Article Per Year (5 Year)
p-Index From 2020 - 2025
1.564
P-Index
This Author published in this journals
All Journal JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Digital Teknologi Informasi JPM: JURNAL PENGABDIAN MASYARAKAT Kontribusi: Jurnal Penelitian dan Pengabdian Kepada Masyarakat Jurnal Esensi Infokom: Jurnal Esensi Sistem Informasi dan Sistem Komputer Journal of Software Engineering and Information System (SEIS) Journal of International Multidisciplinary Research
Hafsari, Rizka
Unknown Affiliation
Aerospace Engineering Humanities Astronomy Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology Public Health Social Sciences Transportation Other

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Journal of Software Engineering and Information System (SEIS)

 1 
Prediksi Curah hujan di Kota Pekanbaru Menggunakan lSTM (Long Short Term Memory) Hendra, Yos; Mukhtar, Harun; Baidarus; Hafsari, Rizka
Journal of Software Engineering and Information System (SEIS) Vol. 3 No. 2 (2023)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v3i2.5606

Abstract

Based on data obtained from BMKG Pekanbaru City in 2010-2020 there was an increase anddecrease in the intensity of rainfall that occurred in Pekanbaru city. The increase in rainfall in thecity of Pekanbaru will cause problems such as the occurrence of flooding of several roads and severalareas in the city of Pekanbaru and the occurrence of other unexpected disasters that will causeproblems and experience difficulties. In overcoming this problem, research was conducted in the formof Rainfall Prediction in Pekanbaru City Using LSTM (Long Short Term Memory) using 2 methods,namely in finding the accuracy of the error rate using RMSE (Root Mean Square Error) and MSE(Mean Square Error). The results showed that the predictions made were quite good. With the lowesterror rate of 21,328 in the train and 454,901 in the test, the composition of the train data and the testdata half gave the best results.
ANALISIS KESUBURAN PERTANIAN MELALUI IRIGASI DENGAN MENGGUNAKAN METODE K-MEANS CLUSTERING Mukhtar, Harun; Syafutri, Trimaiyuza Maulina; Rahman, Rayhan Aulia; Putra, Afyuadri; Hafsari, Rizka
Journal of Software Engineering and Information System (SEIS) Vol. 4 No. 2 (2024)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v4i2.7599

Abstract

Indonesia is an agricultural country where the majority of its population makes a living from agriculture. The agricultural sector is a very important sector for economic development in an agricultural country like Indonesia. Poor irrigation facilities greatly affect the results of the agricultural sector. Crop quality is based on many factors such as the characteristics of the irrigation process, including the amount of air and irrigation time. Overwatering irrigation can cause air wastage, soil freezing disease, yellowing of plant leaves, wilting of plant leaves, and many other problems. K-Means clustering is a method used to group data into one or more groups or clusters. The advantages of the K-Means algorithm are that it is easy and simple to implement, scalability, speed in convergence, and the ability to adapt to sparse data. K-Means to group agricultural land based on soil fertility and rainfall data, found that this grouping can help in more efficient irrigation planning. The clustering results show that agricultural land can be divided into three main clusters based on soil fertility and irrigation. Soil fertility is formed into three clusters based on the level of soil fertility using the Kmeans algorithm which can also be effective in helping in the Indonesian agricultural sector. By adding technological elements, the results provided will of course be even better.
Co-Authors Afin Wirdiansyah, Muhammad Amalia Ihsan, Luthfa Ardiansya, M Aryanto Aryanto Aryanto Baidarus Bangun, Elsi Titasari Br Br Bangun, Elsi Titasari Chaniago, Wahyu Ramadhan Dharma Aji, Bima Dhea Anaqoh, Fatahillah Doni Winarso, Doni Edo Arribe, Edo Fernando, Aldi Fhingkan Agustina, Fatimah Ghina Salsabila, Oktaviani Harun Mukhtar Hosman, Alifa Irawan, Gilang Julieta Bagio, Zakiya Khosyi Pasadana, Rifqi Pratama, Sarhan Putra, Afyuadri Rahman, Rayhan Aulia Risnal Diansyah, Risnal Ryandi, Muhammad Sari, Rani Aprilia Syafutri, Trimaiyuza Maulina Syahril Syahril Syahril Wide Mulyana Wijaya, Setiawan Ardi Wijaya, Setyawan Ardhi Wulan Dari, Tri Yos Hendra, Yos