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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Sains dan Teknologi: Jurnal Keilmuan dan Aplikasi Teknologi Industri JURNAL PENDIDIKAN TAMBUSAI Jurnal Ilmiah Media Sisfo JOURNAL OF SCIENCE AND SOCIAL RESEARCH JOISIE (Journal Of Information Systems And Informatics Engineering) INTI Nusa Mandiri Jurnal Ekonomi Manajemen Sistem Informasi Jurnal Teknologi Dan Sistem Informasi Bisnis JATI (Jurnal Mahasiswa Teknik Informatika) Indonesian Journal of Electrical Engineering and Computer Science Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Pendidikan Guru (JPG) Journal of Applied Data Sciences Bulletin of Computer Science Research JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Ipteks Terapan : research of applied science and education Journal of Education Research Algoritme Jurnal Mahasiswa Teknik Informatika Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Hasi Penelitian Dan Pengkajian Ilmiah Eksakta - JPPIE Jurnal Ekonomika Dan Bisnis Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Sains dan Teknologi Jurnal Komtekinfo Indonesian Journal Computer Science (ijcs) Intellect : Indonesian Journal of Learning and Technological Innovation SATIN - Sains dan Teknologi Informasi Jurnal Quancom: Jurnal Quantum Komputer Journal of Information System and Education Development Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) The Indonesian Journal of Computer Science CSRID
Ramadhanu, Agung -
Universitas Putra Indonesia “YPTK” Padang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Implementasi Algoritma K-Means Pada Pengolahan Citra Untuk Deteksi Bentuk Dan Material Gelas putri, kamila amaliah; 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.v7i4.2267

Abstract

Digital image processing is a branch of computer science that plays a significant role in automating object identification processes. This study presents the implementation of the K-Means Clustering algorithm for detecting the shape and material of drinking glasses based on digital images. The research methodology involves several stages, including image data collection, color space conversion from RGB to Lab, image segmentation using K-Means Clustering, and feature extraction of shape and texture. The K-Means algorithm is employed to cluster image pixels into multiple groups according to color similarity and texture patterns, thereby enabling the classification of glasses based on their material (glass, plastic, or clay) and shape. The experimental results demonstrate that the proposed method achieves a high level of accuracy in object identification and can be effectively implemented within a Matlab-based system. Consequently, this approach offers a potential solution for the automation of drinking container identification in various industrial and research applications.
Hybrid Data Mining with the Combination of K-Means Algorithm and C4.5 to Predict Student Achievement Ramadhanu, Agung; Defit, Sarjon; Kareem, Shahab Wahhab
International Journal of Artificial Intelligence Research Vol 5, No 2 (2021): December 2021
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (480.052 KB) | DOI: 10.29099/ijair.v6i1.225

Abstract

Getting academic achievement is the dream of every student who studies at higher education, especially undergraduate level. Undergraduate students aspire to the highest achievement (champion) at the last achievement of their studies. However, students cannot predict whether these students with the habits that have been done and the current conditions will make them excel or not. Apart from that, of course, students also want to know what factors and conditions influence the achievement the most. The objective to be achieved in this research is how to predict which number of students among them are predicted to excel (champion) at the end of the semester with a combination of the K-Means and C4.5 methods. Besides, the purpose of this study reveals how the K-Means algorithm performs data clustering of student data who will excel or not and how the C4.5 algorithm predicts students who have been grouped. Data processing in this study uses the Rapid Miner software version 9.7.002. The result of this research is that it is easier to group data in numerical form than data in polynomial form. Other results in this study were that out of 100 students, 27 students (27%) were predicted to excel (champions) and 73 (73%) did not achieve (not champions).
Penerapan Algoritma K-Means Clustering dalam Segmentasi Citra Sayuran: Wortel, Kol, dan Terong Berbasis Matlab atiqah, sri; Ramadhanu, Agung
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3622

Abstract

The process of identifying vegetable quality faces a major challenge due to its reliance on manual inspection, which istime-consuming, inconsistent, and highly dependent on the observer’s subjectivity. This study aims to examine theapplication of the K-Means Clustering algorithm in the digital image segmentation of three types of vegetables—carrots,cabbages, and eggplants—to evaluate the algorithm’s ability to separate the main object from the background and assessidentification accuracy based on shape and texture features. The research employs an exploratory method with aconceptual prototype approach. The dataset consists of 30 digital images (10 for each vegetable type) obtained throughdirect image acquisition under controlled lighting conditions. All images were processed using MATLAB R2023a andconverted from the RGB color space to the CIELab (Lab) color space* prior to segmentation using the K-Meansalgorithm. After segmentation, shape features (area, perimeter, eccentricity) and texture features based on the Gray LevelCo-occurrence Matrix (GLCM) were extracted. Quantitative analysis was conducted to evaluate the segmentationaccuracy and the effectiveness of object separation. The results show that the K-Means algorithm successfully separatedthe main objects from the background with 100% accuracy and high consistency. This approach is considered feasible asan initial model for an automatic identification system for agricultural commodities based on digital imagery, withpotential for further development through dataset expansion and comparison with other algorithms.
Implementasi Image Processing untuk Klasifikasi Citra Sapi, Gajah, dan Iguana dengan K-Means Maharani, Filsha Rifi; Ramadhanu, Agung
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3626

Abstract

The rapid technological developments have made significant contributions in various fields, but the main problem faced in animal research and conservation is the limitations of manual identification methods that are time-consuming and prone to human error. In addition, visual differences between species often cause difficulties in the process of accurately classifying animal images. This study aims to develop an automatic classification system based on the K-Means Clustering method in identifying three animal species, namely cattle (Bos taurus), elephants (Loxodonta africana and Elephas maximus), and iguanas (Iguanidae). The research method includes several main stages, namely image acquisition, preprocessing by converting RGB to LAB color space, image segmentation using the K-Means Clustering algorithm, and extraction of shape and texture features with Eccentricity, Energy, and Homogeneity parameters. The dataset used consists of 30 images, 10 for each species. The results were analyzed using a confusion matrix to measure the level of classification accuracy. The results showed that the system was able to classify all images with an accuracy level of 100% without any misclassification between classes. Confusion matrix analysis reinforced these findings by demonstrating fully correct identification for all samples. These findings demonstrate the effectiveness of the K-Means Clustering method in grouping animal images with striking visual differences and offer potential applications in conservation and intelligent farming systems.
Implementasi Pengolahan Citra untuk Klasifikasi Jenis Bunga Matahari, Mawar, dan Tulip Menggunakan Algoritma K-Means Clustering ., Ulfa; Ramadhanu, , Agung
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3627

Abstract

Manual identification and classification of ornamental flower varieties is time-consuming and highly dependent onindividual expertise, resulting in identification errors that impact the production value chain and operational efficiency ofthe horticulture industry. This research aims to implement an automated classification system for three types ofornamental flowers (sunflower, rose, and tulip) using K-Means Clustering method with visual feature analysis to improveidentification accuracy and computational efficiency. The research methodology includes acquisition of 210 high-qualitybalanced flower images (70 samples per class), preprocessing with RGB to HSV color space transformation, segmentationusing K-Means with k=3, and extraction of 10 multi-dimensional features encompassing morphology, color, and GLCMtexture. The dataset was divided into 80% training and 20% testing using stratified sampling with K-Fold CrossValidation. Performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The researchresults demonstrate overall accuracy of 88.89% with sunflower achieving F1-score of 0.98 (0% error), rose 0.86 (14.3%error), and tulip 0.85 (19% error). Aspect ratio, solidity, and mean red channel intensity proved to be the mostdiscriminative features. Misclassification predominantly occurred in the rose-tulip pair (71.4%) due to red spectrum coloroverlap and morphological variation. K-Means algorithm demonstrated optimal balance between accuracy,computational efficiency (0.3s/image), and interpretability, although it has limitations on low feature separability. Thisstudy is limited to a small dataset (210 images) and controlled conditions, requiring real-world validation for bettergeneralization
Klasifikasi Jenis Kendaraan (Helikopter, Mobil, Motor) Menggunakan Metode K-Means Clustering pada Pengolahan Citra Nurjannah, Farah; Ramadhanu, Agung
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3631

Abstract

Digital image-based vehicle type classification still faces obstacles because the identification process is generally done manually, so it takes a long time and has the potential to result in object recognition errors. This condition indicates the need for an image processing-based automation system that is able to recognize vehicle types accurately and efficiently. This study aims to develop a vehicle image classification system (helicopters, cars, and motorcycles) using the K-Means Clustering method to improve identification accuracy based on visual characteristics. This study was conducted with a quantitative approach through four main stages, namely image preprocessing (RGB to LAB conversion and size normalization), segmentation using the K-Means Clustering algorithm, extraction of shape features (metric, eccentricity) and texture (contrast, correlation, energy, homogeneity) based on Gray Level Co-occurrence Matrix (GLCM), and evaluation of accuracy using a confusion matrix. The research dataset consists of 30 vehicle images divided equally for each class. The results show that the combination of the K-Means Clustering method and GLCM feature extraction is able to classify three types of vehicles with an accuracy level reaching 100%. These findings prove that the K-Means method is effective for vehicle image recognition automation, and can be used as a basis for developing artificial intelligence-based visual identification systems in the future.
A Implementasi K-Means Clustering dalam Segmentasi Citra Hewan pada Kucing, Kambing, dan Burung Delvi, Syerlin Aprilia; Ramadhanu, Agung
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3632

Abstract

Image segmentation is one of the most important challenges in digital image processing because it determines the successof separating the main object from the background so that visual information can be further analyzed. The problem ariseswhen the object has complex color, texture, and shape characteristics, as in animal images that often have color patternssimilar to their surroundings, making object boundaries difficult to distinguish clearly. This study aims to apply the KMeans Clustering method in the process of animal image segmentation—specifically for cats, goats, and birds—and toevaluate its effectiveness in identifying and separating the main object from the background. The method used is the KMeans Clustering algorithm, an unsupervised learning technique that groups image pixels based on color similarity in theRGB color space through an iterative process until centroid stability is achieved and clusters representing different imageregions are formed. The results show that the K-Means method can produce good segmentation performance for imageswith uniform lighting and simple backgrounds but experiences a decrease in accuracy when the object’s color is similar toits environment. Overall, this algorithm is effective, simple, and can serve as a foundation for developing automatedanimal image identification and classification systems
Penerapan K-Means Clustering untuk Klasifikasi Citra Aksesoris Ekstraksi Warna dan Tekstur GLCM Zubaidah, Rima Puti; Ramadhanu, Agung
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3633

Abstract

The main problem in accessory image recognition lies in the similarity of physical shapes among objects such as bracelets, necklaces, and earrings, which often causes difficulties in the automatic classification process. This study aims to develop an accessory image classification system capable of accurately grouping objects based on a combination of color and texture features using the K-Means Clustering algorithm. The method used includes several preprocessing stages such as resizing images to ensure uniform dimensions and normalizing pixel values to achieve consistent data scales. Color features were extracted using RGB and HSV histograms to represent color variations, while texture features were obtained through the Gray Level Co-occurrence Matrix (GLCM) method with four parameters: contrast, correlation, energy, and homogeneity. All extracted features were then combined and analyzed using the K-Means algorithm with k=3, corresponding to the number of accessory categories. The results show that combining color and texture features produces a more optimal cluster separation compared to using single-feature extraction. The K-Means algorithm successfully grouped accessory images according to their respective categories with high consistency. These findings have potential applications in digital catalog management systems and product recommendation systems on e-commerce platforms.
Penerapan K-Means Clustering Pada Pengolahan Citra Jam Digital, Analog dan Monograph dengan Matlab Dinantia, Triend; Ramadhanu, Agung
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3634

Abstract

Manual grouping of clock types is time-consuming and prone to errors, necessitating an automatic method to accuratelyclassify digital, analog, and chronograph clocks. This study aims to implement the K-Means Clustering method ingrouping clock types using image processing techniques with Matlab. The applied method involves image processing withcolor space conversion from RGB to LAB, texture feature extraction using Gray-Level Co-occurrence Matrix (GLCM),and grouping using K-Means Clustering algorithm. Analysis was performed by calculating silhouette coefficient andDavies-Bouldin Index to evaluate cluster quality. Results show three clusters formed: analog clocks, digital clocks, andchronograph clocks with 99% accuracy, where 30 out of 30 image data were correctly identified. K-Means Clusteringmethod is proven effective and accurate in determining clock categories.
PENERAPAN METODE IMPORTANCE PERFORMANCE ANALYSIS (IPA) UNTUK MENGUKUR KUALITAS SISTEM INFORMASI ULANGAN HARIAN Syahputra, Hadi; Ramadhanu, Agung; Bayuputra, Ramdani
Jurnal Ekonomi Manajemen Sistem Informasi Vol. 1 No. 4 (2020): Jurnal Ekonomi Manajemen Sistem Informasi (Maret 2020)
Publisher : Dinasti Review

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31933/jemsi.v1i4.172

Abstract

SMA N 9 Padang merupakan salah satu sekolah yang sudah menerapkan teknologi dalam bidang pendidikan dengan sebaik-baiknya. Sekolah tersebut membangun sistem informasi yang diberi nama BeeSmart, yang merupakan sistem informasi pertama yang digunakan di sekolah tersebut untuk siswa dan guru dalam melaksanakan ulangan harian secara terkomputerisasi. BeeSmart tersebut diharapkan dapat menjadi pedoman untuk sekolah lain yang belum menerapkan teknologi dalam pembelajaran, oleh karena itu sebagai pelopor sistem informasi ulangan harian BeeSmart tentu harus memenuhi standar kualitas sistem yang baik dan jauh dari kekurangan. Dengan menggunakan metode Importance Performance Analysis (IPA), maka kualitas dari sistem informasi ulangan harian ini dapat diukur dan diperbaiki lebih baik.
Co-Authors ., Ulfa Afriadi Afriadi Afriadi, A Agsera, Nilam Agus Salim, David Agusty, Dhia Fadhila Ahmad Syarif ahmad yani Akbar, Syifa Chairunnissa Deliva Al-arrafi, Muhammad Ikhsan Andry Novrianto Angga Angga Anggara Putra, Febri Antoni Antoni Arsyah Arsyah atiqah, sri Avezrima Rahmamuthi Bayuputra, Ramdani Berta Agus Petra Betriana Roza, Yesi Betriana, Yesi Chairunnissa Deliva Akbar, Syifa Chan, Fajri Rinaldi Delvi, Syerlin Aprilia Desi Permata Sari Desi Permata Sari Desi Permata Sari Desi Permata Sari Devi Maryuni Devita, Retno Dhia Fadhila Agusty Dicky Imansyah, Muhammad Dila, Rahmah Dinantia, Triend Dodi Guswandi Enggari, Sofika Erlanda, Hadrian Fadila Cahyani Putri Fajri Saputra, Charisman Fajrul Islami Febri Hadi Fiki Pratama Firmansyah, Ryan Firna Yenila Fitri Yeni Gafari, Abuzar Gunadi Widi Nurcahyo Hadi Syahputra Hadi Syahputra Hadi Syahputra Putra Halifia Hendri Hanna Pratiwi Harnaranda, Jefri Hasmaynelis Fitri Hendri, Hallifia Hidayati, Dzil Hidayattullah, Hafis Hikmi, Zakiya Honestya, Gabriela Husna Arsyah, Rahmatul Ilmawan, Fachrul Imrah, Imrah Sari Irfan Rizki Nur Irsyad, As'Ary Sahlul Jehan Harka Johan Harlan Jufriadif Na`am, Jufriadif Kareem, Shahab Wahhab Karseno, Doni Khomsi, Ahmad Larissa Navia Rani M.Iqbal, M.Iqbal Maharani, Filsha Rifi Majid, Mazlina Abdul Mardison Mardison Mardison Mardison Mardison Marfalino, Hari Masri, Taufik Mokti Isra Mokti Isra Muhammad Idris Muhammad Raihan Zaky Muhammad Raihan Zaky Muhammad Yusuf Nabila Frisca Oktavia Nadia, Nadia Aini Hafizhah Nasution, Amir Salim Khairul Rijal Nasution, Annio Indah Lestari Negoro, Wahyu Saptha Neni Sri Wahyuni Nengsi Neni Sri Wahyuni Nengsi Neni Sri Wahyuni Nengsi Neni Sri Wahyuni Nengsih Neni Sri Wahyuni Nengsih Neni Sri Wayuni Ningsih Neni Sri Wayuni Ningsih Ningsih, Neni Sri Wayuni Nurdiansyah, Ali Nurhaliza Nurhaliza Nurjannah, Farah Permata, Edo Pertiwi, Yuliana Pratama, Dede Putra, Kharisma Utama Putra, Ramdani Bayu putri, kamila amaliah Rahmad Rahmad Rahmad, R Raja Ayu Mahessya Rani, Larissa Navia Repelita Witri Rheza Thresya Rianti, Eva Riati, Itin Rindy Citra Dewi Riyan Saputra, Riyan Rizky Gusrianto Rosa, Imelda Rosda Syelly Sajida, Mayang salim, alfajri Saputra, Charisman Fajri Saputra, Randy Sarjon Defit Selvia, Dina Silfia Andini, Silfia Sisi Hendriani Sofika Enggari Sofika Enggari Sofika Enggari Sovia, Rini Suci Wahyuni Sularno Sularno Sumijan, S Sutri, Ridwan Syafri Arlis Syafrika Deni Rizki Syafrika Deni Rizki, Syafrika Deni Syafril Syafril Syafril, S Syalsabilla, Adinda Teri Ade Putra Tesa Vausia Sandiva Tomi, Zebbil Billian Utama Putra, Kharisma Utari, Utari Armila Vidyanti, Angela Citra Wiratama, Aditya Wirdawati, Wira Witri, Repelita Yagus Valentino Harefa Yanti, Rahma Yasmin, Nabila Yasmin, Nabilla Yemi, Leonardo Yesi Betriana Roza, yesibetriana_18 Yogi Wiyandra Yolanda Yolanda, Yolanda Yosfand, Windra Yuhandri Yuhandri Yuhandri Yulihartati, Sandra Zubaidah, Rima Puti