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All Journal Elektron Jurnal Ilmiah Adi Widya : Jurnal Pengabdian Masyarakat Dinamisia: Jurnal Pengabdian Kepada Masyarakat SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Compiler JUTEKIN (Jurnal Manajemen Informatika) AMALIAH: JURNAL PENGABDIAN KEPADA MASYARAKAT JMAI (Jurnal Multimedia & Artificial Intelligence) JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Budimas : Jurnal Pengabdian Masyarakat International Journal of Advances in Data and Information Systems Jurnal Teknik Informatika (JUTIF) Konstelasi: Konvergensi Teknologi dan Sistem Informasi Jurnal Teknik Informatika Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal ADAM : Jurnal Pengabdian Masyarakat Journal Of Information System And Artificial Intelligence Jikom: Jurnal Informatika dan Komputer Jurnal Pengabdian Masyarakat Bangsa Jurnal Informatika: Jurnal Pengembangan IT Jurnal Pengabdian Kepada Masyarakat Radisi Jurnal Pengabdian Kepada Masyarakat Bersinergi Inovatif Jurnal Pengabdian Masyarakat Mentari Exhibition and Seminar on Science and Creative Technology – Al Azhar Proceeding International Journal of Informatics Engineering and Computing
Indah Susilawati
Universitas Mercu Buana Yogyakarta
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : journal of information system and artificial intelligence

 1 
Prototipe Sistem Deteksi Ketersediaan Lahan Parkir Menggunakan Metode Algoritma Canny Edge Ferry Pradana Putra; Indah Susilawati
Journal Of Information System And Artificial Intelligence Vol. 1 No. 2 (2021): Journal of Information System and Artificial Intelligence Vol I, No II Mei.2021
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (355.764 KB) | DOI: 10.26486/jisai.v1i2.20

Abstract

Pada setiap tempat parkir terdapat petugas parkir di pintu masuk maupun keluar untuk melayani pemberian karcis parkir serta pembayaran biaya parkir. Sangat jarang petugas parkir berkeliling di tempat parkir, untuk mengetahui ketersediaan dari tempat parkir. Hal ini menyebabkan banyaknya pengendara yang harus berkeliling mencari tempat parkir sehingga memakan banyak waktu para pengendara. Kesulitan mencari tempat parkir juga menyebabkan kemacetan dikarenakan kendaraan cenderung bergerak lebih lambat untuk mencari slot parkir yang kosong. Maka dirasa diperlukan suatu sistem yang mendeteksi ketersediaan lahan parkir secara real time agar para pengendara tidak kebingungan mencari lahan parkir yang kosong untuk kendaraan mereka. Penelitian dilakukan dengan OpenCV sebagai library pemrograman bahasa python dengan algoritma canny edge dengan posisi kamera berada diatas kendaraan. Berdasarkan pengujian yang dilakukan dengan posisi kamera berada diketinggian 40cm dengan menggunakan threshold 510 mendapatkan akurasi 77%. Namun ada beberapa hal yang mempengaruhi akurasi seperti,ketinggian,cahaya, dan letak objek.
Sistem Pakar Mendiagnosa Penyakit Pencernaan Pada Manusia Menggunakan Metode Forward Chaining dan Certainty Factor Arif Wijayanto; Indah Susilawati
Journal Of Information System And Artificial Intelligence Vol. 2 No. 1 (2021): Journal of Information System and Artificial Intelligence Vol II, No I November
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (444.071 KB) | DOI: 10.26486/jisai.v3i1.56

Abstract

Digestive disease is the most common disease and often found in health community centers. Digestive diseases attack organs in the digestive system which can interfere the functions of other systems. If ignored, it can make the person’s condition become more severe. To solve the problem, the researcher attempted to design a smart system to help people to early recognize digestive diseases such as GERD, dyspepsia, cholera, hepatitis, appendicitis, dysentery and hemorrhoids. Based on the symptoms fed by the patients into the system, the system will use the forward chaining method and certainty factor as an inference machine that will produce a disease diagnosis. Of 36 patient data tested on the system and matched with the experts’ validation, 34 of them matched. The system has an accuracy level of 94.4% of the tested data. The results are expected to provide an initial medium of consultation to help people diagnose digestive diseases. Keywords: Certainty Factor, Forward Chaining, digestive diseases, expert system.
Klasifikasi Jenis Aglaonema Berdasarkan Citra Daun Menggunakan Convolutional Neural Network (CNN) Yoga Purna Irawan; Indah Susilawati
Journal Of Information System And Artificial Intelligence Vol. 2 No. 2 (2022): Journal of Information System and Artificial Intelligence Vol II, No II Mei 202
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (428.427 KB) | DOI: 10.26486/jisai.v2i2.57

Abstract

Aglaonema or popularly known in Indonesia as "Sri Rejeki" is a leaf ornamental plant fancied by many people. This plant has unique leaves with beautiful and diverse shapes, colors, and patterns. Various ways can be used to identify this plant; one of which is by using an image processing technique in which the process is carried out through feature extraction or classification process. A method/algorithm to classify Aglaonema image is the Convolutional Neural Network (CNN). CNN is an algorithm of Deep Learning and is the development of a Multi Layer Perceptron (MLP). This study used the image of 5 types of Aglaonema leaves with 100 images of each type. The CNN model used in this study was the Alexnet model. Based on 4 experiments using the optimizer and different configurations of epoch values, the highest training validation accuracy value was 98.00%. The system also can classify Aglaonema images well with an accuracy success rate of 96% of 50 images tested.
Sistem Pakar Identifikasi Hama dan Penyakit Pada Umbi Porang dengan Metode Certainty Factor Puput Zahiroh; Indah Susilawati
Journal Of Information System And Artificial Intelligence Vol. 3 No. 1 (2022): Journal of Information System and Artificial Intelligence Vol III, No I Novembe
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jisai.v3i1.108

Abstract

Porang, known as tuber plant (Amorphophalusllus muelleri), is an edible producing plant because it is one family with suweg (elephant foot yam) and walur. Porang plants are widespread and used by the community. High economic value and great business opportunities encourage the community and entrepreneurs to cultivate porang plants. To say the least, they must encounter obstacles such as capital to plant porang, one of the causes of porang cultivation failure is pest and disease attacks. Research on the identification of pests and diseases of porang tubers was carried out using the Certainty Factor method. Firstly, the application development stage begins with the analysis and case study stages that generate information, data requirements and system requirements. Secondly, system and software design produces something, namely context, flow, entity relationship, table, and interface menu designs. The last stage is implementing and unit testing using XAMPP, PHP and MySQL. This research will produce an expert system for pests and diseases on porang tubers that can diagnose various pests and diseases with an average CF confidence value of 94.5% and the percentage of system suitability with expert diagnoses reaching 96.6%.
Co-Authors Aji, Dennis Feliawan Arif Wijayanto Arita Witanti Benedictus Fredika Apriawan Santoso Budi Sulistiyo Jati Budi Sulistiyo Jati Dennis Feliawan Aji Dwi Pramudya, Alvian Ferry Pradana Putra Gideon Rensenbrink Ayal, Justin Heru Wahyudi Imam Edwin Hasym Imam Wirabowo Muhammad Fandi Nurdiansyah Mutaqin Akbar Nugraeni Nugraeni Nur Alamsyah Paramitalaksmi, Ratri Pebiani, Misa Prasetyaningrum, Putri puput zahiroh Purwanto, Hendri Rahmatuloh, Muhamad Rendi Saputra Risqi, Yoga Alfa Rizkyansyah, M Daffa Rogawati, Nesya Safitri, Della Nur Setyaningsih, Putry Setyaningsih, Putry Wahyu Sidiq Purnomo, Agus Sipata, Daud Imanuel Siti Istiqomah Suharjo, Imam Supatman Supatman Supatman Supatman, Supatman Wibowo, Ibnu Surya Yoga Purna Irawan Zakaria Habib Al-Ra’zie