Article Per Year (5 Year)
p-Index From 2021 - 2026
5.495
P-Index
This Author published in this journals
All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Informatika dan Teknik Elektro Terapan Infotech Journal Journal of Information System Jurnal Teknologi dan Sistem Komputer Jurnal Informatika JURNAL LENTERA : Kajian Keagamaan, Keilmuan dan Teknologi JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Jurnal TAM (Technology Acceptance Model) JURNAL ILMIAH INFORMATIKA ILKOM Jurnal Ilmiah DoubleClick : Journal of Computer and Information Technology INTECOMS: Journal of Information Technology and Computer Science Jiko (Jurnal Informatika dan komputer) Jurnal ULTIMATICS Jurnal Informasi dan Komputer STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Progresif: Jurnal Ilmiah Komputer Jurnal Pendidikan dan Konseling Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak Journal of Applied Engineering and Technological Science (JAETS) Generation Journal JATI (Jurnal Mahasiswa Teknik Informatika) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Wahana Pendidikan Paradigma
Betha Nurina Sari
Program Studi Informatika, Universitas Singaperbangsa Karawang
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

Published : 52 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 52 Documents
Search

 1   2   3   4   5 
Analisis User Interface Meningkatkan Pengalaman Pengguna Menggunakan Usability Testing pada Aplikasi Android Course Wira Buana; Betha Nurina Sari
DoubleClick: Journal of Computer and Information Technology Vol 5, No 2 (2022): Perkembangan dan Transformasi Teknologi Digital
Publisher : Universitas PGRI Madiun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25273/doubleclick.v5i2.11669

Abstract

Abstrak: User Interface adalah tampilan dari sebuah produk yang berfungsi menjembatani sistem dengan pengguna atau user, dimana tampilan UI bisa berupa warna, bentuk serta tulisan yang menarik pada aplikasi mobile. Dengan kurangnya persiapan dan rancangan yang belum matang, maka pada aplikasi mobile tersebut kurang berjalan maksimal dan mengakibatkan pengguna ingin berpindah ke aplikasi yang lain. Tujuan penelitian ini yaitu analisis tingkat user interface pada aplikasi android Course Online menggunakan usability testing. Pada penelitian ini dilakukan dengan menggunakan usability testing yaitu pengujian usability menggunakan metode SUS dengan mengukur kepuasan pengguna dengan 10 pertanyaan secara online. Penelitian ini data informasi yang didapat secara online ini adalah pengguna aplikasi Course Online android berbasis internet ini akan memberikan langsung efek kepada pengguna terhadap aplikasi android Course Online yang dilangsungkan. Sampel data yang diambil dalam penelitian ini adalah 30 orang mahasiswa yang mencoba aplikasi ini. Dalam teknik ini analisis data penelitian informasi yang digunakan merupakan analisis deskriptif dengan persentase data, kemudian dideskripsikan untuk mengukur tingkat kemudahan penggunaan dalam aplikasi android Course Online. Hasil dari penelitian ini skor yang di dapat melalui kuesioner yang disebarkan secara online ini mendapatkan skor SUS 78,3. Pada sisi acceptability ranges menempati level marginal high, pada sisi adjektif rating berada pada posisi OK, dan terakhir pada sisi grade scale menempati grade B. Kata kunci: Analisis, Usability Testing, User Interface
Sistem Pakar Deteksi Dini HIV/AIDS Dengan Metode Forward Chaining Dan Certainty Factor Bayu Adhi Pamungkas; Apriade Voutama; Betha Nurina Sari; Susilawati Susilawati
INTECOMS: Journal of Information Technology and Computer Science Vol 4 No 1 (2021): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v4i1.2461

Abstract

Setiap tahun grafik jumlah kasus HIV di Indonesia terus mengalami peningkatan, tetapi jumlah tersebut masih diperkirakan karena peningkatan stigma dan diskriminasi yang menyebabkan masyarakat enggan untuk melakukan pemeriksaan HIV. Untuk mengatasi hal tersebut diperlukan sebuah sistem pakar sehingga masyarakat dapat melakukan pemeriksaan awal HIV melalui perangkat masing-masing tanpa perlu datang ke klinik. Tujuan penelitian ini adalah untuk merancang, mengimplementasikan, dan mengembangkan sistem deteksi dini HIV/AIDS dengan menggunakan metode rantai maju dan faktor kepastian . Metode penelitian yang digunakan yaitu ESDLC, yang terdiri dari penilaian, perolehan pengetahuan, perancangan, pengujian, dan dokumentasi.Hasil evaluasi sistem yang dilakukan menggunakan kuesioner terhadap 50 responden menunjukan hasil dari segi tampilan memiliki persentase sebesar 82,3% dan dari segi manfaat sebesar 82,2% dapat dikatakan bahwa sistem dapat diterima oleh masyarakat dengan interpretasi sangat kuat.
Cluster Analysis of Covid-19 Distribution Using K-Means Clustering Algorithm Ato Sugiharto; Betha Nurina Sari; Tesa Nur Padilah
INTECOMS: Journal of Information Technology and Computer Science Vol 4 No 2 (2021): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v4i2.2776

Abstract

Coronavirus disease (covid-19) has become a global concern after on January 20, 2020, three people were killed in the city of Wuhan, Hubei province, China. Covid-19 was first reported to have entered Indonesia on March 2, 2020, with two cases. This study aims to conduct a cluster analysis of the distribution of COVID-19 cases in West Java province as of April 1, 2021 with the variables of isolation, recovery, and death. By using the elbow method, the difference in SSE in each cluster, the silhouette graph, and the factoextra diagram, the optimum number of clusters is 3, the evaluation results show the Dunn index value = 0.4776, connectivity = 9.4738, and silhouette = 0.5839 (data structure reasoned). The clustering results show a good variance of 75.8%. Cluster 1 consists of 1 city/district, cluster 2 consists of 6 cities/districts, and cluster 3 consists of 20 cities/districts.
Implementasi K-Means Clustering Ujian Nasional Sekolah Menengah Pertama di Indonesia Tahun 2018/2019 Agil Aditya; Ivan Jovian; Betha Nurina Sari
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 4, No 1 (2020): Januari 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v4i1.1784

Abstract

Clustering is an activity that aims to group a data that has a similarity between one data with another data. K-Means clustering is a non-hierarchical data clustering method that attempts to partition existing data into one or more clusters / groups. In this study clustering was conducted using the K-Means algorithm using data on the achievements of the National Middle School National Examination in 2018 obtained from the official website of the Center for Education and Culture Assessment of the Ministry of Education and Culture of the Republic of Indonesia. The results of the cluster with the K-Means algorithm are obtained for cluster 1 there are 14 provinces, cluster 2 there are 5 provinces, and cluster 3 there are 15 provinces with cluster 1 level is a cluster with a high national test score, cluster 2 is a cluster with a low national test score and a cluster 3 is a cluster with moderate national examination scores. While the results of the evaluation of the K-Means algorithm with the number of clusters 3 produce an evaluation value of Connectivity 11,916, Dunn 0.246 and Silhouette 0.464.
Perbandingan Naïve Bayes dan Support Vector Machine untuk Klasifikasi Ulasan Pelanggan Indihome Aan Rohanah; Dwi Latifah Rianti; Betha Nurina Sari
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 6, No 1 (2021)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (445.224 KB) | DOI: 10.30998/string.v6i1.9232

Abstract

IndiHome is an internet service provider from PT. Telekomunikasi Indonesia, Tbk with the widest internet coverage in Indonesia. Customer satisfaction is one of the things that must be considered in a company, including the IndiHome company. IndiHome's customer service satisfaction level can be seen from customer reviews via Twitter social media. This study discusses the classification of IndiHome customer reviews by applying the CRISP-DM research stages and the application of the Naïve Bayes Classifier algorithm and the Linear Support Vector Machine Kernel. Customer review data were obtained from Twitter, totaling 1000 tweets using the Rapid Miner and R library tools. The preprocessing stages applied were cleansing, case folding, tokenizing, word conversion, stopword, and stemming. The results of data visualization are presented in the form of a word cloud which is categorized based on positive and negative opinions of words that often appear. The results showed that the application of the Support Vector Machine Kernel Linear algorithm is better than the Naïve Bayes Classifier algorithm with an accuracy value of 82.11%, 76.44% precision, 88.01% recall, and an AUC value of 0.909.
PENERAPAN ALGORITMA NAÏVE BAYES DENGAN BACKWARD ELIMINATION UNTUK PREDIKSI WAKTU TUNGGU ALUMNI MENDAPATKAN PEKERJAAN Abimanyu Widhiantoyo; Betha Nurina Sari; Dadang Yusuf
JIKO (Jurnal Informatika dan Komputer) Vol 4, No 3 (2021)
Publisher : Journal Of Informatics and Computer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v4i3.3272

Abstract

Perguruan tinggi memiliki peranan yang signifikan dalam pengembangan kualitas pendidikan manusia. Merancang kurikulum dan strategi pendidikan yang tepat dapat menghasilkan lulusan yang berkualitas. Tracer Study menjadi salah satu metode untuk melacak status pekerjaan alumni setelah lulus dari pendidikannya. Fasilkom Unsika adalah salah satu fakultas yang ada di Universitas Singaperbangsa Karawang. Dari banyaknya jumlah lulusan yang dihasilkan, sejauh ini di Fasilkom Unsika belum pernah dilakukan pelacakan terhadap status pekerjaan alumni. Oleh karena itu pelacakan perlu dilakukan untuk nantinya dilakukan proses Data Mining. Dari proses Data Mining kemudian dihasilkan suatu pengetahuan. Penelitian ini bertujuan untuk memprediksi waktu tunggu alumni mendapatkan pekerjaan dengan menggunakan algoritma Naïve Bayes dan dibandingkan dengan algoritma Naïve Bayes dengan fitur seleksi Backward Elimination. Metodologi Data Mining yang digunakan yaitu Cross-Industry Standard Process for Data Mining (CRISP-DM). Penelitian menggunakan kelas label CEPAT dan LAMBAT dengan menerapkan sembilan skenario K-Folds Cross Validation. Hasilnya menunjukkan bahwa algoritma Naïve Bayes dengan fitur seleksi Backward Elimination meraih performa terbaik dengan nilai Accuracy 68,52% dan Kappa 0,370. Kesimpulan dari penelitian ini yaitu algoritma Naïve Bayes dengan fitur seleksi Backward Elimination terbutki dapat meningkatkan hasil evaluasi pada prediksi waktu tunggu alumni mendapatkan pekerjaan.
Implementasi Metode Double Exponential Smoothing dalam Memprediksi Pertambahan Jumlah Penduduk di Wilayah Kabupaten Karawang Andini Diyah Pramesti; Mohamad Jajuli; Betha Nurina Sari
Ultimatics : Jurnal Teknik Informatika Vol 12 No 2 (2020): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v12i2.1688

Abstract

The density and uneven distribution of the population in each area must be considered because it will cause problems such as the emergence of uninhabitable slums, environmental degradation, security disturbances, and other population problems. In the data obtained from the 2010 population census based on the level of population distribution in Karawang District, the area of West Karawang, East Karawang, Rengasdengklok, Telukjambe Timur, Klari, Cikampek and Kotabaru are zone 1 regions which are the densest zone with a population of 76,337 people up to 155,471 inhabitants. This research predicts / forecasting population growth in the 7 most populated areas for the next 1 year using Double Exponential Smoothing Brown and Holt methods. This study uses Mean Absolute Percentage Error (MAPE) to evaluate the performance of the double exponential smoothing method in predicting per-additional population numbers. Forecasting results from the two methods place the Districts of East Telukjambe, Cikampek, Kotabaru, East Karawang, and Rengasdengklok in 2020 to remain in zone 1 with a range of 76,337 people to 155,471 inhabitants. Whereas in the Districts of Klari and West Karawang are outside the range in zone 1 because both districts have more population than the range in zone 1. From the results of MAPE both methods are found that 6 out of 7 districts in the method Holt's double exponential smoothing produces a smaller MAPE value compared to the MAPE value generated from Brown's double exponential smoothing method. It was concluded that in this study the Holt double exponential smoothing method was better than Brown's double exponential smoothing method.
Clustering Productivity of Rice in Karawang Regency Using the Fuzzy C-Means Method Suna Mulyani; Betha Nurina Sari; Azhari Ali Ridha
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 2 (2020): Spetember 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i2.10415

Abstract

Rice is a major food commodity that has a strategic role in the development of community nutrition, agriculture and the economy in Indonesia. Karawang Regency is known as a city of rice barns which is one of the largest rice producing and supplying regions in the province of West Java and even Indonesia. The importance of rice as a staple food in Karawang Regency needs to ensure rice productivity remains stable. Data Mining is a data mining technique that produces an output in the form of knowledge. The purpose of this study is to classify the productivity of rice plants so as to know the area of high rice productivity in Karawang Regency. The data used in this study were 180 data from 30 districts. Data grouping will use the Fuzzy C-Means (FCM) algorithm which is a data clustering technique where the existence of each data point in a cluster is determined by the degree of membership. With Silhouette Coefficient evaluation techniques the results of clustering obtained in 2010, 2011, 2013, 2014 and 2015 show that the results of grouping have a good structure that is above 0.5. Only in 2012 showed that the grouping results had a weak structure of 0.49.
Random Forest Algorithm for Prediction of Precipitation Aji Primajaya; Betha Nurina Sari
Indonesian Journal of Artificial Intelligence and Data Mining Vol 1, No 1 (2018): March 2018
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (443.435 KB) | DOI: 10.24014/ijaidm.v1i1.4903

Abstract

Predicting rainfall needs to be done as one of such effort to anticipate water flooding. One of the algorithm that can be used to predict rainfall is random forest. The porpose of the research is to create a model by implementing random forest algorithm. The research method consist of four steps: data collection, data processing, random forest implementation, analysis. Random forest implementation with using training set resulted model that has accurracy 71,09%, precision 0.75, recall 0.85, f-measure 0.79, kappa statistic 0.33, MAE 0.35, RMSE 0.46, ROC Area 0.78. Implementation of random forest algorithm with 10-fold cross validation resulted the output with accurracy 99.45%, precision 0.99, recall 0.99, f-measure 0.99, kappa statistic 0.99, MAE 0,09, RMSE 0.14, ROC area 1.
PERBANDINGAN ALGORITMA CART DAN K-NEAREST NEIGHBOR UNTUK PREDIKSI LUAS LAHAN PANEN TANAMAN PADI DI KABUPATEN KARAWANG Muhammad Fadhlil Aziz; Sofi Defiyanti; Betha Nurina Sari
Jurnal TAM (Technology Acceptance Model) Vol 9, No 2 (2018): Jurnal TAM (Technology Acceptance Model)
Publisher : LPPM STMIK Pringsewu

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (366.087 KB)

Abstract

Karawang regency is known as one of the nation rice granaries because the are many areas of rice fields, especially rice. But the transfer of function from agricultural land into industrial or recidential area can change the geographical structure of Karawang regency previously filled with agricultural land into industrial and property areas. Data mining is a technique of extracting an information from large data. One of them regression techniques. In predicting something a dataset of a numeric data type usually uses a regression technique. In this study used regression techniques to predict the area of harvested land in Karawang regency by using tools WEKA 3.8.2. The resulting comparison is seen from correlation coefficient, mean absolute error, and root mean squared error. In comparison algorithm used the same scenario is cross validation 10 folds. The result of the experiment using the same scenario shows that both algorithm can be used to predict the area of harvest area in Karawang regency. The result of evalution with same scenario shows that CART algorithm has better performance than KNN algorithm with correlation coefficient 0,9646, MAE 498,6229, and RMSE 834,0204
Co-Authors ., Garno Aan Rohanah Abimanyu Widhiantoyo Adam Mukti Wibisono Afifa Atira Agil Aditya Agil Aditya Ahmad Khusaeri Ammellia Putri, Zahra Andini Diyah Pramesti Anggri, Anggri Anisa Nur Hasanah Apriade Voutama aqillah Aqillah Fadia Haya Arbizal, Yusril Assyifa Khalif Ato Sugiharto Audy Sukma Putera Aulia Kusnadi, Putri Azhari Ali Ridha Bayu Adhi Pamungkas Bintang Selviana Budi Arif Dermawan Dadang Yusuf Darmawan, Adam Delpidat, Derrenz Dinda Fitriani Duwo Jiwo Saputro, Adi Dwi Latifah Rianti Fathimatuz Zahra Febriandika Dian Nurcahyo Fida Nafisah Giustin garno, Garno Hannie, Hannie Hasanah, Anisa Nur Hayah, Najmil Ibnu Alfitra Salam Ilham, Septian Ilyas Shiddiq Pratama Intan Purnamasari Iqbal Maulana Iskandar Zulkarnaen Ivan Jovian Katon Wahyudi Putra Khalif, Assyifa Lusen, Vieri Luthfiyyah, Ibtihal Qomariyyah Margaretta, Putri Mayasari, Rini Muhamad Khandava Mulyadien Muhammad Diki Hendriyanto Muhammad Fadhlil Aziz Nabilla Audy, Ide Nur Padilah, Tesa Nurhasanah, Desi Nurhusaeni, Muhamad Nurohmah, Yunita Padilah, Tesa Nur Rahmat Alfanza Rahmawati, Erika Rahmayani, Fenny Ratna Mufidah Reynaldi Azhar Ridha, Azhari Ali Ridwan, Muhammad Hafizh Rini Mayasari Rizal Fadilah Sani Shalihamidiq Senjaya, Rico Silvana Fitria Mandang Sisca Yuliantina Sisca Yuliatina Sita Alden Sofi Defiyanti Suna Mulyani Susilawati Susilawati Syafrida Nabila Salsabila Tesa Nur Padilah Tesa Nur Padilah Topan Adib Amrulloh, Ibnu Vania, Putri Wira Buana Yanti, Fajri