 360 training samples and 120 testing samples. Image preprocessing involves resizing, HSV conversion, noise reduction through morphological operations, and feature extraction based on color and shape characteristics. The extracted features are used to train and test an ELM model. To improve classification performance and address potential overfitting in traditional ELM, this study introduces a new development called the Extended Extreme Learning Machine (EELM). The key innovation lies in modifying the calculation of the output weights βj, where a regularization term is introduced using ridge regression to stabilize learning and improve generalization. Experimental results show that the proposed system achieves 100% accuracy on the training data and an average accuracy of 83.3% on the testing data. The system also demonstrates robustness in handling varying lighting conditions and fruit shapes. These improvements enable EELM to better handle noisy or complex data by preventing over-reliance on randomly initialized hidden layer parameters. Consequently, EELM demonstrates improved reliability, making it more suitable for deployment in resourceconstrained real-world environments such as mobile or embedded systems.
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

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.
GoogLeNetMP: A Development of GoogLeNet Architecture for Multi-Class Microplastic Classification in Subsurface Water Image Halifia Hendri; Yuhandri Yuhandri; Agung Ramadhanu
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1372

Abstract

Microplastic pollution has become a major environmental concern due to its persistence in marine ecosystems and its potential impact on aquatic organisms and human health. Automatic detection of microplastic particles in underwater environments remains challenging because of turbidity, low contrast, light distortion, and the visual similarity between microplastics and natural marine objects. This study proposes GoogLeNetMP, an enhanced GoogLeNet-based deep learning architecture for multi-class classification of subsurface marine images into four categories: primary microplastics, secondary microplastics, non-microplastics, and marine biota. The proposed framework integrates basic image preprocessing (resizing and noise reduction) with a modified GoogLeNetMP architecture designed to intrinsically handle fine-grained feature extraction under degraded conditions, thereby minimizing the reliance on complex external enhancement pipelines. A dataset of underwater images acquired from the coastal waters of Padang, Indonesia, was used for model development and evaluation. Experimental results show that GoogLeNetMP outperformed the standard GoogLeNet model, achieving 95.75% accuracy, 92.80% sensitivity, 97.00% specificity, and an F1-score of 92.06%. The proposed model also demonstrated more stable training convergence and better discrimination of visually challenging classes. The architecture is designed to internalize the robust feature extraction process, thereby minimizing the reliance on extensive external enhancement pipelines while maintaining standard normalization steps for input consistency. These findings indicate that GoogLeNetMP is a promising approach for AI-based marine pollution monitoring and decision support in sustainable coastal management.
Co-Authors Aggy Pramana Gusman Agung Ramadhanu Chan, Fajri Rinaldi Dendi Novrizal Deri Marse Putra Dicky Wiransyah Dodi Guswandi Emil Salim Enggari, Sofika Eva Rianti Febri Hadi Haniva Budiana Deswary Harkamsyah Andrianof Hasri Awal Husna Arsyah, Rahmatul Ifvan Yurisvi JohanEudes Saleilei Larissa Navia Rani, Larissa M.Hafizh Mardison Mardison Mardison Mardison Mardison Mardison Mardison Mardison Muhammad Habib Yuhandri Muhammad Reza Putra Nanda Tommy Wirawan Negoro, Wahyu Saptha Okta Andrica Putra Pondrinal, Muhammad Retno Devita Richi Andrianto Riska Robianto Romi Hardianto Sarjon Defit Silfia Andini Sofika Enggari Sofika Enggari Syafrika Deni Rizki Wijaya, Romi Yasmin, Nabilla Yuhandri Yuhandri, Yuhandri Yusvi Diana