Analysis and Identification of International Tourist Visits to Indonesia Based on Data Warehouse Implementation

Larasati, Indira Septianita; Ardhana, Valian Yoga Pudya; Oktaviani, Alika; Pongtambing, Yulita Sirinti; Sampetoding, Eliyah A M; Sarungallo, Lopinta; Rahmi, Rahmi

doi:10.30865/ijics.v6i2.4537

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Ju-Long develops a forecasting model that focuses on conditions of uncertainty and insufficient information in analyzing and understanding systems through research on the conditional analysis, prediction and decision-making (Pratiwi & Achmad, 2019). This model is known as the Gray theory or GM (d,p), where d indicates the number of differentiations and p indicates the many research variables (Ahdika, 2018).…”

Section: Methodsmentioning

confidence: 99%

Forecasting the Number of Jogja-Solo KRL Passengers with the Gray Method (1,1), Moving Average and Exponential Smoothing

Priyanto¹,

Churniawan²,

Oktaria³

et al. 2023

JWS

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Introduction: The development of the Jogja Solo railway transportation, one of which is the electrification of the railway line. This electrification will increase the capacity of the number of passengers because it can cut travel time so that there will be more and more trips. The government's plan to extend the electrification of the KRL line to Kulonprogo and Madiun is an opportunity for growth in the number of Jogja Solo KRL passengers. This study aims to find the best forecasting model for the number of KRL Jogja Solo passengers among short-term forecasting models. Methods: The forecasting method used is the Gray Method (1.1), Moving Average and Exponential Smoothing. Furthermore, based on the forecasting results of each method, the error rate will be sought based on the MAPE, MAD and MSD values. Result: The error prediction value among the three selected methods, namely Gray (1,1), second-order moving average and single exponential smoothing, the results obtained from the single exponential smoothing method have the slightest error value compared to the others. So this method is the best method chosen to predict the number of Jogja Solo KRL passengers. Conclusion: the single exponential smoothing method is the best forecasting method because it has the mirror value, namely MAPE of 51, MAD of 50,308 and MSD of 3,891,632,651. The prediction results for the 14th-period passengers obtained a result of 202,067 people. The prediction results for the 14th-period passengers obtained a result of 202,067 people. Pengembangan transportasi perkeretaapian Jogja Solo, salah satunya dilakukan elektrifikasi jalur perkeretaapian. Elektrifikasi ini akan meningkatkan kapasitas jumlah penumpang karena dapat memangkas waktu tempuh sehingga perjalanan semakin bertambah banyak. Rencana pemerintah untuk memperpanjang elektrifikasi jalur KRL sampai Kulonprogo dan Madiun menjadi suatu peluang pertumbuhan jumlah penumpang KRL Jogja Solo. Penelitian ini bertujuan untuk mencari model peramalan jumlah penumpang KRL Jogja Solo terbaik diantara model peramalan jangka pendek. Metode peramalan yang digunakan adalah Metode Grey (1,1), Moving Average dan Exponential Smoothing. Selanjutnya berdasarkan hasil peramalan masing-masing metode akan dicari tingkat kesalahannya berdasarkan nilai MAPE, MAD dan MSD. Hasil perbandingan menunjukkan metode single exponential smoothing merupakan model terbaik dengan nilai kesalalahan paling rendah yaitu MAPE sebesar 51, MAD sebesar 50.308 dan MSD sebesar 3.891.632.651. Hasil prediksi penumpang periode ke 14 diperoleh hasil sebesar 202.067 orang

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Section: Methodsmentioning

confidence: 99%

Forecasting the Number of Jogja-Solo KRL Passengers with the Gray Method (1,1), Moving Average and Exponential Smoothing

Priyanto¹,

Churniawan²,

Oktaria³

et al. 2023

JWS

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“…Adanya aplikasi survey kerawanan pemilu sebagai upaya untuk mendapatkan data dan informasi yang lebih akurat dan cepat terkait kerawanan dalam pelaksanaan pemilihan umum (pemilu). Pemilu merupakan proses demokrasi yang penting dalam sebuah negara, namun seringkali terdapat berbagai masalah dan tantangan yang dapat mengganggu kelancaran pemilihan (Larasati, et al, 2022).…”

Section: Pendahuluanunclassified

Aplikasi Survei Kerawanan Pemilu Berbasis Web Menggunakan Metode SDLC

Valian Yoga Pudya Ardhana,

M. Dermawan Mulyodiputro,

Lilik Hidayati

2024

INSOLOGI: Jurnal Sains dan Teknologi

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Election vulnerability refers to the potential for interference, fraud, or threats to the integrity and democratic process in a general election. This can include certain variations such as vote manipulation, voter intimidation, misuse of state resources, as well as other legal violations that can affect the fairness and validity of election results. The solution to overcome vulnerabilities in elections is to use a survey application. The existence of an election vulnerability survey application is an effort to obtain more accurate and faster data and information regarding vulnerabilities in the implementation of general elections (elections). The election vulnerability survey application is a tool that helps minimize the risk of insecurity and improves the integrity and quality of elections in implementing democratic principles. By using this technology and application, it is hoped that monitoring and supervision of election vulnerabilities can be carried out more effectively and efficiently. The information collected can be processed quickly and accurately, so that it can provide. Testing the questionnaire involved 10 admins and respondents who were surveyors and respondents who would be surveyed and it was found that this election vulnerability survey application had been tested well and was successful.

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“…Apabila keterlambatan penerbangan terjadi, maka secara langsung akan menyebabkan banyak kerugian. Bagi penumpang, keterlambatan penerbangan menyebabkan ketidaknyamanan perjalanan pada penumpang dan traveler, suasana hati yang buruk serta kerugian ganda waktu dan ekonomi [2]. Bagi bandar udara keterlambatan penerbangan mempengaruhi operasional normal bandar udara [3].…”

Section: Pendahuluanunclassified

Prediksi Flight Delay Berbasis Algoritma Neural Network

Ardhana,

Syam,

Ramadani

et al. 2022

jieee

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Pada penerbangan komersial salah satu masalahyang tidak dapat dihindari adalah keterlambatan penerbangan. Keterlambatan penerbangan (flight delay) adalah terjadinya perbedaan waktu antara waktu keberangkatan atau kedatangan yang dijadwalkan dengan realisasi waktu keberangkatan atau kedatangan. Dengan terjadinya keterlambatan penerbangan, maka secara langsung akan menyebabkan banyak kerugian. Pada penelitian ini dibangun sebuah model untuk memprediksi keterlambatan penerbangan yang akan terjadi kedepannya dengan menggunakan metode pengenalan pola yaitu neural network. Neural network atau biasa disebut juga jaringan syaraf tiruan adalah suatu metode komputasi yang meniru sistem jaringan syaraf biologi. Metode ini menggunakan elemen perhitungan non-linear dasar yang disebut neuran yang diorganisasikan sebagai jaringan yang saling berhubungan, sehingga mirip dengan jaringan saraf manusia. Dengan menerapkan model neural network pada data latih sebanyak 20 epichs dalam waktu 11 menit. Akurasi akhir dari data latih yaitu 81,89%, sementara akurasi pada data tes yaitu 81,59%.

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Analysis and Identification of International Tourist Visits to Indonesia Based on Data Warehouse Implementation

Cited by 3 publications

References 9 publications

Forecasting the Number of Jogja-Solo KRL Passengers with the Gray Method (1,1), Moving Average and Exponential Smoothing

Forecasting the Number of Jogja-Solo KRL Passengers with the Gray Method (1,1), Moving Average and Exponential Smoothing

Aplikasi Survei Kerawanan Pemilu Berbasis Web Menggunakan Metode SDLC

Prediksi Flight Delay Berbasis Algoritma Neural Network

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