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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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Expert System to Determine Camera Price Using VIKOR Method Purwanto, Hendri; Susilawati, Indah
International Journal of Informatics Engineering and Computing Vol. 1 No. 1 (2024): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v1.i1.27

Abstract

This study presents the design and implementation of an expert system for determining the best camera recommendations for beginners, utilizing a web-based platform to deliver tailored results based on user input. The VIKOR method is employed to evaluate and rank camera products, ensuring optimal recommendations by balancing multiple criteria such as technical specifications, usability, and cost-effectiveness. Analysis of five camera products revealed that the Fujifilm X-A5 achieved the lowest VIKOR value of 0, indicating it as the top recommendation for beginners. The system's calculations were validated against manual computations, achieving a 100% accuracy rate, and thereby confirming the reliability of the expert system. The proposed system not only enhances decision-making efficiency but also significantly reduces the time required for experts to rank and evaluate multiple camera products. This approach demonstrates the potential of integrating multi-criteria decision-making methods into expert systems to provide accurate, user-centric recommendations in real time.
Restaurant Recommendation Decision Support System Using Topsis System Rogawati, Nesya; Susilawati, Indah; Witanti, Arita
EXSACT-A Vol 1, No 1 (2023)
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/exc.v1i1.2248

Abstract

The vast technology development in the culinary aspect makes all kinds of information could be acquired easily. Information is needed to be one of some considerations when a person is going to book a seat in a restaurant. Hungryhub is restaurant booking service provider which helps customers to be able to make a reservation online. This research background is Hungryhub website development innovation which offers so many restaurants. The aim of this research is to help customers upon making decisions with restaurants' recommended option alternatives.This research is using Technique for Other Reference by Similarity to Idea Solution (Topsis). Data is collected from documentation and interviews. The documentation is obtained from survey fulfillment by the users which would be processed and references of the restaurant recommendations for the users themselves. The interviews are done with the Hungryhub operational team to get the restaurants' data which have cooperated with Hungryhub. The topsis method is chosen because it has a concept that chosen alternatives are alternatives which have the shortest range to the ideal positive solutions and have the farthest range to the ideal negative solutions. The result of this research is a recommendation system which could display alternative restaurants' ranking result.
Identifikasi Daging Ayam Kampung Segar Dengan Daging Ayam Kampung Basi Menggunakan Metode Learning Vector Quantization Aji, Dennis Feliawan; Susilawati, Indah
JMAI (Jurnal Multimedia & Artificial Intelligence) Vol. 4 No. 2 (2020): JMAI (Jurnal Multimedia & Artificial Intelligence)
Publisher : LPPM Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kampong chicken meat is meat obtained from kampong chicken. kampong chicken meat is considered expensive because they take longer time to grow up, a lot of people cheat by selling stale kampong chicken meat. The characteristics used to identify the meat’s image are homogeneity, contrast, average and variants. The number of data used in this research consists of two classes, each class has 30 image data, the total data is 60 training data. Whereas for test data, each class used 20 test data with a total of 40 test data. During the training process using LVQ parameters, there were 2 best percentages of 90%, namely on alpha 0.001 with a dec alpha of 0.2 and alpha 0.01 with a dec alpha of 0.9. The identification performed using the final weight from alpha 0.01 and dec alpha 0.9 had an 90% accuracy level with 4 iterations. The best performance from 40 test data using this software was with alpha 0.01 and dec alpha 0.9, which reached 90%.
Klasifikasi Citra Virus SARS-COV Menggunakan Deep Learning Susilawati, Indah; supatman, supatman; Witanti, Arita
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.4587

Abstract

Various variants of the SARS-COV virus emerged from 2003 to early 2022. This resulted in a heavy burden on the health sector in carrying out its duties and public services. It would be very helpful if a computer-assisted application was available that could distinguish between the variants of the SARS-CoV virus. From a scientific point of view, this is an opportunity for information technology to play its role to classify SARS-COV variants using supporting algorithms, including the use of artificial intelligence. Artificial intelligence-based and computer-assisted processes are certainly more immune to negative effects due to repetitive works and fatigue. In this study, Classification of the SARS-COV Virus Image Using Deep Learning (CNN) was carried out based on microscopic data called Transmission Electron Microscopy (TEM) images. The aim of the research is to produce a neural network (CNN/Deep Learning) that has been trained to classify two types of variants of the SARS virus, namely SARS-COV and SARS-COV2. The research phase includes data collection, data pre-processing (consists of the image format conversion and enhancing process), and the classification stage. The classification is carried out using both of the original and enhanced image data. The highest classification accuracy was obtained when the original image data was used, namely 77.66%. It was also found that the classification accuracy increased with an increase in the input image size, but the image data enhancing process used was not able to increase the classification accuracy beyond the classification accuracy achieved when using the original image.
Pemanfaatan teknologi kecerdasan buatan untuk guru kinderstation school Budi Sulistiyo Jati; Mutaqin Akbar; Indah Susilawati
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 8, No 3 (2024): September
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v8i3.25728

Abstract

AbstrakKemajuan dalam bidang teknologi informasi dan komunikasi telah menyebabkan perubahan besar dalam berbagai aspek kehidupan, terutama dalam bidang pendidikan. Era pendidikan dewasa ini ditandai oleh integrasi teknologi canggih seperti AI, machine learning, dan internet of things (IoT) dalam proses pembelajaran. Untuk mengatasi permasalahan tersebut, program pengabdian kepada masyarakat ini dirancang untuk meningkatkan pemahaman dan keterampilan praktis guru dan pengajar di Kinderstation School Yogyakarta dalam memanfaatkan teknologi AI dalam proses pembelajaran. Kegiatan pelatihan dilaksanakan dalam dua kali pertemuan (10 Juli 2024 dan 17 Juli 2024), dimana pertemuan pertama membahas mengenai pengenalan teknologi AI dan pertemuan kedua merupakan pelatihan penggunaan teknologi AI dalam dunia pendidikan. Sebelum pertemuan pertama diberikan pre-test untuk mengukur tingkat pemahaman awal para peserta mengenai teknologi AI dalam dunia pendidikan. Nilai rata-rata (mean) berkisar antara 2,24 hingga 3,53, menunjukkan bahwa sebagian besar peserta belum memiliki pemahaman yang mendalam tentang AI. Kemudian setelah pertemuan kedua, diberikan lagi post-test untuk mengukur tingkat pemahaman para peserta setelah pelatihan. Nilai rata-rata (mean) yang berkisar antara 4,06 hingga 4,35 menunjukkan peningkatan pemahaman dan keyakinan yang cukup besar di kalangan peserta. Hasil pengukuran pada pre-test dan post-test menunjukkan bahwa pelatihan pemanfaatan teknologi AI dalam dunia pendidikan berhasil meningkatkan pemahaman, keyakinan, dan kesiapan peserta dalam mengadopsi AI. Peningkatan di semua aspek menunjukkan bahwa pelatihan telah memberikan dampak positif yang signifikan. Kata kunci: guru; kecerdasan buatan; pelatihan; teknologi. AbstractInformation and communication technology advances have brought significant changes in various aspects of life, including education. Today's educational era is characterized by integrating advanced technology such as AI, machine learning, and the internet of things (IoT) in education. To overcome these problems, this community service program is designed to increase the understanding and practical skills of Kinderstation School Yogyakarta teachers in utilizing AI technology in the learning process. The workshops were carried out in two meetings (10 July 2024 and 17 July 2024), where the first meeting discussed the introduction of AI technology, and the second meeting was workshop on the use of AI technology in education. Before the first meeting, a pre-test was given to measure participants' initial understanding of AI technology in education. The mean scores ranged from 2.24 to 3.53, indicating that most participants do not yet have a deep understanding of AI. Then, after the second meeting, post-test was given to measure participants' knowledge after the workshop. The mean values ranging from 4.06 to 4.35 indicate a significant increase in understanding and confidence among participants. The measurement results in the pre-test and post-test show that workshops on the use of AI technology in education increased participants' understanding, confidence, and readiness to adopt AI. The increase in all aspects shows that the workshop has had a significant positive impact. Keywords: artificial intelligence; teacher; technology; workshop.
Implementasi Convolution Neural Network (CNN) untuk Deteksi Penyakit pada Daun Jagung Berbasis Citra Digital Imam Wirabowo; Indah Susilawati
Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer) Vol 5 No 1 (2025): Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitekt
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakadata.v5i1.1046

Abstract

Jagung merupakan komoditas pangan utama kedua di Indonesia yang sering menghadapi masalah penyakit pada daun seperti Blight, Common Rust, dan Gray Leaf Spot. Identifikasi penyakit secara manual masih mengandalkan pengamatan langsung yang bersifat subjektif dan kurang efektif untuk skala luas. Penelitian ini bertujuan mengembangkan sistem deteksi penyakit daun jagung berbasis Convolutional Neural Network (CNN) yang efisien untuk perangkat dengan spesifikasi komputasi rendah. Dataset yang digunakan terdiri dari 2.960 citra daun jagung dengan tiga kategori penyakit yang diperoleh dari publikasi akademik, dataset terbuka, dan pengambilan langsung. Preprocessing meliputi resizing ke 224×224 piksel, normalisasi, dan augmentasi data sederhana. Model CNN dibangun dengan arsitektur sequential berlapis yang terdiri dari tiga blok Conv2D dan MaxPooling2D, flatten layer, dense layer 128 neuron dengan ReLU, dropout 0.5, dan output layer dengan aktivasi softmax. Evaluasi dilakukan dengan delapan kombinasi parameter validation split (0.2 dan 0.3), zoom range (0.2 dan 0.4), dan epoch (20 dan 50). Hasil terbaik diperoleh pada kombinasi validation split 0.2, zoom range 0.2, dan epoch 20 dengan akurasi validasi 90.03% dan loss 0.2574. Confusion matrix menunjukkan performa seimbang pada ketiga kelas penyakit dengan precision, recall, dan F1-score rata-rata 0.90. Model ini terbukti efisien, akurat, dan cocok untuk implementasi pada perangkat dengan keterbatasan komputasi, memberikan solusi praktis bagi petani dalam deteksi dini penyakit tanaman jagung.
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