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All Journal Agromet CESS (Journal of Computer Engineering, System and Science) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Ilmiah FIFO Faktor Exacta Petir Kilat Jurnal Manajemen Informatika JURNAL TEKNIK TERANG : Jurnal Pengabdian Pada Masyarakat Menerangi Negeri
Haris, Abdul
Institut Teknologi PLN
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Journal : Faktor Exacta

 1 
Penerapan Metode Fuzzy Tsukamoto untuk Menentukan Kualitas Proposal Layak Hibah Abdul Haris
Faktor Exacta Vol 12, No 1 (2019)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v12i1.3458

Abstract

Penelitian ini bertujuan untuk mengembangkan suatu aplikasi pengajuan proposal Penelitian dan Pengabdian Masyarakat guna mempermudah proses pangajuan, monitoring dan evaluasi prosposal penelitian dengan menggunakan metode Fuzzy Tsukamoto, metode ini cukup mampu untuk untuk memetaka kualitas proposal yang layak hiba berdasarka katogori yang telah ditetapkan. Selain itu Aplikasi dibuat untuk memperbaiki proses yang masih konvensional menjadi proses yang terkompterisasi dari proses pengajuan sampai pada proses pelaporan selain itu juga mempermudah para reviewer untuk untuk memeriksa proposal dan laporan serta membantu pihak-pihak yang terkait. Dari hasil pengujian penerapan metode Tsukamoto pada aplikasi ini mengahasilkan akurasi yang cukup baik dengan tingkat kesalahan yang rendah namun perlu ada pengukuran pembanding dengan penggunakan metode lain.
Mikro-Irigasi Cerdas dengan Sprinkler Menggunakan Fuzzy Logic Pada Lahan Terbatas Untuk Pertanian 4.0 Abdul Haris; Hengki Sikumbang; L.M Syahrul Anwar
Faktor Exacta Vol 14, No 4 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i4.10742

Abstract

Most irrigation systems in Indonesia use surface irrigation systems or conventional irrigation, which are still heavily influenced by Earth's gravity, making it very difficult to manage and monitor. while current technological developments are almost evenly distributed throughout Indonesia with an already very good internet network, so that it can be used to support agricultural systems 4.0. In this study, researchers used intelligent computing technology on micro irrigation with Fuzzy Logic algorithm and Sugeno inference to decide when irrigation water is distributed to sprinkler irrigation systems based on a predetermined range value, then the results are evaluated to see the accuracy of the model that has been made before testing in the actual environment. The purpose of this research is to produce smart micro irrigation technology that can be used on limited land and lack of water so that it can help facilitate the work of farmers.
Klasifikasi Citra Penyakit Daun Cabai Menggunakan Algoritma Learning Vector Quantization Puji Catur Catur Siswipraptini; Abdul Haris; Winda Novita Sari
Faktor Exacta Vol 16, No 2 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i2.15900

Abstract

The problem often occurs in chili leaves is organisms that interfere with chili plants which can reduce chili production. There are chili plant diseases that are difficult for farmers to recognize by using their eyes and without using tools. The purpose of this study was to produce a model capable of identifying chili leaf diseases based on leaf colour in order to make it easier for farmers to identify chili leaf diseases, especially  Phytophthora, Anthracnose, and Cercospora diseases, using the Learning Vector Quantization (LVQ) classification algorithm. Data was collected in the form of digital images of 30 chili leaves which were processed by resizing and transforming RGB to HSV which then proceeded to Canny Edge detection process with the aim of getting patterns from images of chili leaves. The result of testing LVQ algorithm using a confusion matrix get an accuracy of 80%, the precision value of 80%, recall value of 82%, and f-1 score of 81%. 
Co-Authors Abdurrasyid Abdurrasyid Abdurrasyid Abdurrasyid, Abdurrasyid Alan Burhan Aminuyati Budi Indra Setiawan Djunaidi, Karina Edwin Hendrian Efy Yosrita Efy Yosrita Hengki Sikumbang Hengki Sikumbang Herman Bedi Agtriadi, Herman Bedi Indrianto Indrianto L.M Syahrul Anwar Luqman Luqman Luqman Luqman Luqman, Luqman Marimin , Monica Sianturi Muhammad Jafar Elly Nina Nirmaya Ningrum, Rahma Farah Puji Catur Catur Siswipraptini Puji Catur Siswipraptini Puji Catur Siswipraptini Puji Puji Randi Adrika Putra Renaldi Bagas P. Riki Ruli A. Siregar, Riki Ruli A. Rizqia Cahyaningtyas Rosida Nur Aziza Rosida Nur Aziza Rosida Nur Aziza Rosida Nur Aziza Rosida Nur Aziza Sri Wahjuni Trisma Juwita Winda Novita Sari