Article Per Year (5 Year)
p-Index From 2019 - 2024
3.706
P-Index
This Author published in this journals
All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Nuansa Informatika Infotech Journal Journal of Information Technology and Computer Science (JOINTECS) Bina Insani ICT Journal Jurnal Pilar Nusa Mandiri Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Informatika Universitas Pamulang Jurnal Sisfokom (Sistem Informasi dan Komputer) INTECOMS: Journal of Information Technology and Computer Science J-SAKTI (Jurnal Sains Komputer dan Informatika) STRING (Satuan Tulisan Riset dan Inovasi Teknologi) JISICOM (Journal of Information System, Infomatics and Computing) Journal of Information System, Applied, Management, Accounting and Research JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Pendidikan dan Konseling Generation Journal Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Journal Sensi: Strategic of Education in Information System Dharma Raflesia : Jurnal Ilmiah Pengembangan dan Penerapan IPTEKS BERNAS: Jurnal Pengabdian Kepada Masyarakat J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Teknologi Sistem Informasi Innovative: Journal Of Social Science Research Jurnal INFOTEL
Yuyun Umaidah
Departemen Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Singaperbangsa Karawang, Karawang, 41361
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Neuroscience Nursing Public Health Social Sciences Veterinary Other

Published : 26 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 26 Documents
Search

 1   2   3 
PENERAPANALGORITMA K-NEAREST NEIGHBOR(K-NN) DENGAN PENCARIAN OPTIMALUNTUK PREDIKSI PRESTASI SISWA Yuyun Umaidah; Purwantoro Purwantoro
Journal of Information System, Informatics and Computing Vol 3 No 2 (2019): JISICOM : Volume 3, Nomor 2, December 2019
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (800.897 KB)

Abstract

Pendidikan merupakan hal yang penting untuk meningkatkan kualitassiswa. Dengan pendidikansiswa dapat mencapaihasil-hasil yang diperoleh yaitu prestasi. Prestasi merupakan wujud nyata kualitas yang diperolehsiswa atas usaha dan kerja keras dalam belajar. Penelitian ini memanfaatkan teknik data miningmenggunakanalgoritma K-Nearest Neighbor(K-NN) dengan pencarian K-Optimalmenggunakanmetode k-fold cross validationuntuk memprediksi prestasi siswa. Kriteria yang digunakan adalah: Les Tambahan, Jurusan, Nilai rata-rata rapor mata pelajaran pokok, Nilai rata-rata rapor mata pelajaran penjurusan, Nilai kedisiplinan, Jarak Tempuh, Ekstrakurikuler, Organisasi, dan Prestasi.Metodologi yang digunakan adalah CRISP-DMdan Performa Algoritma dilihat dari nilai accuracy, precision, recall, dan AUCdengan melakukan pemilihan k-fold cross validation(k=2,k=3, k=4, k=5, k=6,k=7, k=8, k=9, k=10). Setelah diperoleh hasil terbaik dari pemilihan k-fold cross validationakan dilakukan pengujian dengan pemilihan klasterk-NN(klaster1, klaster 2, klaster 3, klaster 4 dan klaster 5). Dari penelitian diperoleh hasil terbaik terdapatpada k=5 (5-fold cross validation) pada klaster 2 dengan hasil accuracy= 93.63%, precision=95.77%, recall=96.58% dan AUC=0.782. Kata Kunci: K-nearest neighbor, k-fold cross validation,k-optimal, CRISP-DM
SISTEM PENDUKUNG KEPUTUSAN PENERIMAAN KARYAWAN DENGAN METODE ANALYTICAL HIERARCHY PROCESS PADA PT.CRESYN INDONESIA Purwantoro Purwantoro; Yuyun Umaidah
Journal of Information System, Applied, Management, Accounting and Research Vol 3 No 4 (2019): JISAMAR : Volume 3, Nomor 4, November 2019
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1140.181 KB)

Abstract

Pesatnya perkembangan dunia industri membawa dampak tersendiri untuk keberadaan industri di Indonesia.[2]Untuk menghadapi tekanan persaingan tersebut perusahaan harus berupaya meningkatkan kualitas sumber daya manusianya[1] sehingga dapat menjadi keunggulan kompetitif bagi perusahaan. PT. Cresyn Indonesia merupakan salah satu pelaku yang bergerak di bidang industri, Dalam hal ini perusahaan harus menemukan orang yang tepat bagi suatu jabatan tertentu[4] sehingga orang tersebut mampu bekerja secara optimal dan dapat bertahan di perusahaan untuk waktu yang lama. Berhasil tidaknya suatu perusahaan ditentukan oleh unsur karyawan yang melakukan pekerjaan.Perusahaan harus selektif untuk memilih dan menerima karyawan baru, mulai daripendidikan hingga pengalaman bekerja.Perusahaan-perusahaan mempunyai kriteria-kriteria yang berbeda-beda satu dengan yang lainnya.Untuk mendukung usaha perusahaan tersebut dalam penerimaan karyawan maka diperlukan sistem pendukung keputusan penerimaan karyawan yang tepat bagi perusahaan, karena sistem penerimaan karyawan harus disesuaikan dengan kondisi dari perusahaan saaat ini, dimana metode yang dianggap sesuai dengan kondisi perusahaan saat ini adalah dengan menggunakan metode analytical hierachy process (AHP), Hasil yang diperoleh pada goal berdasarkan kriteria penerimaan karyawan menunjukkan 34.0 % pengalaman,28.1% pendidikan,9.2% tes psikologi,7.3% tes kesehatan,5.8% wawancara
Sistem Pendukung Keputusan Rekomendasi Topik Skripsi Menggunakan Naïve Bayes Classifier Farid Farid; Ultach Enri; Yuyun Umaidah
JOINTECS (Journal of Information Technology and Computer Science) Vol 6, No 1 (2021)
Publisher : Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31328/jointecs.v6i1.2076

Abstract

Setiap mahasiswa dituntut untuk melakukan kewajiban, salah satunya berupa penelitian. Sebagai wujud nyata proses akhir menuju sarjana setiap mahasiswa diharuskan membuat artikel ilmiah dalam bentuk buku yang diberi nama skripsi. Selama ini proses menentukan topik skripsi mahasiswa dilakukan secara manual, baik pembimbing skripsi yang memberi masukan atau ide diperoleh dari berbagai makalah penelitian. Dan proses penentuan topik skripsi tanpa menggunakan sistem terkomputerisasi. Maka dari itu peneliti membuat penelitian ini agar dapat membantu Mahasiswa dalam menentukan topik skripsi yang sesuai dengan kompetensi Mahasiswa. Metode penelitian ini menggunakan metode pengembangan data mining dan perangkat lunak dengan menerapkan algoritma Naïve Bayes Classifier ke sistem berbasis website. Hasil dari penelitian ini adalah sistem pendukung keputusan yang dapat memberikan rekomendasi topik skripsi berdasarkan data nilai mata kuliah pilihan. Nilai accuracy model terbaik yang diimplementasikan pada sistem ini adalah sebesar 69,27%. Nilai akurasi kurang baik karena jumlah data yang tidak seimbang pada setiap kategori topik skripsi.
ANALYSIS OF KARAWANG ONLINE SALES CUSTOMER SATISFACTION USING CUSTOMER SATISFACTION INDEX (CSI) METHOD Hannie Hannie; Ultach Enri; Yuyun Umaidah
Jurnal Pilar Nusa Mandiri Vol 16 No 1 (2020): Publishing Period for March 2020
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1038.308 KB) | DOI: 10.33480/pilar.v16i1.1111

Abstract

Karawang is one of the industrial cities. Most industry players look at Karawang as a strategic city to run a business. Many products have been produced from Karawang. However, there are lack in promoting, marketing the product and expanding the marketing area. The analysis of consumer satisfaction in Karawang is to determine the satisfaction of Karawang consumers to the prospects of promising online sales. Service attributes can be included in increasing online sales at Karawang using the Customer Satisfaction Index (CSI) method. The result of the Customer Satisfaction Index (CSI) is 78.43% which means that overall consumers who live in Karawang and have been shopped online are satisfied with the development of online shopping. This research was conducted in Karawang. The data used are primary data and secondary data. The sampling method is a non-probability sampling method, while the non-probability sampling method used sampling purposes.
PREDICTION OF PUBLIC SERVICE SATISFACTION USING C4.5 AND NAÏVE BAYES ALGORITHM Yuyun Umaidah; Ultach Enri
Jurnal Pilar Nusa Mandiri Vol 17 No 2 (2021): Publishing Period for September 2021
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v17i2.2403

Abstract

One of the things that has often been questioned lately is in the field of public services, especially in terms of the quality or service quality of government agencies to the community, the Manpower and Transmigration Office of Kab. Karawang is a government agency in charge of public services. where one of the tasks is to make an AK.1 card (yellow card), based on this problem the Manpower and Transmigration Office of Kab. Karawang Regency. Karawang seeks to improve service quality in order to satisfy consumers by distributing questionnaires to every consumer who is making an AK card.1. In this study, we will apply the C4.5 and Naïve Bayes algorithms to predict the satisfaction of public services with the nominal type of dataset used. The evaluation is done based on a comparison of the level of accuracy, precision, recall, and F-Measure using a confusion matrix. From the research that has been carried out, the Naïve Bayes algorithm with 70% training data distribution and 30% testing is able to provide better predictive results than the C4.5 algorithm as evidenced by the accuracy value = 96.89%, precision = 95.50%, recall = 95.00% and f-measure = 94.60%.
Analisis Sentimen Review Pelanggan Restoran Menggunakan Algoritma Support Vector Machine Dan K-Nearest Neighbor Bintang Sifa Amalia; Yuyun Umaidah; Rini Mayasari
SITEKIN: Jurnal Sains, Teknologi dan Industri Vol 19, No 1 (2021): Desember 2021
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/sitekin.v19i1.14861

Abstract

Pada masa pandemi adanya virus corona ini hampir sebagian orang berdiam diri dirumah untuk mematuhi peraturan yang sudah dirancang oleh pemerintah. Bahkan sebagian orang kewalahan mencari bahan makanan atau malas untuk memasak dirumah sebab bahan makanan untuk dimakan telah habis, karena itu seiring berkembangnya teknologi memanfaatkan dengan memesan makanan pesan antar secara online salah satu restoran yang menyediakan jasa pesan antar makanan yaitu solaria. Pada penelitian kali ini akan menganalisis sentiment review pelanggan restoran yang masuk ke dalam 2 kelas yaitu positif dan negatif menggunakan algoritma support vector machine (svm) dan algoritma k-Nearest Neighbor (knn) dan menggunakan metode Crisp-dm untuk membandingkan hasil klasifikasi antara kedua algoritma tersebut. Hasil pengujian membuktikan bahwa algortima SVM memiliki hasil kinerja lebih baik daripada algoritma k-NN pada kasus ini menghasilkan nilai akurasi sebesar 81.92%.
Perbandingan Algoritma SVM, Random Forest Dan XGBoost Untuk Penentuan Persetujuan Pengajuan Kredit Mohammad Rizal Givari; Mochammad Riszky Sulaeman; Yuyun Umaidah
NUANSA INFORMATIKA Vol 16, No 1 (2022)
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (325.813 KB) | DOI: 10.25134/nuansa.v16i1.5406

Abstract

Credit is an option for seeking funding for most economic activities. The demand for credit is currently growing very rapidly, in line with the increasing financial needs of the community, especially in developing countries such as Indonesia. Credit analysis needs to be carried out to achieve proper and safe lending. Credit analysis is an observation to see the feasibility of a credit problem. From this analysis, the creditworthiness of the recipient will be known. This study uses the CRISP-DM methodology which consists of 6 stages, namely Bussines Understanding, Data Understanding, Data preparation, Modeling Evaluation, and Deployment by applying the classification method by comparing the SVM, Random Forest, and XGBoost algorithms. This research uses an open source dataset obtained from Kaggle. The results of the research using the SVM, random forest, and XGBoost algorithms get the highest accuracy, recall, precision values in the XGBoost model with 82% accuracy, 70% recall, and 92% precision.
Analisis Sentimen Media Sosial Twitter Dengan Algoritma K-Nearest Neighbor Dan Seleksi Fitur Chi-Square (Kasus Omnibus Law Cipta Kerja) Aditiya Yoga Pratama; yuyun Umaidah; Apriade Voutama
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.386

Abstract

The use of information technology is growing rapidly, marked by public opinion that can be conveyed indefinitely through social media. One of the social media that is Twitter. Twitter is considered easier to retrieve information related to existing opinions and sentiments due to the limited character in a tweet made by users and there is the hashtag "#" which can be searched easily regarding the current hotly discussed situation. Some time ago, there was a lot of discussion regarding the ratification of the omnibus work copyright law. Tweets in the form of lively sentiments adorn the hashtag “#omnibuslaw” and other related hashtags. This study discusses reviews in the form of tweets related to omnibus law with the Chi Square selection feature and the K-Nearest Neighbor algorithm using R Studio. Data were taken as many as 500 tweets related to the omnibus law. The methodology used is Knowledge Discovery in Databases. Data labeling is carried out by experts who are divided into positive and negative sentiments. The results of the modeling using K-Fold Cross Validation, the highest accuracy is obtained with a 25% feature use scheme (Chi Square feature selection), and the value of k = 5 in KNN is 81.4%. Testing on the model was carried out using 100 random data and obtained 83% accuracy, 100% precision, 15% recall and 26.08% F-Measure value. Of the 500 data taken, the word "people" is the most dominating word. Of the 500 data taken, 78.8% negative sentiments and 21.2% positive sentiments.
Sentimen Analisis Komentar Toxic pada Grup Facebook Game Online Menggunakan Klasifikasi Naïve Bayes Renaldy Permana Sidiq; Budi Arif Dermawan; Yuyun Umaidah
Jurnal Informatika Universitas Pamulang Vol 5, No 3 (2020): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v5i3.6571

Abstract

Toxic comments are comments made by social media users that contain expressions of hatred, condescension, threatening, and insulting. Social media users who are on average still teenagers with a nature that still cannot be controlled completely becomes a matter of great concern when they comment, their comments can be studied as text processing. Sentiment analysis can be used as a solution to identifying toxic comments by dividing them into two classifications. Where the data used amounted to 1,500 taken from social media Facebook in the private group Arena of Valor community. The dataset is divided into 2 classes: toxic and non-toxic. This research uses Naive Bayes with TF-IDF transformation and Information Gain feature selection and use distribution ratio 80:20. It will be compared the results of the evaluation where Naive Bayes without transformation, using TF-IDF transformation, and TF-IDF using Information Gain feature selection. The results of the comparison of evaluations from confusion matrix that have been carried out obtained the best classification model is to use the ratio of training and testing data 80:20 with TF-IDF transformation resulting in an accuracy of 75%, precision of 63%, recall of 67%, and F-measure of 64%.
Text Mining Untuk Analisis Sentimen Pelanggan Terhadap Layanan Uang Elektronik Menggunakan Algoritma Support Vector Machine Fajar Romadoni; Yuyun Umaidah; Betha Nurina Sari
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 9, No 2 (2020): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v9i2.903

Abstract

Electronic money is a cashless payment instrument whose money is stored in media server or chip that can be moved for the benefit of payment transactions or fund transfers. In Indonesia, there are already many electronic money products, one of which is OVO. OVO is very popular with the people of Indonesia because it offers many promos such as discounts and cashback. But over time, that much promotion is detrimental to OVO shareholders, so the portion of promo given by OVO to its customers is finally reduced. That incident caused many pros and cons opinions about OVO, one of them is on social media Twitter. Sentiment analysis can be used as a solution to process the opinions of OVO customers on Twitter. This study aims to classify the customer opinions on OVO services into positive and negative classes. This study uses the Support Vector Machine algorithm with 3852 data taken from Twitter with keyword @ovo_id using web scraping techniques. The dataset divided into two classes, 2034 positive and 1818 negative sentiment data. The classification process is carried out with four splitting data scenarios, with 60:40, 70:30, 80:20, 90:10 data ratio and with four kernel such as linear, rbf, sigomid, and polynomial. The final results show that the greatest accuracy value obtained by linear kernel with 90:10 data ratio which gets an accuracy value of 98.7%.
Co-Authors Aditiya Yoga Pratama Ahmad Ridhoi Fajri Apriade Voutama Asep Jamaludin Bintang Sifa Amalia Budi Arif Dermawan Chaerur Rozikin Dina Amelia Alzahra Dwi Latifah Rianti Euis Saraswati Fajar Romadoni Falih Pramataning Dewi Farid Farid Hannie, Hannie Hendi Permana I Ketut Manu Mahatmayana Ika Fitrianti Indrajit Wicaksana Jaman, Jajam Haerul Kardo Trihandoko Khoirunnisa Hamidah Mayland Trifena Mochammad Riszky Sulaeman Mohammad Rizal Givari Muhammad Nur Yasin Nur Maelani Asih Priskila Siwi Aryni Purwantoro Purwantoro Renaldy Permana Sidiq Rianti, Winda Richard Julianno Soeganda Rini Mayasari Risna Alfiani Suhaeni, Suhaeni Tiara Destiana Titan Nugraha Ultach Enri Ultach Enri