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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Community Diversity and Engagement
Sugianto
Universitas Islam Majapahit
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Social Sciences

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Prediksi Indeks Harga Konsumen Menggunakan Metode Long Short Term Memory (LSTM) Berbasis Cloud Computing Soffa Zahara; Sugianto; M. Bahril Ilmiddafiq
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 3 (2019): Desember 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (656.725 KB) | DOI: 10.29207/resti.v3i3.1086

Abstract

Long Short Term Memory (LSTM) is known as optimized Recurrent Neural Network (RNN) architectures that overcome RNN’s lact about maintaining long period of memories. As part of machine learning networks, LSTM also notable as the right choice for time-series prediction. Currently, machine learning is a burning issue in economic world, abundant studies such predicting macroeconomic and microeconomics indicators are emerge. Inflation rate has been used for decision making for central banks also private sector. In Indonesia, CPI (Consumer Price Index) is one of best practice inflation indicators besides Wholesale Price Index and The Gross Domestic Product (GDP). Since CPI data could be used as a direction for next inflation move, we conducted CPI prediction model using LSTM method. The network model input consists of 28 variables of staple price in Surabaya and the output is CPI value, also the entire development of prediction model are done in Amazon Web Service (AWS) Cloud. In the interest of accuracy improvement, we used several optimization algorithm i.e. Stochastic Gradient Descent (sgd), Root Mean Square Propagation (RMSProp), Adaptive Gradient(AdaGrad), Adaptive moment (Adam), Adadelta, Nesterov Adam (Nadam) and Adamax. The results indicate that Nadam has 4,008 RMSE’s value, less than other algorithm which indicate the most accurate optimization algorithm to predict CPI value.
Peramalan Data Indeks Harga Konsumen Berbasis Time Series Multivariate Menggunakan Deep Learning Soffa Zahara; Sugianto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (449.416 KB) | DOI: 10.29207/resti.v5i1.2562

Abstract

Multivariate Time Series based forecasting is a type of forecasting that has more than one criterion changes from time to time that it can forecast based on historical patterns of data sequences. The Consumer Price Index (CPI) issued regularly every month by the Statistics Indonesia calculated based on data observations. This study is a development of previous research that only used on type of algorithm to predict CPI value resulting poor of accuracy due to lack of architecture variations testing. This study developed a CPI forecasting model with a new approach about using several types of deep learning algorithms, namely LSTM, Bidirectional LSTM, and Multilayer Perceptron with architectural variations of the number of neurons and epochs. Furthermore, this study adapt ADDIE model of Research and Development method. Based on the results, the best accuracy is obtained from the LSTM Bidirectional with 10 neurons and 2000 epoch resulting 3,519 of RMSE value. Meanwhile, based on the average RMSE value for the whole test, LSTM gets the smallest average of RMSE followed Bidirectional LSTM and Multilayer Perceptron with the RMSE value 4,334, 5,630, 6,304 respectively.
USE AND UTILIZATION OF VILLAGE INFORMATION SYSTEM TO MAXIMIZE POPULATION AND ADMINISTRATIVE DATABASES IN PLOSOBUDEN VILLAGE LAMONGAN REGENCY eko; Sugianto; Erly Ekayanti Rosyida; Pipit Sari Puspitorini; AH. Hasan Bashori; Fera Yuliana
IJCDE (Indonesian Journal of Community Diversity and Engagement) Vol. 4 No. 1 (2023): Vol. 4 No. 1 , 2023
Publisher : LEMBAGA PENELITIAN, PENGABDIAN PADA MASYARAKAT, PENINGKATAN AKTIVITAS INSTRUKSIONAL, PENINGKATAN DAN PENJAMINAN MUTU UNIVERSITAS ISLAM MAJAPAHIT

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36815/pengabdian.v4i1.2487

Abstract

Weak archiving systems, correspondence related to services to village communities and the lack of village government regarding population and population data, so this service has the goal of developing Plosobuden Village a village website to facilitate archiving and population administration. The method carried out in the service process is carried out in several stages, namely location surveys, maturation of concepts in the internal service team, asking for input and criticism of village web creations to village officials, finalizing village web concepts and contents internally for the service team, training and assistance in village web operations. to village officials and evaluation. The results of the service show that village officials are very enthusiastic about the village web to facilitate archiving in terms of correspondence and population data archiving. The results of the evaluation are the lack of basic population input data so that the use of the village web cannot be maximized.
Co-Authors AH. Hasan Bashori eko Erly Ekayanti Rosyida Fera Yuliana M. Bahril Ilmiddafiq Pipit Sari Puspitorini Soffa Zahara