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PENERAPAN METODE BACKRPOPAGATION UNTUK IDENTIFIKASI HURUF HIJAIYAH TULISAN TANGAN Damayanti, Ariesta; Syahara, Pujiatus
Jurnal Sistem Informasi Vol 10, No 1 (2018): April
Publisher : Major of Information Systems Faculty of Computer Science Sriwijaya University

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

Hijaiyah letters are Arabic spelling letters that are the original language of the Qur'an. Just like other types of letters, the hijaiyah has certain shapes and characteristics that will form a certain pattern. By using the concept of artificial neural networks, can dibanguun a system that can recognize the pattern by doing the previous training. One of the most commonly used meotodes in artificial neural network paradigms is the crawling or backpropagation buffer. This hijaiyah letters identification system is built using the handwritten hijaiyah image data of 150 images. The feature or feature taken from the image is the binary value of the letter pattern and the number of objects contained in the letters. Prior to the feature extraction process, the image first passes the preprocessing stage consisting of color binerization, object widening, cropping, and resizing. The result obtained by backpropagation method is the system is able to recognize handwriting hijaiyah pattern well. All training data have been correctly identified, while as many as 150 test data can be identified as 77 letters with an accuracy of 51.33%. This accuracy value is obtained with the architectural arrangement of the number of hidden layer neurons = 60, minimum error = 0.001 and maximum iteration = 10000. Keywords: backpropagation, biner, hijaiyah, , pattern, preprocessing

Penentuan Tingkat Kerawanan Penyebaran Leptospirosis Menggunakan Inferensi Fuzzy Tsukamoto Ariesta Damayanti
Jurnal Sistem Komputer dan Informatika (JSON) Vol 1, No 1 (2019): September 2019
Publisher : STMIK Budi Darma

Cases of leptospirosis in Indonesia mainly occur in areas that often experience floods and areas where the majority of its citizens work as farmers. Special Region of Yogyakarta (DIY) was the province with the most leptospirosis cases in Indonesia in 2011. In 2010-2011 an extraordinary event (KLB) of leptospirosis occurred in Bantul district and in 2014 the number of leptospirosis cases in Bantul district increased by 76 cases.. Based on Kementerian Kesehatan report, data shows that there has been an outbreak of leptospirosis in Bantul , so in addition to epidemiological data necessary case information is also needed to determine the geographic case risk factors and mitigation efforts.In the processing of digital maps for GIS , often found important objects that are not appropriate in its processing can not even be excluded because of uncertainty owned. Applications are made in this study was built and designed by the architectural Tsukamoto fuzzy inference method for handling uncertainty. The results of the application is the visualization of the spread of the disease leptospirosis vulnerability maps based determinants that also involves uncertainty factors that will be resolved with the Tsukamoto fuzzy inference method for use as detection and prevention against the spread of disease leptospirosis in the future

TEKNOLOGI FIREBASE UNTUK APLIKASI LAPOR AKAKOM Rudy Cahyadi; Ariesta Damayanti; Irwan Setiawan
JURNAL INFORMATIKA DAN KOMPUTER Vol 4, No 1 (2019)
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

The mechanism for conveying aspirations in STMIK AKAKOM by academicians and how the responses given have not been able to run well. This happens because the media used to convey these aspirations are only through suggestion boxes or by conveying them directly to the parties concerned. The follow-up given by the related parties was felt to still take a long time. Therefore, the application of aspirations and information is needed to accommodate the aspirations and dissemination of information by utilizing Firebase technology. Some features used in making this application are Firebase Firestore to make data updates in real time and the application can be used offline. Firebase Cloud Messaging is used to create push notifications. This application is divided into two parts. The web-based application functions to manage information and aspirations used by the admin of STMIK AKAKOM and the Android-based application functions to receive information and send aspirations to be used by STMIK AKAKOM students. The results of this study are the application of aspirations and information by utilizing Firebase technology. From the testing that has been done, it is obtained that the application can function as an application to manage student reports and aspirations.

ALGORITMA NAÃVE BAYES UNTUK PREDIKSI JUMLAH PENDAFTAR ULANG PADA PENERIMAAN MAHASISWA BARU Ariesta Damayanti
JoMMiT Vol 3, No 2 (2019)
Publisher : Politeknik Negeri Media Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46961/jommit.v3i2.338

STMIK AKAKOM Yogyakarta setiap tahunnya melakukan penerimaan mahasiswa baru yang dilakukan oleh bagian marketing dan admisi, penerimaan mahasiswa baru sangat penting untuk STMIK AKAKOM Yogyakarta karena operasional kampus dibiayai olehÂ pemasukan yang berasal dari SPP mahasiswa. Sehingga diperlukan suatu sistem untuk bisa melakukan prediksi jumlah mhasiswa baru setiap tahunnya, sebagai informasi bagi manajemenÂ sebagai dasar pengelolaan kegiatan kampus.NaÃ¯ve Bayes adalah pengklasifikasian statistik yang dapat digunakan untuk memprediksi probabilitas keanggotaan suatu class. NaÃ¯ve Bayes didasarkan pada teorema Bayes yang memiliki kemampuan klasifikasi seperti decision tree dan neural network. NaÃ¯ve Bayes digunakan untuk memprediksi jumlah mahasiswa baru dengan menggunakan data pendaftar ulang di tahun sebelumnya yang memiliki atribut yaitu asal kota, gelombang, program studi, penghasilan orang tua,Â nilai U N dan status registrasi, sehingga pihak marketing dan admisi STMIK AKAKOM Yogyakarta mendapat gambaran jumlah mahasiswa baru ditahun depan.Hasil dari penelitian ini adalah sistem yang dapat memprediksi data dengan kelas yaitu registrasi dan tidak registrasi. Dari 1704 data testing yang di proses menggunakan sistem didapatkan hasil prediksi registrasi sebanyak 1226 data dan tidak registrasi 478 data. Untuk pengujian dari 731 data didapatkan hasil prediksi 679 data terprediksi benar dan 52 data salah prediksi. Tingkat akurasi probabilitas yang didapatkan sebesar 92,88%.

PENGEMBANGAN APLIKASI QnA UNTUK PENDAFTARAN MAHASISWA BARU STMIK AKAKOM Muhammad Agung Nugroho; Ariesta Damayanti; Muhammad Fahrur Rifai; Syamsu Windarti
Journal of Information System Management (JOISM) Vol. 2 No. 2 (2021): Januari
Publisher : Universitas Amikom Yogyakarta

STMIK AKAKOM annually opens new student registration through offline and online media. Through online media, several media such as websites, social media and email are used. However, on media such as social media, there are questions that often arise regarding new student registration information. With limited human resources to always be online for 24 hours, an alternative model is needed to provide answers to these questions, even though the social media manager offline. Nowadays. In the term of technological developments, it is possible to create a model of knowledge base in the form of a summary of questions and answers to certain topics. This knowledge base can use as a model for creating a prototype application that can provide answers if there are questions related to new student registration. This study aims to provide convenience in the question and answer process by using a Google Dialogflow.

PENINGKATAN KAPASITAS KELOMPOK DESA WISATA WUKIRSARI DALAM PEMASARAN ONLINE MENGGUNAKAN SOSIAL MEDIA MARKETING Ariesta Damayanti; Agung Nugroho; Syamsu Windarti
Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia. Vol 1, No 1 (2022): Maret 2022
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Berdasarkan peta Pariwisata Kabupaten Bantul, Kabupaten Bantul merupakan wilayah yang memilikilebih dari 70 (tujuh puluh) objek wisata yang tersebar di hampir seluruh wilayah. Salah satu wilayah diKabupaten Bantul yang memiliki potensi wisata adalah Kecamatan Imogiri. Kecamatan Imogiri tercatatmemiliki tiga desa wisata yaitu desa wisata Kebonagung, desa Wisata Karangtengah, serta yang terakhiradalah Desa Wisata Wukirsari. Desa Wukirsari sendiri memiliki 7 (tujuh) potensi wisata yang tersebardi beberapa dusun. Sementara itu, dengan pesatnya perkembangan teknologi digital selama ini danmeluasnya penggunaan sosial media, pola pemasaran pun kini telah bergeser ke arah pemasarandengan memanfaatkan media digital tersebut. Fenomena penggunaan media daring dalam pemasaranini mendorong kelompok sadar wisata Wukirsari untuk memaksimalkan teknologi komputer dan internetsebagai media untuk melakukan pemasaran secara online. Pemanfaatan website khusus wisata danmedia sosial bagi pengembangan pemasaran di Desa Wukirsari sangat memungkinkan untuk dilakukan,karena jika diperhitungkan mengenai jangkauan dan sebaran media online ini lebih besar dibandingkanmedia pemasaran yang kita kenal selama ini dengan model konvensional. Sehingga diharapkan dapatlebih mendorong kunjungan wisata ke Desa Wukirsari sehingga akan menambah pendapatan DesaWukirsari. Tujuan dari program pengabdian pada masyarakat ini adalah agar mitra memiliki pengetahuandan ketrampilan memanfaatkan media sosial dan internet sebagai media pemasaran digital. Selainitu agar mitra memiliki pemahaman dan pengetahuan mengenai konten untuk digital marketing. Untukmendukung sistem online marketing diberikan pelatihan penggunaan internet dan jejaring sosial sertaakan digunakan mailchimp sebagai sarana membantu penyebaran informasi produk.

Analisis Sentimen Tindakan Pemerintah Indonesia Dalam Penanganan Covid-19 Menggunakan Metode Support Vector Machine Ariesta Damayanti; Helda Ludya Safitri; Rudy Cahyadi
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 2 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5341

Corona Virus Disease 2019 (Covid-19) which has hit the world including Indonesia since the beginning of 2020 is an outbreak that has become a serious threat to world health. The Indonesian government is taking various actions to deal with this problem, while the public, with the existence of social media, has provided many responses to these government policies. Twitter is one of the social media that is widely used by the public to convey comments in the form of responses, suggestions, to criticism of the government regarding the handling of Covid-19. The comments that appear should be used by the government as part of the reference in evaluating a policy or action taken in handling Covid-19. So that one way that can be used to deal with this is one of the methods that exist in the domain of text mining, namely sentiment analysis. This research was conducted by analyzing sentiment using the Support Vector Machine (SVM) method with the Kernel Radial Basis Function (RBF). Tweets will be classified into positive, negative and neutral sentiments, so that the percentage of each opinion category can be known. This study uses data of 600 tweets obtained from the results of scraping using a Twitter scraper. The result of this study is that the training accuracy rate is 77% in classifying positive, negative, and neutral sentiments. From the results of the data classification, it was found that most of the tweets consisted of negative sentiments.

Implementation of Classification Decision Tree and C4.5 Algorithm in selecting Insurance Products Redjeki, Sri; Damayanti, Ariesta; Hudianti, Erna; Nasyuha, Asyahri Hadi
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13444

Every insurance customer will receive a policy card, as a sign that the person is included in the insurance and is obliged to pay the insurance premium, the amount of which has been determined by the company in accordance with the agreement. Premium payments are Insurance's biggest source of income. Unfavorable economic conditions often cause customers not to pay their premiums by the specified time limit, resulting in a delay in completing the recording of premium income. This research aims to find out the right type of insurance product for prospective customers. The research method used is Classification Decision Tree. Classification Decision Tree is a research method used to examine existing facts systematically based on research objects, existing facts to be collected and processed into data, then explained based on theory so that in the end it produces a conclusion. This research is for selecting the right type of insurance product for prospective customers based on the age and income categories of prospective customers. Insurers must be more careful, especially in selecting prospective customers, and in determining the right type of insurance product for prospective customers so that the power in selecting the right type of insurance product for prospective customers is right at the intended target.

ALGORITMA NAÃVE BAYES UNTUK PREDIKSI JUMLAH PENDAFTAR ULANG PADA PENERIMAAN MAHASISWA BARU Damayanti, Ariesta
JoMMiT Vol 3 No 2 (2019): Artikel Jurnal Volume 3 Issue 2, Desember 2019
Publisher : Politeknik Negeri Media Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46961/jommit.v3i2.338

STMIK AKAKOM Yogyakarta setiap tahunnya melakukan penerimaan mahasiswa baru yang dilakukan oleh bagian marketing dan admisi, penerimaan mahasiswa baru sangat penting untuk STMIK AKAKOM Yogyakarta karena operasional kampus dibiayai olehÂ pemasukan yang berasal dari SPP mahasiswa. Sehingga diperlukan suatu sistem untuk bisa melakukan prediksi jumlah mhasiswa baru setiap tahunnya, sebagai informasi bagi manajemenÂ sebagai dasar pengelolaan kegiatan kampus.NaÃ¯ve Bayes adalah pengklasifikasian statistik yang dapat digunakan untuk memprediksi probabilitas keanggotaan suatu class. NaÃ¯ve Bayes didasarkan pada teorema Bayes yang memiliki kemampuan klasifikasi seperti decision tree dan neural network. NaÃ¯ve Bayes digunakan untuk memprediksi jumlah mahasiswa baru dengan menggunakan data pendaftar ulang di tahun sebelumnya yang memiliki atribut yaitu asal kota, gelombang, program studi, penghasilan orang tua,Â nilai U N dan status registrasi, sehingga pihak marketing dan admisi STMIK AKAKOM Yogyakarta mendapat gambaran jumlah mahasiswa baru ditahun depan.Hasil dari penelitian ini adalah sistem yang dapat memprediksi data dengan kelas yaitu registrasi dan tidak registrasi. Dari 1704 data testing yang di proses menggunakan sistem didapatkan hasil prediksi registrasi sebanyak 1226 data dan tidak registrasi 478 data. Untuk pengujian dari 731 data didapatkan hasil prediksi 679 data terprediksi benar dan 52 data salah prediksi. Tingkat akurasi probabilitas yang didapatkan sebesar 92,88%.

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