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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ILKOM Jurnal Ilmiah Jurnal Matematika UNAND REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Jurnal Inovasi Teknik Informatika Journal of Computer Networks, Architecture and High Performance Computing Journal of Applied Data Sciences Journal of Computer Scine and Information Technology Jurnal Abdimas ADPI Sains dan Teknologi Jurnal Ipteks Terapan : research of applied science and education pendidikan, science, teknologi, dan ekonomi Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Komtekinfo
Teri Ade Putra
Universitas Putra Indonesia YPTK Padang
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Penerapan Metode Apriori untuk Pembelian di Minimarket Pradani Ayu Widya Purnama; Teri Ade Putra; Riandana Afira; Romi Wijaya
REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer Vol. 9 No. 1 (2025): Volume 9 Nomor 1 Januari 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/remik.v9i1.14249

Abstract

Agar selalu dapat bertahan dalam persaingan bisnis ataupun minimarket, para pelaku bisnis minimarket harus terus mengembangkan bisnis mereka. Meningkatkan kualitas produk, penambahan jenis produk, pengurangan biaya operasional dan dilakukan analisis data adalah beberapa hal yang dapat ditingkatkan oleh pihak minimarket. Algoritma apriori adalah salah satu algortima terbaik dalam data mining, dengan menggunakan algoritma apriori dan metode market basket analisis ini, peneliti dapat memahami pola pembelian konsumen dengan menganalisis produk-produk yang dibeli. Penelitian ini menghasilkan sebanyak 4 aturan (aturan 1 : Jika pelanggan membeli Produk Susu, mereka cenderung membeli Produk Roti, aturan 2 : Jika pelanggan membeli Produk Roti, mereka cenderung membeli Produk Susu, aturan 3 : Jika pelanggan membeli Produk Roti dan Produk Chiki, mereka cenderung membeli Produk Susu, aturan 4 : Jika pelanggan membeli Produk Susu dan Produk Permen, mereka cenderung membeli Produk Roti). 4 aturan yang didapat tersebut dapat dijadikan sebagai salah satu referensi bagi pihak minimarket dalam mendukung keputusan untuk melakukan tindakan yang bisa meminimalisir terjadinya penumpukan barang.
A Modified Watershed Algorithm for Rice Plant Growth Stage Analysis Putra, Teri Ade; Yuhandri, Yuhandri; Ramadhanu, Agung
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1117

Abstract

Information technology plays a crucial role in enhancing various sectors, including agriculture. In particular, technological advancements in crop monitoring are essential for sustainable food production, where accurate growth analysis is vital. Image-based approaches have emerged as a promising tool for assessing crop growth, particularly in rice plants. This study aims to enhance rice plant image segmentation using an improved Watershed algorithm, integrating the Laplacian operator and Distance Transform. This study utilizes a Support Vector Machine (SVM) classifier for segmenting and classifying rice plant growth stages, achieving accuracy, precision, recall, and F1-score metrics. The dataset consists of 1080 images of rice plants, with 74 images used for training, 31 for testing, and 975 images for validation. The image processing pipeline involves preprocessing steps such as grayscale conversion, normalization, color segmentation, Otsu thresholding, filtering, and edge detection. Following preprocessing, the Watershed algorithm is applied in two scenarios: the conventional method and the enhanced method with the Laplacian operator and Distance Transform. Performance evaluation is based on accuracy, precision, recall, and F1-score metrics. The results show that the enhanced Watershed algorithm significantly outperforms the conventional method, achieving an accuracy of 99.58%, precision of 80.55%, recall of 79.92%, and an F1-score of 81.50%. While there is a slight imbalance in precision and recall, the model demonstrates reliable performance in identifying rice plant growth. This study confirms that integrating the Laplacian operator and Distance Transform into the Watershed algorithm significantly improves segmentation accuracy, supporting the development of automated monitoring systems in smart farming. Furthermore, this approach opens avenues for application in other crops and diverse environmental conditions.
Co-Authors Abulwafa Muhammad Afira, Riandana Afriadi, Damrul Alvi Martunasia Anam, M Khairul Ariandi, Vicky Aulia Fitrul Hadi Ayu, Purnama Boy cenly dwi Sandes Pasella Dinul Akhiyar Elva, Yesri elva, yesri Firdaus, Muhammad Bambang Gema, Rima Liana Gunawan, Chichi Rizka Ikhsan Ikhsan Irzal Arief Wisky Iskandar, Riki Jafnihirda, Lika Karfindo, Karfindo Lika Jafnihirda Mahessya, Raja Ayu Muhammad Hafizh Pandu Pratama Putra, Pandu Pratama Pradani Ayu Widya Purnama Pradani Ayu Widya Purnama Purnama, Ayu Widya Purnama, Pradani Ayu Widya Purnama, Pradani Widya Pradani Putra, Ondra Eka Rahman, Sepsa Nur Rahmat , Muhammad Arif Raja Ayu Mahessya Raja Ayu Mahessya Raja Ayu Mahessya Ramadhanu, Agung Reza Suriadinata Riandana Afira Riki Iskandar Rio Andika Malik Romi Wijaya Sarjon Defit Sepsa Nur Rahman Yesri Elva Yuhandri Yuhandri, Yuhandri