Article Per Year (5 Year)
p-Index From 2021 - 2026
1.383
P-Index
This Author published in this journals
All Journal Abdimas Dewantara bit-Tech Algor Abdi-Dharma RUBINSTEIN
Fenriana, Indah
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Social Sciences Other

Published : 8 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Algor

 1 
PERANCANGAN APLIKASI PREDIKSI PENYAKIT JANTUNG MENGGUNAKAN METODE NAÏVE BAYES Timotius, Adrian; Fenriana, Indah
ALGOR Vol. 5 No. 2 (2024): Creativity Influence Technology
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/algor.v5i2.2360

Abstract

There is limited information on how to diagnose disease in the community, especially heart disease and a lack of awareness of the types of heart disease and related symptoms in predicting heart disease. The Naive Bayes algorithm is one of the algorithms that is often used in data mining to predict the likelihood of heart disease based on existing risk factors. The problem to be solved is how the diagnostic results are obtained from system design using the Naïve Bayes algorithm in diagnosing heart disease. This research technique uses the Naïve Bayes Algorithm. As for the method of data collection is the dataset and heritage studies. As for the results of his research, the naïve Bayes algorithm in the field of data mining computer science, especially in the classification of heart disease, is proven to be implemented properly. Tests carried out using the manual method and using the RapidMiner software produce accuracy as a measure of the accuracy of the algorithm in diagnosing heart disease. In addition, it also has a higher level of accuracy compared to the linear regression algorithm. the naïve Bayes method has an accuracy of 86.26%, while the linear regression algorithm only has an accuracy of 83%.
Prototype Alat Pendeteksi Suhu Tubuh Manusia Untuk Membuka Pintu Secara Otomatis Menggunakan Sensor Suhu GY-906 Berbasis Android Pada Gereja MCC Tangerang Pratama, Jonathan Marcellino; Fenriana, Indah
ALGOR Vol. 4 No. 2 (2023): Updated Technology
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/algor.v4i2.1720

Abstract

Recieved: September 24, 2022Final Revision: February 28, 2023Available Online: March 24, 2023Menjaga kesehatan merupakan hal yang sangat penting bagi kehidupan terutama di saat masa pandemi Covid-19. Salah satu protokol yang diberlakukan bagi masyarakat berkegiatan di ruang umum atau fasilitas terbuka adalah memeriksa suhu tubuh. Dalam penelitian ini dibuat rancangan prototype alat pendeteksi suhu tubuh manusia berbasis mikrokontroler yang memiliki pengingat apabila suhu tubuh berada diatas angka 37.5 dan terhubung ke perangkat komputer melalui koneksi internet. Data suhu tubuh juga ditampilkan pada LCD 16x2 (cm) yang terdapat pada alat. Tujuan dari penelitian ini adalah untuk membuat prototype alat Pendeteksi suhu tubuh manusia untuk membuka pintu secara otomatis dengan menggunakan sensor GY-906 sebagai sensor suhu. NodeMCU ESP 8266 sebagai alat memproses data yang kemudian memberikan hasilnya pada LCD untuk di tampilkan dan aplikasi Monitoring Temperature, sensor ultrasonic digunakan untuk mendeteksi jarak suatu objek, lalu motor servo akan merespon perintah berdasarkan masukan dari sensor ultrasonic dan hand sanitizer otomatis untuk mengukur tingkat keberhasilan alat akan dilakukan pengujian. Indikator sensor suhu digunakan untuk mengukur suhu tubuh dan hand sanitizer. Kemudian data tersebut disimpan pada App Inventor yang akan melakukan monitoring data suhu tubuh dan ruangan. Selanjutnya data tersebut mengeluarkan informasi daftar nama dan suhu pada layar Smartphone. Hasil keluaran informasi tersebut berupa pemberitahuan mengenai kondisi tubuh dimana ia sedang mengalami normal atau panas. Berdasarkan hasil penelitian dapat disimpulkan bahwa monitoring suhu tubuh menggunakan NodeMCU ESP 8266 berhasil dibuat dan dapat memberikan informasi secara realtime mengenai temperature tubuh. Alat ukur suhu tubuh ini dapat membantu menampilkan hasil sensor yang telah terbaca ditampilkan dalam bentuk aplikasi android.
PENERAPAN METODE SINGLE EXPONENTIAL SMOOTHING DALAM APLIKASI PERAMALAN PENCAIRAN KREDIT PADA PT. BPR MAGGA JAYA UTAMA Tandika, Verga; Fenriana, Indah
ALGOR Vol. 5 No. 1 (2023): Sustainability of Information Technology
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/algor.v5i1.2409

Abstract

This research aims to apply the single exponential smoothing method in forecasting credit disbursement at PT. BPR Magga Jaya Utama. The single exponential smoothing method is a forecasting method that is suitable for forecasting time series data with a stable trend. Credit disbursement data at PT. BPR Magga Jaya Utama during the period January 2020 to December 2022 was used as research data. Data was analyzed using Microsoft Excel software. The research results show that the single exponential smoothing method can provide credit disbursement forecasting with a high level of accuracy. The resulting forecasting model can be used as a reference in making decisions regarding credit disbursement at PT. BPR Magga Jaya Utama. In this research, the best alpha value for the single exponential smoothing method was 0.3, with a Mean Absolute Percentage Error (MAPE) value of 3.94%. In conclusion, this research proves that the single exponential smoothing method can be applied in forecasting credit disbursement at PT. BPR Magga Jaya Utama. Accurate forecasting results can help PT. BPR Magga Jaya Utama in making better and more appropriate decisions regarding credit disbursement.
Co-Authors Akbar, Junaidi Christian, Fernando Daniawan, Benny Dyah Safitri, Ramona Hariyanto, Susanto Lasut, Desiyanna Lim, Marsel Pratama, Jonathan Marcellino Putra, Dicky Surya Dwi Ramona Safitri, Ramona Rino Rino Tandika, Verga Tanjung, Naia Putri Timotius, Adrian velistin, Fellix Wiejaya, Andre