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All Journal Jurnal Teknimedia: Teknologi Informasi dan Multimedia Jurnal Dinamika Informatika (JDI)
Ferry Wahyu Wibowo
Magister Teknik Informatika, Universitas Amikom Yogyakarta
Computer Science & IT Control & Systems Engineering Engineering

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Perancangan Sistem Pakar Final Check Motor Matic Menggunakan Metode Forward Chaining Studi Kasus Ahass 9677 Wahit Desta Prastowo; Ferry Wahyu Wibowo; Kusrini Kusrini
Jurnal Dinamika Informatika Vol 8 No 1 (2019): Jurnal Dinamika Informatika
Publisher : Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (455.887 KB)

Abstract

Meningkatnya minat para konsumen terhadap produk sepeda motor matic khususnya produk dari honda kini sangat tinggi. Minat pembelian motor honda matic yang terus meningkat berbanding terbalik dengan jumlah teknisis maupun pusat layanan, sehingga mengakibatkan ketidakseimbangan antara layanan service AHASS 9677 dengan pengguna motor matic. Hal ini ditunjukkan oleh menumpuknya antrian mencapai 30 unit dalam sehari sehingga konsumen mengantri berjam-jam walaupun hanya untuk sekedar bertanya guna mengetahui kerusakan dan solusi serta estimasi biaya dari kerusakan kendaraan yang dimiliki. Kemajuan teknologi dapat digunakan sebagai upaya mempertahankan pelanggan dan mengatasi masalah antrian yang menumpuk dengan salah satu cara menerapkan ilmu keecerdasan buatan (Artificial Inteligence) dengan membuat Sistem Pakar (Expert System) menggunakan metode Forward Chaining yang dapat menerima inputan gejala kerusakan dan memberikan analisis kerusakan dan solusi kemudian memberikan estimasi biaya service. Kata kunci— Sistem Pakar, Motor Honda Matic, Forward Chaining. Abstract The increasing interest of consumers towards motorcycle products, especially products from Honda, is now very high. The interest in purchasing Honda matic motorcycles that continues to increase is inversely proportional to the number of technicians and service centers, resulting in an imbalance between the AHASS 9677 service service and motorcycle users. This is indicated by the accumulation of queues reaching 30 units in a day so that consumers queue for hours even though only to ask questions to find out the damage and solutions and the estimated cost of damage to the vehicle owned. Technological advances can be used as an effort to retain customers and overcome queue problems that accumulate with one way of applying artificial intelligence by making an Expert System using the Forward Chaining method that can accept damage symptom input and provide damage analysis and solutions. then provide estimated service costs. Keywords— Expert System, Honda Matic Motor, Forward Chaining.
OPTIMASI HYPERPARAMETER CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI PENYAKIT TANAMAN PADI Afis Julianto; Andi Sunyoto; Ferry Wahyu Wibowo
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 3 No. 2 (2022): Desember 2022
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v3i2.77

Abstract

Plant disease is a challenge in the agricultural sector, especially for rice farmers. Identification of diseases on rice leaves is the first step to eradicating and treating diseases, to minimize crop failure. With the rapid development of the convolutional neural network (CNN), rice leaf disease can be recognized well without the help of an expert. The MobileNet-V2 architecture is used to classify rice leaf diseases due to its small size but good performance. To improve the performance of the CNN model, a hyperparameter consisting of an epoch, batch size, learning rate, and optimizer. This study purpose to have hyperparameters optimal The dataset used consists of 3 classes of diseases that attack the leaves of rice plants, including blast, blight, and tungro. Based on the experiments that have been carried out, the determination of hyperparameters greatly influences the model performance. Hyperparameter with epochs, batch sizes 32 learning rate and optimizer gives the most optimal results with accuracy 97.56%, precision 97.64%, recall 97.57%, and f1-score 97.57%.
Co-Authors Afis Julianto Andi Sunyoto Kusrini Kusrini Prastowo, Wahit Desta