Article Per Year (5 Year)
p-Index From 2019 - 2024
1.203
P-Index
This Author published in this journals
All Journal InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan) JOIV : International Journal on Informatics Visualization JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Mantik Penusa Information System Journal (INFOS)
Ike Verawati
Universitas Amikom Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

Published : 8 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 8 Documents
Search

 1 
Sistem Penunjang Keputusan Produksi Kopi Wine Gayo Menggunakan Algoritma Fuzzy Tsukamoto Ike Verawati; Junaidi Sarifullah
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 5, No 1 (2020): InfoTekJar September
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v5i1.2293

Abstract

Jumlah stok barang adalah hal yang harus diperhatikan oleh perusahaan, hal ini berarti banyaknya jumlah produksi haruslah optimal, Jdin Roastery adalah Salah satu pabrik yang melakukan produksi terhadap kopi Wine Gayo. Ketidak - optimalan dalam menentukan jumlah produksi akan berpengaruh terhadap pelanggan dan internal perusahaan tersebut, Logika fuzzy merupakan salah satu metode untuk melakukan analisis yang mengandung ketidakpastian. Metode tsukamoto adalah salah satu metode Fuzzy yang dapat digunakan dalam menentukan jumlah persediaan stok barang yang optimal berdasarkan data permintaan, persediaan, dan data produksi.Hasil dari penelitian ini diharapkan, metode fuzzy tsukamoto dapat memberikan penunjang untuk menentukan jumlah produksi dengan nilai yang optimal.
Penerapan Data Mining Untuk Rencana Penambahan Stok Produk Menggunakan Algoritma Apriori Ike Verawati; Mahendra Wishnu P
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 6, No 1 (2021): InfoTekJar September
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v6i1.3884

Abstract

Data mining adalah suatu metode yang memungkinkan para pengguna untuk mengakses data secara cepat atau menggali data yang tersembunyi dari sebuah data yang besar. Sebagai contoh, data mining dapat digunakan untuk mencari informasi kombinasi item dalam suatu penjualan, menentukan penerimaan kelayakan bantuan, memprediksi prestasi pada siswa, dll. Penerapan data mining sangat bermanfaat bagi suatu perusahaan atau lembaga yang ingin melakukan analisa data yang sangat besar, Karena penggunaan data mining bagi suatu perusahaan mampu menganalisa secara cepat, tepat dan akurat.Toko Rianni merupakan salah satu perusahaan yang bergerak dibidang ritel yang mana perusahaan ini harus memenuhi kebutuhan konsumen setiap harinya. Maka dari itu pihak toko dituntut untuk mengambil keputusan yang tepat dalam menentukan rencana penambahan stok produk sehingga dapat meninggkatkan strategi penjualan. Dengan memanfaatkan data transaksi penjualan, pihak toko dapat mengetahui kebiasaan pelanggan atau perilaku pelanggan mengenai apa saja produk yang sering banyak terjual pada toko rianni. Untuk mengetahui produk yang banyak terjual, dapat digunakan aturan assosisasi, yaitu teknik data mining untuk menemukan aturan asosiasi suatu kombinasi item dengan menggunakan algoritma apriori akan menghasilkan pola kombinasi item dan rule. Hasil dari analisa ini akan memudahkan pihak toko dalam rencana penambahan stok produk pada toko rianni, sehingga dapat meninggkatkan strategi pemasaran.
SISTEM PENUNJANG KEPUTUSAN DALAM MENENTUKAN PRIORITAS PENERIMA BANTUAN RUMAH TIDAK LAYAK HUNI ike verawati; Sefri Ferian Erlangga
Information System Journal Vol. 4 No. 1 (2021): Information System Journal (INFOS)
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/infosjournal.2021v4i1.553

Abstract

Bantuan Perumahan Tidak Layak Huni (RTLH) adalah salah satunya program pemerintah untuk mengurangi kemiskinan di syarat kebutuhan perumahan. Program telah dijalankan di berbagai daerah, termasuk di desa buara, Karanganyar kecamatan, kabupaten Purbalingga. Bantuan ini dapat diperoleh jika memenuhi persyaratan kriteria yang telah ditentukan antara lain luas tanah, lantai tidak keramik, dinding tidak layak, struktur atap yang membahayakan penghuninya, pendapatan keluarga, dan jumlah tanggungan. Masalahnya adalah proses yang telah dilakukan oleh Desa Desa Buara tetap dilaksanakan keluar secara subjektif dengan hanya mempertimbangkan hasil dari survey agar bantuan tidak tepat sasaran. Di dalam penelitian, sistem pendukung keputusan (SPK) dibuat menggunakan metode Simple Additive Weighting (SAW) berdasarkan di situs web. Penelitian ini bertujuan untuk memberikan kemudahan dalam menentukan calon penerima Unlivable Rumah Tangga (RTLH) agar tepat sasaran. Hasil studi ini berupa sistem pendukung keputusan untuk menentukan prioritas penerima Rumah Tidak Layak Huni (RTLH) berdasarkan kriteria yang telah ditentukan.
Penerapan Algoritma Dempster Shaferberbasis Android Pada Sistem Pakar Untuk Mendiagnosa Kerusakan Motor Matic Sumarni Adi; Ike Verawati
Jurnal Mantik Penusa Vol. 22 No. 1 (2018): Special Issue
Publisher : Lembaga Penelitian dan Pengabdian (LPPM) STMIK Pelita Nusantara Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (783.637 KB)

Abstract

Expert System is a computer-based system that combines knowledge, facts, and reasoning techniques in solving problems in a particular field like an expert. Expert systems can function as consultants who advise users as well as assistants to experts. One way to overcome and help detect a person's motor motor damage is to create an expert system based on Android as a media for consultation and monitoring so that the user understands what is happening with his motorbike. The Dempster Shafer method is a non monotonous reasoning method used to find inconsistencies due to the addition or subtraction of new facts that will change the existing rules, so the Dempster Shafer method allows one to be safe in doing the work of an expert in this matter is a mechanic. The purpose of this study was to apply the Dempster Shafer uncertainty method to the expert system to diagnose damage to the motorbike and also measure the accuracy of the Dempster Shafer inference engine. The diagnostic results of damage to the matic motor generated by the expert system are the same as the results of manual calculations using the Dempster Shafer inference engine theory which is processed from the results of interviews with the mechanic motor mechanic. So it can be concluded that an android-based expert system that has been built can be used to diagnose damage to the Matic motor. Keywords:Dempster Shafer, Motor Matic, Expert System, Android 
DIAGNOSA KECANDUAN GADGET PADA ANAK MENGGUNAKAN CERTAINTY FACTOR Ike Verawati; Maria Yonessa Purwalasari
Jurnal Mantik Penusa Vol. 3 No. 3 (19): COmputer Science
Publisher : Lembaga Penelitian dan Pengabdian (LPPM) STMIK Pelita Nusantara Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (324.194 KB)

Abstract

Expert system is a system that uses human knowledge, where the knowledge is put into a computer and then used to solve problems that usually require expertise or human expertise. The expert system can function as a consultant who gives advice to users as well as advice for experts. One way to make it easier for parents of children aged 17-17 years to find out the level of addiction experienced by children is to build a web-based expert system to diagnose the level of gatget addiction in children aged 3-17 years web-based. The certainty factor method is a method for determining certainty. The purpose of this study is to build a web-based expert system to diagnose the level of gadget addiction in children aged 3-17 years, to simplify diagnosing the level of addiction experienced by children. The results of the gadget addiction diagnosis level produced by the expert system are the same as the results of the manual calculation using the certainty factor method. So it can be concluded that the web-based expert system that has been built can be used to diagnose the level of gadget addiction in children aged 3-17 years.
An Intrusion Detection System Using SDAE to Enhance Dimensional Reduction in Machine Learning Hanafi Hanafi; Alva Hendi Muhammad; Ike Verawati; Richki Hardi
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.990

Abstract

In the last decade, the number of attacks on the internet has grown significantly, and the types of attacks vary widely. This causes huge financial losses in various institutions such as the private and government sectors. One of the efforts to deal with this problem is by early detection of attacks, often called IDS (instruction detection system). The intrusion detection system was deactivated. An Intrusion Detection System (IDS) is a hardware or software mechanism that monitors the Internet for malicious attacks. It can scan the internetwork for potentially dangerous behavior or security threats. IDS is responsible for maintaining network activity under the Network-Based Intrusion Detection System (NIDS) or Host-Based Intrusion Detection System (HIDS). IDS works by comparing known normal network activity signatures with attack activity signatures. In this research, a dimensional reduction and feature selection mechanism called Stack Denoising Auto Encoder (SDAE) succeeded in increasing the effectiveness of Naive Bayes, KNN, Decision Tree, and SVM. The researchers evaluated the performance using evaluation metrics with a confusion matrix, accuracy, recall, and F1-score. Compared with the results of previous works in the IDS field, our model increased the effectiveness to more than 2% in NSL-KDD Dataset, including in binary class and multi-class evaluation methods. Moreover, using SDAE also improved traditional machine learning with modern deep learning such as Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). In the future, it is possible to integrate SDAE with a deep learning model to enhance the effectiveness of IDS detection
Algoritma Naïve Bayes Classifier Untuk Analisis Sentiment Pengguna Twitter Terhadap Provider By.u Ike Verawati; Bagas Sonas Audit
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4132

Abstract

The development of the internet which has increased in recent years has made it easy for people to give their opinion on a product. By.u, as a new internet service provider, has made many new users share their opinions with each other. Many by.u users give their opinions through social media, especially twitter. From these problems, research was conducted using sentiment analysis. The research stages consisted of collecting data from social media Twitter, preprocessing data, weighting TF-IDF data and classifying using the Naïve Bayes Classifier algorithm. To get the best evaluation results, a comparison of training data and test data was carried out. Data classification is done automatically after cleaning the data in the preprocessing process. There are 2 labels for the data resulting from the automatic classification, namely positive and negative. The dataset after classification will be used as training data and test data. The datasets to be tested are divided into 3 numbers, namely the number of 1000 datasets, 2000 datasets, and 3000 datasets. The test was carried out 3 times for each dataset. The accuracy test is carried out using a confusion matrix. The test results with the highest accuracy were obtained by the nave Bayes classifier with a multinomial model of 85%.
Klasifikasi Penyakit Daun Padi Menggunakan KNN dengan GLCM dan Canny Edge Detection Ike Verawati; Ridwan Al Akhyar Aunurrohim
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.6906

Abstract

Rice plants have an important role in human survival, especially in Indonesia where rice plants are the staple food source for most of the population. The Central Statistics Agency reported that rice consumption in Indonesia reached 28.69 million tons in 2019. In the same year, rice production in Indonesia reached 31.31 million tons. However, production results decreased compared to the previous year, which amounted to 33.94 million tons. One of the factors causing the decline in quality and even death of rice plants is pests and disease. According to the International Rice Research Institute, every year farmers lose an average of 37 percent of their harvest due to pest and disease attacks. The Food and Agriculture Organization also reported a similar thing, where 20 to 40 percent of world food production failures were caused by pests and diseases. Farmers' lack of knowledge and the limited number of experts result in ineffective disease diagnosis. Therefore, a step or method is needed so that the disease detection process in rice plants becomes more effective. This research uses the K-Nearest Neighbor classification algorithm with Gray Level Co-Occurrence Matrix and Canny Edge Detection to classify diseases in rice plants. The result is that Canny Edge Detection has a positive influence on method performance with accuracy reaching 91.67%, precision 87.37% and recall 87.50% at k=7.
Co-Authors Alva Hendi Muhammad Bagas Sonas Audit Hanafi Hanafi Junaidi Sarifullah Mahendra Wishnu P Maria Yonessa Purwalasari Richki Hardi Ridwan Al Akhyar Aunurrohim Sefri Ferian Erlangga Sumarni Adi