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Mega Silfiani
Institut Teknologi Kalimantan
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Analisis Peramalan Inflasi Di Kota Balikpapan Menggunakan Metode ARIMA Lembang, Gebryani Rante; Silfiani, Mega; Fitria, Irma
SPECTA Journal of Technology Vol. 7 No. 3 (2023): SPECTA Journal of Technology
Publisher : LPPM ITK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35718/specta.v7i3.1026

Abstract

Uncontrolled inflation is one of the problems in a country's economy. This is because inflation is used as a reference for monetary policy. However, controlling the inflation rate is relatively difficult to do. Therefore, an accurate inflation rate forecast is needed so that it can predict future inflation. This research aims to predict future inflation using the ARIMA method. The data used in this research is inflation in Balikpapan City from January 2016 to December 2022. From the analysis results, the best ARIMA method for predicting inflation in Balikpapan City is ARIMA([1,2,12],0,[6]) which has an RMSE value of 0.22886. Further research that can be carried out to improve the accuracy of Balikpapan City inflation forecasting is the use of combined methods or adding independent variables that are able to explain Balikpapan City inflation in the future.
Empowering New Capital Zones: East Kalimantan’s Economic District Outlooks Using Location Quotient and Cluster Analysis Silfiani, Mega; Nurlaily, Diana; Fitria, Irma
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol 10, No 2 (2024)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.v10i2.21962

Abstract

This research focuses on investigating the economy of the new capital buffer zone by identifying and clustering its leading sectors in GRDP (Gross Regional Domestic Product) of East Kalimantan. The identification of a region’s leading sector through LQ (Location Quotient) index has proven to be effective. In addition, k-means clustering and Self-Organizing Maps (SOM) are adopted to provide comprehensive insights. The results show that LQ index quickly identifies the main sectors in each district of East Kalimantan. In addition, the kmeans clustering has better performance than SOM based on the Silhouette coefficient. This meticulous analysis confirms the existence of two distinct clusters, one including eight members and the other consisting of only two. Anticipating future research endeavours, the exploration of various approaches for constructing clusters, encompassing both hierarchical and non-hierarchical approaches, provides the potential to enhance the performance of clusters. By investigating this structure, a more comprehensive comprehension of the economic framework of East Kalimantan can be achieved, as well as its potential role as a buffer for the capital region.
Peramalan Harga Beras Grosir Indonesia Menggunakan SARIMA, Triple Exponential Smoothing, dan Time Series Regression Magh Heryan Tudaan; Mega Silfiani
JPNM Jurnal Pustaka Nusantara Multidisiplin Vol. 3 No. 1 (2025): February : Jurnal Pustaka Nusantara Multidisiplin
Publisher : SM Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59945/jpnm.v3i1.358

Abstract

Peramalan harga beras grosir di Indonesia merupakan hal penting dalam mendukung ketahanan pangan dan stabilitas ekonomi. Tujuan dari penelitian ini adalah untuk mendapatkan metode terbaik dalam melakukan peramalan terhadap harga beras grosir di Indonesia. Penelitian ini menggunakan metode Seasonal Autoregressive Integrated Moving Average (SARIMA), Triple Exponential Smoothing, dan Regresi Time Series. Data yang digunakan berupa data bulanan harga beras grosir di Indonesia periode Januari 2010-Desember 2020. Root Mean Absolute Error (RMSE) dan Mean Percentage Absolute Error (MAPE) digunakan untuk membandingkan akurasi ketiga metode dalam melakukan peramalan. Model SARIMA yang dihasilkan untuk data harga beras grosir di Indonesia, yaitu SARIMA (1,1,0)(0,1,1)[12] dengan RMSE dan MAPE sebesar 501,88 dan 3,56%. Triple Exponential Smoothing diperoleh α, β, γ, dan φ masing masing sebesar 0,9999, 0,0766, 1e-04, dan 0,9777 dengan RMSE dan MAPE sebesar 181,515 dan 1,18%. Model Regresi Time Series yang dihasilkan untuk data harga beras yaitu Z_t= 7116,49 + 49,325X dengan RMSE dan MAPE sebesar 1121,93 dan 8,95%. Metode yang terbaik untuk melakukan peramalan pada harga beras grosir di Indonesia adalah Triple Exponential Smoothing.
Application of Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) for Stock Forecasting Silfiani, Mega; Hayati, Farida Nur; Azka, Muhammad
Jurnal Statistika dan Komputasi Vol. 2 No. 1 (2023): Jurnal Statistika dan Komputasi
Publisher : Universitas Nahdlatul Ulama Sunan Giri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/statkom.v2i1.1594

Abstract

Background: Stock price forecasting assists investors to anticipate risks and opportunities in making prudent investments and maximizing returns. Objective: This study aims to identify the most accurate model for stock forecasting. Methods: This paper utilized the daily closing stock price of Unilever Indonesia, Tbk (UNVR) from January 1, 2018 to July 31, 202.  Double Seasonal Autoregressive Integrated Moving Average (DSARIMA), was utilized in this study. Mean Absolute Scaled Error (MASE) and Median Absolute Percentage Error (MdAPE) are used to compare forecasting accuracy. Results: Following conducting each model, we assessed that the best models are DSARIMAX (0,1,[4]) ([3],1,1)5(1,1,0)253, regarding MASE and MdAPE corresponding to approximately 1.423 and 0.111. The scope of this study has limitations to a test set for one-month forecast periods. Conclusion: As stock prices rise, investors require precise forecasts. Models of forecasting must perform well. This analysis shows how the DSARIMA generate forecasts stock prices more accurately. This investigation evaluated the closing stock price of UNVR. Both MASE and MdAPE assess prediction. After analyzing each model, DSARIMAX (0,1,[4])([3],1,1)5(1,1,0)253 has the lowest MASE and MdAPE values, 1.423 and 0.111, respectively. The procedure lasted one month. Research may combine forecasts and improve their accuracy.  
Peramalan Jumlah Penumpang Kapal di Pelabuhan Balikpapan dengan SARIMA Khoiriyah, Nurhastivania Sohifatul; Silfiani, Mega; Novelinda, Resti; Rezki, Surya Muhammad
Jurnal Statistika dan Komputasi Vol. 2 No. 2 (2023): Jurnal Statistika dan Komputasi
Publisher : Universitas Nahdlatul Ulama Sunan Giri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/statkom.v2i2.2303

Abstract

Latar   Belakang: Peramalan jumlah kedatangan penumpang kapal dalam negeri di pelabuhan dalam negeri sangat penting untuk antisipasi lonjakan penumpang. Tujuan: Tujuan dari penelitian ini adalah mendapatkan model terbaik untuk peramalan jumlah kedatangan penumpang kapal. Metode: Penelitian ini menggunakan metode Seasonal Autoregressive Integrated Moving Average (SARIMA). Data jumlah kedatangan penumpang kapal dalam negeri di Pelabuhan Balikpapan dari Januari 2017 sampai dengan Desember 2021. Root mean absolute error (RMSE) digunakan untuk membandingkan akurasi peramalan. Hasil: Model SARIMA yang dihasilkan  untuk jumlah kedatangan penumpang kapal dalam negeri di Pelabuhan Balikpapan yaitu SARIMA(1,0,0)(1,0,0)12 dan SARIMA(1,0,0)(0,0,1)12 dengan RMSE masing-masing sebesar 9442.62 dan 9608.54. Kesimpulan: Model terbaik untuk peramalan jumlah kedatangan penumpang kapal di Pelabuhan Balikpapan adalah SARIMA(1,0,0)(1,0,0)12.
Perbandingan Beberapa Metode Univariat Time Series pada Peramalan Curah Hujan Silfiani, Mega; Lumintang, Indah Ayu; Winda, Retno Lelly
Jurnal Statistika dan Komputasi Vol. 3 No. 1 (2024): Jurnal Statistika dan Komputasi
Publisher : Universitas Nahdlatul Ulama Sunan Giri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/statkom.v3i1.2730

Abstract

Latar   Belakang: Peramalan curah hujan sangat penting untuk berbagai bidang seperti pertanian, manajemen sumber daya air, keamanan, transportasi dan perencanaan wilayah kota. Tujuan: Penelitian ini diarahkan untuk mendapatkan model terbaik dalam peramalan curah hujan di Kota Sampali, Sumatra Utara. Metode: Metode dalam penelitian ini meliputi Seasonal Autoregressive Integrated Moving Average (SARIMA), Time Series Regression (TSR) dan Triple Exponential Smoothing (TES). Data curah hujan bulanan Kota Sampali mulai Januari 2011 sampai dengan Desember 2022. Ukuran akurasi yang digunakan untuk perbandingan hasil peramalan yang dihasilkan oleh berbagai model dalam penelitian ini adalah Root Mean Absolute Error (RMSE). Hasil: Curah hujan Kota Sampali memiliki pola musiman sehingga sesuai dengan metode SARIMA, TSR dan TES yang masing-masing dapat mengakomodasi pola musiman. Model terbaik yang dihasilkan dari masing-masing metode SARIMA, TSR dan TES adalah SARIMA(0,0,0)(0,1,1)12, TSR dengan tujuh variabel yang signifikan dan TES dengan parameter level, tren dan seasonal masing-masing sebesar 0,2, 0,2 dan 0,2.. Kesimpulan: Model terbaik untuk peramalan curah hujan Kota Sampali adalah Triple Exponential Smoothing. Kata kunci: Peramalan curah hujan, RMSE, SARIMA, Time Series Regression, Triple Exponential Smoothing.  
Comparison of Several Univariate Time Series Methods for Inflation Rate Forecasting Salfina, Salfina; Hernanda, Yunissa; Silfiani, Mega
Eigen Mathematics Journal Vol 7 No 2 (2024): December
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v7i2.200

Abstract

Forecasting inflation is very crucial for a country because inflation is one of indicator to measure development of the country. This study aims to evaluate the effectiveness of three univariate time series methods i.e., ARIMA (Autoregressive Integrated Moving Average), Double Exponential Smoothing (DES), and Trend Projection (TP), in forecasting Indonesia’s monthly inflation rates using data from 2018 to 2022. The analysis identifies DES as the most accurate method, evidenced by its lowest Root Mean Square Error (RMSE) value of 2.9296, outperforming ARIMA and TP, which have RMSE values of 13.1479 and 3.47053, respectively. Consequently, DES was selected as the preferred model for forecasting inflation over the next 36 month, with the forecasts indicating a consistent downward trend in inflation throughout the year. While these findings highlight DES's effectiveness, the study also acknowledges limitations, including its reliance on univariate models that do not incorporate other economic variables, and the potential limitations of the dataset’s specific time frame. To address these limitations, future research should consider multivariate models, integrate machine learning techniques, and conduct scenario analyses to improve forecast accuracy and robustness. Despite these constraints, the study provides valuable insights into inflation forecasting in Indonesia, offering a practical tool for policymakers and contributing to more informed economic decision-making.
Perbandingan Peramalan Jumlah Kasus Kecelakaan Lalu Lintas Kota Balikpapan dengan Linear Trend Analysis dan Double Exponential Smoothing Silfiani, Mega
Equiva Journal Vol 1 No 1 (2023)
Publisher : Jurusan Matematika dan Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35718/equiva.v1i1.757

Abstract

Kecelakaan mengakibatkan berbagai kerugian sehingga penting untuk mengantisipasi kecelakaan secara akurat. Penelitian ini bertujuan untuk membandingkan dua metode untuk meramalkan jumlah kasus kecelakaan lalu lintas yang akan terjadi di Kota Balikpapan. Metode tersebut adalah linear trend analysis dan double exponential smoothing. Jumlah kasus kecelakaan lalu lintas yang terjadi di Kota Balikpapan dari Januari 2019 hingga Agustus 2022 merupakan dataset yang digunakan untuk penelitian ini. Hasil pemodelan menunjukkan bahwa linear trend analysis dengan RMSE 2,73 merupakan model yang paling akurat untuk memperkirakan jumlah kasus kecelakaan lalu lintas di Kota Balikpapan. Nilai RMSE untuk prediksi menggunakan metode double exponential smoothing adalah 2,86. Penelitian lanjutan yang dikembangkan dari penelitian ini dapat menggunakan pengaruh peraturan pembatasan sosial masyarakat akibat COVID 19 dan juga menerapkan metode machine learning yang cocok untuk sampel kecil.
Forecasting the Number of Domestic Aircraft Passengers at Sultan Aji Muhammad Sulaiman Sepinggan Balikpapan Airport with Holt-Winters Exponential Smoothing Silfiani, Mega
BESTARI BPS Kalimantan Timur Vol. 5 No. 1 (2025): Vol. 5 No. 01 (2025): Bestari 9th Edition
Publisher : BPS Kalimantan Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Air Passenger, Ensemble Averaging, Forecasting, Holt-Winters, MAPE
PELATIHAN DASAR TEKNOLOGI BAGI SISWA SEKOLAH DASAR UNTUK MENINGKATKAN KOMPETENSI SISWA Nurlaily, Diana; Sari, Surya Puspita; Silfiani, Mega; Rama, Luthfy Ahmad; S, Hadiyyan Falakha; Sembiring, Brema; Iqbal, Muhammad
JMM (Jurnal Masyarakat Mandiri) Vol 8, No 5 (2024): Oktober
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v8i5.26453

Abstract

Abstrak: Kompetensi teknologi dasar merupakan hal dasar yang perlu dimiliki oleh siswa. Pemebelajaran terkait teknologi dasar perlu diberikan kepada siswa sejak dini. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk membekali siswa sekolah dasar terkait teknologi dasar khususnya Ms. Word. Metode yang digunakan pada kegiatan ini adalah pengajaran yang dilaksanakan di dalam kelas. Pada proses pengajaran siswa diberi materi dan melaksanakan praktikum langsung menggunakan Ms. Word. Mitra kegiatan ini adalah siswa SD kelas 6. Jumlah siswa yang mengiktui pelatihan sebanyak 58 siswa yang dibagi menjadi kelas A, B, dan C. Dari hasil kuesioner yang disebarkan kepada siswa setelah kegiatan pengajaran didapatkan informasi bahwa kegiatan ini meningkatkan kemampuan siswa dalam menggunakan Ms.Word sebesar 72.41%.Abstract: Basic technological competency is a basic thing that students need to have. Learning related to basic technology needs to be given to students from an early age. This community service activity aims to equip elementary school students with basic technology, especially Ms. Word. The method used in this activity is teaching carried out in the classroom. In the teaching process, students are given material and carry out direct practicums using Ms. Word. The partners of this activity are 6th grade elementary school students. The number of students who participated in the training was 58 students who were divided into classes A, B, and C. From the results of the questionnaire distributed to students after the teaching activity, information was obtained that this activity increased students' ability to use Ms. Word by 72.41%.
Co-Authors Agung Prabowo Akbar Taufik Amrullah Aprilianti, Dwi Savira Aulia Putri Salsabila Aulia, Aleyda Kania Farra Azka, Muhammad Christianto Credidi Khala Diana Nurlaily Diana Nurlaily Dyah Wahyu Apriani Farida Nur Hayati Farida Nur Hayati Ferdy Novri Firdaus, Andi Adam Fitria, Irma Fitria, Irma Fitriani, Yustina Hanifatuzzuhro, Retno Lelly Winda Hariyadi, Fahri Dwi Hayati, Farida Nur Hermayanti, Naura Attya Hernanda, Yunissa Khoiriyah, Nurhastivania Sohifatul Lembang, Gebryani Rante Lumintang, Indah Ayu Magh Heryan Tudaan Muhammad Iqbal Novelinda, Resti Nurlaily, Diana Rama, Luthfy Ahmad Rezki, Surya Muhammad S, Hadiyyan Falakha Salfina Salfina, Salfina Sembiring, Brema Suhartono Suhartono Surya Puspita Sari, Surya Puspita Wijayanti, Anis Fadillah Winda, Retno Lelly