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All Journal International Journal of Electrical and Computer Engineering PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Sinkron : Jurnal dan Penelitian Teknik Informatika Information System for Educators and Professionals : Journal of Information System Jurnal Sisfokom (Sistem Informasi dan Komputer) JurTI (JURNAL TEKNOLOGI INFORMASI) Krea-TIF: Jurnal Teknik Informatika E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Journal of Computer System and Informatics (JoSYC) Madaniya Jurnal Kajian Ilmiah Journal of Applied Computer Science and Technology (JACOST) Jurnal Atma Inovasia Prosiding University Research Colloquium INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Jurnal Informatika Polinema (JIP) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Muhammad Resa Arif Yudianto
Universitas Muhammadiyah Magelang
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Social Sciences Other

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Journal : Prosiding University Research Colloquium

 1 
Classification of Tangerines on Fruit Ripening Levels Using K-Nearest Neighbor Algorithm Irfan Rasyid; Imam Saputra; Raden Kartika Satya Suryanegara; Muhammad Resa Arif Yudianto; M Maimunah
Prosiding University Research Colloquium Proceeding of The 15th University Research Colloquium 2022: Mahasiswa (Student Paper Presentation) B
Publisher : Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (220.028 KB)

Abstract

This journal reviews the classification of the maturity level of tangerines based on HSV using the K-Nearest Neighbor (KNN) method. This study aims to make it easier for the public to distinguish ripe and unripe when choosing citrus fruits and also to avoid fruit shops selling unripe oranges so as not to harm sellers or buyers. We take the data sources used in this study ourselves. In this study, we use the K-Nearest Neighbors (KNN) method. This method is used in the image classification process by relying on the results of feature extraction that have previously been trained. This method selects the nearest neighbor from the training dataset, then determines the closest distance value or the smallest distance value that will produce the classification output. The results of the accuracy in using this method have reached 93% with a value of k=7.
Classification of Avocado Ripeness Levels using Naïve Bayes Method Ira Nuryani; Aldi Muhammad Nur Fadli; Nadila Dwi Saputri; Alfira Nisa Fadhilah; Muhammad Resa Arif Yudianto; M Maimunah
Prosiding University Research Colloquium Proceeding of The 15th University Research Colloquium 2022: Mahasiswa (Student Paper Presentation) B
Publisher : Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (306.03 KB)

Abstract

During this time most people in determining the ripeness of avocados for personal consumption is not difficult because they can distinguish themselves but another case if used for production, which requires a lot of labor to group ripe and raw avocados. One of the innovations in information and communication technology in agriculture and plantations is the use of classification methods with naïve bayes algorithms. The formula of the problem in this study is how to do the classification on the ripeness of avocados and see the accuracy rate of the data. The purpose of this study is to classify avocado ripeness and to acquire intelligent systems, so that it becomes the first step towards the implementation stage. Based on the results and analysis that has been done, it can be concluded that the Naive Bayes method is considered capable in classifying avocado ripeness by using RGB color features. The accuracy in testing using Naïve Bayes method reached 83.34%. The performance obtained from this intelligent system is also effective and efficient so that the classification of avocado ripeness can be implemented.
Classification of Tangerines on Fruit Ripening Levels Using K-Nearest Neighbor Algorithm Rasyid, Irfan; Saputra, Imam; Suryanegara, Raden Kartika Satya; Yudianto, Muhammad Resa Arif; Maimunah, M
Prosiding University Research Colloquium Proceeding of The 15th University Research Colloquium 2022: Mahasiswa (Student Paper Presentation) B
Publisher : Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This journal reviews the classification of the maturity level of tangerines based on HSV using the K-Nearest Neighbor (KNN) method. This study aims to make it easier for the public to distinguish ripe and unripe when choosing citrus fruits and also to avoid fruit shops selling unripe oranges so as not to harm sellers or buyers. We take the data sources used in this study ourselves. In this study, we use the K-Nearest Neighbors (KNN) method. This method is used in the image classification process by relying on the results of feature extraction that have previously been trained. This method selects the nearest neighbor from the training dataset, then determines the closest distance value or the smallest distance value that will produce the classification output. The results of the accuracy in using this method have reached 93% with a value of k=7.
Classification of Avocado Ripeness Levels using Naïve Bayes Method Nuryani, Ira; Fadli, Aldi Muhammad Nur; Saputri, Nadila Dwi; Fadhilah, Alfira Nisa; Yudianto, Muhammad Resa Arif; Maimunah, M
Prosiding University Research Colloquium Proceeding of The 15th University Research Colloquium 2022: Mahasiswa (Student Paper Presentation) B
Publisher : Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

During this time most people in determining the ripeness of avocados for personal consumption is not difficult because they can distinguish themselves but another case if used for production, which requires a lot of labor to group ripe and raw avocados. One of the innovations in information and communication technology in agriculture and plantations is the use of classification methods with naïve bayes algorithms. The formula of the problem in this study is how to do the classification on the ripeness of avocados and see the accuracy rate of the data. The purpose of this study is to classify avocado ripeness and to acquire intelligent systems, so that it becomes the first step towards the implementation stage. Based on the results and analysis that has been done, it can be concluded that the Naive Bayes method is considered capable in classifying avocado ripeness by using RGB color features. The accuracy in testing using Naïve Bayes method reached 83.34%. The performance obtained from this intelligent system is also effective and efficient so that the classification of avocado ripeness can be implemented.
Co-Authors Acihmah Sidauruk Agus Qomaruddin Munir Aldi Muhammad Nur Fadli Alfira Nisa Fadhilah Arazka Firdaus Anavyanto Ardhin Primadewi Arham Rahim Arif Dwi Laksito Arya Prayoga Bayu Agustian Bekti Wulandari Bonita Destiana Burhanudin, Nazil Ilham Darmawan, Briga Devi Oktaviani Dimas Sasongko Dzul Fadli Rahman Ema Utami Endah Ratna Arumi, Endah Ratna Fadhilah, Alfira Nisa Fadli, Aldi Muhammad Nur Febriyanto, Yusril Hanif Al Fatta Hanif Al Fatta Hasani, Rofi Abul Imam Saputra Imam Saputra Ira Nuryani Irfan Rasyid Irfan Rasyid Jalaluddin, Alif Kusrini Kusrini Kusrini, Kusrini Lusi Nurlatifah Maimunah Maimunah Maimunah, M Maimunah, Maimunah Masduki Zakarijah Meidar Hadi Avizenna Nadhir Fachrul Rozam Nadila Dwi Saputri Nuryani, Ira R Arri Widyanto Raden Kartika Satya Suryanegara Rahman, Dzul Fadli Rahmi, Alfie Nur Ramadhana Setiyawan Rasyid, Irfan Rofi Abul Hasani Sambodo Sriadi Pinilih Saputri, Nadila Dwi Satriyo Agung Dewanto Setiya Nugroho Sukmasetya, Pristi Suryanegara, Raden Kartika Satya Tika Novita Sari Tuessi Ari Purnomo Tuessi Ari Purnomo Uky Yudatama, Uky Yusril Febriyanto Zaenal Mustofa Zakariah, Masduki