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%.
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.
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%.
Jamur adalah organisme eukariotik yang dibagi menjadi kingdom tertentu yang disebut "Fungi". Jamur memiliki karakteristik yang berbeda dengan makhluk lainnya. Jenis jamur yang paling terkenal adalah jamur tiram dan jamur kuping yang populer di pasar dan toko bahan makanan. hal ini menyebabkan masyarakat masih belum banyak mengenal jenis jenis jamur lainnya. Dalam mengidentifikasi jenis jamur untuk mempermudah masyarakat mengetahui ciri-ciri dari beberapa jenis jamur. Oleh karena itu proses klasifiksi jamur perlu dilakukan secara otomatis dengan sistem komputer sehingga diharapkan dapat mempermudah masyarakat untuk mengenali jenis-jenis jamur. Pada penelitian ini menggunakan metode GLCM untuk ekstraksi ciri dan metode KNN untuk proses klasifikasi jenis jamur. Data yang digunakan sebanyak 607 gambar yang di peroleh dari website Kaggle dengan judul "Mushrooms classification" dengan 5 macam dataset Jamur. Sebanyak 80% gambar sebagai data training dan 20% gambar lainnya untuk data uji tingkat akurasi tertinggi yang didapatkan sebesar 73%.
ABSTRAKDi era pandemi Covid-19 yang terjadi saat ini telah membawa dampak besar di berbagai negara, termasuk di negara Indonesia. Setiap negara mempunyai kebijakan sendiri dalam menangani wabah ini, mulai dari diwajibkan untuk menjaga jarak, penerapan protokol kesehatan, hingga adanya penyaluran bantuan sosial dari pemerintah dengan tujuan terpenuhi nya semua kebutuhan masyarakat terutama yang terdampak langsung akibat pandemi ini. Bantuan sosial tersebut bermacam macam, diantaranya adalah Bantuan Sosial Tunai, Bantuan Sembako, Program Keluarga Harapan, dan masih banyak lagi. Terkait sistem untuk pendataan masyarakat yang menerima bantuan sosial juga menjadi sangat penting sehingga data warga yang terpilih mendapatkan bantuan sosial lebih tepat sasaran dan juga lebih akurat. Oleh karena itu, perlu dibuatkan suatu sistem pengelolaan bantuan agar dapat membantu pengurus RT untuk menyalurkan bantuan sosial secara lebih akurat dan efisien. Tujuan dilakukannya pengabdian ini adalah meningkatkan ketepatan dalam penyaluran bantuan dari pemerintah kepada seluruh masyarakat. Dalam pembuatan sistem informasi pengelolaan bantuan berbasis web yang efektif, efisien dan akurat perlu menggunakan metode waterfall atau metode pengembangan perangkat lunak dengan melalui tahapan observasi, Analisis Kebutuhan atau Wawancara, Studi pustaka, Desain sistem, Implementasi dan sistem testing, dan Maintenance sistem. Manfaat yang di dapat dari pengabdian ini yaitu hasil analisa dapat digunakan untuk merancang sistem informasi pengelolaan bantuan sosial di Kampung Pulojahe, RT 006 RW 014 Jakarta Timur. Kata kunci: bantuan sosial; pengelolaan bantuan; warga ABSTRACTIn the current era of the Covid-19 pandemic, it has had a major impact in various countries, including Indonesia. Each country has its own policy in dealing with this outbreak, starting from being required to maintain distance, implementing health protocols, to distributing social assistance from the government with the aim of meeting all community needs, especially those directly affected by this pandemic. There are various kinds of social assistance, including Cash Social Assistance, Basic Food Assistance, Family Hope Program, and many more. Regarding the system for collecting data on people who receive social assistance, it is also very important so that the data on citizens who are selected to receive social assistance is more targeted and also more accurate. Therefore, it is necessary to develop an aid management system in order to assist RT management in distributing social assistance more accurately and efficiently. The purpose of this service is to increase accuracy in the distribution of assistance from the government to the entire community. In making an effective, efficient and accurate web-based assistance management information system, it is necessary to use the waterfall method or software development method by going through the stages of observation, needs analysis or interviews, literature study, system design, implementation and system testing, and system maintenance. The benefit from this service is that the results of the analysis can be used to design an information system for the management of social assistance in Pulojahe Village, RT 006 RW 014 East Jakarta. Keywords: social assistance; assistance management; resident
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