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All Journal JOIN (Jurnal Online Informatika) Progresif: Jurnal Ilmiah Komputer Jurnal Teknik Informatika (JUTIF) Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen
Farida Nur Aini
Department Of Information Technology, Universitas Respati Yogyakarta, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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Rancang Bangun Sistem Informasi Pelayanan pada Pusat Kesehatan Masyarakat Ahmad Sahal; Zaidir Zaidir; Farida Nur Aini
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.v19i1.1115

Abstract

Optimal service is the main goal of the Community Health Center as a center for public health services. Utilizing reliable information technology to improve services is urgently needed. The problem at the City Health Center in Manggarai Regency, East Nusa Tenggara is that the presentation of data in each polyclinic is still not integrated and systematic, starting from registration, especially giving medical record numbers, patient care, and administering drugs, so it still needs to be improved. This paper presents an Information System model for services at Puskesmas, which was developed using the Waterfall method. The developer software used is VB.Net and PostgreSQL, which are tested using the Black Box testing method. The resulting puskesmas service information system can be used for services ranging from patient registration, services to polyclinics to drug services that can be carried out, so that it can facilitate services while at the same time presenting the necessary reports and improving the performance of the puskesmas.Keywords: Design and Build; Information Systems; Community Health Center Services; Medical Records AbstrakPelayanan yang optimal adalah tujuan utama Puskesmas yang menjadi pusat pelayanan kesehatan masyarakat. Memanfaatkan teknologi informasi yang handal untuk peningkatan pelayanan sangat dibutuhkan. Permasalahan di Puskesmas Kota Kabupaten Manggarai Nusa Tenggara Timur adalah penyajian data di setiap poliklinik masih belum terintegrasi dan sistematis, mulai dari pendaftaran terutama pemberian nomor rekam medis, perawatan pasien, dan pemberian obat, sehingga masih perlu di tingkatkan. Paper ini menyajikan model Sistem Informasi untuk pelayanan pada Puskesmas, yang dikembangkan menggunakan metode Waterfall. Perangkat lunak pengembang yang pakai adalah VB.Net dan PostgreSQL, yang diuji menggunakan metode pengujian Black Box testing. Sistem informasi pelayanan puskesmas yang dihasilkan, dapat digunakan untuk pelayanan mulai dari pendaftaran pasien, pelayanan ke poli sampai pelayanan obat dapat dilakukan, sehingga dapat mempermudah pelayanan sekaligus dapat menyajikan laporan yang diperlukan serta meningkatkan kinerja Puskesmas.Kata Kunci: Rancang Bangun; Sistem Informasi; Layanan Puskesmas; Rekam Medis
Multi-Step Vector Output Prediction of Time Series Using EMA LSTM Mohammad Diqi; Ahmad Sahal; Farida Nur Aini
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1037

Abstract

This research paper proposes a novel method, Exponential Moving Average Long Short-Term Memory (EMA LSTM), for multi-step vector output prediction of time series data using deep learning. The method combines the LSTM with the exponential moving average (EMA) technique to reduce noise in the data and improve the accuracy of prediction. The research compares the performance of EMA LSTM to other commonly used deep learning models, including LSTM, GRU, RNN, and CNN, and evaluates the results using statistical tests. The dataset used in this study contains daily stock market prices for several years, with inputs of 60, 90, and 120 previous days, and predictions for the next 20 and 30 days. The results show that the EMA LSTM method outperforms other models in terms of accuracy, with lower RMSE and MAPE values. This study has important implications for real-world applications, such as stock market forecasting and climate prediction, and highlights the importance of careful preprocessing of the data to improve the performance of deep learning models.
BIG DATA CONCEPT ANALYSIS FOR AGRICULTURAL SUITABLE LAND GEOGRAPHIC INFORMATION SYSTEM APPROACH Agus Qomaruddin Munir; Farida Nur Aini; Evrita Lusiana Utari; Naufal Naja Hafidhah
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 4 (2023): JUTIF Volume 4, Number 4, August 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.4.1328

Abstract

Big data analysis for agriculture provides farmers with a comprehensive view of the concept of increasing agricultural productivity using the effectiveness of irrigation canals, predicting rainfall to determine outcrop patterns, and identifying the adequacy of agricultural land. It also allows farmers to optimize irrigation, increasing yields while reducing costs and environmental impact. It also will enable farmers to optimize irrigation; Rainfall predictions are used to determine cropping patterns and identify suitability for permits. It can also be used to deal with weather patterns and climate change, allowing farmers to adapt their practices to reduce the impact of climate change, ultimately protecting their crops and currency. This research aims to develop plant productivity through several stages of research and the use of methods. The methods used in this study are 1)Prediction of water discharge using the linear regression method; 2)Prediction of Rainfall for Planting Pattern Training using the SARIMA method, and 3)Suitability of Agricultural Land using the Cluster Area Analysis Approach. The results of this study are that in the Sleman region, the adequacy of water for agricultural areas is in the excellent category (fulfilled), cropping pattern spending is divided into 2, namely dry and wet months. In the wet months (high rainfall), rice is suitable for planting from January to May; for the dry months between June and October, tobacco, soybeans, corn, peanuts, green beans, cassava, and sweet potatoes. As for land suitability, it consisted of 46025.36 Ha (81%) suitable and 10811.48 Ha not suitable for use.
Sistem Informasi Presensi Dengan Validasi Radius Lokasi Menggunakan Formula Haversine (Studi Kasus : PT. PICSI) Indra Listiawan; Zaidir; Sugeng Winardi; Farida Nur Aini
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 21 No 1 (2023): Januari 2023
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v21i1.21

Abstract

Sistem presensi atau absensi karyawan pada PT. PICSI Yogyakarta yang digunakaan saat ini adalah dengan finger print yaitu sistem presensi yang menggunakan alat dengan deteksi sidik jari. Masalah yang timbul dari peralatan presensi finger print ini ada beberapa hal. Masalah pertama adalah kepekaan alat, alat presensi ini kadang-kadang memerlukan waktu lama untuk mengenali sidik jari karyawan, yang kedua letak alat finger print terlalu jauh dari ruang karyawan. Sebagai pemecahan masalah tersebut adalah diusulkannya sistem informasi presensi dengan validasi radius lokasi menggunakan formula Haversine, sedangkan untuk mengetahui koordinat pengguna digunakan teknologi Google Map API
Multi-Step Vector Output Prediction of Time Series Using EMA LSTM Mohammad Diqi; Ahmad Sahal; Farida Nur Aini
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1037

Abstract

This research paper proposes a novel method, Exponential Moving Average Long Short-Term Memory (EMA LSTM), for multi-step vector output prediction of time series data using deep learning. The method combines the LSTM with the exponential moving average (EMA) technique to reduce noise in the data and improve the accuracy of prediction. The research compares the performance of EMA LSTM to other commonly used deep learning models, including LSTM, GRU, RNN, and CNN, and evaluates the results using statistical tests. The dataset used in this study contains daily stock market prices for several years, with inputs of 60, 90, and 120 previous days, and predictions for the next 20 and 30 days. The results show that the EMA LSTM method outperforms other models in terms of accuracy, with lower RMSE and MAPE values. This study has important implications for real-world applications, such as stock market forecasting and climate prediction, and highlights the importance of careful preprocessing of the data to improve the performance of deep learning models.
Co-Authors Agus Qomaruddin Munir Evrita Lusiana Utari Indra Listiawan Mohammad Diqi Naufal Naja Hafidhah Sahal, Ahmad Sugeng Winardi Zaidir