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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Techno Nusa Mandiri : Journal of Computing and Information Technology Technologia: Jurnal Ilmiah
F. Lia Dwi Cahyanti
Universitas Nusa Mandiri
Computer Science & IT

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Komparasi Pemilihan Platform Belanja Online Dengan Menggunakan Metode Simple Additive Weighting (SAW) Dan Profile Matching. Qorinul Ahlamiyah; Rani Irma Handayani; F. Lia Dwi Cahyanti
Bianglala Informatika Vol 10, No 2 (2022): Bianglala Informatika 2022
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/bi.v10i2.13181

Abstract

Abstrak - Perkembangan bisnis di Indonesia yang sangat pesat akhir-akhir ini adalah bisnis secara online. Belanja online atau e-commerce adalah salah satu cara berbelanja melalui media elektronik yaitu handphone, laptop, komputer dan lain sebagainya. Belanja online merupakan salah satu akses yang mudah untuk membeli segala kebutuhan karena pembeli dan penjual tidak perlu susah payah untuk bertemu langsung di toko. Pembeli bisa melihat barang yang diinginkan melalui media elektronik yang telah dihubungan oleh internet kemudian dapat memesan barang sesuai dengan pilihan lalu jika sudah sesuai maka pembeli melakukan pembayaran melalui transfer uang elektronik yang telah disediakan di online shop yang telah dipilih. Penelitian ini dibuat dengan menggunakan metode Simple Additive Weighting (SAW) dan metode Profile Matching. Penelitian ini dilakukan dengan mengumpulkan data dan hasil analisis untuk mendapatkan informasi yang harus disimpulkan. Dengan kriteria yang digunakan meliputi nilai kualitas, kepercayaan, kemudahan serta harga barang. Dari hasil pengumpulan data yang telah dilakukan, diperoleh hasil alternatif Shopee sebagai nilai tertinggi dengan perhitungan menggunakan metode SAW dan profile matching dengan nilai 0,95 dan 4,625. Selanjutnya untuk Tokopedia dengan nilai 0,89 dan 4,5. Selanjutnya Bukalapak dengan nilai 0,85 dan 4,375. Dan kemudian Lazada dengan nilai 0,81 dan 4,125. Bobot yang diberikan pada setiap kriteria mempengaruhi hasil akhir penentuan pemilihan aplikasi jasa online shop. Perubahan nilai bobot pada suatu kriteria juga akan mempengaruhi hasil akhir perhitungannya.Kata Kunci : Sistem Pendukung Keputusan, Pemilihan Aplikasi Online Shop, Simple Additive Weighting, Profile Matching Abstract  - The rapid development of business in Indonesia lately is online business. Online shopping or e-commerce is one way of shopping through electronic media, namely cellphones, laptops, computers and so on. Online shopping is one of the easiest ways to buy everything you need because buyers and sellers don't have to bother to meet in person at the store. The buyer can see the desired item through electronic media that has been connected to the internet and then can order the goods according to the choice and if it is appropriate, the buyer makes a payment via electronic money transfer that has been provided at the selected online shop. This research was made using the Simple Additive Weighting (SAW) method and the Profile Matching method. This research was conducted by collecting data and analysis results to obtain information that must be concluded. The criteria used include the value of quality, trustworthiness, convenience and the price of goods. From the results of data collection that has been carried out, the Shopee alternative results obtained as the highest value with calculations using the SAW and Profile matching methods with values of 0.95 and 4.625. Furthermore, for Tokopedia with a value of 0.89 and 4.5. Furthermore, Bukalapak with a value of 0.85 and 4.375. And then Lazada with a value of 0.81 and 4.125. The weight given to each criterion affects the final result of determining the selection of an online shop service application. Changes in the weight value on a criterion will also affect the final result of the calculation.Keywords: Decision Support System, Online Shop Application Selection, Simple Additive Weighting, Profile Matching
Analysis and Design of UI/UX Mobile Applications for Marketing of UMKM Products Using Design Thinking Method Eva Zuliana Dewi; May Fransisca; Rani Irma Handayani; F. Lia Dwi Cahyanti
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2022): Article Research: Volume 7 Number 4, October 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i4.11505

Abstract

The “Product UMKM Online” website is a marketplace or e-commerce that is used as a medium to facilitate UMKM actors in Indonesia in marketing their products and helping UMKM actors to further increase their product sales and help play an important role in the UMKM business process in Indonesia. however, the author found on the website there are still some shortcomings, so the author researched. this study aims to provide recommendations as reference material in the form of a prototype design so that it can be developed in the future. in this study, the author uses a Design Thinking method approach to be able to produce a UI/UX design that fits the user’s needs. There are several processes in the research method that the author uses, namely empathize, define, ideate, prototype, and test. The prototype stage goes through several stages, namely understanding a problem, designing a solution than making a prototype which is then tested on several users. The application of the Design Thinking method in this research is expected to improve the user’s experience better than before.
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 Period of September 2023
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.
Co-Authors Eva Zuliana Dewi Fajar Sarasati Firasari, Elly May Fransisca Qorinul Ahlamiyah Rani Irma Handayani Widi Astuti