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All Journal Teknika CESS (Journal of Computer Engineering, System and Science) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan INTECOMS: Journal of Information Technology and Computer Science JURNAL PENDIDIKAN TAMBUSAI J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Sains dan Teknologi Journal of Applied Engineering and Technological Science (JAETS) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Pengabdian Nasional (JPN) Indonesia J-SAKTI (Jurnal Sains Komputer dan Informatika) AJAD : Jurnal Pengabdian kepada Masyarakat Jurnal Pendidikan Sains dan Komputer International Journal Software Engineering and Computer Science (IJSECS) International Journal Education and Computer Studies Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Nasional Teknologi Komputer Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Artificial Intelligence and Digital Business CKI On Spot Sistem Komputer & Teknologi Intelegensi Artifisial (SIKOMTIA) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Pengabdian Nasional (JPN) Indonesia Journal Innovations Computer Science
Sarimole, Frencis Matheos
Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika
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Journal : journal of applied engineering and technological science jaets

 1 
Classification of Maturity Levels in Areca Fruit Based on HSV Image Using the KNN Method Frencis Matheos Sarimole; Anita Rosiana
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 1 (2022): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (423.475 KB) | DOI: 10.37385/jaets.v4i1.951

Abstract

Areca nut (Areca catechu) is a kind of palm plant that grows in Asia and Africa, the eastern part of the Pacific and in Indonesia itself, areca nut can also be found on the islands of Java, Sumatra and Kalimantan. At the stage of classifying the maturity of the betel nut so far, it is still using the manual method which at that stage has subjective weaknesses. Based on these problems, researchers will create a system that is able to classify the level of maturity of areca nut using HSV feature extraction with assistance at the classification stage using the KNN method. In this study, 842 datasets were used which were divided into 3 types of classes, namely ripe, unripe and old fruit. The dataset was divided into 683 training data and 159 test data. In the next stage, the data is tested using the K-Nearest Neighbor method by calculating the closest distance using k = 1. From the results of the calculation of the closest distance k1 produces an accuracy rate of 87.42%. Kata kunci— Matlab, Areca Ripeness, KNN, HSV.
Classification of Durian Types Using Features Extraction Gray Level Co-Occurrence Matrix (GLCM) AND K-Nearest Neighbors (KNN) Frencis Matheos Sarimole; Achmad Syaeful
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 1 (2022): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (451.242 KB) | DOI: 10.37385/jaets.v4i1.959

Abstract

Durian is one of the most popular fruits because it has a delicious taste and distinctive aroma. It has different shapes and types, especially from thorns and different colors and has fruit parts that are also not the same as other parts. In terms of fruit selection, care must be taken because consumers generally still find it difficult to distinguish physically identified types of Durian fruit due to limited knowledge of the types of Durian fruit and require a relatively long time and accuracy in sorting. Therefore, there is a need for a method to sort the types of Durian fruit effectively and efficiently. Namely image segmentation based on the classification of the types of Durian fruit to help consumers. The method used is Gray Level Co-Occurrence Matrices for feature extraction, while to determine the proximity between the test image and the training image using the K-Nearest Neighbor method based on texture based on the color of the Durian fruit obtained. Extraction features using the GLCM method based on angles of 0°, 45°, 90° and 135°. Then the KNN method is used for the classification of characteristic results using K = 3. In this study, 1281 data training was used and 321 data testing was used, resulting in an accuracy of 93%.
Classification Of Guarantee Fruit Murability Based on HSV Image With K-Nearest Neighbor Frencis Matheos Sarimole; Muhammad Ilham Fadillah
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 1 (2022): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (522.607 KB) | DOI: 10.37385/jaets.v4i1.929

Abstract

Guava bol is one of the fruits from Indonesia that is favored by many Indonesian people. The guava itself has a soft and dense flesh texture compared to water guava. The guava itself has a pink color if it is raw but if the guava is ripe it will be dark red. From a glance, when viewed from human vision, it is very easy to distinguish between them, but from most people it is still difficult to distinguish which guava is ripe, half-ripe and unripe guava because of differences in opinion from one human eye to another. Based on these problems, researchers have developed a system that is able to detect the maturity level of guava fruit by utilizing the Hue Saturation Value (HSV) feature extraction with K-Nearest Neighbor (KNN). The data used in this study were 465 datasets which were divided into 324 training data and 141 test data. The data had classes, namely ripe, half-cooked, and raw. The data is then classified using the K-Nearest Neighbor method by calculating the closest distance with a value of K = 3. From this study resulted in an accuracy of 97.16%.
Co-Authors Abdillah, Junindo Abdulloh Achmad Syaeful Aditya Zakaria Hidayat Ahmad Baidowi Akbar, Firman Aulia Akbar, Yuma Alannuari, Fiky Alwi Renaldhy Amelia, Ika Andrian Nur Ihsan Anita Rosiana Apriyanto, Kevin Jonathan Ari Ramadhan Arinal, Veri Aryanti, Putri Gea Awang Hariman, Aloisius Azis, Abd Barronzoeputra, Gaoeng Qalbun Beay, Richardviki Bere Tae, Chelvyn Erikson Betty Yel, Mesra Betty Yel, Mesra Bili, Yudisman Ferdian Bimantoro, Dava Sevtiandra Brian - Pangestu Candra Milad Ridha Eislam Dadang Iskandar Mulyana` Dava Septya Arroufu Diadi, Randitia Ridad Fadhil Khanifan Achmad Fahmi Chairulloh Fahmi, Hakon Feni Putriani Fentri Boy Pasaribu Fiky Alannuari Ginting, Yafet Nikolas Guntara, Arya Hakim, Lukamanul Haryati Heri Rizky Firdaus Ikhwanul Kurnia Rahman Iqhlima, Salabila Listania Karim, Lutfi Kudrat, Kudrat Kurnia, Mega Tri Lingga, Tracy Olivera Lutfi Karim Marjuki Marliani, Tiara Meilisa Miftahul Huda Muhammad Ilham Fadillah Novianto, Firza Nufaisa Almazar Nugraha, Pramudya Nur Arif Khairudin Nurmayanti, Laily Nurmaylina, Vivi Oky Tria Saputra7 Praja Raymond , Samuel Pramudya Nugraha Purwandono, Eddy Purwanto, Helmi Purwasih, Intan Rahmah, Shafira Azzahra Nurul Raihan, Farid Raihanah, Syifa Randitia Ridad Diadi Rasiban Richardviki Beay Rizky Adawiyah Romadan, Diva Putra Saepudin Septian, Wahyu Septiansyah, Muhamad Aqil Septianto, Ahas Eko Setiawan, Kiki Siahaan, Bangun Sidiq, Bagas Maulana Siti Nur Hidayati Sugiono Sugiono Sugiyono Surapati, Untung Sutisna Syaeful, Achmad Tanjung, Cici Yolanda Tasya Aisyah Amini Tracy Olivera Lingga Tundo Tundo, Tundo Untung Wahyudi Wibawa, Andri Putra Widianto Putro, Faris Wijayanto, Willy Yakob, Galih Satria Yuliantoro, Dita Tri