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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Ilmiah Media Sisfo JOURNAL OF SCIENCE AND SOCIAL RESEARCH JOISIE (Journal Of Information Systems And Informatics Engineering) INTI Nusa Mandiri Jurnal Teknologi Dan Sistem Informasi Bisnis JATI (Jurnal Mahasiswa Teknik Informatika) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pendidikan Guru (JPG) JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Journal of Education Research Algoritme Jurnal Mahasiswa Teknik Informatika Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Komtekinfo Indonesian Journal Computer Science (ijcs) Journal of Information System and Education Development Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) The Indonesian Journal of Computer Science CSRID
Ramadhanu, Agung
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Improved feature extraction method and K-means clustering for soil fertility identification based on soil image Ramadhanu, Agung; Hendri, Halifia; Enggari, Sofika; Andini, Silfia; Devita, Retno; Rianti, Eva
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp2001-2011

Abstract

This research is conducting analysis of digital land images using digital image processing techniques. The main purpose of the research is to classify soil fertility based on two-dimensional RGB colored digital soil images. The research is done by extracting features and shapes from the soil image. The research uses methods of segmentation, extraction, and identification against digital soil images. This research is carried out in three stages. The first phase of this research is image pre-processing which begins with the conversion of RGB color image to Grayscale then color conversion to binary which subsequently performs noise reduction with the method Three-layer median filter. The second stage is a process that is divided into the first two stages, namely the process of segmentation by grouping RGB color images into L*a*b which is continued by clustering using the K-means clustering method. The second is the extraction of characteristics of the soil image which is characteristic of shape and texture. The final stage is the identification of soil images that are clustered into two types: fertile soils and unfertile soil. The study achieved an accuracy of 85% which could accurately identify 20 images while inaccurately classifying 5 images out of a total of 25 input images.
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

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.
KLASTERISASI KESEHATAN DAUN MENGGUNAKAN K-MEANS CLUSTERING DENGAN TEKNIK PENGOLAHAN CITRA Pratama, Dede; Ramadhanu, Agung
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.v8i2.3087

Abstract

Abstract: Leaves are an important part of plants that greatly influence the health and quality of the plant. Poor leaf conditions, such as yellow or degraded leaves, can indicate problems in plant growth or health. Manual classification of fresh and yellow leaves can be time-consuming and often inconsistent, necessitating a more efficient automated method. The main goal of this research is to implement an image processing-based automatic classification system to classify fresh and yellow leaves based on visual features such as color, texture, and shape, to improve efficiency and consistency in plant health monitoring. To distinguish brightness and color, images are processed by converting the RGB color space to LAB. Using the K-Means Clustering algorithm, images are grouped into two clusters, each consisting of fresh leaves and yellow leaves. The data used in this research consists of eight images, each comprising four images of fresh leaves and four images of yellow leaves. The research results show that this method successfully classified fresh and yellow leaves with an accuracy rate of 100%, with 8 out of 8 images correctly identified. The K-Means Clustering method has been demonstrated as an effective and accurate method for determining leaf health conditions. Keywords: Fresh Leaves, Yellow Leaves, K-Means Clustering, Image Processing,                 Feature Extraction Abstrak: Daun merupakan bagian penting dari tumbuhan yang sangat mempengaruhi kesehatan dan kualitas tanaman. Kondisi daun yang buruk, seperti daun kuning atau daun yang terdegradasi, dapat menunjukkan adanya masalah dalam pertumbuhan atau kesehatan tanaman. Klasifikasi daun segar dan daun kuning secara manual dapat memakan waktu dan sering kali tidak konsisten, sehingga diperlukan metode otomatis yang lebih efisien. Tujuan utama dari penelitian ini adalah untuk mengimplementasikan sistem klasifikasi otomatis berbasis pengolahan citra dalam mengklasifikasikan daun segar dan daun kuning berdasarkan fitur visual seperti warna, tekstur, dan bentuk, guna meningkatkan efisiensi dan konsistensi dalam proses pemantauan kesehatan tanaman. Untuk membedakan kecerahan dan warna, gambar diproses dengan mengubah ruang warna RGB ke LAB. Dengan menggunakan algoritma K-Means Clustering, gambar dikelompokkan ke dalam dua kelompok, masing-masing terdiri dari daun segar dan daun kuning. Data yang digunakan dalam penelitian ini terdiri dari delapan gambar, masing-masing terdiri dari empat gambar daun segar dan empat gambar daun kuning. Hasil penelitian menunjukkan bahwa metode ini berhasil mengklasifikasikan daun segar dan daun kuning dengan tingkat akurasi 100%, dengan 8 dari 8 gambar teridentifikasi dengan benar. Metode K-Means Clustering telah ditunjukkan sebagai metode yang efektif dan akurat untuk menentukan kondisi kesehatan daun. Kata kunci:  Daun Segar, Daun Kuning, K-Means Clustering, Pengolahan Citra,  Ektraksi Fitur.
KLASTERISASI BUNGA TEROMPET DAN BUNGA KAKI ITIK DENGAN METODE K-MEANS BERBASIS PENGOLAHAN CITRA Masri, Taufik; Ramadhanu, Agung
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.v8i2.2661

Abstract

Abstract: Clustering is one of the methods in data processing that aims to group objects based on certain similarities. This study aims to cluster Trumpet Flower (Brugmansia) and Balsam Flower (Impatiens Balsamina) using the K-Means method based on digital image processing. The image processing begins with a pre-processing stage, including grayscale conversion, noise reduction, and object segmentation. Next, image features are extracted to obtain information on texture, color, and shape. The extracted feature data is then analyzed and grouped using the K-Means algorithm, where the clustering results are evaluated based on grouping accuracy and inter-cluster consistency. The study results show that the K-Means method can effectively cluster Trumpet Flower and Balsam Flower with high accuracy, depending on the input image quality and clustering parameters. This study highlights the great potential of the K-Means algorithm in image processing applications, particularly for visual-based object identification and grouping. Keywords: K-Means; clustering; image processing; Trumpet flower; Balsam flower Abstrak: Klasterisasi merupakan salah satu metode dalam pengolahan data yang bertujuan untuk mengelompokkan objek-objek berdasarkan kemiripan tertentu. Penelitian ini bertujuan untuk melakukan klasterisasi terhadap bunga Terompet ( Brugmansia ) dan bunga Kaki Itik ( Impatiens Balsamina ) menggunakan metode K-Means berbasis pengolahan citra digital. Proses pengolahan citra diawali dengan tahap pra-pengolahan yang meliputi konversi ke skala abu-abu, pengurangan noise, serta segmentasi objek. Selanjutnya, fitur citra diekstraksi untuk mendapatkan informasi tekstur, warna, dan bentuk. Data fitur yang dihasilkan kemudian dianalisis dan dikelompokkan menggunakan algoritma K-Means, di mana hasil klasterisasi dinilai berdasarkan akurasi pengelompokan dan konsistensi antar kelompok. Hasil penelitian menunjukkan bahwa metode K-Means mampu mengelompokkan bunga Terompet dan bunga Kaki Itik dengan tingkat akurasi yang tinggi, tergantung pada kualitas citra masukan dan parameter pengelompokan. Studi ini menunjukkan potensi besar algoritma K-Means dalam aplikasi pengolahan citra khususnya untuk identifikasi dan pengelompokan objek berbasis visual. Kata kunci: K-Means;klasterisasi;pengolahan citra;Bunga terompet ;Bunga kaki itik
IMPLEMENTASI METODE K-MEANS UNTUK MENGKLASTERISASI JENIS BUAH NAGA DENGAN TEKNIK PENGOLAHAN CITRA Idris, Muhammad; Ramadhanu, Agung
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.v8i2.3091

Abstract

Abstract: This research aims to implement the K-Means method for clustering red and yellow dragon fruit types using image processing techniques. Image processing is carried out by utilizing features such as color, texture, and shape from dragon fruit images obtained using a digital camera. The captured images are then processed through preprocessing stages such as conversion to a specific color space  and feature extraction to describe the visual characteristics of the dragon fruit. Afterward, the K-Means algorithm is applied to cluster the images based on the similarity of their features. The clustering results show that the K-Means method is effective in distinguishing between red and yellow dragon fruit types, with a satisfactory accuracy rate. This study contributes to the development of an automated classification system for dragon fruit type identification based on images, which can be applied in agriculture, especially in the processing and distribution of products. Keyword: K-Means Method; Red Dragon Fruit Clustering; Yellow Dragon Fruit; Image Processing. Abstrak: Penelitian ini bertujuan untuk mengimplementasikan metode K-Means dalam mengklasterisasi jenis buah naga merah dan buah naga kuning menggunakan teknik pengolahan citra. Pengolahan citra dilakukan dengan memanfaatkan fitur-fitur warna, tekstur, dan bentuk dari citra buah naga yang diperoleh menggunakan kamera digital. Citra yang diambil kemudian diproses melalui tahap pra-pemrosesan seperti konversi ke ruang warna tertentu dan ekstraksi fitur untuk mendeskripsikan karakteristik visual dari buah naga. Setelah itu, algoritma K-Means diterapkan untuk mengelompokkan citra berdasarkan kemiripan fitur yang dimilikinya. Hasil pengklasteran menunjukkan bahwa metode K-Means efektif dalam memisahkan jenis buah naga merah dan kuning, dengan tingkat akurasi yang memadai. Penelitian ini memberikan kontribusi dalam pengembangan sistem klasifikasi otomatis untuk identifikasi jenis buah naga berdasarkan citra, yang dapat diterapkan dalam bidang pertanian, terutama dalam proses pengolahan dan distribusi produk. Kata kunci:  Metode K-Means; Pengklasteran buah naga merah; Buah naga kuning; Pengolahan citra.
Implementation of Extreme Learning Machine Based on HSV Color Features for Marine Animal Image Classification Hidayati, Dzil; Pertiwi, Yuliana; Ramadhanu, Agung
Techno.Com Vol. 24 No. 3 (2025): Agustus 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i3.13490

Abstract

Recognizing sea animals is a significant challenge in digital image recognition. This is due to the diverse visual characteristics of marine animals, including morphological shapes, body surface colors, and textures displayed in images. Environmental factors also influence image quality, such as underwater lighting conditions, water turbidity, and other external elements. To address these classification challenges, one proposed approach is the use of the Extreme Learning Machine (ELM) method, which can be implemented by utilizing HSV (Hue, Saturation, Value) color features as the main input. The HSV color space is chosen because it more closely resembles the way humans perceive colors. In this model, color is separated into three main components: hue represents the type of color, saturation indicates the intensity or purity of the color, and value refers to its brightness or darkness. The dataset consists of several classes of marine animals such as clams, squids, and shrimp, collected from high-resolution image datasets. Test results show that the ELM model can classify images with competitive accuracy, achieving up to 83% accuracy in a much shorter training time compared to traditional learning methods. This study demonstrates that combining HSV color features with the ELM algorithm can be an efficient approach for classifying marine animal images.   Keywords - Shell, Squid, Shrimp, ELM,HSV
IMPLEMENTASI PRINCIPAL COMPONENT ANALYSIS DAN KNEAREST NEIGHBORS DALAM KLASIFIKASI TANAMAN JAHE, KUNYIT, DAN LENGKUAS Yesi Betriana Roza, yesibetriana_18; Ramadhanu, Agung
INTI Nusa Mandiri Vol. 20 No. 1 (2025): INTI Periode Agustus 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v20i1.6441

Abstract

Ginger (Zingiber offivinale), turmeric (curcuma longa), and galangal (Alpinia galanga) plants are the result of Indonesia's wealth which has high economic and health value. This type of plant has high economic and health value, so its accurate identification is very important in the agricultural and pharmaceutical fields. By combining image classification methods, PCA, KNN, this research aims to develop a system that can identify ginger, turmeric, and galangal automatically and accurately. It is hoped that this system can not only provide a solution for efficient plant identification, but can also contribute to the management of natural resources and the development of herbal plant-based products in Indonesia. Data collected by taking pictures and then processed using MATLAB. This research aims to identify ginger, turmeric and galangal plants using euclidean distance and extract shape and texture characteristics. Shape feature extraction using RGB, HVS, and Area. This research implements the PCA and K-Nearest Neighbor methods in classifying data. Meanwhile, the KNN method is applied by measuring the closest distance between the test data and the training data. In this research there are labels and attributes, labels taken from the level of fruit maturity and attributes obtained from the results of image feature extraction. These attributes are R(red), G(green), B(blue), H(hue), S(saturation), V(value), Area. The accuracy results obtained from the classification of ginger, turmeric and galangal plants using the KNN method were 80% with a K=3 value obtained from 8 test data with accurate classification, and 20% from 2 test data with inaccurate classification.
IMPLEMENTASI METODE EXTREME LEARNING MACHINE UNTUK KLASIFIKASI JENIS MOBIL idris, muhammad; Fajri Saputra, Charisman; Ramadhanu, Agung
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.3714

Abstract

Abstract: The rapid development of automotive technology has led to a wide variety of car types with increasingly complex features and characteristics. Alongside this advancement, the need for fast and accurate automatic classification systems has become essential, particularly in the field of vehicle image recognition. This study aims to implement the Extreme Learning Machine (ELM) method to classify vehicle types based on digital images. The research focuses on classifying three types of vehicles (Bus, Pickup, and SUV), using a dataset of 10 images per class. The test results show that ELM is capable of classifying vehicle types with competitive accuracy compared to conventional methods, while offering significantly more efficient computation time. This research demonstrates the potential of ELM as a practical solution to support intelligent vehicle recognition systems in the modern automotive industry. The classification achieved an accuracy rate of 91.67%. Keywords: Extreme Learning Machine (ELM), Vehicle Type Classification, Image Processing, Pattern Recognition. Abstrak: Perkembangan teknologi otomotif yang pesat telah menghasilkan beragam jenis mobil dengan fitur dan karakteristik yang semakin kompleks. Seiring dengan kemajuan ini, kebutuhan akan sistem klasifikasi otomatis yang cepat dan akurat menjadi sangat penting, terutama dalam bidang pengenalan citra kendaraan. Tujuan penelitian ini untuk mengimplementasikan metode Extreme Learning Machine (ELM) dalam mengklasikasi jenis mobil berdasarkan citra digital. Penelitian ini mengklasifikasikan 3 jenis mobil (BUS, Pickup dan SUV) dengan dataset masing masing 10 gambar. Hasil pengujian menunjukkan bahwa ELM mampu melakukan klasifikasi jenis mobil dengan akurasi yang kompetitif dibandingkan metode konvensional, dengan waktu komputasi yang jauh lebih efisien. Penelitian ini menunjukkan potensi ELM sebagai solusi praktis dalam mendukung sistem cerdas berbasis pengenalan kendaraan dalam dunia otomotif modern. Hasil yang didapat dari penelitian ini adalah sebesar 91,67% untuk tingkat akurasinya. Kata kunci: Extreme Learning Machine (ELM), Klasifikasi Jenis Kendaraan, Pemrosesan Citra, Pengenalan Pola.
Identification of Signature Authenticity Using Binary Extraction and K-nearest Neighbor Feature Methods Vidyanti, Angela Citra; Riati, Itin; Ramadhanu, Agung
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2063

Abstract

This research focuses on identifying the authenticity of signatures, which is an important part of the field of biometrics. Identification of signature authenticity has wide applications, including in document security, financial transactions, and identity verification in general. The problem to be resolved is the lack of an effective and efficient method for identifying signature authenticity. The method used is the binary extraction method and the K-nearest Neighbor feature. The main contribution of this research is to propose a new approach in identifying signature authenticity by combining binary extraction methods and K-nearest Neighbor features. This approach is expected to increase the accuracy and efficiency of the signature authenticity identification process. The results of this research are the development of a new model or algorithm for identifying the authenticity of signatures. After testing and validation, the accuracy level of the results of identifying the authenticity of this signature is 75%.
Implementasi Algoritma K-Means untuk Klasifikasi Citra Biota Laut: Gurita, Lobster, dan Kerang Laut Dicky Imansyah, Muhammad; Ramadhanu, Agung
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 7 No 4 (2025): Oktober 2025
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v7i4.2271

Abstract

Advances in digital image processing technology and machine learning, such as clustering, have contributed to increased efficiency in various sectors, including marine and fisheries. Octopus, lobsters, and shellfish are high-value fishery commodities that have traditionally been classified manually, with the potential for subjectivity and inefficiency. This study aims to develop a digital image classification model for marine biota using the K-Means Clustering method equipped with image processing techniques. The methods applied include converting the RGB color space to L*a*b, segmentation with K-means, shape feature extraction (metric, eccentricity) and GLCM texture (contrast, correlation, energy, homogeneity). The results show that this method is effective in identifying the three types of marine biota with an average accuracy of 95% based on testing on 30 images. The implementation of K-means Clustering has been proven to be accurate and consistent in the automation of marine biota classification.
Co-Authors Afriadi Afriadi Afriadi, A Agus Salim, David Ahmad Syarif ahmad yani Akbar, Syifa Chairunnissa Deliva Al-arrafi, Muhammad Ikhsan Angga Angga Anggara Putra, Febri Antoni Antoni Chairunnissa Deliva Akbar, Syifa Chan, Fajri Rinaldi Devita, Retno Dicky Imansyah, Muhammad Dila, Rahmah Enggari, Sofika Erlanda, Hadrian Fajri Saputra, Charisman Gafari, Abuzar Gunadi Widi Nurcahyo Hadi Syahputra Halifia Hendri Harnaranda, Jefri Hidayati, Dzil Honestya, Gabriela Ilmawan, Fachrul Irsyad, As'Ary Sahlul Karseno, Doni Khomsi, Ahmad Mardison Mardison Masri, Taufik Muhammad Idris Nadia, Nadia Aini Hafizhah Negoro, Wahyu Saptha Nurdiansyah, Ali Permata, Edo Pertiwi, Yuliana Pratama, Dede Putra, Kharisma Utama Rahmad Rahmad Rahmad, R Rianti, Eva Riati, Itin Riyan Saputra, Riyan Rosa, Imelda Sajida, Mayang salim, alfajri Saputra, Randy Sarjon Defit Selvia, Dina Silfia Andini, Silfia Sumijan, S Sutri, Ridwan Syafril Syafril Syafril, S Syalsabilla, Adinda Tesa Vausia Sandiva Utama Putra, Kharisma Vidyanti, Angela Citra Wirdawati, Wira Yanti, Rahma Yasmin, Nabila Yasmin, Nabilla Yemi, Leonardo Yesi Betriana Roza, yesibetriana_18 Yolanda Yolanda, Yolanda Yosfand, Windra Yuhandri Yulihartati, Sandra