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All Journal Gaung Informatika Jurnal Nasional Teknologi Informasi dan Aplikasinya
Dwi Retnoningsih
universitas Sahid Surakarta
Computer Science & IT Engineering

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SISTEM PENGELOLAAN KENDARAAN DINAS DI PEMERINTAH KOTA SALATIGA Alfiyanus Shaf’at; Dwi Retnoningsih; Hardika Khusnuliawati
JURNAL GAUNG INFORMATIKA Vol 13 No 2 (2020): Jurnal Gaung Informatika, Volume 13, Nomor 2 Juli 2020,
Publisher : Universitas Sahid Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47942/gi.v13i2.539

Abstract

The optimal management of regional property has become a basic requirement for local governments in realizing good governance. Official vehicles as one part of regional property have a significant function. However, the process of managing official vehicles in regional organizations of Salatiga government is still not optimal. For example, the frequency of vehicle maintenance schedules are missed and vehicle tax payments are due. The change of official vehicle users also often occurs and it will be a problem if the process is not recorded properly. This study aims to design an applications in supporting regional organizations at Salatiga government for managing data and presenting information related to official vehicles. This study used the Linear Sequential Model / Waterfall Model. The data collection covered observation, interviews and literature study. The application wasmade in the form of a desktop application using the Node.js platform and it is based on javascript and utilizes the electron framework. The database used SQLite. Analysis and design of the system used a structured method through the stages of making normalization, relations between tables, entity relationship diagrams, data dictionaries, data flow diagrams, flowcharts, and interface design. The resulting application is tested using the blackbox testing method. The analysis of test results shows that the application is functioning normally according to the expected results. The main function of managing data on the use and maintenance of official vehicles has been successful through all the test scenarios conducted.
Implementasi Metode LSTM untuk Prediksi Harga Saham PT Indofood CBP Sukses Makmur TBK Endritha Pramudya; Dwi Retnoningsih; Diyah Ruswanti
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 4 (2025): JNATIA Vol. 3, No. 4, Agustus 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v03.i04.p24

Abstract

High stock price fluctuations make stock prices difficult to predict accurately. Therefore, a predictive analysis approach that utilizes historical data in addition to machine learning methods is needed to help estimate price movements more effectively. This study aims to determine the performance of the Long Short-Term Memory (LSTM) method in predicting the stock prices of PT Indofood CBP Sukses Makmur Tbk based on historical data. LSTM is a type of artificial neural network that is effective in processing time series data due to its ability to capture long term relationships between data. Historical data is used to train the LSTM model. The results show that the LSTM model is effective in predicting stock prices, with an average accuracy of 80.5%. Sukses Makmur Tbk based on historical data. LSTM is one type of artificial neural network that is effective in processing time series data due to its ability to capture long-term relationships between data points. The data used consists of ICBP stock closing prices from January 2019 to May 2025. The methods used include data cleaning, data normalization, data splitting, model design, prediction, denormalization, and evaluation using the Mean Absolute Percentage Error (MAPE) metric. The research results demonstrate that the LSTM model performs well in recognizing time series data patterns, as indicated by the lowest MAPE value of 1.43, at the combination of 100 epochs and a batch size of 32.
Co-Authors Alfiyanus Shaf’at Endritha Pramudya Hardika Khusnuliawati, Hardika Ruswanti, Diyah