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All Journal dCartesian: Jurnal Matematika dan Aplikasi Jurnal Ilmiah Informatika dan Ilmu Komputer d'Cartesian: Jurnal Matematika dan Aplikasi Indonesian Journal of Intelligence Data Science (IJIDS)
Yohanes A.R Langi
Universitas Sam Ratulangi
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Library & Information Science Mathematics

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Peramalan Banyaknya Penumpang Bandar Udara Internasional Sam Ratulangi Manado Dengan Metode Winter's Exponential Smoothing dan Seasonal ARIMA Priscilia Felicia Angel Tambuwun; Nelson Nainggolan; Yohanes A.R Langi
d'CARTESIAN:Jurnal Matematika dan Aplikasi Vol. 12 No. 1 (2023): Maret 2023
Publisher : Universitas Sam Ratulangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35799/dc.12.1.2023.48066

Abstract

The Winter's Exponential Smoothing method is used to overcome seasonal patterns in data. This method is divided into two models, namely additive and multiplicative models. While the Seasonal ARIMA method is an ARIMA method used to solve seasonal time series. The data used is secondary data from PT. Angkasa Pura I (Persero) Sam Ratulangi International Airport Manado for the period January 2015 to December 2022. The purpose of this research is to determine the model for forecasting the number of passengers at PT.Angkasa Pura I (Persero) Sam Ratulangi International Airport Manado, as well as to compare the Winter's method Exponential Smoothing and Seasonal ARIMA based on the smallest MSD value. The results of the comparison of the two methods with the smallest MSD value are the Winter's Exponential Smoothing method with the multiplicative model equation. The results of the analysis on arriving passengers, namely the exponential smoothing of the original data (α) is 0.9, the smoothing of the trend pattern (β) is 0.1, and the smoothing of the seasonal pattern (γ) is 0.1. With the results of the 2023 forecast, namely: January 95,046, February 87,154, March 98,462, April 97,391, May 110,061, June 103,098, July 130,360, August 118,165, September 108,790, October 115,673, November 112,114, and December 136.40. The results of the analysis on domestic passenger departures are α = 0.9, β = 0.1, and γ = 0.2. With the results of forecasting the number of departures in 2023, namely January 108.900, February 88.588, March 100.646, April 98.066, May 111.638, June 112.963, July 126.684, August 111.471, September 111.872, October 116.211, November 111.990, and December 117.431.
Peramalan Banyaknya Penumpang Bandar Udara Internasional Sam Ratulangi Manado Dengan Metode Winter's Exponential Smoothing dan Seasonal ARIMA Priscilia Felicia Angel Tambuwun; Nelson Nainggolan; Yohanes A.R Langi
d\'Cartesian: Jurnal Matematika dan Aplikasi Vol. 12 No. 1 (2023): Maret 2023
Publisher : Sam Ratulangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35799/dc.12.1.2023.48066

Abstract

The Winter's Exponential Smoothing method is used to overcome seasonal patterns in data. This method is divided into two models, namely additive and multiplicative models. While the Seasonal ARIMA method is an ARIMA method used to solve seasonal time series. The data used is secondary data from PT. Angkasa Pura I (Persero) Sam Ratulangi International Airport Manado for the period January 2015 to December 2022. The purpose of this research is to determine the model for forecasting the number of passengers at PT.Angkasa Pura I (Persero) Sam Ratulangi International Airport Manado, as well as to compare the Winter's method Exponential Smoothing and Seasonal ARIMA based on the smallest MSD value. The results of the comparison of the two methods with the smallest MSD value are the Winter's Exponential Smoothing method with the multiplicative model equation. The results of the analysis on arriving passengers, namely the exponential smoothing of the original data (α) is 0.9, the smoothing of the trend pattern (β) is 0.1, and the smoothing of the seasonal pattern (γ) is 0.1. With the results of the 2023 forecast, namely: January 95,046, February 87,154, March 98,462, April 97,391, May 110,061, June 103,098, July 130,360, August 118,165, September 108,790, October 115,673, November 112,114, and December 136.40. The results of the analysis on domestic passenger departures are α = 0.9, β = 0.1, and γ = 0.2. With the results of forecasting the number of departures in 2023, namely January 108.900, February 88.588, March 100.646, April 98.066, May 111.638, June 112.963, July 126.684, August 111.471, September 111.872, October 116.211, November 111.990, and December 117.431.
Peramalan Indeks Harga Konsumen Di Kota Palu Menggunakan Metode Arima (Autoregressive Integrated Moving Average) Dalam Model Intervensi Fungsi Step Ilka Soldarima Landa; Djoni Hatidja; Yohanes A.R Langi
Indonesian Journal of Intelligence Data Science Vol 3 No 1 (2024): Volume 3 No 1 2024
Publisher : Faculty of Mathematics and Natural Sciences Sam Ratulangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35799/ijids.v3i1.55581

Abstract

Analisis intervensi adalah sebuah pendekatan dalam analisis runtun waktu yang digunakan untuk memahami dampak beberapa peristiwa yang menyebabkan perubahan pola data pada suatu titik waktu t. Penelitian ini bertujuan untuk melakukan prediksi terhadap Indeks Harga Konsumen di kota Palu dari bulan Januari hingga Juni 2023 menggunakan metode ARIMA dengan fungsi step. Data yang digunakan dalam penelitian ini adalah Indeks Harga Konsumen di Kota Palu dari Januari 2015 hingga Desember 2022. Pada bulan Januari 2020, terjadi sebuah peristiwa yang signifikan atau intervensi yang berlangsung dalam jangka waktu yang cukup lama. Oleh karena itu, model intervensi yang diduga menggunakan fungsi step dengan orde b=0, s=36, dan r=0. Setelah melakukan analisis terhadap model ARIMA (1,2,2), didapatkan model intervensi yang kemudian digunakan untuk melakukan prediksi terhadap IHK kota Palu selama 6 bulan ke depan. Hasil prediksi yang diperoleh adalah 116.23, 116.27, 116.23, 116.18, 116.13, dan 116.08. Kata kunci: Analisis Intervensi, Kota Palu,Peramalan.
Sistem Informasi Penyewaan Alat Camping (Outdoor) Berbasis Web pada Kalasey Outdoor Manado Miracle Recky Buge; Yohanes A.R Langi; Rillya Arundaa; Eric Alfonsius; Wisard Kalengkongan; Stephano Ngangi
Jurnal Ilmiah Informatika dan Ilmu Komputer (JIMA-ILKOM) Vol. 5 No. 2 (2026): Volume 5 Nomor 2 September 2026
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jima-ilkom.v5i2.86

Abstract

Kalasey Outdoor Manado merupakan usaha penyewaan perlengkapan camping yang masih menghadapi kendala dalam pengelolaan proses penyewaan, seperti pengecekan ketersediaan alat secara manual, pencatatan data pelanggan, pencatatan transaksi, pengembalian, pembayaran, dan pembuatan laporan. Penelitian ini bertujuan untuk merancang dan mengembangkan sistem informasi penyewaan alat camping berbasis web pada Kalasey Outdoor Manado. Metode pengembangan sistem yang digunakan adalah Rapid Application Development (RAD) yang terdiri dari tahap Requirements Planning, User Design, Construction, dan Cutover. Data penelitian diperoleh melalui observasi, wawancara, dokumentasi, serta studi literatur. Sistem dikembangkan menggunakan PHP sebagai bahasa pemrograman, MySQL sebagai basis data, HTML, CSS, JavaScript, untuk tampilan, dan XAMPP sebagai server lokal. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan memiliki fitur login, registrasi pelanggan, katalog alat, detail alat, transaksi penyewaan, pembayaran, pengembalian, pengelolaan data alat, pengelolaan data pelanggan, dan laporan penyewaan. Pengujian menggunakan Black Box Testing menunjukkan bahwa fitur utama sistem berjalan sesuai dengan hasil yang diharapkan. Dengan demikian, sistem ini dapat membantu Kalasey Outdoor Manado dalam mengelola penyewaan alat camping secara lebih terstruktur, efisien, dan terkomputerisasi.
Co-Authors Alfonsius, Eric Djoni Hatidja Ilka Soldarima Landa Miracle Recky Buge Nelson Nainggolan Priscilia Felicia Angel Tambuwun Rillya Arundaa Stephano Ngangi Wisard Kalengkongan