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All Journal Jurnal Informatika dan Teknik Elektro Terapan METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi
Sarwanti, Ruth Tika
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Computer Science & IT Economics, Econometrics & Finance

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IMPLEMENTASI ETL DAN TOPIC MODELING MENGGUNAKAN ALGORITMA LATENT DIRICHLET ALLOCATION UNTUK IDENTIFIKASI TOPIK KRIMINAL PADA BERITA ONLINE Medistarani, Ririn; Kamila, Ahista Tasya; Pradana, Bintang Ary; Sarwanti, Ruth Tika; Rozikin, Chaerur
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3S1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3S1.8041

Abstract

Peningkatan angka kriminalitas di Indonesia berdampak pada semakin masifnya pemberitaan kejahatan di media daring. Data berita yang tidak terstruktur memerlukan pendekatan sistematis agar dapat dianalisis secara efektif. Penelitian ini bertujuan untuk mengimplementasikan proses Extract, Transform, Load (ETL) dan metode topic modeling menggunakan algoritma Latent Dirichlet Allocation (LDA) untuk mengidentifikasi topik kriminal pada artikel berita online, khususnya dari portal Detik.com. Data dikumpulkan melalui teknik web scraping, kemudian diproses melalui tahapan transformasi untuk pembersihan dan standarisasi, serta dimuat ke dalam basis data agar lebih terorganisasi. Selanjutnya, dilakukan text preprocessing dan representasi teks menggunakan Bag of Words sebelum dimodelkan dengan LDA. Hasil penelitian menunjukkan bahwa dari 4.105 artikel kriminal, diperoleh 7 topik utama, yaitu pencurian dan kekerasan fisik, kejahatan seksual dan kekerasan anak, kejahatan politik dan pelanggaran HAM, kekerasan bersenjata dan separatisme, kejahatan finansial dan narkotika, pembunuhan dan penganiayaan, serta pelanggaran hukum dan penyalahgunaan wewenang. Analisis tren menunjukkan dua topik dominan sepanjang periode penelitian, yakni pencurian serta kejahatan seksual. Temuan ini menegaskan pentingnya integrasi ETL dan LDA untuk memahami pola kriminalitas secara sistematis, serta dapat menjadi dasar pengambilan kebijakan berbasis data.
Perbandingan Convolutional Neural Network dan Algoritma Machine Learning Konvensional untuk Klasifikasi Kemiskinan Multidimensional di Indonesia Sarwanti, Ruth Tika; Umaidah, Yuyun
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No2.pp275-281

Abstract

Multidimensional poverty in Indonesia is a complex phenomenon involving various interconnected social, economic, and structural aspects. Conventional approaches to poverty classification often fail to capture non-linear interaction patterns and spatial dependencies inherent in multidimensional socio-economic data. This research aims to compare the performance of Convolutional Neural Networks (CNN) with conventional machine learning algorithms such as Random Forest and XGBoost in classifying multidimensional poverty in Indonesia. The research method employs a comparative quantitative approach using data from the 2023 National Socio-Economic Survey (Susenas) by BPS, covering 8.000 household observations. The target variable is multidimensional poverty status based on the Multidimensional Poverty Index (MPI) with a 1/3 cutoff. Data was split 70:30 for training and testing, with preprocessing including missing value imputation, one-hot encoding, and Min-Max scaler normalization. The CNN model was designed with a two-convolutional layer architecture, while Random Forest used 200 decision trees and XGBoost with 200 estimators. Research results demonstrate that CNN provides the best performance with 82.4% accuracy, outperforming Random Forest (80.1%) and XGBoost (81.2%). Important variable analysis reveals that housing infrastructure conditions, household head education level, and sanitation access are key factors in determining multidimensional poverty, providing strategic input for formulating more targeted poverty alleviation policies.
Perbandingan Convolutional Neural Network dan Algoritma Machine Learning Konvensional untuk Klasifikasi Kemiskinan Multidimensional di Indonesia Sarwanti, Ruth Tika; Yuyun Umaidah
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Multidimensional poverty in Indonesia is a complex phenomenon involving various interconnected social, economic, and structural aspects. Conventional approaches to poverty classification often fail to capture non-linear interaction patterns and spatial dependencies inherent in multidimensional socio-economic data. This research aims to compare the performance of Convolutional Neural Networks (CNN) with conventional machine learning algorithms such as Random Forest and XGBoost in classifying multidimensional poverty in Indonesia. The research method employs a comparative quantitative approach using data from the 2023 National Socio-Economic Survey (Susenas) by BPS, covering 8,000 household observations. The target variable is multidimensional poverty status based on the Multidimensional Poverty Index (MPI) with a 1/3 cutoff. Data was split 70:30 for training and testing, with preprocessing including missing value imputation, one-hot encoding, and Min-Max scaler normalization. The CNN model was designed with a two-convolutional-layer architecture, while Random Forest used 200 decision trees and XGBoost with 200 estimators. Research results demonstrate that CNN provides the best performance with 82.4% accuracy, outperforming Random Forest (80.1%) and XGBoost (81.2%). Important variable analysis reveals that housing infrastructure conditions, household head education level, and sanitation access are key factors in determining multidimensional poverty, providing strategic input for formulating more targeted poverty alleviation policies.
Co-Authors Kamila, Ahista Tasya Medistarani, Ririn Pradana, Bintang Ary Rozikin, Chaerur Umaidah, Yuyun Yuyun Umaidah