Exploratory Data Analysis terhadap Kepadatan Penumpang Kereta Rel Listrik

Samosir, Feliks Victor Parningotan; Mustamu, Loudry Palmarums; Anggara, Erik Dwi; Wiyogo, Albertus Indarko; Widjaja, Andreas

doi:10.28932/jutisi.v7i2.3700

Cited by 2 publications

(3 citation statements)

References 1 publication

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“…Exploratory Data Analysis (EDA) memiliki karakteristik fleksibel yang diperlukan untuk melakukan identifikasi dan investigasi suatu fenomena yang muncul pada saat melakukan penelitian empiris. Pengaplikasian Exploratory Data Analysis (EDA) tergantung pada konteks dan tergantung pada rincian analisis [8] [11].…”

Section: Exploratory Data Analysis (Eda)unclassified

“…Tujuan Exploratory Data Analysis (EDA) adalah mencari pola data. Exploratory Data Analysis (EDA) juga digunakan diantaranya untuk mengoptimalkan pengetahuan mengenai data, menghasilkan variabel yang penting, mendeteksi outlier, anomali pada data, dan menguji asumsi awal [11] [12]. Hal-hal inilah yang dapat digunakan dalam memperkaya analisis data dan membantu mengoptimalkan hasil klasifikasi.…”

Section: Exploratory Data Analysis (Eda)unclassified

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Pcos Disease Classification Using Feature Selection Rfecv and Eda With KNN Algorithm Method

Pitaloka,

Kusnawi

2023

J. Tek. Inform. (JUTIF)

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Polycystic ovary syndrome is an endocrine disorder of the ovaries that causes hormonal disturbances in women of reproductive age, where androgen secretion in the ovaries of women with Polycystic Ovary Syndrome (PCOS) is excessive compared to normal women. This usually occur in women with obesity which is characterized by irregular menstrual cycles, chronic anovulation, hyperandrogenism, and even infertility. Efforts are used to treat this disease in the form of hormone therapy, laparoscopic ovarian drilling, and in-vitro fertilization. However, these three therapies are focused on symptomatic therapy and are less effective in treating PCOS-related infertility. Detecting PCOS disease early is very necessary so that prevention and treatment can be carried out immediately. Therefore, a classification is carried out to detect PCOS disease by being able to analyze data that has a high degree of accuracy. The method used for the classification of PCOS disease is using the K Nearest Neighbor (KNN), method which previously carried out the feature selection process, namely the Exploratory Data Analysis (EDA), method which is used for the data analysis process by means of an analysis approach to data to find out the most accurate method and using the Recursive Feature Elimination and Cross-Validation (RFECV) selection method which ranks the features based on their level of importance to the prediction process. Further, the data classification process uses the K-Nearest Neighbors (KNN) algorithm. The results of the Exploratory Data Analysis (EDA) feature selection process produce 10 data attributes that are used and are continued by the Recursive Feature Elimination and Cross-Validation (RFECV) process by producing the 7 most important attributes used and finally the K-Nearest Neighbors (KNN) method has a high level high accuracy by producing an accuracy value of 93%, precision 82%, recall 100%, and F1 score 90%.

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Section: Exploratory Data Analysis (Eda)unclassified

Pcos Disease Classification Using Feature Selection Rfecv and Eda With KNN Algorithm Method

Pitaloka,

Kusnawi

2023

J. Tek. Inform. (JUTIF)

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“…Tahap-tahap penelitian yang dilakukan dalam tahun pertama dapat dilihat pada Gambar 1. adalah Exploratory Data Analysis (EDA) [6]. EDA digunakan untuk menjawab pertanyaan-pertanyaan, membuat hipotesis untuk analisis lanjutan, atau membantu kita dalam menyiapkan data untuk membuat model [7]. Hasil tahap ini adalah model hasil insight dari data yang sudah di visualisasikan.…”

Section: Metodeunclassified

Analisis Data Eksploratif Capaian Indikator Kinerja Utama 3 Fakultas Teknik

Hidayat

Tolago

Dako

et al. 2023

JJEEE

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Data mentah yang belum di olah akan memperlambat proses penyampaian data, dengan menggunakan eksplorasi data analisis data yang akan di sampaikan akan lebih jelas dan dapat dipahami dengan mudah oleh pembaca. Data yang digunakan adalah data capaian Indikator Kinerja Utama (IKU) 3 Fakultas Teknik UNG Tahun 2021. IKU 3 terkait data dosen berkegiatan di luar kampus. Data IKU 3 FT UNG diperoleh dari capaian IKU 3 UNG yang telah diverifikasi Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi Tahun 2021. Data tersebut kemudian dilakukan Exploratory Data Analysis (EDA), sehingga didapatkan insight (wawasan) terhadap data tersebut. Proses yang terjadi adalah proses pengenalan masalah, pengumpulan data, pembersihan data dan kemudian analisis data eksplorasi. dari data yang didapatkan setelah melakukan EDA adalah untuk meningkatkan capaian IKU 3 Fakultas teknik UNG adalah dengan melakukan pengembangan terhadap dosen baru dan juga dosen yang belum pernah melakukan kegiatan di luar kampus. Dari data itu didapatkan pula data prodi yang paling banyak menyumbang dosen berkontribusi yaitu sebanyak 83%, dan juga prodi yang menyumbang paling rendah sekitar 18%. Dengan didapatkannya hasil ini, penentu kebijakan dapat mengambil keputusan untuk melakukan hal-hal yang dapat meningkatkan capaian IKU 3 Fakultas Teknik UNG. Raw data that has not been processed will slow down the process of conveying data, by using data exploration data analysis that will be conveyed will be clearer and can be easily understood by readers. The data used is the Main Performance Indicator (IKU) 3 achievement data for the Faculty of Engineering UNG in 2021. IKU 3 is related to lecturer data on activities outside the campus. The IKU 3 FT UNG data was obtained from the achievements of the IKU 3 UNG which have been verified by the Ministry of Education, Culture, Research and Technology in 2021. The data was then carried out by Exploratory Data Analysis (EDA), so that insight was obtained from the data. The process that occurs is the process of problem identification, data collection, data cleaning and then exploratory data analysis. from the data obtained after carrying out the EDA is to improve the achievement of IKU 3 UNG Faculty of engineering is to develop new lecturers and also lecturers who have never carried out activities outside the campus. From the data, it was also obtained that the study programs that contributed the most were lecturers, namely 83%, and also the study programs that contributed the lowest, around 18%. By obtaining these results, policy makers can make decisions to do things that can improve IKU 3 achievements at the UNG Faculty of Engineering.

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Exploratory Data Analysis terhadap Kepadatan Penumpang Kereta Rel Listrik

Cited by 2 publications

References 1 publication

Pcos Disease Classification Using Feature Selection Rfecv and Eda With KNN Algorithm Method

Pcos Disease Classification Using Feature Selection Rfecv and Eda With KNN Algorithm Method

Analisis Data Eksploratif Capaian Indikator Kinerja Utama 3 Fakultas Teknik

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