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All Journal Jurnal Riset Informatika SENSITEK Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat
Sri Wulandari
Universitas AMIKOM yogyakarta
Computer Science & IT

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Komparasi Metodologi Penentuan Kebutuhan Spesifikasi Sistem Dalam Pengembangan Sistem Informasi Akademik Wahyu Wijaya Widiyanto; Robi Wariyanto; Sri Wulandari; Fendy Prasetyo Nugroho; Muqorobin -
Proceeding Seminar Nasional Sistem Informasi dan Teknologi Informasi 2018: Proceeding Seminar Nasional Sistem Informasi dan Teknologi Informasi (SENSITEK)
Publisher : STMIK Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/pss.v1i1.230

Abstract

Pada saat dihadapkan pada pemilihan metodologi pengembangan sistem, banyak diantara pengembang proyek perangkat lunak yang akhirnya bingung. Padahal salah menentukan metodologi, dapat mempengaruhi kepenyusunan jadwal, staffing proyek, biaya dan lain-lain. Oleh karena itu, pemilihan metodologi merupakan bagian yang penting, tidak hanya pada saat pengembangan sistem informasi namun juga pada pengembangan proyek-proyek perangkat lunak lainya. Dalam makalah ini membahas mengenai pengembangan sistem informasi akademik dengan 4 model/metode yaitu waterfall, Rapid Aplication Developtmen (RAD), Prototype, dan Spiral dari segi kelebihan dan kelemahan, tujuan dari pembahasan ini agar pengembang dapat melakukan implementasi pengembangan sistem khususnya sistem informasi akademik dengan pemilihan metodologi yang tepat.Kata kunci: komparasi, Rapid Aplication Developtment (RAD), waterfall, Prototype, Spiral
SISTEM PAKAR DIAGNOSA HAMA DAN PENYAKIT TANAMAN PADI DENGAN METODE BAYES Sri Wulandari; Muhammad Fajrian Noor; Ajie Kusuma Wardhana; Kusrini Kusrini
Jurnal Informa : Jurnal Penelitian dan Pengabdian Masyarakat Vol 5 No 2 (2019): Juni
Publisher : Politeknik Indonusa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (321.727 KB) | DOI: 10.46808/informa.v5i2.83

Abstract

Perkembangan teknologi saat ini berkembang dengan pesat dimana meliputi berbagai bidang seperti bidang pendidikan, kesehatan bahkan bidang pertanian. Sistem pakar merupakan salah satu pemanfaatan perkembangan teknologi, yaitu suatu aplikasi komputer kecerdasan buatan untuk memecahkan masalah seperti keahlian seorang pakar pada bidang tertentu berdasarkan pengetahuan dan fakta sehingga dapat memberikan solusi yang memuaskan. Sistem pakar pada bidang pertanian dapat membantu petani untuk mengatasi masalah pada tanamannya dengan melihat gejala yang ada pada tanaman tersebut. Sistem pakar untuk mendiagnosa hama dan penyakit padi diharapkan dapat membantu untuk mengetahui secara tepat dan cepat jenis hama dan penyakit yang menyerang tanaman padi tanpa harus menunggu petugas pertanian. Penelitian ini akan mengimplementasikan metode Bayes pada sistem pakar untuk mendiagnosa hama dan penyakit tanaman padi.
Prediction of Rainfall and Water Discharge in The Jagir River Surabaya with Long-Short-Term Memory (LSTM) Retzi Yosia Lewu; Slamet Slamet; Sri Wulandari; Widdi Djatmiko; Kusrini Kusrini; Mulia Sulistiyono
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.v5i3.558

Abstract

Flood disasters can occur at any time when the factors for the amount of river water discharge and rainfall intensity tend to be high, so preparations and ways of handling are needed to anticipate flood disasters quickly, precisely, and accurately for the Surabaya Public Works Service. One of the steps to predict and analyze the status of the flood disaster alert level is by calculating predictions based on rainfall and the amount of river water discharge. This study uses the Long-Short Term Memory (LSTM) algorithm to predict rainfall and river water discharge on the Jagir River in Surabaya. The LSTM method is a model commonly used for predictions based on time series data. The data obtained are rainfall data and water discharge on the Jagir River, Surabaya, which will be used as training and testing data to make predictions. The results of implementing the LSTM method using data training of 70% and data testing of 30% on rainfall data using the best epoch, namely at epoch ten by producing tests on data testing can have a Mean Absolute Error (MAE) performance of 4.5 and Root Mean Square Error (RMSE) of 9.7. Whereas the water discharge variable uses the best epoch, namely at epoch 75, by producing data testing data which can have a Mean Absolute Error (MAE) performance of 11.49 and a Root Mean Square Error (RMSE) of 9.63.
Prediction of Rainfall and Water Discharge in The Jagir River Surabaya with Long-Short-Term Memory (LSTM) Retzi Yosia Lewu; Slamet Slamet; Sri Wulandari; Widdi Djatmiko; Kusrini Kusrini; Mulia Sulistiyono
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.v5i3.239

Abstract

AbstractFloods can occur at any time if the amount of river water discharge and rainfall intensity tends to be high, so preparations and ways of handling are needed to anticipate flooding quickly, precisely, and accurately for the Surabaya City Public Works Service. One of the steps to predict and analyze the status of the flood disaster alert level is to calculate predictions based on rainfall and the amount of river water discharge. This study uses the Long-Short Term Memory (LSTM) algorithm to predict using a time series dataset of rainfall and river water discharge in the Jagir River, Surabaya. This data is used to make predictions with the proportion of 70% training data and 30% testing data. Data normalization is performed in intervals of 0 and 1 using a min-max scaler and activated using ReLU (Rectified Linear Unit) and Adam Optimizer. The process continues by repeating the process to enter iterations, or epochs until it reaches the specified epoch (n). The data is then normalized to their original values and visualized. The model was evaluated and produced acceptable performance evaluation results for the rainfall variable, namely at epoch (n) = 75 for training data, namely a score of 0.054 for MAE and 0.099 for RMSE. In contrast, data testing was given a score of 0.041 for MAE and 0.091 for RMSE. As for the water discharge variable, the performance evaluation shows the difference between the training and testing data. Results of training data MAE = 11.10 and RMSE=18RMSE =18.61.61 at epoch (n) = 150. Results of data testing MAE = 11.37 and RMSE = 21.08 at epoch (n) = 100. These results indicate an anomaly that needs to be discussed in further research.
Co-Authors Ajie Kusuma Wardhana Fendy Prasetyo Nugroho Kusrini Kusrini Muhammad Fajrian Noor Mulia Sulistiyono Muqorobin - Retzi Yosia Lewu Robi Wariyanto Slamet Slamet Wahyu Wijaya Widiyanto Widdi Djatmiko