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All Journal ComEngApp : Computer Engineering and Applications Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Teknologi Bahan dan Barang Teknik Jurnal Teknik Informatika C.I.T. Medicom Pelita Masyarakat Madaniya Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Community Empowerment Jurnal Sisfotek Global Jurnal Ilmiah Teknik Informatika & Elektro (JITEK)
Muhathir, Muhathir
Universitas Medan Area
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Physics Public Health Social Sciences

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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

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Performance Evaluation Of Variations Boosting Algorithms For Classifying Formalin Fish From Photos Fadlisyah Fadlisyah; Muhathir Muhathir
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 6 No. 2 (2023): Issues January 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i2.6614

Abstract

Fish is one of the foods that are often consumed by humans to complete the protein in the body. Indonesia is rich in animal protein so some rogue traders to avoid losses due to rotten fish, sellers process fish to make it look fresh by fishing with formalin liquids so that buyers think the fish is still fresh, this problem is often found in the market so that it takes the right solution. The solution offered is to apply Machine Learning (Boosting Algorithm) to classify fresh fish and fish that have been planned with formalin by utilizing the extraction of GLCM features. The findings of this study indicate a variation of boosting algorithms can provide solutions to this problem. Accuracy, precision, recall, f1-score, f2-score, and Jaccard score in tilapia fish with a value of 0.95, 0.95, 0.95, 0.95, 0.95, 0.95 this result is obtained by extreme gradient boosting variations with the highest achievements compared to variations Other things are also similar to Tamban fish with a value of 0.78, 0.775, 0.78, 0.825, 0.77, 0.632. The effect of boosting algorithm on the measurement of model performance looks very increased in tilapia fish while in tamban fish do not provide maximum results, but these findings are better
Performance Analysis of Naive Bayes Variation Method in Spice Image Classification Using Histogram of Gradient Oriented (HOG) Feature Extraction Taufik Ismail Simanjuntak; Muhathir Muhathir; Fadlisyah Fadlisyah; Ira Safira
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 1 (2023): Issues July 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i1.7957

Abstract

Indonesia has a lot of natural wealth of spices. The diversity of spices is an inseparable aspect of Indonesian history. Spices and seasonings are biological resources that have long played an important role in human life. Indonesian spices have almost the same color and shape. The purpose of this study was to analyze the performance of the Naïve Bayes variation method in classifying spices using a Histogram Of Oriented Gradient (HOG) feature extraction. Based on 3 tests, the performance of the four Naïve Bayes variation methods carried out in this study, it can be seen that testing 5 types of spices using the Gaussian Naïve Bayes method obtained the best performance with an accuracy of 0.946, a precision of 0.95, a recall of 0.945, f1 score of 0.947, f beta score of 0.946, and Jaccard score of 0.90. Where as using the Complement Naïve Bayes method gets the lowest performance. From the results of this study it can be concluded that by utilizing HOG feature extraction and the Naïve Bayes variation method, maximum classification results are obtained in classifying spices. To obtain more accurate classification results, consider using other methods and other feature extraction
Co-Authors Al Khowarizmi Amri Ismail Tumanggor Arief Goeritno Aripin Rambe Asmaul Husna Ayu Pariyandani Azmi, Fadhillah Cahyo Hasanudin Cut Lika Mestika Sandy Cut Try Utari Deti Indah Kiranti Diah Ayu Larasati Dian Ifantiska Effiati Juliana Hasibuan Eka Pirdia Wanti Ellis Susmawati FADHILLAH AZMI Fadlisyah Fadlisyah Fadlisyah Fadlisyah FAUZI . Hashina Qiamu Mumtaziah Hayani Wulandari Indra Muda Insidini Fawwaz Ira Safira Ira Safira Juliansyah Putra Tanjung Karynda Natalie Theofilus Leonardi Paris Hasugian M Hamdani Santoso Mahardika Abdi Prawira Tanjung Mahmudah Salwa Gianti Melisah Melisah Merri Hafni Muchammad Takdir Sholehati Muhammad Hamdani Santoso N P Dharshinni Nadzifah Nadzifah Neneng Yulia Barky Nurul Khairina Rambe, Yunita Syafitri Reydo Trisno Pangestu Reyhan Achmad Rizal Rifa Alia Syahidah Rizki Muliono Sri Juwita Subairi Subairi Susilawati Susilawati Susilawati, Susilawati Syahputra, Dinur Syifaul Fuada Taufik Ismail Simanjuntak Theofil Tri Saputra Sibarani Wahyu Hidayah Yuhefizar Yuhefizar Zulfikar Sembiring