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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Nuansa Informatika Jurnal Informatika dan Teknik Elektro Terapan Infotech Journal Sistemasi: Jurnal Sistem Informasi Journal of Information Technology and Computer Science (JOINTECS) Bina Insani ICT Journal 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 JURNAL PENDIDIKAN TAMBUSAI 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 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 Innovative: Journal Of Social Science Research Jurnal INFOTEL

Yuyun Umaidah

Departemen Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Singaperbangsa Karawang, Karawang, 41361

Author-ID : 2425772

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 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

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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)

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)

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

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.

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

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

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.

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

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

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%.

Tren Marketplace Berdasarkan Klasifikasi Ulasan Pelanggan Menggunakan Perbandingan Kernel Support Vector Machine Dwi Latifah Rianti; Yuyun Umaidah; Apriade Voutama
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 6, No 1 (2021)
Publisher : Universitas Indraprasta PGRI Jakarta

Currently, many Indonesian people like to conduct online trading transactions. However, a number of business people find it difficult to choose a marketplace to market their products. One of the reasons is because they rarely pay attention to the marketplace trends that consumers are discussing. Therefore, analyzing trends on social media such as Twitter, it becomes very important for business people to understand the pattern of consumer tendencies towards their services or products. So the purpose of this study is to create a model that can analyze marketplace trends based on the classification of customer reviews on Twitter using the SVM algorithm. The kernels used are linear, RBF, sigmoid, and polynomial with parameter optimization using grid search. The methodology used is KDD. The results of the evaluation of the best classification model are the sigmoid kernel with 92% accuracy, 92% precision, 92% recall, and 92% F1 score and parameters C=100, =0.01, and r=1. Market trend results based on the highest percentage of positive reviews are Tokopedia, Shopee, and lastly Bukalapak.

PENERAPAN KNOWLEDGE MANAGEMENT SYSTEM BERBASIS INFORMASI DESA UNTUK MENINGKATKAN LAYANAN PUBLIK DESA DUKUH KARYA Purwantoro Purwantoro; Yuyun Umaidah; Ultach Enri
Jurnal Techno Nusa Mandiri Vol 15 No 2 (2018): Techno Nusa Mandiri : Journal of Computing and Information Technology Periode Se
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

The rank of EGDI level (E-Government Development Index) in Indonesia has decreased and it reflects the development conditions of EGovernment in Indonesia are not maximally covering: the level of public services, there is a disparity in public services in the village, the support of information technology devices is still limited, the absence of village archive management, the absence of a system that can manage knowledge, there is no synergy between village apparatus. Dukuh Karya is a village that belongs to Karawang Regency, Rengasdengklok District. Dukuh Karya village has a population of around 6000 and mostly the residents has income from agricultural products. The need for knowledge managers in the village is to manage properly and implement a Knowledge Management System (KMS) based on village information systems can improve public services in the Dukuh Karya village. By applying the concept of KMS to be able to collect and manage all available knowledge. Building Knowledge, collect, store and use it so that the village government can be more transparent and accountable to improve public services. The implementation of KMS is a way for village apparatus to identify, create, represent, distribute, and enable the adaptation of insights and experiences consisting of knowledge, both owned by individuals and knowledge that is inherent in the process or standard of service procedures that have the main objective to maintain and effectively transfer knowledge that is important to improve the quality of service of village government apparatus to its citizens.

Prediksi Kepuasan Customer Terhadap Performance Terapis Baby Massage Menggunakan Algoritma Naive Bayes Risna Alfiani; Yuyun Umaidah
Bahasa Indonesia Vol 9 No 1 (2022): Bina Insani ICT Journal (Juni) 2022
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/biict.v9i1.1794

Abstrak: Dimasa sekarang dunia usaha mengalami perkembangan yang sangat pesat, sehingga memicu ketatnya persaingan antar sesama pelaku usaha. Kepuasan customer merupakan salah satu faktor terpenting dalam keberlangsungan sebuah perusahaan. Beberapa tahun belakangan ini kesadaran konsumen akan pentingnya massage untuk tumbuh kembang bayi semakin meningkat. Ketatnya persaingan bisnis baby massage menyebabkan perusahaan harus memperhatikan kualitas pelayanannya. Penelitian ini dilakukan dengan tujuan untuk mengetahui besarnya tingkat akurasi klasifikasi yang dihasilkan dari prediksi kepuasan pelanggan terhadap performa terapis sebuah baby massage dengan menggunakan algoritma Naïve Bayes Penelitian ini menggunakan data penilaian kepuasan pelanggan yang diambil dengan metode penyebaran kuesioner kepada pelanggan sebuah baby massage di kota Surakarta. Data tersebut akan diolah dan dianalisis menggunakan aplikasi RapidMiner untuk mendapatkan hasil yang dibutuhkan. Hasil dari penelitian ini adalah prediksi pola kepuasan pelanggan baby massage yang memetakan penilaian pelanggan menjadi dua label yaitu puas dan tidak puas. Hasil yang diperoleh dari pengolahan data menggunakan algoritma Naive Bayes menunjukkan hasil prediksi dengan nilai akurasi sebesar 98,21% yang berarti algoritma Naive Bayes cocok untuk memprediksi kepuasan pelanggan terhadap performa terapis baby massage. Kata kunci: data mining, kepuasan, Naive Bayes, prediksi, Rapid Miner Abstract: Nowadays, the business world is experiencing very rapid development, thus triggering intense competition among business actors. Customer satisfaction is one of the most important factors in the sustainability of a company. In recent years, consumer awareness of the importance of massage for baby's growth and development has increased. The tight competition in the baby massage business causes companies to pay attention to the quality of their services. This study was conducted with the aim of knowing the magnitude of the classification accuracy resulting from the prediction of customer satisfaction on the performance of a baby massage therapist using the Naïve Bayes algorithm. The data will be processed and analyzed using the RapidMiner application to get the required results. The results of this study are predictions of baby massage customer satisfaction patterns that map customer ratings into two labels, namely satisfied and dissatisfied. The results obtained from data processing using the Naive Bayes algorithm show prediction results with an accuracy value of 98.21%, which means that the Naive Bayes algorithm is suitable for predicting customer satisfaction with the performance of baby massage therapists. Keywords: data mining, Naive Bayes, prediction, Rapid Miner, satisfaction

Co-Authors Ahmad Ridhoi Fajri Altolyto Sitanggang Apriade Voutama Asep Jamaludin Bintang Sifa Amalia Budi Arif Dermawan Chaerur Rozikin Dina Amelia Alzahra Dwi Latifah Rianti Dwinda Putri Nuria Fajar Romadoni Falih Pramataning Dewi Farid Farid Ganes Wisnu Cahya Bagaskara Hendi Permana I Ketut Manu Mahatmayana Indrajit Wicaksana Ismail Adhiya Adha Jaman, Jajam Haerul Kardo Trihandoko Khoirunnisa Hamidah Mayland Trifena Milla Rochmawati 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 Riza Ibnu Adam Suhaeni, Suhaeni Tiara Destiana Titan Nugraha Ultach Enri Ultach Enri

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