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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Sosial Budaya Jurnal Edukasi dan Penelitian Informatika (JEPIN) Seminar Nasional Teknologi Informasi Komunikasi dan Industri J I M P - Jurnal Informatika Merdeka Pasuruan Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Sistem Komputer dan Informatika (JSON) Journal of Education Informatic Technology and Science
Novi Yanti
Universitas Islam Negeri Sultan Syarif Kasim Riau
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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Penerapan Struktur Backpropagation Pada Jaringan Syaraf Tiruan Untuk Mendeteksi Gangguan Penyakit Tropis Novi Yanti
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2018: SNTIKI 10
Publisher : UIN Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (202.182 KB)

Abstract

 Tropical disease is the most common diseases in tropical and subtropical regions. Many factors affected the spread of these diseases, such as poor sanitation and bad environment. Islam establishes the principles in maintaining health through the cleanliness, wudu, and taking bath regularly. Technology through the expert system development tried to transform the expertise knowledge into computers that can mimic the workings of the human brain. One of the methods applied is Artificial Neural Network (ANN) with backpropagation structure. This method detected the tropical diseases of patients, including Dengue Hemorrhagic Fever (DHF) and Typhoid Fever to perform the appropriate treatment as early as possible. ANN diagnosed the type of diseases by identifying the pattern of symptoms in patients. ANN training was presented using 80% of training data and 20% test data. The binary sigmoid activation function [0 1] is used. The learning rate (α) values 0.05, 0.1, 0.2, 0.5, 0.75 and the hidden layers values 10, 50 and 100 are used in testing process. ANN trained the input symptoms, thus the results proposed whether patients affected by any kinds of tropical disease or not. Keywords: DBD, hidden layer, JST, learning rate, Tifoid
Implementasi Metode Learning Vector Quantization (LVQ) Untuk Sentimen Analisis Terhadap Aplikasi Go-Jek Pada Playstore Arif Pratama Budiman; Elvia Budianita; Novi Yanti; Reski Mai Candra
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 3 (2022): Juni 2022
Publisher : Program Studi Teknik Informatika, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i3.4287

Abstract

Abstrak— Perkembangan teknologi dan informasi pada saat ini sangat pesat, terutama di Indonesia. Salah satu teknologi yang berkembang pesat di Indonesia saat ini adalah  teknologi dalam bidang transportasi yaitu, transportasi online. Dengan adanya transportasi online ini sangat membantu segala aktifitas masyarakat. Terdapat beberapa platform tranasportasi yang ada di Indonesia, salah satu yang terkenal adalah transportasi online Gojek. Apliikasi Gojek dapat dengan dengan mudah di download pada google plyastore. Playstore adalah sebuah aplikasi yang berguna untuk mengunduh berbagai aplikasi. Playstore juga menyediakan fitur penilaian terhadap aplikasi yang tersedia di platform tersebut, dengan adanya fitur penilaian ini pengguna dapat memberikan penilaiannya dan juga berkomentar terhadap aplikasi yang digunakannya. Dengan adanya fitur komentar ini maka dapat di lakukannya sentimen analisis untuk mengetahui sentimen publik terhadap suatu aplikasi. Dalam penelitian ini langkah awal yang dilakukan adalah mengumpulkan data dan juga memberikan laber terhadap seluruh data, pada penelitian ini terdapat 3 label yaitu positif, netral, dan Negatif dengan jumlah 900 data. Selanjutnya melakukan proses analisa preprocessing dan juga dilanjutkan dengan proses pembobotan TF, kemudian baru dilakukannya proses klasifikasi menggunakan metode Learning Vector Quantization (LVQ). Hasil pengujiannya sendiri dilakukan dengan menggunakan metode Confussion Matrix. Berdasarkan dari proses dan hasil  pengujian yang di lakukan di dapatkan akurasi terbaik pada perbandingan 90 : 10 sebesar 84,44% yang sebagian besar bernilai positif.  Kata Kunci: transportasi online, Sentiment analysis , Playstore, Klasifikasi, Learning Vector Quantization Abstract— The development of technology and information is currently very fast, especially in Indonesia. One technology that is developing rapidly in Indonesia today is technology in the field of transportation, namely online transportation. The existence of online transportation is very helpful for all community activities. There are several transportation platforms in Indonesia, which is well-known online transportation called Gojek. The Gojek application can be easily downloaded on the Google Playstore. Playstore is an application that is useful for downloading various applications. Playstore also provides an assessment feature for applications available on the platform, with this assessment feature users can provide an assessment and also comment on the applications they use. With this comment feature, sentiment analysis can be done to find out public sentiment towards an application. In this study, initial step was taken to collect data and also provide a label for all data, in this study there were 3 labels, namely positive, neutral, and negative with a total of 900 data. Next, perform the preprocessing analysis process and also continue with the TF-IDF weighting process, then the classification process is carried out using the Learning Vector Quantization (LVQ) method. The results of the test itself are carried out using the Confusion Matrix method. Based on the process and the results of the tests , the best accuracy obtained is at ratio of 90 : 10 by 84,44%,which is most of it are positiveKeywords:Online Transportation,sentiment analysis , Playstore, Classification, Learning Vector Quantization 
Sistem Pakar Diagnosa Gangguan Stress Pasca Trauma Menggunakan Metode Certainty Factor Marliana Safitri; Fitri Insani; Novi Yanti; Lola Oktavia
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 4 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6309

Abstract

Mental health disorder or commonly called Mental Health Disorder is a disturbing psychological behavior and is followed by traumatic events such as shock shell, war fatigue, accidents, victims of sexual violence, and the covid pandemic. Cases of post traumatic stress disorder data from Indonesian Psychiatric Association amounted to 80% of 182 examiners experiencing symptoms of post-traumatic stress due to exposure to covid, 46% experienced severe symptoms, 33% moderate, 2% mild and others did not show symptom. This study aims to diagnose post traumatic stress disorder using the assurance factor method with 35 symptom data and 3 levels of post traumatic stress disorder as a knowledge base. The certainty factor is a circulation management method and a decision-making strategy using the confidence factor in the system. Based on the research results of the expert system for diagnosing post traumatic stress disorder, the test results obtained an accuracy of 80%. The results of the accuracy of this expert system indicate that the expert system can potentially be used to diagnose post traumatic stress disorder.
Analisa Dini Gangguan Disleksia Anak Sekolah dengan Metode Backpropagation Novi Yanti; Adil Setiawan; Sarjon Defit
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 9, No 2 (2023): Volume 9 No 2
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v9i2.64588

Abstract

Disleksia sering disalah artikan sebagai kebodohan atau kemalasan pada anak. Gejala disleksia dikenal dengan gangguan belajar yang meliputi mengenal huruf, mengeja, membaca, dan menulis. Meskipun gejala disleksia tidak terlihat dengan jelas, kondisi ini dapat berdampak pada perkembangan pola belajar anak. Tujuan penelitian adalah untuk mengidentifikasi gejala disleksia sedini mungkin agar tidak mengganggu perkembangan belajar pada anak. Selain itu, penelitian juga bertujuan untuk mengevaluasi keakuratan teknik yang digunakan. Analisa menggunakan metode jaringan syaraf tiruan dengan teknik backpropagation dengan memberikan nilai bobot, sehingga dapat memberikan nilai input dengan benar. Penelitian menggunakan 150 dataset, 40 variabel input dan 40 lapisan tersembunyi. Keluaran yang diharapkan mencakup disleksia atau non-disleksia. Hasil implementasi dan pengujian untuk data latih dan data uji terbaik adalah 90:10. Dengan nilai epoch maksimum 5000 dan nilai error target 0,001. Metode backpropagation dapat memberikan hasil akurasi terbaik 100% pada learning rate 0,5. Sehingga metode backpropagation dapat dengan baik mendeteksi gangguan disleksia pada anak sejak dini.
Co-Authors Afriani, Defi Albis Ya Albi Arif Pratama Budiman Eka Pandu Cynthia Elvia Budianita Fitri Insani Fitri Insani Ivo Dwi Ananda Lola Oktavia Marliana Safitri Miftahul Ghifrah Sembiring Muhammad Affandes Muhammad Irsyad MUHAMMAD YUSUF Nasriah Nasriah Nazruddin Safaat H Safaat H Pizaini Pizaini Rahmad Kurniawan Reski Mai Candra Sarjon Defit Setiawan, Adil Yelvi Fitriani Yelvi Fitriani Yusra, Yusra