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All Journal TeknoIS : Jurnal Ilmiah Teknologi Informasi dan Sains
Lis Utari
Sekolah Tinggi Ilmu Komputer Binaniaga, [https://orcid.org/0000-0003-1334-856X]
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Rekomendasai Tenaga Pengajar Pada Sekolah TK Nasional Plus Dengan Menggunakan Metode Simple Additive Weighting (SAW) Dini Erlita; Lis Utari
Teknois : Jurnal Ilmiah Teknologi Informasi dan Sains Vol 11, No 2 (2021): November
Publisher : Sekolah Tinggi Ilmu Komputer Binaniaga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jbs.v11i2.116

Abstract

In accepting teachers, each school will offer various criteria for prospective teachers. The process of accepting teachers at the Kinderfield Cibinong School is still done manually, where a file selection and comparison with existing criteria will be carried out. Based on the 8 (eight) criteria obtained from interviews with the principal, the weight value of each criterion is also obtained. The application of the method Simple Additive Weighting (SAW) to this problem is one of the most appropriate and effective ways. The existence of a system for the recommendation of teaching staff in national kindergarten schools plus will be more effective and efficient in its use. For the implementation of the system, the compiler uses the vb.net programming language and SQL Server as the database. With the Decision Support System, it can help in determining which prospective teachers will be accepted and the process will be faster
Penerapan Metode Naïve Bayes untuk Prediksi Minat Baca Berdasarkan Usia Lis Utari; Yawmil Ulfah
Teknois : Jurnal Ilmiah Teknologi Informasi dan Sains Vol 11, No 1 (2021)
Publisher : Sekolah Tinggi Ilmu Komputer Binaniaga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jbs.v11i1.104

Abstract

Reading is one of the activities favored by all circles of society, and a place that can be visited for reading activities is the library. One of them is the Pajajaran Sports Hall Library, based on the data obtained from more than 100 visitors who come to visit the Pajajaran Sports Hall Library each year. However, visitors who borrow do not necessarily have a high reading interest. In this study, an application was made that can predict library visitors who have a high reading interest. In order to predict future reading interest using the method Naive Bayes. It uses variables obtained from borrowing data and carried out preprocessing and obtained several variables used to make predictions, namely name, book category, number of late days, age group, and level of reading interest. This is done to predict the reading interest of visitors to the Pajajaran GOR Library, who have a high level of reading interest and a low level of reading interest. So that the librarian can find out the level of reading interest of the visitors, and the collection of books in the Pajajaran GOR Library can be increased based on the preferred book category of all ages. The accuracy test has also been carried out using the formula confusion matrix with an accuracy value of 80%, which means that the application that has been made is suitable for use.
Identifikasi Gejala Penyakit Pada Domba menggunakan Metode Teorema Bayes Dan Certainty Factor Lis Utari; Mauladanny Fajar Ilhami
Teknois : Jurnal Ilmiah Teknologi Informasi dan Sains Vol 12, No 1 (2022): January
Publisher : Sekolah Tinggi Ilmu Komputer Binaniaga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jbs.v12i1.134

Abstract

Domba (Ovies aries) adalah  salah  satu  jenis ternak sumber  protein hewani  yang banyak dipelihara oleh masyarakat.  Domba  diklasifikasikan  dalam  filum Chordata  (hewan  bertulang  belakang),  kelas Mammalia  (hewan  menyusui),  ordo Artiodactyla  (hewan  berteracak),  famili Bovidae (hewan  memamah  biak).   Banyaknya Penyakit pada ternak dapat menimbulkan kerugian ekonomi yang cukup besar bagi peternak khususnya dan masyarakat luas pada umumnya. Karena banyak penyakit ternak yang tidak hanya menyerang ternak tetapi juga dapat menular kepada manusia disebut penyakit “ZOONOSIS”. Metode penelitian yang popular di gunakan dalam sistem pakar di antarnya Naïve Bayes dan Certainty Factor, Fuzzy KNearest Neighbor (F-KNN ), Teorema Bayes dan Certainty Factor.  Yang akan digunakan dalam penelitian ini adalah Teorema Bayes dan Certainty Factor.  Teorema Bayes adalah sebuah teorema dengan dua penafsiran berbeda. Sedangkan Certainty Factor menyatakan kepercayaan dalam sebuah kejadian sesuai dengan bukti dan penilaian seorang pakar. Metode ini menggunakan suatu nilai dalam mengasumsikan derajat keyakinan seorang pakar terhadap suatu data. 
Pemetaan Kompetensi Siswa Untuk Mengikuti Seleksi Lomba Kejuruan Multimedia Menggunakan Metode K-Means Lis Utari; Rio Geraldy
TeknoIS : Jurnal Ilmiah Teknologi Informasi dan Sains Vol 12, No 2 (2022): Volume 12, No 2 (2022): July
Publisher : Universitas Binaniaga Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jbs.v12i2.151

Abstract

The process of mapping competent students who will take part in the competition is the selection of students who have the best scores in the vocational field, which is carried out by the academic side in order to include students who are competent in the vocational field to be included in the vocational competition. So far, the school still has difficulties in selecting competent and less competent students due to the limitations of the presentation and processing of the data carried out. To facilitate the mapping of competent students in the vocational field, this activity is computerized into software. Time accuracy and effectiveness are important factors so that the mapping process of competent students can run well and as it should. In this research, an application is made that can cluster competent students so that they can take part in Multimedia vocational competitions appropriately and accurately using the K-Means Algorithm. It applies variables such as Academic Values and Attitude Values.
Penerapan Convolutional Neural Networks Menggunakan Edge Detection Untuk Identifikasi Motif Jenis Batik Lis Utari; Ammar Zulfikar
TeknoIS : Jurnal Ilmiah Teknologi Informasi dan Sains Vol 13, No 1 (2023)
Publisher : Universitas Binaniaga Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jbs.v13i1.184

Abstract

Batik is the work of the Indonesian nation which is a combination of art and technology by the ancestors of the Indonesian people. UNESCO designated batik as a Humanitarian Heritage for Masterpieces of the Oral and Intangible Heritage of Humanity, followed by a presidential decree on 2 October 2009 which was designated as Indonesia's National Batik Day. In maintaining the existence of batik in the era of technological advances, they have utilized Artificial Intelligence and Machine Learning technologies. Even though research on batik motifs has become a common topic, there are still many mistakes found during the process of identifying motifs. As is well known, Indonesia has various types of traditional batik which have very diverse colors, patterns and motifs. This is the main problem in the identification of batik motifs, where it is found that many batik motifs adopt or have similarities in either pattern or color to other batik motifs, giving rise to thin predictions when identified with other motifs. This study uses the Convolutional Neural Networks (CNN) method using Edge Detection to identify batik motifs. Using the main dataset of 1106 images divided into 4 classes, namely Kawung, Megamendung, Merak Ngibing, and Parang motifs. The research is focused on comparing the results of prediction effectiveness on the CNN model with Canny edge detection (CNN-Canny) and the CNN model with Sobel edge detection (CNN-Sobel). The training process for each model is applied the same configuration to the dataset with a ratio of 8:2. Then in the model augmentation the functions of random flip, random zoom, and random invert are applied. The results obtained from learning the machine learning model at the testing and validation stage on the CNN-Sobel model obtained an accuracy of 91.2% in the training process and 91.8% in the validation process. Meanwhile, CNN-Canny got 90.5% accuracy in the training process and 86.2% in the validation process. The results of the comparison of the performance of the two models which are mapped to the confusion matrix table on 16 data testing in the form of images of batik motifs that have never been studied by each model show that the CNN-Sobel model can work more optimally than the CNN-Canny model with an accuracy comparison percentage of 94%. compared to 76% in the process of identifying batik motifs.
Co-Authors Ammar Zulfikar Dini Erlita Mauladanny Fajar Ilhami Rio Geraldy Yawmil Ulfah