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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) TEKNOLOGI INFORMASI Jurnal Teknologi Informasi dan Ilmu Komputer REKAYASA Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Ilmiah Biosaintropis (Bioscience-Tropic) Fountain of Informatics Journal Journal of Development Research JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA EnviroScienteae JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Journal of Information Systems and Informatics Jurnal Mnemonic International Journal of Advances in Data and Information Systems Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurnal Cahaya Mandalika Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Janitra Informatika dan Sistem Informasi J-Intech (Journal of Information and Technology) Internet of Things and Artificial Intelligence Journal Ta'dib Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal ilmiah teknologi informasi Asia
Mokhamad Amin Hariyadi
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Physics Social Sciences Other

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Penerapan Long Short-Term Memory dan TF-IDF dalam Mengklasifikasikan Sentimen Publik terhadap Kebijakan Efisiensi Anggaran 2025 Maharani, Hamidah Lutfiyanti; Hariyadi, Mokhamad Amin; Abidin, Zainal
Teknologi Informasi : Teori, Konsep, dan Implementasi : Jurnal Ilmiah Vol 16 No 2 (2025): JURNAL TEKNOLOGI INFORMASI: Teori, Konsep dan Implementasi Vol 16 No 2 Tahun 202
Publisher : STMIK PPKIA Pradnya Paramita

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36382/jti-tki.v16i2.605

Abstract

Tanggapan masyarakat terhadap suatu kebijakan seringkali bervariasi dan perlu dilakukan klasifikasi untuk memahami gambaran keseluruhan respons publik. Salah satu contohnya adalah kebijakan Efisiensi Anggaran 2025, yang menjadi fokus penelitian ini. Dilakukan analisis tweet yang terkait dengan kebijakan tersebut menggunakan model LSTM untuk mengklasifikasikan sentimen publik menjadi dua kategori: positif dan negatif. Pembobotan kata dilakukan menggunakan metode TF-IDF, dan hasilnya menunjukkan nilai akurasi 94,38%, precision 93,75%, recall 89,44%, dan F1-score 91,55% pada epoch 10 dan batch size 32 pada rasio pembagian data 80:20. Dari hasil tersebut model LSTM terbukti baik dalam mengklasifikasikan data teks.
Utilizing Long Short-Term Memory (LSTM) Networks for Predicting Seismic-Induced Building Damage: A Bawean Region Case Study Zarkoni, Ahmad; Almais, Agung Teguh Wibowo; Crysdian, Cahyo; Hariyadi, Mokhamad Amin; Pagalay, Usman; Sugiharto , Tomy Ivan
Jurnal Ilmiah Teknologi Informasi Asia Vol 20 No 1 (2026): Volume 20 Issue 1 2026 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v20i1.1212

Abstract

This study examines the feasibility of employing Long Short-Term Memory (LSTM) networks to estimate earthquake-induced building damage using a focused dataset derived from the continuous 8-day mainshock–aftershock sequence that occurred in March 2024. A total of 483 events were analyzed, utilizing three readily available source parameters: magnitude, depth, and epicentral distance to predict the corresponding EMS-98 damage grade. The motivation for using an LSTM architecture stems from its capacity to model temporal dependencies within sequential seismic activity, despite the limited size of the dataset. The best-performing single-split model (B4) achieved a test R^2 of 0.5738 and an RMSE of 0.2997 on the held-out set. However, to obtain a more robust assessment of the model’s generalizability, a 5-fold TimeSeriesSplit cross-validation was conducted. The cross-validation procedure yielded a mean R^2 of 0.49 with a standard deviation of 0.27, and a mean RMSE of 0.33 with a standard deviation of 0.16. These results demonstrate that the LSTM model provides a credible baseline model for exploratory damage estimation, although a substantial portion of the variance remains unexplained due to the absence of geotechnical, soil amplification, and structural fragility information. The findings highlight the potential of sequence-based modeling for rapid damage estimation and underscore the need for integrating site-specific and structural variables in future work to enhance predictive accuracy.
Career Path Mapping Using the Random Forest Method for Vocational High School Graduates Imami, Nia Kurniawati; Hariyadi, Mokhamad Amin; Arif, Yunifa Miftachul
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.9297

Abstract

Vocational high school students are prepared to enter the labor market. However, field facts show that many graduates work outside their field of expertise. To address this, the special job placement bureau (Bursa Kerja Khusus/BKK) plays an important role as it connects graduates with industry. In addition, BKK provides pre-employment training such as interview preparation and soft-skill development. This study aims to develop a classification-based career-path mapping system integrated with BKK functions. The data used are scores of eight competency dimensions for vocational students obtained from BKK. The method employed is the Random Forest algorithm. We conduct hyperparameter tuning with cross-validation. Results show Random Forest achieves accuracy of 0.895 and an F1 score of 0.905. These results indicate that optimizing for F1 yields the best balance between precision and recall while maintaining high overall accuracy. Overall, this study confirms a trade-off between overall accuracy and inter-class balance (F1): constrained tree depth tends to maximize accuracy, whereas unconstrained depth benefits F1. Random Forest proves reliable and stable for the classification task in this career-path mapping
Co-Authors Agung Sedayu Agung Teguh Wibowo Almais Ainul Yaqin Ajib Hanani Akbar, Galuh Muhammad Iman Akkad, Muhammad Iqbal Arafat, Ihsan Bagus Fahad Badruz Zamanil Charis Cahyo Crysdian Cahyo Crysdian Cahyo Crysdian Cahyo Crysdian Cahyo Crysdian Eva Yustina Fathir Fathir Fathir Fresy Nugroho Hamid, Abdulhalim Hamid Salih Imam Rofiki Imami, Nia Kurniawati Imamudin, M. Imamudin Irwan Budi Santoso Juniardi Nur Fadila Kurniawan, Andang Kurniawan, Puan Maharani Maharani, Hamidah Lutfiyanti Mahmudiah, Rikha Rizki Mahsun, Muhammad Muhaimin, Afif Muhammad Andryan Wahyu Saputra Muhammad Farid Wadjdi Muhammad Isa Ansori Nur Elyta Febriyanty Nur Hidayah Hanifah Putra, Kurniawan Tri Rahmatmulya, Revaldi Ramadan, Afrijal Rizqi Ririen Kusumawati Setiyaris Setiyaris Setiyawan, Niko Heri Sifaulloh, Hafizzudin Sri Harini Sugiharto , Tomy Ivan Suhartono Syaifudin Ramadhani Usman Pagalay Wiyono, Masdar Yuliawan, Audi Bayu Yunifa Miftachul Arif Yustina, Eva Zainal Abidin Zarkoni, Ahmad