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All Journal Format : Jurnal Imiah Teknik Informatika JIKA (Jurnal Informatika)
Aksani, Muhammad Luthfi
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Computer Science & IT Decision Sciences, Operations Research & Management

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RANCANG BANGUN SISTEM INFORMASI E-DOCUMENT KEPENDUDUKAN PADA DESA PASIR JAYA Septarini, Ri Sabti; Sugiyani, Yani; Aksani, Muhammad Luthfi; Nuramalia, Eva
Jurnal Informatika Vol 7, No 1 (2023): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v7i1.7187

Abstract

Kantor Desa Pasir Jaya merupakan salah satu instansi pemerintah yang masih menggunakan sistem manual dalam proses pengarsipan datanya, terutama dalam data kependudukan. Oleh karena itu, diperlukan sistem yang lebih terkomputerisasi untuk membantu petugas dalam pengarsipan. Dalam penelitian ini, penulis menggunakan metode URS dalam analisisnya, metode scrum dalam pengembangan sistem dan metode black box dalam pengujiannya. Sistem informasi e-Document pada Desa Pasir Jaya yang dihasilkan dengan menggunakan metode Scrum secara nyata dapat memenuhi kebutuhan pengguna sesuai dengan product backlog yang dapat dilihat dari fiturnya.
Klasifikasi Kepribadian Berdasarkan Dimensi Ekstraversi Berbasis Data Mining Menggunakan Extremely Randomized Trees Yanuardi, Yanuardi; Basri, Firdiansyah Firdaus; Aksani, Muhammad Luthfi
FORMAT Vol 14, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2025.v14.i2.008

Abstract

Personality is one of the fundamental aspects that distinguishes individual behavior, thought patterns, and interaction styles. The extraversion dimension, which is part of the Big Five Personality Traits framework, reflects an individual’s tendency to engage in social interactions with two main poles, namely introvert and extrovert. Identifying personality based on this dimension has various applications, ranging from education to employee recruitment. This study aims to develop a personality classification model based on the extraversion dimension using the Extremely Randomized Trees (ERT) algorithm and to compare its performance with other algorithms, namely Decision Tree, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The dataset used in this study was obtained from the Kaggle platform, consisting of 2,900 entries and including social behavior indicators represented by five numerical variables and two categorical variables. The research methodology involves data preprocessing, exploratory data analysis, model construction, and evaluation using confusion matrix, precision, recall, F1-score, accuracy, and ROC-AUC. The results indicate that ERT achieves the best performance compared to the other algorithms. The ERT model obtained an accuracy of 92.69%, an F1-score of 0.9269, and a ROC-AUC of 0.9429, outperforming SVM (F1 0.9173; AUC 0.9300), KNN (F1 0.9086; AUC 0.9146), and Decision Tree (F1 0.8879; AUC 0.8876). The superiority of ERT is supported by its tree-based ensemble mechanism with high randomization, which enhances generalization, reduces variance, and captures complex non-linear interactions among behavioral variables. Therefore, ERT is proven to be effective in consistently distinguishing introvert and extrovert tendencies.
Co-Authors Firdiansyah Firdaus Basri Nuramalia, Eva Septarini, Ri Sabti Yani Sugiyani, Yani Yanuardi Yanuardi