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All Journal Jurnal Fourier LOSARI: Jurnal Pengabdian Kepada Masyarakat Journal of Data Insights Emerging Statistics and Data Science Journal
M. Al Haris
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Transportation Other

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Journal : Journal of Data Insights

 1 
Peramalan Indeks Harga Konsumen Kota Semarang dengan Metode Autoregressive Integrated Moving Average: Forecasting Consumer Price Index (CPI) of Semarang City using Autoregressive Integrated Moving Average (ARIMA) Method Sesotyaning Harum Prabuningrat; M. Al Haris; Nadia Khoirunnafisa Salma; Putri Wahyu Muharamah; Muhammad Saifuddin Nur
Journal of Data Insights Vol 1 No 1 (2023): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v1i1.124

Abstract

Indeks Harga Konsumen (IHK) merupakan salah satu indikator untuk menentukan tingkat stabilitas ekonomi suatu negara. IHK dapat memberikan informasi mengenai perkembangan harga barang dan jasa yang dibayar oleh konsumen, khususnya masyarakat kota. Pemerintah selalu menjaga mengenai presentase perubahan nilai IHK agar tetap rendah dan stabil sehingga mampu memberikan kesejahteraan untuk masyarakat. Oleh karena itu, perlu adanya peramalan data IHK untuk membantu pemerintah dalam menyusun kebijakan kedepannya. Salah satu metode yang tepat untuk meramalkan data IHK Kota Semarang yaitu dengan menggunakan model time series dengan proses Autoregressive Integrated Moving Average (ARIMA). Berdasarkan hasil analisis diperoleh Model ARIMA terbaik adalah ARIMA (0,1,1). Model terbaik menghasilan nilai kesalahan prediksi berdasarkan nilai MAPE sebesar 6,07% yang menandakan bahwa kemampuan model dalam memprediksi IHK Kota Semarang sangat akurat.
Prediction of Covid-19 Cases in Indonesia Using the Auto Regressive Integrated Moving Average Method: Prediksi Kasus Covid-19 di Indonesia Menggunakan Metode ARIMA Sawiah Adam, Asriyanti; Safira, Rahma; M. Al Haris; Amri, Saeful
Journal of Data Insights Vol 3 No 1 (2025): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v3i1.212

Abstract

This study discusses the use of the ARIMA (Auto Regressive Integrated Moving Average) model to predict the number of COVID-19 cases in Indonesia based on previous data. The results of the analysis show that the ARIMA (1,0,0) model is the most accurate in predicting the spread of COVID-19. Based on this model, the prediction results obtained that confirmed COVID-19 data from January to December 2022 are predicted to decrease. The number of confirmed cases of COVID-19 until December 2022 is predicted to reach 20,0365 cases of spread. So this Covid-19 case still needs special and more serious attention from the government and the public must still be vigilant because based on the results of the study there have been no signs of a significant decrease in the spread of Covid-19 cases. This study provides important insights for the government, medical personnel, and the public in planning strategies for preventing and handling the pandemic
K-Nearest Neighbor (KNN) Method for Weather Data Prediction: Penerapan Metode K-Nearest Neighbour (KNN) Untuk Prediksi Data Cuaca Putri, Agata Dwi Putri; M. Al Haris; Fauzi, Fatkhurokhman; Amri, Saeful
Journal of Data Insights Vol 3 No 1 (2025): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v3i1.214

Abstract

The weather tends to change frequently every day, so weather forecasts are made to be used as an early warning if sudden weather changes occur. By forecasting the weather, losses can be minimized and people are alert to carry out outdoor activities. From this problem, the K-Nearest Neighbor (KNN) method was applied. This method is expected to provide accurate and efficient information to obtain weather predictions for existing conditions. The data used is secondary data. After conducting research on training data (old data) amounting to 80% and test data (new data) amounting to 20%. The accuracy results from the testing data predictions are 75% with a value of k = 8.
Co-Authors Ali Imron Amri, Saeful Dannu Purwanto Fauzi, Fatkhurokhman Febrianti, Fatika Lovina Firdatul Fahria Ihsan Fathoni Indah Fitriyani Linda Puspitasari Miftakhul Haris Muhammad Rifqy Ardiansyah Muhammad Saifuddin Nur Nadia Khoirunnafisa Salma Prizka Rismawati Arum Putri Wahyu Muharamah Putri, Agata Dwi Putri Rahma Nurmalita Safira, Rahma Sawiah Adam, Asriyanti Sesotyaning Harum Prabuningrat Siti Hamidah Ardhy Suci Laeliyah