Article Per Year (5 Year)
p-Index From 2021 - 2026
7.717
P-Index
This Author published in this journals
All Journal Syntax Jurnal Informatika Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi INTECOMS: Journal of Information Technology and Computer Science J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jurnal Riset Informatika JOISIE (Journal Of Information Systems And Informatics Engineering) Journal of Information System, Applied, Management, Accounting and Research Jurnal Abdimas Mahakam METIK JURNAL Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jusikom: Jurnal Sistem Informasi Ilmu Komputer Systematics Zonasi: Jurnal Sistem Informasi Jurnal Informasi dan Teknologi Buana Information Technology and Computer Sciences (BIT and CS) JURSIMA (Jurnal Sistem Informasi dan Manajemen) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer JIKA (Jurnal Informatika) Jurnal Sistem Komputer dan Informatika (JSON) Infotek : Jurnal Informatika dan Teknologi Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Journal of Engineering, Science and Information Technology Djtechno: Jurnal Teknologi Informasi Jurnal Tika Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Jurnal Minfo Polgan (JMP) Society: Jurnal Pengabdian Masyarakat Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Komtekinfo Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Malcom: Indonesian Journal of Machine Learning and Computer Science JUSIFOR : Jurnal Sistem Informasi dan Informatika Golden Ratio of Data in Summary Jurnal Ilmiah Teknik Informatika dan Komunikasi Innovative: Journal Of Social Science Research JURSIMA Journal Of Artificial Intelligence And Software Engineering VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Jurnal Sistem Informasi dan Manajemen Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial
Novalia, Elfina
Universitas Buana Perjuangan Karawang
Religion Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences 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 Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

Published : 62 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 62 Documents
Search

 1   2   3   4   5 
Implementasi Algoritma K-Means dalam Analisis Klasterisasi Penyebaran Penyakit Hiv/Aids Tita Puspita Sari; April Lia Hananto; Elfina Novalia; Tukino Tukino; Shofa Shofia Hilabi
Infotek: Jurnal Informatika dan Teknologi Vol. 6 No. 1 (2023): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v6i1.7423

Abstract

The most crucial component of everyone's life is their health, especially for young people, health problems that often arise in the younger generation are promiscuity or free sex. HIV/AIDS cases were reported in the last 30 years, from 1992 to 2022 as many as 2,052 were infected with HIV/AIDS. Dozens of them are students and college students. This study's objective was to cluster the total cases of HIV/AIDS based on sub-districts in Karawang district. Seeing which areas need more attention in dealing with cases of HIV/AIDS transmission and the areas with the highest cases can serve as a manual for choosing the highest areas and these areas can be the main focus. The approach adopted for this study is data mining. To solve the existing problems, the authors use the K-Means algorithm using 4 clusters to find out which sub-district groups have very high, high, medium and low numbers of HIV/AIDS cases by calcualating the centroid/mean of the cluster data. The results of the study contained 4 clusters as follows: cluster 0 with low criteria earned 73%, cluster 1 with very high criteria earned 3%, cluster 2 with medium criteria earned 7%, and cluster 3 with high criteria earned 17%.
Klasifikasi Hasil Penjualan Minuman Ringan Pada Koperasi Berdasarkan Jenis Barang Menggunakan Algoritma K-Means Clustering Awaljan Situmorang; Tukino Tukino; Elfina Novalia; Sandi Ahmad
Jurnal Tika Vol 7 No 3 (2022): Jurnal Teknik Informatika Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (508.815 KB) | DOI: 10.51179/tika.v7i3.1565

Abstract

The joint cooperative store is one of the efforts given by the joint cooperative management to increase cooperative income by calculating profits every year and distributing them to cooperative members in the form of money, commonly known as SHU or the remaining results of operations. However, there are still shortcomings in the implementation of cooperative sales management, one of which is the sale of soft drinks. There are still errors in determining the high and low volume of beverage sales. This research will help cooperative managers to categorize beverage sales data so that customer demand for soft drinks can be fulfilled properly. The data collected from January 2020 to September 2022 is the sale of 11,945 drinks from 15 soft drinks at the Koperasi Bersama store. This research aims to group the sales recapitulation results into a cluster using a data mining approach using the K-Means clustering algorithm. Grouping sales data according to its characteristics. The results of this study indicate that 1 soft drink is included in cluster 0 which is classified as high sales volume, while 14 soft drinks are included in cluster 1 which is classified as low sales volume.
Klasterisasi Data Jamaah Umrah pada Tanurmutmainah Tour Menggunakan Algoritma K-Means Muhamad Djaka Permana; April Lia Hananto; Elfina Novalia; Baenil Huda; Tukino Paryono
Jurnal KomtekInfo Vol. 10 No. 1 (2023): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v10i1.332

Abstract

Wisata religi khususnya umroh dan haji semakin diminati oleh masyarakat saat sekarang ini. Tanurmutmainah Tour merupakan salah satu agen travel yang bergerak dibidang jasa wisata relegi untuk memberikan layanan umroh dan haji. Fakta yang terjadi bahwa Tanurmutmainah Tour memiliki banyak data jamaah yang berbeda-beda, sehingga permasalahan yang dihadapi dalam hal ini adalah sulitnya menemukan pengetahuan seputar strategi yang dibutuhkan dalam pengembangan. Berdasarkan permasalahan tersebut maka, penelitian ini bertujuan untuk menggali pengetahuan yang tersembunyi dari data jemaah umroh dan haji dengan menggunakan algoritma K-Mean Cluster. Algoritma tersebut digunakan untuk melakukan pengelompokan data guna melihat minat calon jemaah umroh dan haji dalam memilih paket yang telah disediakan. Pengelompokan tersebut akan menyajikan kategori C1 (Sangat Diminati), C2 (Diminati) dan C3 (Kurang Diminati). Proses kluster nantinya akan menguji sejumlah 27 dataset penelitian calon jemaah umroh haji yang tercatat didatabase sistem Tanurmutmainah Tour. Berdasarkan proses kinerja algoritma K-Means, bahwa hasil proses kluster menghasilkan 38% kelompok sangat diminati dengan paket kamar Quad, 34% kelompok diminati dengan paket kamar Triple, dan 28% kelompok kurang diminati untuk paket kamar Double. Hasil tersebut dapat disimpulkan bahwa kinerja algoritma K-Means telah sesuai untuk melakukan proses klusterisasi kategori paket yang akan dipilih bagi calon jemaah umroh dan haji Tanurmutmainah Tour. Dengan hasil tersebut maka kontribusi penelitian mampu memberikan informasi baru kepada pihak pengelola Tanurmutmainah dalam strategi pelayanan kepada calon jemaah umroh dan haji.
Prediction of Rice Field Planted Area with CRISP-DM Using Classification and Regression Tree (Cart) Algorithms : Prediksi Luas Tanam Sawah dengan CRISP-DM Menggunakan Algoritma Classification and Regression Tree (Cart) Elfina Novalia; Apriade Voutama; Garno
SYSTEMATICS Vol 5 No 1 (2023): April 2023
Publisher : Universitas Singaperbangsa Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35706/sys.v5i1.8755

Abstract

Every year the area of paddy fields in Karawang Regency has increased and decreased due to land conversion. Climate change also causes changes in the amount of rain and rain patterns that cause shifts in the beginning of the season and the planting period. If the decrease in planting area will be affected, then the price of rice will increase and farmers will maintain the area and not convert their rice fields to function, therefore a study was conducted to predict the rice planting area in order to know the description of the area of rice planted in Karawang Regency will increase. , decreased or stabilized. So the search for information on the data on the area of rice planting in Karawang Regency was carried out. A total of 180 data were processed using data mining techniques so that they could mine information from the data. Data mining is a technique of extracting or new discoveries from large data and then extracting the data into information that can later be used. Experiments were carried out using the CART algorithm and cross validation using the Weka tools. The results of the evaluation carried out can be concluded that the CART algorithm using different K values provides different evaluation results. The performance of the algorithm is seen from the accuracy, precision, recall and F-Measurement, thus providing different performance values for each result. The value of k=8 has the highest accuracy value, which is 90% with precision 0.918%, recall 0.906% and F-measure 0.949%.
Pengelompokkan Data Obat-Obatan Pada Pelayanan Kesehatan Menggunakan Algoritma K-Means Clustering Anita Saptiani; Baenil Huda; Elfina Novalia; Arif Budimansyah Purba
JURSIMA (Jurnal Sistem Informasi dan Manajemen) Vol 10 No 3 (2022): Jursima Vol.10 No.3 Desember 2022
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i3.510

Abstract

ABSTRAK Pada perencanaan kebutuhan obat yang akurat maka pengadaan obat itu menjadi lebih efektif serta efisien, sehingga dapat tersedia dengan jenis dan jumlahnya sesuai dengan yang dibutuhkan. Clustering data mining ini bisa dipakai menganalisa pemakaian obat, perencanaan dan pengelolaan obat di Puskesmas . Metode yang akan diaplikasikan yaitu metode clustering pada data obat menggunakan algoritma K-Means yang dapat membagi data pada cluster sehingga data yang mempunyai kesamaan akan dijadikan satu kelompok dan data yang berbeda akan dikelompokkan pada kelompok lainnya. Tujuan penelitian ini yaitu mengelompokkan data obat di Puskesmas Karangsambung yang dapat digunakan sebagai acuan untuk pengambilan keputusan dalam perencanaan dan persediaan obat di Puskesmas Karangsambung. Pada hasil penelitian ini yaitu mengelompokkan tingkat pemakaian obat pada Puskesmas Karangsambung, yang datanya diambil dari tahun 2019 sampai 2022. Data yang dihasilkan dikelompokan menjadi 3 cluster, yang nantinya pemakaiannya dikelompokkan tinggi, sedang, rendah.
Pengelompokkan Data Obat-Obatan Pada Pelayanan Kesehatan Menggunakan Algoritma K-Means Clustering Anita Saptiani; Baenil Huda; Elfina Novalia; Arif Budimansyah Purba
JURSIMA (Jurnal Sistem Informasi dan Manajemen) Vol 10 No 3: Jursima Vol.10 No.3
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i3.510

Abstract

ABSTRAK Pada perencanaan kebutuhan obat yang akurat maka pengadaan obat itu menjadi lebih efektif serta efisien, sehingga dapat tersedia dengan jenis dan jumlahnya sesuai dengan yang dibutuhkan. Clustering data mining ini bisa dipakai menganalisa pemakaian obat, perencanaan dan pengelolaan obat di Puskesmas . Metode yang akan diaplikasikan yaitu metode clustering pada data obat menggunakan algoritma K-Means yang dapat membagi data pada cluster sehingga data yang mempunyai kesamaan akan dijadikan satu kelompok dan data yang berbeda akan dikelompokkan pada kelompok lainnya. Tujuan penelitian ini yaitu mengelompokkan data obat di Puskesmas Karangsambung yang dapat digunakan sebagai acuan untuk pengambilan keputusan dalam perencanaan dan persediaan obat di Puskesmas Karangsambung. Pada hasil penelitian ini yaitu mengelompokkan tingkat pemakaian obat pada Puskesmas Karangsambung, yang datanya diambil dari tahun 2019 sampai 2022. Data yang dihasilkan dikelompokan menjadi 3 cluster, yang nantinya pemakaiannya dikelompokkan tinggi, sedang, rendah.
DESIGN OF E-COMMERCE DISTRO USING RAPID APPLICATION DEVELOPMENT (RAD) MODEL Apriade Voutama; Garno Garno; Agung Susilo Yuda Irawan; Elfina Novalia
Jurnal Riset Informatika Vol. 4 No. 4 (2022): September 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (799.582 KB) | DOI: 10.34288/jri.v4i4.130

Abstract

Information technology is developing so rapidly that it has become a significant need in all fields. One is in the Business of buying and selling clothes at the Celsius distribution. Sales are still carried out traditionally or using less good places for distribution because they rely only on customers who come to the store, so the innovation of online sales technology or e-business is needed. The creation is to build e-commerce as a medium for buying and selling online with a broader market coverage. E-commerce is a system designed to process goods and services' buying, selling, and marketing through an electronic system. They built this e-commerce using UML (Unified Modeling Language) and PHP-MySql programming language. Some UML diagrams are used, such as Usecases, Activity Diagrams, Sequence Diagrams, and Class Diagrams, and assisted with interface design before being translated into applications. Its e-commerce is built into two parts: the admin panel and the user panel on the system, where this e-commerce is based on a website. The admin panel is managed by the section owner as the manager of online sales management, while the board is the system user who makes transactions from the system. The tests involve random owners and users to get responses to the e-commerce so that they are implemented on an ongoing basis.
Klasterisasi Data Obat dengan Algoritma K-Means (Kasus pada UPTD Puskesmas Curug) kastiawan, Nurhayadi; Huda, Baenil; Novalia, Elfina; Nurapriani, Fitria
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 8, No 1 (2024): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v8i1.771

Abstract

Managing drug supplies is very important because it minimizes drug losses in institutions such as health centers, pharmacies and hospitals, so that drugs of any type are in accordance with the quantity needed. The research aims at grouped drug data, where this case study was carried out at the Curug Health Center UPTD which will be used as a guide in submitting a drug import plan at this health center. The data processed in this research is the 2022 annual report, drug needs plan and proposed drug needs (RKO 2023) at the Curug Health Center UPTD. The data in this research was processed by the K-Means algorithm with the rapidminer tool, where this technique data is grouped by collecting data into clusters. The results obtained were that Cluster 0 was a very low cluster which contained 14 drug items, then Cluster 1 with 12 drug items was a low cluster, Cluster 3 with 2 drug items was a high cluster and Cluster 2 was the highest cluster with 2 items drug.
Implementasi K-Means dan K-Nearest Neighbors pada Kategori Siswa Berprestasi Widyanti, Tyas; Shofiah Hilabi, Shofa; Hananto, Agustia; Tukino; Novalia, Elfina
Jurnal Informasi dan Teknologi 2023, Vol. 5, No. 1
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v5i1.255

Abstract

Penelitian ini menggunakan metode K-Means dan K-NN untuk menentukan siswa berprestasi serta mengkelompokan data menjadi suatu kumpulan data sehingga memperoleh cara menentukan tingkatan siswa beprestasi dengan kategori rendah, cukup, dan tinggi. Setelah menentukan kategori maka bisa dilakukan konsentrasi bimbingan belajar untuk kelas XII khusunya untuk siswa dan siswi yang tedapat pada kategori rendah diharapkan bisa diperhatikan secara khusus guna menciptakan lulusan terbaik di Smkn 3 Karawang.Teknik pengolahan data pada penelitian ini menggunakan perhitungan komputerisasi K-Means dan K-NN yang diterapkan pada aplikasi orange. Tahapan pada pengolahan data ini menyiapkan data nilai siswa kemudian melakukan prepossessing data untuk menghilangkan outlier-nya dan menormalisasikan data menggunakan tools data mining orange akademik siswa TBSM kelas XII dengan metode K-means dan K-NN dari hasil clustering dapat mengetahui tingkat pegelompokan prestasi siswa dan siswi Smkn 3 Karawang pada tahapan selanjutnya menggunakan metode K-NN untuk memprediksi hasil yang lebih baik, dengan metode perhitungan K-NN ini menggunakan dataset K-Means sehingga didapat nilai akurasi yang baik dengan nilai AUC 1.000, Nilai CA 1.000, Nilai F1 1.000, Nilai Precision 1.000 dan Recall 1.000 . Nilai akurasi ini selaku nilai terbaikmenggunakan tata cara lain buat membandingan hasil keakuratan perhitungan.
PREDICTION OF POPULATION GROWTH IN KARAWANG CITY USING MULTIPLE LINEAR REGRESSION ALGORITHM METHOD Desfianthy, Fatiya Hanifah; Hilabi, Shofa Shofiah; Priyatna, Bayu; Novalia, Elfina
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 2 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Currently, Indonesia is experiencing population growth. The factors influencing this growth are the rates of births and deaths. Every year, the population in an area keeps growing. This growth can have various negative impacts on the region. That's why taking action and making predictions about population growth is crucial. The objective of this study is to use a regression algorithm to estimate how fast the population will grow in Karawang City. The data used for this research comes from population records collected by the Karawang City Statistics Agency between 2017 and 2022. To clean, transform, and analyze this data, we employ the Knowledge Discovery in Database (KDD) approach to data mining. By applying linear regression methods with assistance from RapidMiner tools, we have successfully generated predictions based on data that reveal patterns and relationships between variables that influence population growth rates. According to our predictions, there will increase of 338,011 people from 2022 to 2027. This research will assist the Karawang City government in developing plans to minimize negative impacts while optimizing resource utilization such as energy, food, water, and services. Keywords: Multiple Linear Regression, Data Mining, BPS, Rapid Miner
Co-Authors Abdul Hafiz Agung Susilo Yuda Irawan Agustina, Alvi Ahmad Fauzi Ahmad, Sandi Al Khadzik, Fahmi Alfiansyah, Muhammad Rindra Anita Saptiani Apriade Voutama April Hananto April Lia Hananto Arif Budimansyah Purba atikah, dwi Aurel Adhitya Anwar Aviv Yuniar Rahman Awal, Elsa Elvira Awaljan Situmorang Baenil Huda Baenil Huda baktria, leonyka Bayu Yoga Astario Desfianthy, Fatiya Hanifah Diningrat, Cahya Emilia Sukmawati, Cici Fadli, Muhammad Abil Faisal, Muhamad Agus Fauzi, Muhamad Helmi Firdaus, Mohamad Ricky Fitria Nurapriani Garno garno, Garno Garno, Garno Goenawan Brotosaputro Hananto, Agustia Henry Adam Hilabi, Shofa Shofia Hilabi, Shofa Shofiah Hilabi, Shofa Shofiah Huban Kabir Huda, Baenil Indra, Jamaludin Iqbal Maulana Juwita, Ayu Ratna kastiawan, Nurhayadi Lestari, Renita Lutfiah, Siti Muhamad Djaka Permana Nijunnihayah, Uktupi Nurapriani, Fitria Nuriza, Adjeng Putri Nurmayanti, Trisya Paryono, Tukino Prasetya, Rafli Pratama, Daffa Agung Prayono, Tukino Priyatna, Bayu Purba, Arif Budimansyah Rian Pratama Sandi Ahmad Saptiani, Anita Seia Piantara Setiawan, Pratama Wahyu Setiawan, Pratama Wahyu Setiawan, Revi Shofa Shofia Hilabi Shofiah Hilabi, Shofa Situmorang, Awaljan Sopian, Jajang Sukmawati, Cici Emilia Surala, Lyvia Syafana, Vinka Syahri Susanto Tamala, Evi TARMUJI TARMUJI, TARMUJI Tejayanda, Rigger Damaiarta Tita Puspita Sari Tukino Tukino, Tukino tukino, tukino Tukino, Tukino Wahyu Aziz Ramadhani Wahyu, Pratama Widyanti, Tyas Wirlandika, Devri Yoga Astario, Bayu Yusup, Dadang Zhalifunas, Satria Dawas