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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Techno Nusa Mandiri : Journal of Computing and Information Technology EDUMATIC: Jurnal Pendidikan Informatika Technologia: Jurnal Ilmiah Indonesian Journal Computer Science (ijcs) Jurnal Pengabdian Teknik dan Ilmu Komputer (PETIK)
Firasari, Elly
Universitas Nusa Mandiri
Computer Science & IT Education Engineering

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Perancangan E-Commerce Sebagai Media Pemasaran Kerajian Bambu F. Lia Dwi Cahyanti; Fajar Sarasati; Widiastuti Widiastuti; Elly Firasari
Jurnal Pendidikan Informatika (EDUMATIC) Vol 5, No 1 (2021): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v5i1.3275

Abstract

At this time progress economic in Indonesia is very rapidly, This is evidenced by the emergence of many newly pioneered business fields one of which is a company engaged in marketing. Each Company makes various efforts to keep the company be growing. UKM Kerajinan Bambu Brajan in Sleman Yogyakarta has a problem's promotion activities and product sales that are still traditional and manual. The purpose of this study is to design e-commerce as a traffic bamboo handicrafts on UKM  in Brajan village. With the creation of the e-commerce website as the marketing of products is expected to help traffic the product so that it can be widely known to the public. The waterfall model is what is used to design this product in systematic steps. This study produces a  ecommerce website that has been tested for its feasibility using the black box testing method. This Website provides information to consumers about the products in UKM Kerajinan Bambu Brajan, as well as provides a facility of the UKM in the process of selling and marketing products.
COMPARISON OF EIGENFACE AND FISHERFACE METHODS FOR FACE RECOGNITION Elly Firasari; F Lia Dwi Cahyanti; Fajar Sarasati; Widiastuti Widiastuti
Jurnal Techno Nusa Mandiri Vol 19 No 2 (2022): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v19i2.3470

Abstract

Abstract— Biometric information systems have been widely used in the fields of government, shopping centers, education and even security, which offer biological authentication so that the system can recognize its users more quickly. The parts of the human body are identified by a biometric system that has unique and specific characteristics, one of which is the face. Adjustment of facial image deals with objects that are never the same, due to the parts that can change. These changes are caused by facial expressions, light intensity, shooting angle, or changes in facial accessories. With this, the same object with several differences must be recognized as the same object. In this study, the data used were 388 face images and the sata test consisted of 30 face images. Before the face is tested, preprocessing and feature extraction are carried out using the Haar Cascade Classifier and then detected using Eigenface and Fisherface. Based on the research results, the Fisherface method is an algorithm that is accurate and efficient compared to the Eigenface algorithm. The Fisherface algorithm has an accuracy of 88%. while the Eigenface method has an accuracy rate of 76%. Keywords – Haar Cascade Classifier, Eigenface, Fisherface,.
KLASIFIKASI DATA MINING DENGAN ALGORITMA MACHINE LARNING UNTUK PREDIKSI PENYAKIT LIVER F. Lia Dwi Cahyanti; Fajar Sarasati; Widi Astuti; Elly Firasari
Technologia : Jurnal Ilmiah Vol 14, No 2 (2023): Technologia (April)
Publisher : Universitas Islam Kalimantan Muhammad Arsyad Al Banjari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/tji.v14i2.10093

Abstract

Liver merupakan organ tubuh manusia yang memiliki peranan sangat penting seperti mencerna, menyerap, membantu proses pencernaan makanan serta menghancurkan racun di dalam darah. Penyakit hati atau liver yang sudah akut sangat mempengaruhi fungsi-fungsi hati, penyakit hati dapat diketahui dari munculnya gejala klinis maupun fisik yang timbul pada pasien. Penelitian ini membahas tentang klasifikasi penyakit liver pada dataset ILPD yang diambil dari UCI Machine learning Repository menggunakan algoritma machine learning. Dataset terdiri dari 583 record data, 10 kriteria, dan 1 variable kelas berjenis multivariate. Penelitian ini menggunakan beberapa tahapan preprocessing yang dilakukan, diantaranya : Preprocessing Data Dan Eksplorasi Data, Penanganan missing value, feature selection, menerapkan feature correlation dan feature scaling, Analisis menggunakan Algoritma Machine learning. Berdasarkan hasil pengujian yang dilakukan dalam memperoleh nilai akurasi perhitungan klasifikasi menggunakan Algoritma Random Forest memiliki performa  keakuratan yang diukur dengan akurasi sebesar 78,63% sehingga disimpulkan akurasi tersebut lebih unggul dari algoritma lainnya dalam klasifikasi penyakit liver.
CLASSIFICATION OF POTATO LEAF DISEASES USING CONVOLUTIONAL NEURAL NETWORK Elly Firasari; F. Lia Dwi Cahyanti
Jurnal Techno Nusa Mandiri Vol 20 No 2 (2023): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v20i2.4655

Abstract

Potatoes are an agricultural product that has the fourth highest content of wheat flour after corn, wheat, and rice. Although potatoes play a critical role in agriculture, this crop is susceptible to various diseases and pests. There are several potato leaf diseases that are not yet known to farmers. Dry spot potato leaf disease (late blight) and late blight. If not treated, this disease on potato leaves will spread to the stem and reduce crop yields, causing crop failure. By using technology in the form of digital image processing, this problem can be overcome. This research proposes an appropriate method for detecting disease in potato leaves. Classification will be carried out in three classes, namel, Early Blight, Healthy and Late Blight using the Deep Learning method of Convolutional Neural Network (CNN). The data used comes from an online dataset via the kaggle.com page with the file name Potato Disease Leaf Dataset (PLD) totaling 3251 training datasets which are then divided into training, testing, and validation. The processes carried out are image pre-processing, image augmentation, then image processing using a Convolutional Neural Network (CNN). In the classification process using the CNN method with RMSprop optimizer, the accuracy was 97.53% with a loss value of 0.1096.
Pengembangan dan Peningkatan Keterampilan Guru PAUD melalui Pelatihan Microsoft Word: Studi Kasus di PAUD Tunas Bangsa 05 Saputri, Daniati Uki Eka; Firasari, Elly; Khasanah, Nurul; Cahyanti, F. Lia Dwi
Jurnal Pengabdian Teknik dan Ilmu Komputer (Petik) PETIK : Jurnal Pengabdian Teknik dan Ilmu Komputer Vol. 4 No. 1 Juni 2024
Publisher : Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/petik.v4i1.13133

Abstract

The Microsoft Office training, specifically focusing on Microsoft Word, for the teaching staff of PAUD Tunas Bangsa 05 aimed to enhance technological skills to support educational quality. Conducted by a team of lecturers from Universitas Nusa Mandiri on May 18, 2024, the training was met with high enthusiasm from the participants. The methods employed included material presentation, hands-on practice, and Q&A sessions. Evaluation results indicated a significant improvement in participants' understanding and skills in using Microsoft Word. They were able to utilize the application to create more organized and professional documents, positively impacting the teaching-learning process. The activity successfully achieved its objectives and received positive feedback, also indicating the need for further training. Consequently, this training contributes to the professional development of the teaching staff and the enhancement of educational quality at PAUD Tunas Bangsa 05
Kombinasi K-NN dan Gradient Boosted Trees untuk Klasifikasi Penerima Program Bantuan Sosial Firasari, Elly; Khultsum, Umi; Winnarto, Monikka Nur; Risnandar, Risnandar
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 6: Desember 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.0813087

Abstract

Kemiskinan bagi pemerintah Indonesia termasuk masalah yang sulit untuk diselesaikan. Upaya yang dilakukan pemerintah dalam mengatasi kemiskinan di Indonesia yaitudengan  program bantuan sosial meliputiBLT (Bantuan Langsung Tunai), PKH (Program Keluarga Harapan), Raskin (Beras Miskin), dan lain lain. Dalam Pelaksanaan program bantuan sosial saat masih sangat terbatas sehingga dalam penerimaan program bantuan tidak tepat sasaran. Data mining membantu untuk menentukan keputusan dalam memprediksi data di masa yang akan datang. Gradient Boosted Trees dan K-NN merupakan salah satu metode data mining untuk klasifikasi data. Masing-masing metode tersebut memiliki kelemahan. Gradient Boosted Trees menghasilkan nilai persentase akurasi lebih rendah dibanding metode K-NN. Dari permasalahan tersebut maka diusulkan metode kombinasi K-NN dan Gradient Boosted Trees untuk meningkatkan akurasi pada pelaksanaan program bantuan sosial agar tepat sasaran. Metode K-NN, Gradient Boosted Trees, K-NN-Gradient Boosted Treesdilakukan pengujian pada data yang sama untuk mendapatkan hasil perbandingan nilai akurasi. Hasil pengujian membuktikan bahwa kombinasi tersebut menghasilkan nilai persentase yang tinggi dibanding metode K-NN atau Gradient Boosted Trees yaitu 98.17%.AbstractPoverty for the Indonesian government is a problem that is difficult to solve. The efforts made by the government in overcoming poverty in Indonesia are through social assistance programs including BLT (Bantuan Langsung Tunai), PKH (Program Keluarga Harapan), Raskin (Beras Miskin), and others. In the implementation of the social assistance program when it was still very limited, the acceptance of the aid program was not on target. Data mining helps to determine decisions in predicting data in the future. Gradient Boosted Trees and K-NN are data mining methods for data classification. Each of these methods has weaknesses. Gradient Boosted Trees produce lower accuracy percentage values than the K-NN method. From these problems, a proposed method of combination of K-NN and Gradient Boosted Trees is used to improve the accuracy of the implementation of social assistance programs so that it is right on target. The K-NN, Gradient Boosted Trees, and K-NN-Gradient Boosted Trees methods are tested on the same data to get a comparison of the accuracy values. The test results prove that the combination produced a high percentage value compared to the K-NN or Gradient Boosted Trees method that is 98.17%.
Implementasi Deep Learning pada Deteksi Penyakit Daun Kentang dengan Arsitektur InceptionResNetV2 Firasari, Elly; Cahyanti, F. Lia Dwi
Indonesian Journal Computer Science Vol. 4 No. 1 (2025): April 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/n09m6677

Abstract

Kentang (Solanum tuberosum) merupakan salah satu komoditas pangan penting di Indonesia. Namun, produktivitasnya kerap terganggu oleh penyakit daun seperti Early Blight, Late Blight, dan infeksi virus. Identifikasi penyakit secara manual oleh petani masih memiliki keterbatasan dari segi waktu, tenaga, dan akurasi. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi otomatis penyakit daun kentang menggunakan metode deep learning, khususnya dengan arsitektur InceptionResNetV2. Dataset yang digunakan bersumber dari Kaggle dan terdiri dari 3.251 citra yang terbagi dalam tiga kelas: Healthy (816), Early Blight (1.303), dan Late Blight (1.132). Data diproses melalui tahapan praproses berupa resize, normalisasi piksel, dan augmentasi data. Pelatihan dilakukan dengan menggunakan ukuran input 299x299 piksel, batch size 20, dan jumlah epoch sebanyak 20. Hasil pelatihan menunjukkan bahwa model mencapai akurasi pelatihan sebesar 94,20% dan akurasi validasi sebesar 95,30%. Evaluasi model menggunakan confusion matrix menunjukkan kinerja yang baik pada kelas Early Blight dan Healthy, namun model masih mengalami kesulitan dalam membedakan antara Late Blight dan daun Healthy. Secara keseluruhan, model InceptionResNetV2 terbukti efektif dalam mengklasifikasikan penyakit daun kentang dan dapat menjadi solusi pendukung dalam sistem pertanian berbasis teknologi.
Co-Authors Cahyanti, F. Lia Dwi Daniati Uki Eka Saputri F Lia Dwi Cahyanti F. Lia Dwi Cahyanti F. Lia Dwi Cahyanti Fajar Sarasati Fajar Sarasati Fajar Sarasati Khultsum, Umi Risnandar, Risnandar Veithzal Rivai Zainal Widiastuti Widiastuti Widiastuti Widiastuti Winnarto, Monikka Nur