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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Jurnal Ilmiah Wahana Pendidikan Jurnal Informatika Polinema (JIP)
Riza Ibnu Adam
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Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Social Sciences Other

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DETEKSI DAN IDENTIFIKASI CITRA DIGITAL JENIS BERAS MENGGUNAKAN METODE ANFIS DAN PREWITT Nisrina Hasna Nataraharja; Riza Ibnu Adam; Garno Garno
INFOMATEK Vol 22 No 2 (2020): Volume 22 No. 2 Desember 2020
Publisher : Fakultas Teknik, Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/infomatek.v22i2.3250

Abstract

Indonesia termasuk negara penghasil beras terbanyak ketiga di dunia. Berkat itulah terdapat banyak jenis beras di Indonesia. Penelitian ini bertujuan untuk mengidentifikasi jenis beras menggunakan metode Adaptive Neuro Fuzzy Inference (ANFIS) yang dapat dilatih untuk mengidentifikasi jenis-jenis beras. Menggunakan nilai yang terdapat pada ekstraksi ciri bentuk yang meliputi metric dan eccentricity serta ekstraksi ciri tekstur yang meliputi LBP, contrast, correlation, dan energy. Penelitian ini juga bertujuan untuk mengetahui pengaruh penambahan deteksi tepi Prewitt terhadap akurasi dalam identifikasi jenis beras. Deteksi tepi ini ditambahkan pada pengambilan nilai ekstraksi ciri bentuk. Nilai ekstraksi diambil dari 100 citra latih dan 100 citra uji dengan masing-masing 25 citra per jenis beras. Hasil dari penelitian yang dilakukan menunjukkan bahwa ANFIS terbukti cukup baik dalam mengidentifikasi jenis beras dengan rerata akurasi diatas 70%, sedangkan penggunaan deteksi tepi Prewitt berpengaruh 1-5% terhadap akurasi.
Klasifikasi K-NN dalam Identifikasi Penyakit COVID-19 Menggunakan Ekstraksi Fitur GLCM Nisa Nafisah; Riza Ibnu Adam; Carudin Carudin
Journal of Applied Informatics and Computing Vol 5 No 2 (2021): December 2021
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v5i2.3258

Abstract

Covid-19 is a disease that is endemic in various parts of the world including Indonesia, this disease infects the respiratory tract caused by a new type of corona virus. To find out the presence of this virus in the body, medical examinations such as blood tests, radiological examinations can be carried out X-rays (x-rays) and swabs. Therefore, in this study, identification covid-19 disease based on the rongen image from which the image was extracted using the GLCM feature extraction method, namely contrast, correlation, energy, and homogeneity, after obtaining the value from the extraction and then classified using data mining classification method, namely k-nearest neighbor by doing 3 modeling the input value of k. The results obtained from the classification obtained an accuracy of 80% in model 3 with a value of k = 5 and in models 1 and 2 obtained an accuracy of 90% with a value of k = 1 and k = 3.
Klasifikasi Kadar Kolesterol Menggunakan Ekstraksi Ciri Moment Invariant dan Algoritma K-Nearest Neighbor (KNN) Sekar Arum Nurhusni; Riza Ibnu Adam; Carudin Carudin
Journal of Applied Informatics and Computing Vol 5 No 2 (2021): December 2021
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v5i2.3273

Abstract

Cholesterol is a fat that is mostly formed by the body itself, especially in the liver. Cholesterol is very useful for the body but will be very dangerous if it has excessive levels. The impact of excessive cholesterol is the emergence of deadly diseases such as heart disease, stroke and poor blood circulation. In this study, one of the medical sciences that can be used to detect cholesterol levels is Iridology. This iridology itself can be applied in computer science which is often referred to as Digital Image Processing. In this case, the feature recognition method will be used using Moment Invariant feature extraction and the K-Nearest Neighbor Algorithm. Where the data used is the Dataset from Ubiris V1. With the resulting accuracy of 84,8485%.
Analisis K-Means Clustering Pada Pengiriman Produk Bearing Danendra Bima Adhi Pramana; Tifani Amalina; Riza Ibnu Adam
Jurnal Ilmiah Wahana Pendidikan Vol 8 No 15 (2022): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (189.459 KB) | DOI: 10.5281/zenodo.7048988

Abstract

Delivery of finished goods is carried out based on the incoming PO from the customer and received by the Marketing Department. The Marketing Department will make a DO based on the PO that has been received and then the DO will be forwarded to the Shipping Section to make a Packing List. The packing list that has been made will be validated by the section head and the items contained in the packing list will be prepared by the Prepare Subsection. The validated packing list will be printed and forwarded to the Delivery Sub-section for delivery with the goods that are ready to be shipped. Shipping lines are made by the shipping coordinator based on the thoughts of the shipping coordinator, this causes the distance between one driver and another driver to be uneven. To overcome this problem, we need a method of dividing the task of delivering finished goods to customers. The method used is the K-Means Clustering Method. The K-Means Clustering method is a clustering method that partitions data into clusters so that data that have similarities are in the same cluster. In this study, the K-Means Clustering method was proven to be able to minimize the difference in distance between the driver and one PIC with the other driver and PIC. The difference in distance between the manual method and the K-Means Clustering method is 87,203 km.
Identifikasi Citra Digital Jenis Beras Menggunakan Metode Anfis dan Sobel Gansar Suwanto; Riza Ibnu Adam; Garno
Jurnal Informatika Polinema Vol. 7 No. 2 (2021): Vol 7 No 2 (2021)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v7i2.406

Abstract

Beras merupakan salah satu produk unggulan pangan nasional dan produk unggulan pertanian di Indonesia. Banyaknya jenis-jenis beras di Indonesia menyebabkan semakin sulitnya membedakan beras hanya mengandalkan mata saja. Dikarenakan setiap jenis beras memiliki ciri bentuk dan tekstur yang relatif berbeda. Oleh karena itu, citra digital dapat dijadikan langkah awal dalam mengidentifikasi jenis-jenis beras. Penelitian ini bertujuan untuk mengidentifikasi jenis-jenis beras dengan menggunakan pengolahan citra. Pengambilan nilai ciri bentuk menggunakan metode morfologi dan dibandingkan dengan metode sobel. Sedangkan pengambilan nilai ciri tekstur menggunakan metode citra grayscale. Kemudian, nilai bentuk dan tekstur lakukan pengelompokan sesuai jenis beras. Data yang digunakan dalam penelitian ini adalah 140 citra. 100 dari 140 citra tersebut dilakukan pelatihan dengan menggunakan metode ANFIS (Adaptive Neuro Fuzzy Inference System) dengan memanfaatkan nilai bentuk dan tekstur citra. Pengujiaan dilakukan sebanyak 5 kali dengan menggunakan 140 citra tersebut. Hasil pengujian dengan metode ANFIS (Adaptive Neuro Fuzzy Inference System) sebesar 85.2%. Sedangkan, Deteksi tepi sobel dapat mempengaruhi akurasi sebesar 3%.
Co-Authors Carudin Danendra Bima Adhi Pramana Gansar Suwanto Garno Garno Garno Nisa Nafisah Nisrina Hasna Nataraharja Sekar Arum Nurhusni Tifani Amalina