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Pengelolahan Citra Cabai Keriting: Kombinasi Median Filtering dan Algoritma K-Means untuk Pengelompokan Berbasis Fitur Yasmin, Nabilla; Akbar, Syifa Chairunnissa Deliva; Ramadhanu, Agung
Journal of Education Research Vol. 5 No. 4 (2024)
Publisher : Perkumpulan Pengelola Jurnal PAUD Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37985/jer.v5i4.1865

Pengolahan citra digital berperan penting dalam klasifikasi tanaman, termasuk cabai keriting. Penelitian ini mengusulkan metode pengelompokan citra cabai keriting menggunakan algoritma K-Means dengan median filtering sebagai langkah awal untuk mengurangi noise pada citra. Ekstraksi fitur dilakukan dengan model warna RGB untuk fitur warna dan metode Grey Level Co-occurrence Matrix (GLCM) untuk fitur tekstur. Dataset terdiri dari 100 citra, masing-masing 50 citra cabai merah dan hijau keriting, dengan pembagian 60 citra untuk pelatihan dan 40 citra untuk pengujian. Hasil menunjukkan bahwa penggunaan median filtering meningkatkan akurasi klasifikasi, dengan akurasi 95% untuk cabai merah keriting dan 93% untuk cabai hijau keriting, menghasilkan rata-rata akurasi 94%. Temuan ini menegaskan pentingnya median filtering dalam meningkatkan kualitas data untuk pengelompokan citra cabai keriting.

PENERAPAN METODE HYBRID KNN DAN PCA DALAM KLASIFIKASI CABAI MERAH, CABAI HIJAU DAN JERUK MANDARIN Yasmin, Nabilla; Ramadhanu, Agung
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 2 (2025): JATI Vol. 9 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i2.13147

Sistem klasifikasi otomatis berbasis citra digital semakin dibutuhkan dalam sektor pertanian untuk meningkatkan efisiensi seleksi dan distribusi produk. Pengklasifikasian cabai merah, cabai hijau, dan jeruk mandarin secara manual sering kali memakan waktu lama dan rentan terhadap kesalahan manusia. Oleh karena itu, penelitian ini mengusulkan penggunaan kombinasi Principal Component Analysis (PCA) dan K-Nearest Neighbors (KNN) untuk mengklasifikasikan ketiga jenis objek tersebut. Data yang digunakan terdiri dari 16 citra latih dan 10 citra uji, yang diproses melalui tahapan segmentasi, ekstraksi fitur, dan reduksi dimensi menggunakan PCA. Hasil klasifikasi menunjukkan tingkat akurasi mencapai 96,20%, dengan satu data jeruk mandarin yang gagal terdeteksi. Temuan ini membuktikan bahwa metode PCA dan KNN efektif dalam mengklasifikasikan citra dengan akurasi tinggi, memberikan kontribusi terhadap sistem klasifikasi otomatis yang dapat meningkatkan efisiensi dalam distribusi dan seleksi produk agrikultur serta mengurangi potensi kesalahan dalam penilaian kualitas produk secara manual.

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

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.

IDENTIFIKASI TINGKAT KEMATANGAN BUAH MANGGA MENGGUNAKAN METODE K-MEANS CLUESTERING DAN MEDIAN FILTER Yanti, Rahma; Yasmin, Nabilla; Putra, Kharisma Utama; Irawan, Hendri; Sovia, Rini
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 2 (2025): May 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i2.2893

Abstract: This study aims to develop an automatic system for identifying the ripeness level of mangoes using the K-Means Clustering and Median Filter methods. The background of this research is based on the agricultural industry's need for an objective ripeness assessment, as manual methods are often subjective and inefficient. The K-Means Clustering method is used to categorize mango ripeness based on skin color characteristics, while the Median Filter is applied to enhance image quality by reducing noise before clustering. This study utilizes a dataset of 120 mango images, consisting of 47 images for training and 73 images for testing. The results indicate that the combination of these two methods achieves a classification accuracy of 98%. These findings contribute to the development of digital image processing technology for applications in the agricultural and food industries. Keyword: Ripeness identification, K-Means Clustering, Median Filter, Image Processing, Mango. Abstrak: Penelitian ini bertujuan untuk mengembangkan sistem identifikasi tingkat kematangan buah mangga secara otomatis menggunakan metode K-Means Clustering dan Median Filter. Latar belakang penelitian ini didasarkan pada kebutuhan industri pertanian dalam menentukan tingkat kematangan mangga secara objektif, mengingat metode manual sering kali subjektif dan kurang efisien. Metode K-Means Clustering digunakan untuk mengelompokkan tingkat kematangan mangga berdasarkan karakteristik warna kulit, sedangkan Median Filter diterapkan untuk meningkatkan kualitas citra dengan mengurangi noise sebelum dilakukan proses klasterisasi. Penelitian ini menggunakan dataset sebanyak 120 citra mangga, yang terdiri dari 47 citra untuk pelatihan dan 73 citra untuk pengujian. Hasil penelitian menunjukkan bahwa kombinasi kedua metode ini mampu mengklasifikasikan tingkat kematangan mangga dengan akurasi sebesar 98%. Temuan ini memberikan kontribusi dalam pengembangan teknologi pemrosesan citra digital untuk aplikasi dalam industri pertanian dan pangan. Kata kunci: Identifikasi kematangan, K-Means Clustering, Median Filter, Pengolahan Citra, Mangga.

Transformasi Digital dalam Era Society 5.0: Peluang dan Tantangan bagi Start-Up Berbasis Teknologi Yasmin, Nabilla; Jhon Veri
Indo-MathEdu Intellectuals Journal Vol. 6 No. 4 (2025): Indo-MathEdu Intellectuals Journal
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/imeij.v6i4.3650

Digital transformation is a key driver in realizing a technology-based society as envisioned in the concept of Society 5.0. In this context, technology-based start-ups play a strategic role as agents of social innovation and the digital economy. This study aims to systematically examine the opportunities and challenges of digital transformation faced by start-ups in the era of Society 5.0. The method used is a Systematic Literature Review (SLR) following the PRISMA 2020 framework, involving 68 scientific articles published between 2018 and 2025. Data were collected from leading databases such as Scopus, IEEE Xplore, ScienceDirect, and Google Scholar, and analyzed using thematic content analysis and bibliometric analysis with the support of VOSviewer software. The findings reveal that start-ups have significant potential to accelerate social transformation through services based on AI, IoT, and Big Data. However, they also face major challenges such as low digital literacy, infrastructure limitations, unadaptive regulations, and technological access gaps. Common strategies adopted include digital reinvention, strengthening digital human capital, and fostering cross-sector collaboration. This study contributes to a deeper understanding of digital transformation dynamics in the Start-up sector and serves as a strategic reference for policymakers, academics, and industry practitioners.

Analisis Metode Forward Chaining dan Certainty Factor untuk Diagnosa Penyakit pada Ibu Hamil Yasmin, Nabilla; Yuhandri, Yuhandri; Nurcahyo, Gunadi Widi
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.756

The high number of complications that occur during pregnancy and childbirth has the potential to significantly increase the risk of morbidity and mortality in pregnant women. The Maternal Mortality Rate (MMR) reflects the condition of pregnant, delivering, and postpartum mothers, which remains relatively high and is a major concern in the health sector. Based on this, this study aims to develop and evaluate an Expert System based on the Forward Chaining and Certainty Factor methods to diagnose diseases in pregnant women at an early stage, thereby providing fast and accurate medical decision support and minimizing the risk of complications during pregnancy. The Forward Chaining and Certainty Factor methods were chosen for their ability to handle rule-based inference processes and provide certainty level calculations in the diagnosis results. Forward Chaining is used to find solutions based on the symptoms entered by users, while the Certainty Factor helps assign confidence weights to the generated diagnosis. The dataset in this study consists of 30 data samples with 30 types of symptoms experienced by patients as variables. The results show that the Forward Chaining and Certainty Factor methods are capable of producing disease diagnoses in pregnant women with an accuracy rate of 95%. The contribution of this research is to improve the quality of maternal health services through fast and accurate diagnoses by medical personnel and to assist pregnant women in obtaining an initial diagnosis of common diseases during pregnancy.

Pengelolahan Citra Cabai Keriting: Kombinasi Median Filtering dan Algoritma K-Means untuk Pengelompokan Berbasis Fitur Yasmin, Nabilla; Akbar, Syifa Chairunnissa Deliva; Ramadhanu, Agung
Journal of Education Research Vol. 5 No. 4 (2024)
Publisher : Perkumpulan Pengelola Jurnal PAUD Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37985/jer.v5i4.1865

Pengolahan citra digital berperan penting dalam klasifikasi tanaman, termasuk cabai keriting. Penelitian ini mengusulkan metode pengelompokan citra cabai keriting menggunakan algoritma K-Means dengan median filtering sebagai langkah awal untuk mengurangi noise pada citra. Ekstraksi fitur dilakukan dengan model warna RGB untuk fitur warna dan metode Grey Level Co-occurrence Matrix (GLCM) untuk fitur tekstur. Dataset terdiri dari 100 citra, masing-masing 50 citra cabai merah dan hijau keriting, dengan pembagian 60 citra untuk pelatihan dan 40 citra untuk pengujian. Hasil menunjukkan bahwa penggunaan median filtering meningkatkan akurasi klasifikasi, dengan akurasi 95% untuk cabai merah keriting dan 93% untuk cabai hijau keriting, menghasilkan rata-rata akurasi 94%. Temuan ini menegaskan pentingnya median filtering dalam meningkatkan kualitas data untuk pengelompokan citra cabai keriting.

Utilization of Banana Leaf Fiber as a Material for Making False Eyelashes Efrianova, Vivi; Pradana, Samul Martin; Yasmin, Nabilla; Khairani, Aulia; Syafri, Edi
Journal of Applied Agricultural Science and Technology Vol. 9 No. 4 (2025): Journal of Applied Agricultural Science and Technology
Publisher : Green Engineering Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55043/jaast.v9i4.460

False eyelashes are essential tools used to enhance the appearance of the eyes in makeup application. However, most variants of false eyelashes currently available on the market are made from human hair and synthetic materials, which do not guarantee halal quality for Muslim consumers. This study aims to develop a new variant of false eyelashes made from halal-certified natural fibers. The false eyelashes are produced using natural fibers derived from the leaf sheaths of Musa paradisiaca (kepok banana), Musa textilis (abaca banana), and Musa sapientum (ambon banana), which are mechanically processed and crafted using a hanging netting technique, with designs adjusted to fit the shape of the eyes. Based on laboratory tests for tensile strength and modulus of elasticity, abaca banana fibers achieved the highest values at 72.49 g/tex and 1.85 g/tex, respectively. For fiber smoothness testing, kepok banana fibers scored the highest at 10.44 g/tex. In organoleptic tests, abaca banana fibers received the highest score for curliness at 66.7%; for lightness, both kepok and abaca banana fibers shared the highest score at 44.4%; and for neatness, kepok and abaca banana fibers again shared the highest score at 66.7%. In hedonic preference tests, kepok banana fibers scored 44.4%, abaca 50%, and ambon 60%. It can be concluded that false eyelashes made from the leaf sheath fibers of kepok, abaca, and ambon bananas are considered visually suitable for use in makeup based on organoleptic and hedonic evaluations. The results of this study contribute to the development of new variants of halal-certified false eyelashes in the cosmetic industry.

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