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All Journal Jurnal Teknik Informatika UNIKA Santo Thomas KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science MEANS (Media Informasi Analisa dan Sistem) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Jurnal Info Sains : Informatika dan Sains Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Bulletin of Data Science Proceedings of The ICECRS Multidisiplin Pengabdian Kepada Masyarakat (M-PKM) Bulletin of Information System Research
Pristiwanto, Pristiwanto
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Mathematics Nursing

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Journal : Bulletin of Data Science

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Kombinasi Metode ROC dan Metode MAUT dalam Pemilihan Guru pada Madrasah Ibtidaiyah II, Ramadani; Pristiwanto, Pristiwanto; Hasan, Yasir
Bulletin of Data Science Vol 2 No 1 (2022): October 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletinds.v2i1.2324

Abstract

Madrasah Ibtidaiyah Nurul Hidayah, Bandar Khalipah District, is one of the formal education infrastructure under the guidance of the Minister of Religion whose learning process is based on Islam at the elementary school level. This school is in need of Indonesian language teachers. The teacher is a teacher of a science and the teacher in general is a professional educator who has the main task of educating, directing, guiding, training, assessing, and evaluating his students, so in this case the author wants to help the school in the selection of Indonesian language teachers in Madrasah schools. Ibtidaiyah Nurul Hidayah, whose recruitment is still fairly manual. In this study the author has a solution and will solve the problem by applying the Rank Order Centroid (ROC) method and the Multy Attribute Utility Theory (MAUT) method, the author will combine the two methods to determine the best alternative in the selection of Indonesian language teachers in Madrasahs. Ibtidaiyah Nurul Hidayah. The results of the algorithm on the combination of the two methods show that Alternative 5 (A5) has the highest score of 0.832 and ranks 1, thus A5 is the best alternative that will be recommended for teacher selection at Madrasah Ibtidaiyah Nurul Hidayah. The application by combining the Rank Order Centroid (ROC) method and the Multi Attribute Utility Theory (MAUT) method can determine the best alternative in terms of teacher selection.
Kombinasi Metode Discrete Cosine Transform Dan Convolutional Neural Network Dalam Mengidentifikasi Tingkat Kematangan Buah Mangga Berdasarkan Warna Purba, Riski Arnol; Pristiwanto, Pristiwanto; Sihite, A. M. Hatuaon
Bulletin of Data Science Vol 2 No 2 (2023): February 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletinds.v2i2.4497

Abstract

The classification of mango fruit ripeness levels is currently predominantly done manually, which unfortunately has several drawbacks. One of the primary shortcomings is the lack of consistency in accuracy, often resulting in differences among operators conducting the sorting. On the other hand, in image classification processes, a combination of the Discrete Cosine Transform (DCT) and Convolutional Neural Network (CNN) methods is utilized. DCT is a technique commonly used in image processing, especially for image pictures. In this research, there is a proposal to merge the Discrete Wavelet Transform method with the Convolutional Neural Network (CNN). Currently, CNN is one of the methods that provides the most significant results in image recognition. CNN attempts to mimic the image recognition system in the human brain, particularly the visual cortex, allowing it to efficiently process image information. The DCT method is used to transform image data into a frequency image form, which is subsequently employed in feature extraction in the Deep Neural Networks classification method. The research results indicate that the combined method of Discrete Cosine Transform and Convolutional Neural Network achieves the highest accuracy rate of 93.33% in classifying mango fruit ripeness levels. This outcome demonstrates significant potential for automating the mango ripeness classification process with high accuracy, overcoming the inconsistencies associated with manual approaches.
Co-Authors A M Hatuaon Sihite Abdul Halim Hasugian Azlan, Azlan Efori Buulolo Hery Sunandar II, Ramadani Irawan, Muhammad Bima Arya Lestari, Melati Indah Mesran, Mesran Miranda, Rina Moch Tanzil Multazam Nduru, Ewin Karman Nelly Astuti Hasibuan Pulungan, Khairunnisa Purba, Riski Arnol Raihan, Kevin Rian Syahputra Saidi Ramadan Siregar Sembiring, Abdul Sani Sihite, A.M Hatuaon Simangunsong, Lisdawati Siregar, Khoiruddin Yasir Hasan Yasir Hasan