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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Indo-MathEdu Intellectuals Journal
Putra, Kharisma Utama
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Computer Science & IT Economics, Econometrics & Finance Education Mathematics Social Sciences

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PENINGKATAN AKURASI KLASIFIKASI KEMATANGAN PEPAYA BERDASARKAN WARNA DENGAN MEDIAN FILTER, K-MEANS PADA CONVOLUTIONAL NEURAL NETWORK Yosfand, Windra; Putra, Kharisma Utama; Ramadhanu, Agung
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 1 (2025): February 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i1.2781

Abstract

Abstract: The development of technology in the field of digital image processing has become an attraction in itself to make human life easier and has given rise to many applications that can apply it in various fields. Digital image processing methods can transform input images into output images that can be used to identify and classify objects in life. To minimize damage to digital images which is known as noise. Also to reduce the impact of degradation or decrease in image quality caused by noise, colors that are too contrasty or blurry. So a method is needed. One method is the median filter which is used in this research.Keyword: edian Filter, Convolutional Neural Network, Papaya, Classification Abstrak: Dalam perkembangan teknologi di bidang pengolahan citra digital (digital image processing) menjadi daya tarik tersendiri untuk mempermudah kehidupan manusia dan memunculkan banyak aplikasi yang dapat menerapkannya dalam berbagai bidang. Metode digital image processing dapat mentransformasikan citra masukan menjadi citra keluaran yang dapat dimanfaatkan untuk mengidentifikasi dan mengklasifikasi objek dalam kehidupan. Untuk mengurangi meminimalisir kerusakan pada citra digital yang disebut sebagai noise. Juga untuk mengurangi dampak degradasi atau penurunan kualitas citra yang disebabkan oleh derau / noise, warna yang terlalu kontras atau buram. Maka dibutuhkan suatu metode. Salah satu metodenya adalah median filter yang digunakan pada penelitian ini.Kata kunci: Median Filter, Convolutional Neural Network, Pepaya, Klasifikasi
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

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.
Penerapan E-Commerce oleh UMKM: Systematic Literature Review tentang Faktor Keberhasilan dan Hambatan Putra, Kharisma Utama; Veri, Jhon
Indo-MathEdu Intellectuals Journal Vol. 6 No. 5 (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.v6i5.3653

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a crucial role in national economic growth. However, in the digital era, MSMEs' adoption of e-commerce remains suboptimal in addressing current challenges. This study aims to identify the factors contributing to success and obstacles in implementing e-commerce by MSMEs through a systematic literature review (SLR) approach. This method was used to analyze 30 relevant international and national journal articles published between 2018 and 2024. These journals were retrieved from credible academic databases such as Scopus and Google Scholar, and then grouped based on key themes or topics emerging from the data. The study results indicate that success factors include management support, digital literacy, technological infrastructure, and customer trust, while key obstacles include limited human resources, implementation costs, and a lack of understanding of digital regulations. This study provides recommendations for the government and stakeholders to support MSMEs digitalization more strategically.
Co-Authors Hendri Irawan Ramadhanu, Agung Sovia, Rini Veri, Jhon Yanti, Rahma Yasmin, Nabilla Yosfand, Windra