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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Infotech Journal Journal of Information System Jurnal Teknologi dan Sistem Komputer IJCIT (Indonesian Journal on Computer and Information Technology) JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining ILKOM Jurnal Ilmiah DoubleClick : Journal of Computer and Information Technology INTECOMS: Journal of Information Technology and Computer Science Progresif: Jurnal Ilmiah Komputer Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Journal of Applied Engineering and Technological Science (JAETS) JATI (Jurnal Mahasiswa Teknik Informatika) Abdimasku : Jurnal Pengabdian Masyarakat Paradigma
Betha Nurina Sari
Program Studi Informatika, Universitas Singaperbangsa Karawang
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Social Sciences

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PENERAPAN CLUSTERING DBSCAN UNTUK PERTANIAN PADI DI KABUPATEN KARAWANG Betha Nurina Sari; Aji Primajaya
JURNAL INFORMATIKA DAN KOMPUTER Vol 4, No 1 (2019)
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (252.902 KB) | DOI: 10.26798/jiko.v4i1.178

Abstract

Kabupaten Karawang merupakan salah satu kabupaten dengan hasil produksi padi tertinggi di Indonesia untuk kebutuhan pangan provinsi dan nasional, sehingga sering disebut sebagai kota lumbung padi.Kabupaten Karawang terdiri dari 30 kecamatan, masing-masing kecamatan memiliki luas lahan sawah yang berbeda luasnya, sehingga berbeda pula potensi produksi padi sawah yang bisa dipanen. Pemetaan yang dilakukan agar bisa menganalisis terkait karakteristik dari setiap kategori yang dibagikan. algoritma clustering yang diterapkan dalam penelitian ini adalah DBSCAN (Density-Based Spatial Clustering).Tujuan dari penelitian ini adalah menerapkan teknik clustering DBSCAN pada lahan pertanian padi di kabupaten Karawang, melakukan perbandingan evaluasi performa dari teknik clustering DBSCAN yang dilakukan pada penelitian ini.Penerapan teknik clustering DBSCAN (Density-Based Spatial Clustering of Application with Noise) pada lahan pertanian padi di kabupaten Karawang melalui beberapa tahapan penelitian,yaitu  preprocessing, data mining, evaluasi cluster dan visualisasi melalui WEB-GIS. Algoritma DBSCAN diproses melalui R studio dengan bahasa pemrograman R dengan beberapa skenario eksperimen kombinasi masukan epsilon dan minPts. Perbandingan evaluasi performa dari teknik clustering DBSCAN (Density-Based Spatial Clustering of Application with Noise) dilakukan dengan memperhatikan nilai dari average silhoutte width. Besarnya tingkat skor silhoutte menunjukkan kualitas cluster yang terbentuk. Hasil eksperimen pada penelitian ini menunjukkan skor hasil tertinggi 0,74 dengan dua cluster.
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.
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.
Application of C5.0 Algorithm in Prediction of Learning Outcomes in Calculus Subject Fida Nafisah Giustin; Betha Nurina Sari; Tesa Nur Padilah
Journal of Applied Engineering and Technological Science (JAETS) Vol. 3 No. 2 (2022): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (357.91 KB) | DOI: 10.37385/jaets.v3i2.673

Abstract

Calculus is one of the basic subject that must be studied at the computer science faculty of the informatics engineering study program. For some students, especially in the Faculty of Informatics Engineering, calculus is a subject that is considered quite difficult, even though this subject is important for them. And the resulted for some students having to repeat this subject. For this reason, predictions of calculus learning outcomes are carried out by applying the data mining process and using the C5.0 method for the prediction process based on the classification concept that will be carried out. This study applies the Cross Industry Standard Process for Data Mining (CRISP – DM) methodology with the C5.0 algorithm. The results are in the form of a decision tree (Decision tree) and the rules in it using the attributes of guardian, number of family members, status of residence, internet, activity, desire to continue study, the last education of parents (father and mother), parents' occupations, grades on assignments, UAS, and UTS. The C5.0 algorithm is able to predict the results of learning calculus. The evaluation results show that the applied C5.0 algorithm has an accuracy of 95%.
Prediksi Rating Game Menggunakan Algoritme C4.5 Berdasarkan Entertainment Software Rating Board Rahmat Alfanza; Sani Shalihamidiq; Ratna Mufidah; Betha Nurina Sari
Progresif: Jurnal Ilmiah Komputer Vol 19, No 1: Februari 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v19i1.977

Abstract

Games can be played by all ages including children. If the game being played is not in accordance with the child's developmental period, it will have a negative impact on the child. Therefore, the rating on the game is very influential because if there is an error in rating the game, minors can play games that are not in accordance with their developmental needs. The purpose of this research is to create a machine learning model to predict game ratings using data from the ESRB (Entertainment Software Rating Board). This study uses the C4.5 classification algorithm and the python programming language. The data used in this study is game rating data taken from 2020 to 2022. The results of this study indicate that the machine learning model created can predict game ratings with a ratio of 70% training data and 30% testing data, with an accuracy rate of 86%. Keywords: Game; Data Mining; Classification; Algorithm C4.5 AbstrakGame dapat dimainkan oleh semua kalangan usia termasuk usia anak-anak. Jika game yang dimainkan tidak sesuai dengan masa kembang anak maka akan berdampak negatif kepada anak. Oleh sebab itu rating pada game sangat berpengaruh karena apabila terjadi kesalahan terhadap pemberian rating pada game anak dibawah umur dapat memainkan game yang tidak sesuai dengan kebutuhan tumbuh kembangnya. Tujuan penelitian ini adalah membuat model machine learning untuk memprediksi rating pada game dengan menggunakan data dari ESRB (Entertainment Software Rating Board). Penelitian ini menggunakan algoritme klasifikasi C4.5 dan bahasa pemrograman python. Data yang digunakan pada penelitian ini adalah data rating game yang diambil dari tahun 2020 sampai 2022. Hasil dari penelitian ini menunjukkan model machine learning yang dibuat dapat memprediksi rating game dengan perbandingan 70% data training dan 30% data testing, dengan tingkat akurasi sebesar 86%.Kata kunci: Game; Data Mining; Klasifikasi; Algoritme C4.5
Penerapan Naïve Bayes untuk Klasifikasi Kriteria Air Layak Minum dengan Metode CRISP-DM Ibnu Alfitra Salam; Katon Wahyudi Putra; Sisca Yuliatina; Betha Nurina Sari
Paradigma Vol. 25 No. 1 (2023): March 2023 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v25i1.1754

Abstract

With water, living things can do various things easily. The adequacy of water is also important in maintaining human health. Water can be said to be feasible if its content is in accordance with the feasible criteria. From the dataset obtained regarding the feasibility of water for this study, it will calculate the accuracy value obtained using the Naive Bayes algorithm. To simplify the process of processing research data this time using the CRISP-DM methodology which is a stage for data mining. The study uses two tools, namely Rapidminer and Google Collab to compare their accuracy values. By using the two tools in implementing the Naive Bayes algorithm on a potable water quality dataset, an accuracy of 62.8% is obtained. This value is accurate enough to predict the quality of drinking water.
Application of C4.5 Classification in Improving Recitation Fluency in Students Sisca Yuliantina; Betha Nurina Sari
Paradigma Vol. 25 No. 1 (2023): March 2023 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v25i1.1775

Abstract

Fluency in reciting the Koran is learning the recitation of the Qur'an in a tartil way. Based on the observations of researchers, the learning of tajwid in recitation and at school has not been effective so far. Because of this, both teachers and students at recitation or at school need improvement by finding out what can increase fluency in reciting the Koran and what has the most influence on improving fluency. So this study aims to improve the fluency of the Koran in students. The research method used is the Decision Tree data mining classification method with the C4.5 algorithm. The results of data processing with the C4.5 algorithm using the Rapidminer tools are attribute C1(fluency) being the most influential attribute for increasing students' reading fluency and performance data obtained with an accuracy of 83,33%.
Co-Authors Agil Aditya Agil Aditya Anggri, Anggri Apriade Voutama aqillah Aqillah Fadia Haya Ato Sugiharto Bayu Adhi Pamungkas Budi Arif Dermawan Delpidat, Derrenz Dinda Fitriani Fida Nafisah Giustin Hannie, Hannie Ibnu Alfitra Salam Ilham, Septian Ivan Jovian Katon Wahyudi Putra Khusaeri, Ahmad Khusaeri, Ahmad Muhamad Khandava Mulyadien Nurhasanah, Desi Nurhusaeni, Muhamad Padilah, Tesa Nur Rahmat Alfanza Ratna Mufidah Reynaldi Azhar Rini Mayasari Rini Mayasari Sani Shalihamidiq Silvana Fitria Mandang Sisca Yuliantina Sisca Yuliatina Susilawati Susilawati Syafrida Nabila Salsabila Tesa Nur Padilah Tesa Nur Padilah Tia Adha Mariam Putri Ultach Enri Wira Buana Yunita Nurohmah