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All Journal Jurnal Sistem Cerdas Journal of Applied Informatics Science
Lia Farhatuaini
Universitas Islam Negeri Siber Syekh Nurjati Cirebon
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering

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XSentiment-HS: Hierarchical CNN-BiGRU-SVM with Explainable for Indonesian Multi-Level Hate Speech Detection Gina Khayatun Nufus; Rizki Dewantara; Ardi Susanto; Sokid; Lia Farhatuaini; Jaka Septiadi; Mohammad Raihan Akbar
Journal of Applied Informatics Science Volume 2 Issue 1 (2026)
Publisher : GWS Tech Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65897/jais.v2.i1.81

Abstract

Deteksi ujaran kebencian pada media sosial menuntut interpretasi teks yang kompleks karena sifatnya yang spontan dan ambigu, terutama dalam bahasa Indonesia yang kaya akan slang. Tantangan utama saat ini adalah keterbatasan penelitian sebelumnya yang mayoritas hanya melakukan klasifikasi biner tanpa mendeteksi tingkat keparahan konten. Penelitian ini mengusulkan XSentiment-HS, sebuah model deep learning hierarkis dua tahap untuk deteksi multi-tingkat hate speech. Arsitektur model menggabungkan Convolutional Neural Networks (CNN) untuk ekstraksi fitur lokal dan Bidirectional Gated Recurrent Unit (BiGRU) untuk menangkap ketergantungan kontekstual jangka panjang. Model ini juga diperkuat dengan mekanisme Multi-Head Attention dan Support Vector Machine (SVM) sebagai classifier final. Melalui integrasi ini, XSentiment-HS diharapkan mampu mengatasi tantangan ekstraksi fitur dan polisemi secara lebih efektif dibandingkan metode konvensional.
Integration of Particle Swarm Optimization in Bidirectional Memory Networks for Improved Daily Climate Forecasting Accuracy Ardi Susanto; Heru Purnomo Kurniawan; Lia Farhatuaini; Muhammad Iszul Wilsa; Gina Khayatun Nufus; Ananda Sathria Maulana Amri
Journal of Applied Informatics Science Volume 1 Issue 2 (2025)
Publisher : GWS Tech Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65897/jais.v1.i2.85

Abstract

Global climate change has caused rainfall patterns to become increasingly fluctuating and difficult to predict using conventional weather forecasting methods. Accurate daily rainfall prediction is crucial for hydrometeorological disaster mitigation and agricultural sector planning. Although Deep Learning models such as Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) are capable of handling complex time-series data, the manual determination of hyperparameters often results in suboptimal models and entrapment in local optima. This study proposes the integration of the Particle Swarm Optimization (PSO) algorithm to automatically optimize hyperparameters (number of hidden neurons, dropout rate, and learning rate) in LSTM and BiLSTM architectures. The models were evaluated using a multivariate daily climate observation dataset encompassing temperature, humidity, wind speed, and actual rainfall. Experimental results indicate that PSO-based optimization significantly enhances prediction performance compared to baseline models. The PSO-LSTM approach successfully reduced the Root Mean Square Error (RMSE) to 17.59 mm and Mean Absolute Error (MAE) to 9.07 mm, comparable to the performance of PSO-BiLSTM, which achieved an RMSE of 17.59 mm and an MAE of 9.20 mm. These findings prove that automatic parameter tuning using swarm intelligence algorithms can highly optimize sequential neural network architectures in capturing rainfall pattern volatility, making it highly recommended as a foundation for a more accurate early warning system.
Elite-Refined Genetic Algorithm with Hill Climbing Local Search for University Course Scheduling Heru Purnomo Kurniawan; Lia Farhatuaini; Nurul Bahiyah; Ardi Susanto; Muhammad Iszul Wilsa; Gina Khayatun Nufus
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.584

Abstract

Abstract— This paper proposes a hybrid optimization approach combining Genetic Algorithm (GA) and Hill Climbing (HC) to address the university course scheduling problem in the Informatics Study Program at Universitas Islam Negeri Siber Syekh Nurjati Cirebon. The hybrid GA-HC model integrates GA’s global exploration capability with HC's local refinement strategy to minimize hard and soft constraint violations while achieving balanced timetables. The dataset includes 56 course classes, 18 lecturers, and three rooms, with scheduling over five working days and 11 time slots per day. Experimental results demonstrate that GA-HC outperforms pure GA and pure HC in convergence speed, average fitness, and stability of feasible solutions. Parameter tuning analysis further shows that moderate mutation rates and limited HC iterations yield optimal trade-offs between runtime and solution quality. The proposed hybrid framework effectively enhances convergence, reduces conflicts, and improves overall timetable quality, confirming its robustness for large-scale academic scheduling problems.
Co-Authors Ananda Sathria Maulana Amri Ardi Susanto Ardi Susanto Gina Khayatun Nufus Heru Purnomo Kurniawan Jaka Septiadi Mohammad Raihan Akbar Muhammad Iszul Wilsa Muhammad Iszul Wilsa Nurul Bahiyah Rizki Dewantara Sokid