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All Journal Jurnal Mantik Penusa Jurnal Sistem Komputer dan Informatika (JSON) Journal of Applied Data Sciences
Adli Abdillah Nababan
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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ALGORITMA STRING MATCHING BRUTE FORCE DAN KNUTH-MORRIS-PRATT SEBAGAI SEARCH ENGINE BERBASIS WEB PADA KAMUS ISTILAH JARINGAN KOMPUTER Adli Abdillah Nababan; Miftahul Jannah
Jurnal Mantik Penusa Vol. 3 No. 2,Des (2019): Manajemen Dan Informatika
Publisher : Lembaga Penelitian dan Pengabdian (LPPM) STMIK Pelita Nusantara Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (581.609 KB)

Abstract

Search engines are one of the systems used in browsers to make it easier for users to obtain fast and accurate information. In this research, search engines are designed by using two different variations of string matching algorithms to determine the speed of matching strings to text. The text used as a string matching test is a term word in a computer network. The application of string matching is expected to be a reference in choosing the right and efficient algorithm to be applied in search engines. The results of this study show that the brute force algorithm and the knuth-morris-pratt algorithm can be applied well in matching strings to search engines. The knuth-morris-pratt algorithm obtains faster results compared to the brute force algorithm in string matching in terms of runtime.. Keywords :  String Matching, Brute Force, Knuth-Morris-Pratt, terms in the network, Search Engine. AbstrakSearch engine merupakan salah satu sistem yang digunakan pada browser untuk memudahkan user untuk memperoleh informasi yang cepat dan akurat. Pada penelitian ini search engine dirancang dengan menggunakan dua variasi algoritma string matching yang berbeda untuk mengetahui kecepatan dalam proses pencocokan string pada text. Adapun text yang digunakan sebagai pengujian  string matching adalah kata yang berupa istilah dalam jaringan komputer. Penerapan string matching tersebut diharapkan dapat menjadi acuan dalam pemilihan algoritrma string matching yang tepat dan efisien untuk diterapkan dalam search engine. Hasil dari penelitian ini diperoleh bahwa algoritma brute force dan algoritma knuth-morris-pratt dapat di terapkan dengan baik dalam  pencocokan string pada search engine. Algoritma knuth-morris-pratt memperoleh hasil rata-rata lebih cepat di bandingkan dengan algoritma brute force  dalam pencocokan string dari segi runtime. Keywords :  Pencocokan String, Brute Force, Knuth-Morris-Pratt, Istilah dalam Jaringan,Mesin Pencari
Analisis Kecanduan Pornography Berdasarkan Signal Electroencephalogram Menggunakan Algoritma Deep Learning wijaya, william owen; Fernando; Dhanny Rukmana Manday; Allwin M. Simarmata; Adli Abdillah Nababan
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.9326

Abstract

Deteksi objektif kecanduan pornografi masih menjadi tantangan signifikan, seringkali menghambat intervensi klinis yang efektif. Penelitian ini bertujuan mengembangkan model klasifikasi Deep Learning untuk mendeteksi kecanduan pornografi secara non-invasif berdasarkan sinyal Electroencephalography (EEG). Penelitian ini menerapkan arsitektur Gated Recurrent Unit (GRU) untuk menganalisis data EEG yang diperoleh dari 14 responden menggunakan 19 channel. Data mentah diproses melalui tahapan filtering, Independent Component Analysis (ICA) untuk artifact removal, dan normalisasi. Hasil pengujian menunjukkan model GRU yang diusulkan berhasil mencapai akurasi 94,14%, dengan nilai precision dan recall seimbang (94%), serta F1-score 0,94 untuk kedua kelas (kecanduan dan non-kecanduan). Temuan ini menunjukkan bahwa arsitektur GRU sangat efektif untuk membedakan pola aktivitas otak antara individu pecandu dan non-pecandu. Penelitian ini berkontribusi menyediakan landasan ilmiah bagi pengembangan alat bantu diagnosis otomatis yang lebih akurat untuk menunjang program diagnosis dan rehabilitasi.
Adaptive k-Nearest Neighbor Learning for Robust Modal Regression on Multimodal and Heavy-Tailed Data Sutarman Sutarman; Netti Herawati; Adli Abdillah Nababan
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1221

Abstract

Modal regression has attracted increasing attention as an alternative to mean-based regression, particularly in settings characterized by heteroscedasticity, multimodal conditional distributions, and heavy-tailed noise. In such scenarios, estimators based on central tendency may yield predictions that fall in low-density regions of the response space. This paper proposes an adaptive k-nearest neighbor framework for modal regression that integrates entropy-guided neighborhood selection with nonparametric mode estimation, including MeanShift clustering and one-dimensional kernel density estimation. The proposed approach adjusts neighborhood size based on local uncertainty, allowing the regression model to adapt to variations in data density without relying on a globally fixed parameter. Extensive experiments on simulated datasets and real-world benchmarks demonstrate that adaptive modal regression methods generally reduce or stabilize prediction errors relative to fixed-k modal regression and classical kNN mean and median estimators, particularly under heteroscedastic and multimodal conditions, although the magnitude of improvement varies across scenarios. Statistical tests confirm significant differences in most experimental settings, with practical gains ranging from incremental to substantial depending on data complexity. In addition to accuracy, computational behavior is explicitly examined. The findings show a trade-off between computational cost and predictive robustness: entropy-guided adaptive modal regression requires additional runtime due to neighborhood adaptation and density estimation, but this overhead increases proportionally with sample size and remains manageable for medium-sized datasets. Based on these results, adaptive modal regression provides a useful and flexible alternative for regression tasks involving complex and heterogeneous data distributions where robustness is prioritized over minimal computation time.
Co-Authors Allwin M. Simarmata Dhanny Rukmana Manday Fernando Netti Herawati Salsabila, Unik Hanifah Sutarman Sutarman wijaya, william owen