Peramalan Kedatangan Wisatawan ke Suatu Negara Menggunakan Metode Support Vector Machine (SVM)

Mukhtar, Harun; Gunawan, Rahmad; Hariyanto, Amin; Syahril, Syahril; Mulyana, Wide

doi:10.37859/coscitech.v3i3.4211

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Pada penelitian ini digunakan metode penskalaan minmax untuk normalisasi. Metode normalisasi minmax mengubah kumpulan data menjadi skala 0-1 [19]. Rumus berikut dapat digunakan dalam perhitungan MinMaxScaler:…”

Section: Normalisasiunclassified

Klasifikasi Penipuan pada Rekening Bank menggunakan Pendekatan Ensemble Learning

Maghfiroh

Findawati

Indahyanti

2023

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Accounts are a collection of numbers commonly used for all transactions in the banking world, from saving, withdrawing cash, to checking account balances either directly or online using m-banking. In bank accounts and the process of opening them, there are various kinds of criminal acts committed by individuals and groups. As a bank's obligation to prevent crime so that it can provide trust to the community. In an effort to prevent criminal acts of fraud can be solved using data mining techniques, namely classification. The purpose of classification is to predict the class label of an object based on existing attributes. The classification methods used in this research are extreme gradiant boosting (XGBoost) and random forest on 22029 records. In the classification process, this study uses a percentage ratio of 90% train data and 10% test data and tuning parameters processed by randomized search cross validation. The research stages start from preprocessing to evaluation and get a train score of 99.50% and a test score of 99.59% for extreme gradiant boosting (xgboost) while random forest gets a train score of 99.46% and a test score of 99.59%. These results show that the classification results of extreme gradiant boosting (XGBoost) are better than random forest.

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Section: Normalisasiunclassified

Klasifikasi Penipuan pada Rekening Bank menggunakan Pendekatan Ensemble Learning

Maghfiroh

Findawati

Indahyanti

2023

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“…The purpose of this testing phase is to assess the results of the training that has been conducted, with the intention of evaluating how accurate the program is. d) Denormalization of Data The function of this process is to return the normalized values to their original form [19]. Model Evaluation Parameters for ELM There are three metrics used to measure the performance of the classification system that has been constructed: precision, recall, and accuracy.…”

Section: Maximum Data For Each Parameter B) Training Processmentioning

confidence: 99%

Classification of Bulughul Maraam Categories: Prohibitions, Recommendations, and Information Using Extreme Learning Machine and Fasttext

Handayani,

Najiyah,

Wisnuwardana

2023

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Hadith is the second source of Islamic law after the Quran. After the hadiths were compiled, Imam of Hadith created collections of hadiths, one of which is Imam Bukhari who compiled the book Bulughul Maraam, which is considered to have the highest level of authenticity. Digital collections of hadiths can now be found in the form of e-books and web pages, which help in the search for hadiths. The classification of hadiths is necessary to organize them by category, making it easier to search for hadiths based on their categories. Text mining is needed to classify hadiths because it can identify patterns in unstructured text. This research aims to improve the accuracy of classifying recommended, prohibited, and informational hadiths using a dataset of 7008 hadiths, which consists of primary data taken from the book Bulughul Maraam in the Indonesian language. Previously, similar research was conducted in 2017 that classified recommended, prohibited, and obligatory hadiths with an accuracy of 85%, but only for Sahih Bukhari hadiths. In this research, the same classification categories will be examined, proposing a different method, namely the Extreme Learning Machine method and Word2vec Fasttext for text representation with a larger dataset. The results of this research show a model accuracy of 86.31%, 86% precision, and 87% recall, indicating that the proposed model performs well in classifying hadiths.

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“…Pada persamaan 1 diatas X_scaled merupakan variabel input sedangkan X merupakan matrix dataset yang akan dinormalisasi, Xmin merupakan nilai minimun dari setiap kolom dalam matrix dataset, dan Xmax merupakan nilai maksimum dari setiap kolom dalam matrix dataset [13].…”

Section: Normalisasi Dataunclassified

Penggunaan Variabel Event dan Libur Sekolah Dalam Memprediksi Wisatawan Dengan Metode LSTM

Rusmana,

Kusrini,

Kusnawi

2023

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Event yang diadakan diberbagai daerah menjadi daya tarik tersendiri bagi wisatawan untuk datang ketempat tersebut. Liburan musiman sekolah juga menjadi agenda tahunan keluarga untuk pergi ke tempat wisata. Naik turunnya jumlah wisatawan yang datang ke Provinsi NTB memberikan dampak kepada pemerintah daerah, masyarakat sekitar tempat wisata dan pelaku usaha bidang pariwisata. Tujuan penelitian ini untuk melakukan pengujian terhadap variabel event tahunan dan libur sekolah. Datasetyang digunakan didapatkan dari website publik Provinsi NTB yaitu data.ntbprov.go.id dataset tersebut berupa histori jumlah kunjungan wisatawan setiap bulan, dari website disbudpar.ntbprov.go.id didapatkan dataset event tahunan dan dari website kalender pendidikan.com didapat dataset kalender akademik untuk liburan sekolah, dataset yang diambil dari setiap sumber diambil mulai dari tahun 2017 sampai tahun 2022. Dari semua dataset yang didapat bisa dimanfaatkan dalam menggali informasi untuk melakukan prediksi. Dalam melakukan prediksi digunakan Algoritma LSTM dengan menggunakan variabel histori wisatwan, event dan libu sekolah. Penggunaan variabel histori, event dan liburan menghasilkan kinerja MAPE sebesar 20.8% dengan penggunaan data training dan data testing 90/10. Hasil kinerja dengan variabel histori dan liburan saja menghasilkan kinerja MAPE sebesar 38,6%. sedangkan hasil dengan variabel histori dan event saja menghasilkan kinerja MAPE sebesar 23,81%. Ini menunjukan bahwa variabel event dan kalender liburan bisa dengan baik digunakan dalam melakukan prediksi terhadap kedatangan wisatawan di waktu berikutnya. Penelitian ini memperkenalkan pendekatan baru dalam memprediksi jumlah wisatawan dengan menggunakan variabel event tahunan dan kalender libur sekolah dengan menggunakan algoritma LSTM sebagai alat prediksi yang lebih canggih, yang sebelumnya belum banyak dieksplorasi dalam konteks prediksi pariwisata di Provinsi NTB.

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Peramalan Kedatangan Wisatawan ke Suatu Negara Menggunakan Metode Support Vector Machine (SVM)

Cited by 3 publications

References 15 publications

Klasifikasi Penipuan pada Rekening Bank menggunakan Pendekatan Ensemble Learning

Klasifikasi Penipuan pada Rekening Bank menggunakan Pendekatan Ensemble Learning

Classification of Bulughul Maraam Categories: Prohibitions, Recommendations, and Information Using Extreme Learning Machine and Fasttext

Penggunaan Variabel Event dan Libur Sekolah Dalam Memprediksi Wisatawan Dengan Metode LSTM

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