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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science (JOINTECS) Jurnal Sains dan Teknologi
Soffa Zahara
Universitas Islam Majapahit
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

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Prediksi Indeks Harga Konsumen Komoditas Makanan Berbasis Cloud Computing Menggunakan Multilayer Perceptron Soffa Zahara; Sugianto Sugianto
JOINTECS (Journal of Information Technology and Computer Science) Vol 6, No 1 (2021)
Publisher : Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31328/jointecs.v6i1.1702

Abstract

Teknik prediksi merupakan salah satu area dalam data mining dimana menemukan pola dari sekumpulan data yang mengarah pada prediksi di masa depan. Prediksi dalam bidang ekonomi merupakan prediksi yang mendominasi karena merupakan salah satu parameter berkembangnya suatu negara. Indeks Harga Konsumen menggambarkan tingkat konsumsi barang dan jasa pada masyarakat yang dapat dijadikan acuan nilai inflasi. Mayoritas penelitian yang melakukan prediksi nilai Indeks Harga Konsumen sebelumnya hanya melakukan prediksi menggunakan nilai Indeks Harga Konsumen itu sendiri sebagai nilai input dan output. Penelitian ini membangun model peramalan dengan memanfaatkan multi variabel input yaitu 28 jenis harga bahan pokok harian sebagai nilai input untuk meramal nilai Indeks Harga Konsumen di kota Surabaya periode 2014 sampai 2018 dimana keseluruhan pembangunan model prediksi dilakukan di lingkungan Amazon Cloud Services. Sistem prediksi dibangun dengan algoritma Multilayer Perceptron dengan variasi arsitektur jumlah neuron, epoch, dan hidden layer. Berdasarkan hasil pengujian, akurasi terbaik dengan nilai RMSE 3.380  dihasilkan oleh konfigurasi 2 hidden layer,  hidden layer pertama dan kedua mempunyai neuron masing-masing berjumlah 10 dengan epoch sebesar 1000.
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.
Internet of Thing (IoT) untuk Pembuangan Akhir Sampah di Mojokerto Moh. Muslimin; Andhika C.P.; Luki Ardiantoro; Soffa Zahara
INSOLOGI: Jurnal Sains dan Teknologi Vol. 1 No. 6 (2022): Desember 2022
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v1i6.1214

Abstract

As a growing city, Mojokerto has social problems, such as how to manage waste. Waste is basically a material that is wasted or disposed of from a source as a result of human activities or natural processes that have no economic value, and can even have a negative economic impact because handling it requires a process to dispose of it or to clean it up. The volume of waste in Mojokerto reaches 9 tons/day, with a landfill area of ​​14 hectares, with an area of ​​about 55% used. Waste and its management is now an increasingly urgent problem in Mojokerto. As an effort to support sustainable development, it is necessary to find ways to manage waste properly and efficiently through controlled planning in the form of integrated waste management. The problem is the condition of the TPA which cannot be monitored continuously and in real time, the management of waste transportation which is less efficient, this causes piles of waste at the TPS due to delays in the garbage transportation fleet. IoT is one of the solutions, to make life better by monitoring and controlling the process. As a result, this Arduino model design can be used as a solution in the large Mojokerto area, within city limits.
Co-Authors Andhika C.P. Luki Ardiantoro M. Bahril Ilmiddafiq Mohammad Muslimin Sugianto Sugianto Sugianto