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All Journal Akademika : Jurnal Manajemen, Akuntansi, dan Bisnis
MOEHAMMAD NASRI ABDOEL WAHID
Sekolah Tinggi Ilmu Ekonomi Indonesia Malang
Economics, Econometrics & Finance Education

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MENGEMBANGKAN MATERI E-BOOK UNTUK PEMBELAJARAN MATA KULIAH APLIKASI KOMPUTER STATISTIK DI STIE INDONESIA MALANG MOEHAMMAD NASRI ABDOEL WAHID
AKADEMIKA Vol. 21 No. 2 (2023): Agustus 2023
Publisher : Pusat Publikasi dan Penerbitan Karya Ilmiah STIE Indonesia Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51881/jak.v21i2.43

Abstract

Penelitian terapan ini bertujuan merancang e-book untuk memberi kemudahan dalam pemahaman materi kuliah yang memerlukan perhitungan. E-book dikembangkan menggunakan Jupyter-book dengan contoh materi Perhitungan Statistika Pengujian Hipotesis. Metode penelitian, menggunakan ADDIE dengan 5 tahapan yaitu, yaitu Analysis, Design, Development, Implementation dan Evaluation. Metode ini digunakan untuk mengembangkan rekayasa rancang bangun sistem aplikasi web dan phone android berdasar sumber terbuka jupyter-book. Bahasa program menggunakan bahasa python dengan input perhitungan statistika dan output sebuah aplikasi berbasis web (dengan url). Dari evaluasi terbatas pada mahasiswa yang mengikuti perkuliahan Aplikasi Komputer Statistika, didapat nilai visual e-book dan tingkat kemudahan masing-masing memberikan 80 (baik) dan 90 (mudah).
ANALYSIS OF INDONESIAN INFLATION 2006–2024 USING PYTHON LIBRARY: ARIMA, SARIMA, AND EXPONENTIAL SMOOTHING MODELS Moehammad Nasri Abdoel Wahid; Sudarjo
Akademika : Jurnal Manajemen, Akuntansi, dan Bisnis. Vol. 24 No. 2 (2026): Agustus 2026
Publisher : Pusat Publikasi dan Penerbitan Karya Ilmiah STIE Indonesia Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51881/jak.v24i2.205

Abstract

This paper conducts an in-depth analysis of Indonesia’s monthly inflation rate from January 2006 to December 2024, employing advanced time series techniques to uncover underlying patterns and to develop a robust predictive framework. Utilizing Python’s TimeSeriesSplit for cross-validation, we implement and compare multiple forecasting models—specifically ARIMA, SARIMA, and Exponential Smoothing—evaluating their performance across a rolling forecast horizon. The study identifies key periods of volatility linked to the 2008 global financial crisis, domestic fuel subsidy reforms, and the COVID-19 pandemic, and assesses the degree to which seasonal and trend components explain inflation behavior. The SARIMA model selection yields SARIMA(5,1,1)(1,0,1,12), with AIC = 241.576. The seasonal MA coefficient is -0.8062 (t-stat = 0,000), indicating significant seasonal persistence. The lower AIC suggests that the seasonal component improves model fit. Our findings indicate that while seasonal patterns are present, they are relatively mild, and that a SARIMA model incorporating both non-seasonal and seasonal elements yields the most accurate out-of-sample forecasts. The paper contributes a methodological template for inflation forecasting in emerging markets and offers policy-relevant insights on the predictability of Indonesian inflation under structural and shock-driven conditions.
Co-Authors Sudarjo