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PENERAPAN DATA MINING TERHADAP DATA COVID-19 MENGGUNAKAN ALGORITMA KLASIFIKASI Rizka Dahlia; Nanik Wuryani; Sri Hadianti; Windu Gata; Arina Selawati
Jurnal Informatika Vol 21, No 1 (2021): Jurnal Informatika
Publisher : IIB Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/ji.v21i1.2868

Coronavirus 2019 or more commonly referred to as COVID-19 is a type of virus that attacks the respiratory system. Until now the number of spread and the number of deaths caused by this virus continues to increase. As of April 21, 2020, based on data from the WHO, the total number of cases infected with this virus reached 2,397,217 with 162 deaths from all over the world. For South Korea itself, as of March 21, 2020, the total number of infected cases was 10,683 with a total of 237 deaths. In this study, researchers conducted data processing on the spread of COVID-19 in South Korea with Rapidminer using a classification algorithm, namely Naïve Bayes, C4.5, and K-Nearest Neighbor by performing the stages of selection, preprocessing, transfotmating, data mining and interpretation or evaluating the quality of the best accuracy of 80.79% with AUC of 0.881 achieved by the Naïve Bayes algorithm. The distribution of the data found that the influential attribute of the isolated class factor from the patient contained in the sex attribute where more women experienced isolation. Keywords— COVID-19, data mining, classification, C4.5, Naïve Bayes, K-NN

SISTEM INFORMASI PEMBAYARAN SUMBANGAN PARTISIPASI PENDIDIKAN TERINTEGRASI SMS GATEWAY Muhammad Rifqi Firdaus; Muhammad Ifan Rifani Ihsan; Arief Rama Sena; Rizka Dahlia -
Jurnal Infortech Vol 1, No 2 (2019): Desember 2019
Publisher : Universitas Bina Sarana Informatika

Perkembangan teknologi informasi sangat berperan di dunia pendidikan, bahkan bisa menjadi tolak ukur dalam menentukan kualitas suatu pendidikan, mulai dari proses kegiatan pembelajaran hingga pengelolaan informasi manajemen sekolah untuk mendukung proses pendidikan. Di Sekolah Menengah Kejuruan Nahdlatul Ulama , proses pencatatan pembayaran biaya Sumbangan Partisipasi Pendidikan (SPP) masih dilakukan manual, tentu saja hal ini tidak efisien karena akan membutuhkan waktu yang lama apabila ada banyak siswa yaang akan membayarnya. Dalam proses pembuatan laporan, petugas harus merekap ulang setiap bulannya dan diberikan kepada kepala sekolah. Terlebih masih saja ada siswa yang tidak jujur dengan tidak membayarkan uang yang telah diberikan orangtuanya untuk membayar SPP. Tujuan dari penulisan ini yaitu merancang sebuah sistem informasi pembayaran SPP terintegrasi SMS Gateway, dengan tujuan memecahkan masalah tersebut. Metode yang digunakan adalah metode waterfall sebagai metode pengembangan perangkat lunaknya. Hasil dari penelitian yag dilakukan yakni, menciptakan sistem informasi pembayaran SPP yang sudah terkomputerisasi dan terintegrasi SMS Gateway, dimana nantinya pencatatan laporan bisa dilakukan secara cepat, tidak akan ada lagi siswa yang tidak membayarkan biaya SPPnya, laporan SMS akan terkirim langsung kepada oranguta siswa setiap melakukan pembayaran SPP.

Analisis Tingkat Penerimaan Mahasiswa Terhadap Aplikasi Zoom Meeting Sebagai Media Perkuliahan Menggunakan Metode TAM Muhammad Ifan Rifani Ihsan; Rabiatus Saadah; Rizka Dahlia; Badariatul Lailiah; Hendri Mahmud Nawawi
Paradigma Vol. 24 No. 1 (2022): Periode Maret 2022
Publisher : LPPM Universitas Bina Sarana Informatika

The increasingly advanced technology makes it easier for anyone to use it for various activities, such as lecture activities. Especially during the Covid-19 pandemic that has occurred since 2019 until this research was conducted. Covid-19, formerly known as SARS-CoV2, is an outbreak of pneumonia that originates from a virus. This virus was first heard in Wuhan, China in December 2019 (Ciotti et al., 2020). Lectures are conducted online to prevent the spread of this virus. One of the technologies widely used during the Covid-19 pandemic is video conferencing. With video conference meetings can be held even if the people present are far from each other. Zoom is a cloud-based application that is currently often used as a video communication (Azkiya, 2021). Zoom is a video conferencing application that is currently being used in various activities, including lectures. Because it is important to know how much student acceptance of the Zoom application they use for lectures is. The research was conducted using the Technology Acceptance Model or TAM method which consists of three constructs, namely Perceived Usefulness, Perceived Ease of Use and Acceptance of Technology. The data was obtained by distributing questionnaires with a Likert scale of 1 to 5. The data obtained were then calculated using the Structural Equation Model or SEM method consisting of the Outer Model and Inner Model calculations. SEM is an analytical technique that allows testing of a series of simultaneous relationships (Gardenia, 2018). The conclusion is that by taking the R-Square value, the construct in the TAM method is able to measure as much as 63% of student admission case studies on Zoom as a lecture medium. Keywords: Analysis, TAM, Zoom.

SISTEM PAKAR DIAGNOSA PENYAKIT PADA GIGI BERBASIS WEB DENGAN PENALARAN FORWARD CHAINING Muhammad Ifan Rifani Ihsan; Lady Agustine; Rizka Dahlia; Ahmad Fachrurozi
Elkom : Jurnal Elektronika dan Komputer Vol 15 No 2 (2022): Desember : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v15i2.921

Teeth are one of human‘s organs that feeds on food. As an organ, the tooth definitely can be attacked by some disease. There are many cases of dental disease that occur from dental diseases common to people to chronic dental diseases that can be dangerous. The low level of public concern about dental disease is a problem faced today. Evidenced by the small number of people who diligently consult with dentists about the health of their teeth. An alternative option is needed that can make it easier for people to be able to consult or diagnose dental health and disease without having to see a doctor of dental health. Therefore, in this essay an expert system was created to provide alternative choices for people. This expert system was created with the php hypertext preprocessor programming language. Using the forward chaining method as a tracking method. The purpose of making this expert system is to be able to be a substitute for a dental expert so that people can already do the diagnosis of dental disease anywhere and anytime.

Perbandingan Gradient Boosting dan Light Gradient Boosting Dalam Melakukan Klasifikasi Rumah Sewa Rizka Dahlia; Cucu Ika Agustyaningrum
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 6 (2022): Desember 2022
Publisher : Program Studi Teknik Informatika, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i6.5460

Abstrak— Persaingan antar perusahaan tidak akan dapat terhindarkan apalagi terkait tujuan perusahaan dalam mendapatkan omset sebesar-besarnya. Salah satu persaingan yang terjadi adalah dibidang property atau jika lebih spesifik lagi yaitu penyewaan rumah. Sebuah perusahaan harus menentukan strategi bagaimana rumah yang akan disewakan nantinya akan sebanding dengan harga pembangunan. Maka dari itu perusahaan dapat melakukan klasifikasi rumah sewa dalam menentukan hal tersebut. Penelitian ini menggunakan model Gradient Boosting dan Light Gradient Boosting. Hasil yang didapatkan adalah bahwa model Gradient Boosting adalah model yang cocok pada penelitian ini dengan mendapatkan hasil accuracy 84.38%, precision 83.33% dan recall 87.53%. Jika dilihat perbandingan dari confusion matrix, Gradient Boosting memiliki jumlah hasil prediksi data lebih besar dibanding dibanding Light Gradient Boosting.Kata kunci: Rumah Sewa, Data Mining, Gradient Boosting, Light Gradient Boosting Abstract— Competition between companies cannot be avoided, especially regarding the company's goal of getting the maximum turnover. One of the competitions that occurs is in the property sector, or more specifically, house rental. A company must determine a strategy for how the house to be rented out will be comparable to the construction price. Therefore the company can classify rental houses in determining this. This study uses the Gradient Boosting and Light Gradient Boosting models. The results obtained are that the Gradient Boosting model is a suitable model in this study with 84.38% accuracy, 83.33% precision and 87.53% recall. If you look at the comparison of the confusion matrix, Gradient Boosting has a greater number of data prediction results than Light Gradient Boosting.Keywords : House for rent, Data Mining, Gradient Boosting, Light Gradient Boosting

Comparison of Conventional Machine Learning and Deep Neural Network Algorithms in the Prediction of Monkey-Pox Cucu Ika Agustyaningrum; Rizka Dahlia; Omar Pahlevi
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.522

Smallpox syndrome, also known as monkeypox, is an uncommon zoonotic viral infection brought on by the monkeypox virus, which belongs to the genus orthopoxvirus and family Poxviridae. Injury-related mortality in primates ranges from 1 to 10%. Data mining is a method for analyzing data. Deep neural networks and traditional machine learning methods are both used in the data analysis process. The Python programming language is used during the comparison procedure of this research algorithm to generate values for accuracy, f1 score, precision, recall, ROC, and AUC. The test results demonstrate that using sigmoid activation function parameters, the deep neural network algorithm's accuracy is 70.08%, F1 score is 79.18%, precision is 68.59%, recall is 62.65%, and AUC is 62.65%. In comparison to using conventional machine learning algorithms, the adagrad optimizer with learning rate 0.01 and 0.2 dropout has a higher value. The conventional machine learning model algorithm has the best xgboost, F1 score, precision, recall, and AUC scores when compared to other approaches: 64.40%, 64.45%, and 78.14%. According to these numbers, the average fairness disparity between deep neural network algorithms and traditional machine learning is 5.68%, F1 score is 13.79%, precision is 4.14%, recall is 1.75%, and AUC is 1.75%.

Comparison of Conventional Machine Learning and Deep Neural Network Algorithms in the Prediction of Monkey-Pox Cucu Ika Agustyaningrum; Rizka Dahlia; Omar Pahlevi
Jurnal Riset Informatika Vol. 5 No. 3 (2023): June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.217

Smallpox syndrome, or monkeypox, is an uncommon zoonotic viral infection brought on by the monkeypox virus, which belongs to the genus Orthopoxvirus and family Poxviridae. Injury-related mortality in primates ranges from 1 to 10%. Data mining is a method for analyzing data. Deep neural networks and traditional machine learning methods are used in data analysis. The Python programming language is used during the comparison procedure of this research algorithm to generate values for accuracy, f1 score, precision, recall, ROC, and AUC. The test results demonstrate that using sigmoid activation function parameters, the deep neural network algorithm's accuracy is 70.08%, F1 score is 79.18%, precision is 68.59%, recall is 62.65%, and AUC is 62.65%. Compared to conventional machine learning algorithms, the adagrad optimizer has a higher value with a learning rate of 0.01 and 0.2 dropouts. The conventional machine learning model algorithm has the best xgboost, F1 score, precision, recall, and AUC scores compared to other approaches: 64.40%, 64.45%, and 78.14%. According to these numbers, the average fairness disparity between deep neural network algorithms and traditional machine learning is 5.68%, F1 score is 13.79%, precision is 4.14%, recall is 1.75%, and AUC is 1.75%.

Analisis Pola Pembelian Obat Demam Dengan Teknik Data Mining Menggunakan Algoritma Apriori (Studi Kasus: Apotek Ambawang Farma) Rizka Dahlia; Lady Fitriana; Syarah Seimahuira
Technologia : Jurnal Ilmiah Vol 15, No 1 (2024): Technologia (Januari)
Publisher : Universitas Islam Kalimantan Muhammad Arsyad Al Banjari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/tji.v15i1.13907

Masalah: Tingginya kebutuhan masyarakat pada bidang kesehatan khususnya dalam mengobati penyakit demam menjadikan Apotek selaku tempat penjualan obat-obatan untuk dapat memenuhi kebutuhan pengadaan stok obat dalam penelitian ini berfokus pada Apotek Ambawang Farma sebagai salah satu apotek yang terdapat di Kubu Raya. Tujuan: Kebutuhkan akan strategi pemasaran dan meminimalisir kurangnya stok obat saat obat yang ada di etalase telah habis. Metode: Dalam menentukan strategi tersebut, diperlukan proses pengolahan data transaksi penjualan obat demam menggunakan teknik data mining yaitu association rule mining. Pada penelitian ini dilakukan dengan menerapkan algoritma apriori dengan melihat obat demam yang memenuhi minimal support dan minimal confidence. Hasil: Data mining mining menghasilkan aturan asosiasi antar item pada bulan Agustus 2018 sampai dengan Juli 2019 diketahui pola penjualan obat demam jika membeli Emturnas 500mg, maka membeli Mirasic 500mg dengan confidence 87,50%, jika membeli Emturnas 500mg, maka membeli Mirasic 500mg dengan confidence 77,77%, jika membeli Grafadon 500mg dan Mirasic 500mg, maka membeli Emturnas 500mg 85,71%, jika membeli Grafadon 500mg dan Emturnas 500mg, maka membeli Mirasic 500mg dengan confidence 75% dan jika membeli Mirasic 500mg, maka membeli Grafadon 500mg dan Emturnas 500mg dengan confidence 75%. Kesimpulan: Diketahui bahwa obat demam yang paling banyak terjual pada Apotek Ambawang Farma yaitu Mirasic 500mg, Emturnas 500mg, dan Grafadon 500mg

Application of the Finite State Automata (FSA) Method in Indonesian Stemming using the Nazief & Adriani Algorithm lady agustin fitriana; Ali Mustopa; Muhammad Rifqi Firdaus; Rizka Dahlia
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i3.4038

Language is a communication tool commonly used in everyday life. Each country has a different language with predetermined rules. For instance, in the Indonesian language, there are approximately 35 official affixes mentioned in the Big Indonesian Dictionary. These affixes include prefixes (prefixes), infixes (insertions), suffixes (suffixes), and confixes (a combination of prefixes and suffixes). In Information Retrieval, there is a stemming process, which is the process of converting a word form into a base word or the process of transforming variant words into their base form. The theory of language and automata is the foundation of the computer science field that provides the basis for ideas and models of computer systems. In the implementation of the research, several stages were carried out, such as explaining the Nazief & Adriani stemming algorithm, finite state automata, creating pseudocode, and testing using a web-based system, resulting in affixed words becoming the correct base words with 20 affixed words. The results obtained from reading this web-based system, the base word "cinta" (love) used as a test yielded accurate results in accordance with the concept of the Nazief & Adriani stemming algorithm. There are some weaknesses in stemming from suffixes, and the solution is to perform stemming from the prefix position (Prefix).

Perbandingan Gradient Boosting dan Light Gradient Boosting Dalam Melakukan Klasifikasi Rumah Sewa Rizka Dahlia; Cucu Ika Agustyaningrum
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 6 (2022): Desember 2022
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i6.5460

Abstrak— Persaingan antar perusahaan tidak akan dapat terhindarkan apalagi terkait tujuan perusahaan dalam mendapatkan omset sebesar-besarnya. Salah satu persaingan yang terjadi adalah dibidang property atau jika lebih spesifik lagi yaitu penyewaan rumah. Sebuah perusahaan harus menentukan strategi bagaimana rumah yang akan disewakan nantinya akan sebanding dengan harga pembangunan. Maka dari itu perusahaan dapat melakukan klasifikasi rumah sewa dalam menentukan hal tersebut. Penelitian ini menggunakan model Gradient Boosting dan Light Gradient Boosting. Hasil yang didapatkan adalah bahwa model Gradient Boosting adalah model yang cocok pada penelitian ini dengan mendapatkan hasil accuracy 84.38%, precision 83.33% dan recall 87.53%. Jika dilihat perbandingan dari confusion matrix, Gradient Boosting memiliki jumlah hasil prediksi data lebih besar dibanding dibanding Light Gradient Boosting.Kata kunci: Rumah Sewa, Data Mining, Gradient Boosting, Light Gradient Boosting Abstract— Competition between companies cannot be avoided, especially regarding the company's goal of getting the maximum turnover. One of the competitions that occurs is in the property sector, or more specifically, house rental. A company must determine a strategy for how the house to be rented out will be comparable to the construction price. Therefore the company can classify rental houses in determining this. This study uses the Gradient Boosting and Light Gradient Boosting models. The results obtained are that the Gradient Boosting model is a suitable model in this study with 84.38% accuracy, 83.33% precision and 87.53% recall. If you look at the comparison of the confusion matrix, Gradient Boosting has a greater number of data prediction results than Light Gradient Boosting.Keywords : House for rent, Data Mining, Gradient Boosting, Light Gradient Boosting

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