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All Journal Infotech Journal METIK JURNAL bit-Tech Journal of Innovation Information Technology and Application (JINITA) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Information Technology (JIfoTech) D'computare: Jurnal Ilmiah Teknologi Informasi dan Ilmu Komputer Jurnal Informatika Teknologi dan Sains (Jinteks) Teknomatika: Jurnal Informatika dan Komputer Explore
Kusrini
Universitas AMIKOM Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Engineering

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Journal : Journal of Information Technology (JIfoTech)

 1 
Sistem Pendukung Keputusan untuk Menentukan Lahan Budidaya Tanaman Obat Keluarga (TOGA) menggunakan Metode Fuzzy-Gap Kompetensi Yusuf Fadlila Rachman; Akhmad Syarif; Kusrini
Journal of Information Technology Vol 1 No 1 (2021): Journal of Information Technology
Publisher : Institut Shanti Bhuana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46229/jifotech.v1i1.199

Abstract

TOGA or family medicinal plants is one of the plants that can generate personal benefits and mutual benefits. Determination of land is generally necessary because it can help users to be able to plant crops suitable for their land so that they can avoid losses. The purpose of this research is to build a decision support system to assist cultivators in determining the best land for cultivating TOGA. In this study, 5 types of medicinal plants were used, namely ginger, cardamom, turmeric, ginger, and kencur. The criteria used in this study, namely land distance, land conditions, and climatic conditions. The method used in this research is the competency fuzzy-gap. Each criterion chosen by the user will be converted into a fuzzy value, namely 0-1 by using an upward linear function for the benefit criteria and a downward linear function for the cost criterion. The fuzzy values ​​that have been obtained are used in calculations using the competency gap. The test results on manual calculations and in the program obtained the same results as the output of Land 1 is the best land with a value of 7.39625.
Deteksi Otomatis Jerawat Wajah Menggunakan Metode Convolutional Neural Network (CNN) Fajar Sudana Putra; Kusrini; Mei P Kurniawan
Journal of Information Technology Vol 1 No 2 (2021): Journal of Information Technology
Publisher : Institut Shanti Bhuana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46229/jifotech.v1i2.308

Abstract

The development of cosmetology in the world lately is growing rapidly. These developments are balanced by the emergence of cosmetics and skin care from various brands, but not a few negative effects from use, one of which is acne. Acne is one of the problems on the skin, especially the face that arises physiologically because almost everyone has experienced it (Wasitaatmadja, 2010). Acne consists of various types, namely blackheads, whiteheads, papules and cysts (Bhate, K. & Williams, 2013). Not a few people who want to remove and be free from acne. The current technological developments in the field of image processing in recent years with the application of convolutional neural networks have shown significant performance by having a high level of accuracy, for example object detection which recently had image restoration. Therefore, technological developments to facilitate the treatment of acne are urgently needed by medical personnel, especially dermatologists. This research focuses on developing the accuracy of the method using the hough circle transform & Convolutional Neural Network (CNN) method. This study proves the increase in accuracy and accuracy of the object of acne detection using the Convolutional Neural Network (CNN) method. The results of the learning process obtained a CNN model with an accuracy of 99.8% to 100%, so it can be concluded that the CNN method designed in this study can classify images well.
Co-Authors Abdul Ra'uf Alfansani Akhmad Syarif Andi Syahrul Ramdana Angga Kurniawan Asro Nasiri Burhanudin Dwi Prakoso Candra Rusmana Choerun Asnawi dayat subekti Dina Maulina Eko Pramono Ema Utami Fajar Sudana Putra Ferry Wahyu Wibowo Hafidz Sanjaya, Hafidz Hasnan Afif Henderi Hidayatunnisa'i Ibnu Ickwantoro Iqbal Kusnawi Kusnawi M. Rudyanto Arief M.Andi Rohmat MAHMUDI Mei P Kurniawan Muahidin, Zumratul Nuk Ghurroh Setyoningrum Pramudya Insan Rizal Khadarusman Sudarmawan Teguh Arlovin Yusuf Fadlila Rachman