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All Journal Desimal: Jurnal Matematika Jurnal Ilmiah Sinus Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) International Journal of Computer and Information System (IJCIS) Journal of English Language and Education Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Journal of Technology, Mathematics and Social Science Jurnal Pengabdian Masyarakat Ilmu Komputer
Wijayanto, Hendro
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Public Health Social Sciences Other

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A Learning Vector Quantization Approach to Handwritten Mandarin Numeral Recognition Soniya, Septi Vera; Wahyu Utami, Yustina Retno; Wijayanto, Hendro
Jurnal Ilmiah SINUS Vol 24, No 1 (2026): Vol. 24 No. 1, Januari 2026
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/sinus.v24i1.1073

Abstract

Numbers 1 to 10 in Mandarin are also studied in the Mandarin language learning process as basic numbers. Mandarin numbers have a different shape from Arabic numbers and Roman numerals. So it is necessary to recognize the pattern of mandarin numbers to help the learning process of mandarin. Therefore, the purpose of this research is to build an application that applies the Learning Vector Quantization method for handwriting pattern recognition of Mandarin numbers. System testing methods used are Black Box and Confusion Matrix for accuracy testing methods. The application that has been made produces an accuracy of 92.80% with a total of 250 test data. Keyword: Learning Vector Quantization, Pattern Recognition, Mandarin Numbers.
A COMPUTER VISION APPROACH FOR CLASSIFYING CALIFORNIA PAPAYA RIPENESS USING K-NEAREST NEIGHBOR Wulandari, Tyas; Prabowo, Iwan Ady; Utami, Yustina Retno Wahyu; Raharja, Bayu Dwi; Wijayanto, Hendro
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) Vol 14, No 1 (2026): Jurnal Tikomsin, Vol 14, No.1, April 2026
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/tikomsin.v14i1.1091

Abstract

Determining the ripeness level of California papaya is important for harvest decisions, sorting, distribution, and spoilage control. In practice, ripeness identification is still commonly performed visually and is therefore subjective. This study aims to develop a digital image-based classification system for California papaya ripeness using the K-Nearest Neighbor (K-NN) algorithm with Hue and Saturation features in the Hue Saturation Value (HSV) color space. The dataset consists of 90 primary images, divided into 60 training images and 30 testing images, with four ripeness classes: unripe, half-ripe, ripe, and rotten. All images were captured using a Xiaomi Mi A2 Lite smartphone and cropped to 1436 × 1000 pixels. Classification was conducted using Euclidean distance. The value of k was selected empirically through trial and error in the original study, and k = 9 was retained because it produced the most stable result on the available data while reducing the potential for class ties. The evaluation produced 22 correct predictions out of 30 test images, resulting in an accuracy of 73.33%. This revised manuscript strengthens the methodological reporting by clarifying parameter selection, documenting the data distribution and providing a literature-based comparison with alternative methods, such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN). The findings suggest that K-NN with HSV features remains a feasible, low-cost baseline, although its performance should be improved through larger datasets, per-class evaluation reporting, and head-to-head comparisons on the same dataset.
Co-Authors Ady Prabowo, Iwan Arumsari, Arumsari Dorestin, Nacita Agnes Dwi Remawati FADILA, FATIN Fitriani, Putri Asiska Nur Hakim, Yunif Lukman Iwan Ady Prabowo Lathofy, Afan Muhammad Mustofa Nugroho, Zulkifly Setyo Octaviani, Saly Kurnia Prihanto Prihanto, Prihanto Raharja, Bayu Dwi Rahmatika, Rahmatika Retno Tri Vulandari Saptomo, Wawan Laksito Yuly Soniya, Septi Vera Sri Hariyati Fitriasih, Sri Hariyati Sri Harjanto Sri Siswanti Teguh Susyanto Tri Irawati, Tri Tyas Wulandari W, Yustina Retno Waskitho, Anggit Widhi wati, Dwi Rema Widada, Bebas Yustina Retno Wahyu Utami Zaenah, Eny Rahma