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All Journal JIKO (Jurnal Informatika dan Komputer) Jurnal Sistem Komputer dan Informatika (JSON)
Lucia Nugraheni Harnaningrum
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Computer Science & IT Control & Systems Engineering

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Analisis Perbandingan Performa NMF dengan LDA pada Topik Modeling Berita Online Indonesia Latifah Nurrohmah Handayani; Lucia Nugraheni Harnaningrum
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 3 (2026): Maret 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i3.9469

Abstract

Pertumbuhan konten berita digital di Indonesia menciptakan kebutuhan akan metode otomatis untuk mengekstraksi topik-topik utama dari dataset teks berita berskala besar. Penelitian ini melakukan analisis komparatif performa Non-negative Matrix Factorization (NMF) dan Latent Dirichlet Allocation (LDA) dalam tugas topic modeling berita online Indonesia dari tiga media: CNBC Indonesia, Kompas.com, dan Detik.com. Dataset terdiri dari 4.500 artikel berita dengan preprocessing meliputi tokenisasi, penghapusan stopwords, serta ekstraksi fitur menggunakan TF-IDF untuk NMF dan Count Vectorizer untuk LDA. Evaluasi performa dilakukan menggunakan coherence score (Cᵥ), topic diversity, silhouette score, dan uji chi-square untuk distribusi topik antar media. Hasil menunjukkan bahwa NMF memiliki nilai coherence lebih tinggi (0.7544) dibandingkan LDA (0.5600), topic diversity yang lebih baik (0.9400 vs 0.8400), serta efisiensi waktu training yang lebih tinggi (1.60 detik vs 108.30 detik). Uji chi-square mengonfirmasi perbedaan signifikan (p < 0.001) dalam distribusi topik antar media. Berdasarkan hasil evaluasi pada dataset yang digunakan, NMF menunjukkan performa yang lebih baik dibandingkan LDA dalam konteks topic modeling berita online Indonesia.
Improving Memory Efficiency on Android: Leveraging Data Structures for Optimal Performance Muchamad Mafmudin; Lucia Nugraheni Harnaningrum
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 3 (2025): Oktober 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i3.2131

Abstract

This study discusses strategies for increasing memory efficiency in Android application development using optimal data structures. In the era of growing mobile device usage, especially in Indonesia, where the number of users exceeds the total population, memory management has become a major challenge for Android developers. This study analyzes various data structures such as List, ArrayList, MutableList, and LinkedList, as well as comparisons between object and primitive data types in Kotlin. The results show that primitive data structures offer better memory efficiency and execution time than object-based data structures due to their simpler structure and lower complexity. Meanwhile, object data types like MutableList and ArrayList are more efficient for applications that require a balance between flexibility and performance, as they provide both primitive-like characteristics and useful built-in functions. This study also emphasizes the importance of understanding memory management and time complexity in optimizing Android application performance. Testing was conducted using both automated and manual methods. The findings show that Kotlin reduces memory usage by up to 2× and execution time by 28.6\% compared to Java. Among the evaluated structures, primitive arrays and LinkedLists showed the most stable memory performance, while MutableLists offered the best balance for object types.
Co-Authors Latifah Nurrohmah Handayani Muchamad Mafmudin