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This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) CogITo Smart Journal International Journal of Research and Applied Technology (INJURATECH)
Risna Sari
Amikom University Yogyakarta, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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The Systematic Literature Review of the spiral development model: Topics, trends, and application areas Risna Sari; Anggi Muhammad Rifa’i; Muhammad Salimy Ahsan; Mohammad Rezza Pahlevi; M. Ilham Arief
International Journal of Research and Applied Technology (INJURATECH) Vol 2 No 2 (2022): International Journal of Research and Applied Technology (INJURATECH)
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injuratech.v2i2.8372

Abstract

The spiral model is one of the methods used to perform software engineering development and can also be used for development in other fields. This spiral model is the result of a modification from the combination of the waterfall model and prototyping model so that it has many advantages including in each result an evaluation will be carried out, carried out sequentially or systematically, and is more focused in carrying out risk analysis from each stage. Has a function in development to make changes, additions and developments by determining accuracy and speed based on needs. In its implementation the spiral model has been carried out in various fields, but the results of the implementation are not yet known in what scope and how many implementations each year. This study aims to identify the results of the implementation of the spiral model development with data obtained from related papers in the 2012-2022 range. The method used in this study is the Systematic Literature Review (SLR) with the aim of identifying, reviewing, evaluating, and concluding all research on each relevant paper. The results showed that the spiral model development was mostly implemented in software development with a total of 19 papers and in the education sector as many as 17 papers, while the peak of the spiral model development was mostly implemented in 2016 and then increased again in 2021
Analisis Index Vegetation Wilayah Terdampak Kebakaran Hutan Riau Menggunakan Citra Landsat-8 dan Sentinel-2 Risna Sari; Liana Trihardianingsih; Rizki Firdaus Mulya; M. Ilham Arief; Kusrini Kusrini
CogITo Smart Journal Vol. 8 No. 2 (2022): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v8i2.439.282-294

Abstract

Kebakaran hutan telah diidentifikasi sebagai salah satu isu lingkungan utama yang memiliki dampak terhadap keanekaragaman hayati dan iklim global jangka panjang. Riau merupakan salah satu wilayah di Indonesia yang sering mengalami kebakaran hutan. Upaya untuk memulihkan hutan pasca kebakaran dapat dilakukan dengan pengawasan lahan seperti mengamati tingkat vegetasi pada kawasan kebakaran. Dalam penelitian ini, dilakukan analisis untuk mengklasifikasikan tingkat vegetasi kawasan pasca kebakaran dengan memanfaatkan indeks vegetasi dengan tujuan mengetahui tingkat vegetasi pasca kebakaran pada wilayah rawan kebakaran di kabupaten Riau. Model yang digunakan pada penelitian ini memakai algoritma Random Forest dan variabel penentu yang digunakan adalah NDVI, NBR, EVI, dan SAVI. Penelitian ini dilakukan dengan menggunakan 2 citra satelit, yaitu Citra Landsat 8 dan Sentinel-2. Dasaset yang didapatkan menggunakan landsat-8 adalah 1871 data, sedangkan dengan menggunakan sentinel-2 diperoleh 606 data. Akurasi data testing maksimal yang diperoleh dengan menggunakan landsat-8 adalah sebesar 99%, sedangkan dengan menggunakan sentinel-2, diperoleh akurasi maksimal sebesar 94%.
Improved LSTM Method of Predicting Cryptocurrency Price Using Short-Term Data Risna Sari; Kusrini Kusrini; Tonny Hidayat; Theofanis Orphanoudakis
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 1 (2023): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.80776

Abstract

As cryptocurrencies develop, it cannot be denied that crypto prices are volatile. One of the influencing factors is the increasing volume of transactions which attracts the interest of researchers to conduct research in developing coin price predictions from cryptocurrencies. The method, algorithm and amount of data affect the prediction results. In this study, prediction modelling will be carried out using the LSTM method and short-term data. This study will conduct two experiments using the simple LSTM method and utilising multivariate time series with LSTM. The smallest predicted value is obtained using an 80/20 data allocation distribution scenario, input layer LSTM = 360, Epoch = 500, a Solana coin with RMSE = 0.111, R2 = 0.9962. It can be interpreted that short-term data can be used in making predictive models. Still, special attention needs to be paid to the characteristics of the dataset used and the modelling methodology, and it is hoped that the results of this study can be used in further research.
Co-Authors Anggi Muhammad Rifa'i Kusrini Kusrini Kusrini Kusrini Liana Trihardianingsih M. Ilham Arief Mohammad Rezza Pahlevi Muhammad Salimy Ahsan Rizki Firdaus Mulya Theofanis Orphanoudakis TONNY HIDAYAT