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Nazruddin Safaat Harahap
Department of Informatics Engineering, UIN Sultan Syarif Kasim Riau, Pekanbaru 28293, Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology

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A classification of Quran translations using K-nearest neighbors, support vector machine and random forest method Nur Delifah; Nazruddin Safaat Harahap; Surya Agustian; Muhammad Irsyad; Iwan Iskandar
Science, Technology, and Communication Journal Vol. 6 No. 1 (2025): SINTECHCOM Journal (October 2025)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i1.337

Abstract

A Classification of Quranic verses based on topics is one of the efforts to facilitate understanding and searching for information in the holy book, especially for non-Arabic readers. This study aims to test and compare the performance of three text classification methods, namely K-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF), in grouping translated Quranic verses into 15 topic classes, such as Islamic arkanul, faith, the Quran, science and its branches, charity, da'wah, jihad, human and social relations, and others. The dataset used is the English translation of the Quran with full preprocessing and an 80:20 data split for training and testing. The evaluation was carried out using accuracy, precision, recall, and F1-score metrics. The results show that RF achieved the best performance with an average F1-score of 58.48% and testing accuracy of 90.81%. KNN followed with an F1-score of 54.07% and the highest testing accuracy of 92.05%, while SVM produced the lowest F1-score at 50.76% and accuracy of 88.20%. The RF demonstrates a more balanced ability in recognizing all classes, KNN excels in overall accuracy, and SVM performs less optimally in this classification task. This research is expected to serve as a foundation for developing a more intelligent and contextual topic-based verse classification system.
Interpretative comparative analysis of LSTM and random forest for multi-label classification of English Qur’an translation Nur Delifah; Nazruddin Safaat Harahap; Okfalisa Okfalisa; Elvia Budianita
Science, Technology, and Communication Journal Vol. 6 No. 3 (2026): SINTECHCOM Journal (June 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i3.373

Abstract

The rapid growth of digital Qur'anic resources has created a need for automated systems capable of accurately categorizing verses by thematic content. The thematic complexity of Qur'anic text, in which a single verse may simultaneously convey multiple moral, spiritual, and social messages, presents a significant challenge for automated classification systems. This study conducts a comparative and explainable evaluation of long short-term memory (LSTM) and random forest (RF) for multi-label classification of English Qur'an translations across six thematic categories: arkanul Islam, iman, amal, human and community relations, akhlak, and history and story. To address severe class imbalance, synthetic minority over-sampling technique (SMOTE) was applied per label, expanding the training set from 4,489 to 19,658 samples. LSTM captured sequential contextual relationships through integer token embeddings, while RF relied on TF-IDF vector representations. Evaluated on 1,248 unseen test verses, RF achieved a higher macro F1-score (0.2748) compared to LSTM (0.2432), while LSTM retained marginally higher accuracy (79.61% vs. 79.55%). Per-label analysis revealed that both models performed best on lexically explicit labels such as arkanul Islam and iman, but consistently failed on abstract categories such as akhlak, where LSTM recorded near-zero recall of 0.61% and RF only 6.10%. This study contributes empirical evidence that TF-IDF-based SMOTE interpolation is more effective for minority-class augmentation than token-sequence interpolation, and demonstrates that macro F1-score is a more appropriate evaluation metric than accuracy for imbalanced multi-label religious text classification.
Robust cryptocurrency price forecasting using a Bayesian-optimized CNN–LSTM hybrid model Abdul Aziz Sulton; Fitri Insani; Okfalisa Okfalisa; Lestari Handayani; Nazruddin Safaat Harahap
Science, Technology, and Communication Journal Vol. 6 No. 3 (2026): SINTECHCOM Journal (June 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i3.377

Abstract

The rapid growth of cryptocurrency has caused the price movements of digital assets such as Bitcoin (BTC) and Ethereum (ETH) to become highly volatile and difficult to predict. This study aims to develop a cryptocurrency price prediction model using a hybrid convolutional neural network–long short-term memory (CNN-LSTM) architecture optimized with Bayesian Optimization. The data used in this study consisted of daily historical data for Bitcoin and Ethereum from January 1, 2018, to December 31, 2025, obtained from Yahoo Finance. The research stages included data preprocessing, normalization using Min-Max Scaling, sequence generation using the sliding window method (window sizes of 30, 60, and 90), CNN-LSTM model development, hyperparameter optimization using Bayesian Optimization (30, 50, and 100 trials), and evaluation using regression metrics including MSE, RMSE, MAE, MAPE, and R2. The results showed that the hybrid CNN-LSTM model outperformed the standalone CNN and LSTM models, with RMSE reductions of 27% – 59% for BTC and 18% – 19% for ETH. For Bitcoin data, the best model was obtained using 30 trials with a window size of 30, achieving an RMSE of $2,588.33, MAE of $2,004.23, MAPE of 1.99%, and R2 of 0.9468. Meanwhile, for Ethereum data, the best model was obtained using 50 trials with a window size of 60, achieving an RMSE of $138.56, MAE of $99.75, MAPE of 3.27%, and R2 of 0.9737. These results indicate that the combination of CNN-LSTM and Bayesian Optimization is effective for predicting cryptocurrency prices with non-linear and volatile characteristics.
Implementation of the mawaris fiqh hybrid chatbot based on retrieval-augmented generation and rule-based expert system Irpan Afrizal Putra Eriani; Nazruddin Safaat Harahap; Suwanto Sanjaya; Muhammad Irsyad
Science, Technology, and Communication Journal Vol. 6 No. 3 (2026): SINTECHCOM Journal (June 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i3.392

Abstract

Islamic inheritance law (mawaris fiqh) regulates the distribution of inheritance based on the Quran, Sunnah, and ijma’. However, many people still have difficulty in understanding the concept of inheritance and performing accurate inheritance calculations due to the complexity of faraidh rules and limited sources of information about faraidh. This study aims to develop a hybrid-based mawaris chatbot that integrates retrieval-augmented generation (RAG) and rule-based expert system to support both conceptual question answering and deterministic inheritance calculations. This system is implemented using the Voyage-3-Large embedding model, Qdrant vector database, semantic caching, large language models (LLM) for contextual response generation using models from GPT-4o (main) and llama3.2:3b (fallback mode) as well as semantic cache using paraphrase-multilingual-MiniLM-L12-v2. The "Ask Concept" answering mode uses semantic search, confidence router, and RAG, while the "Calculate Inheritance" answering mode uses a rule-based expert system for heir identification, validation, faraidh calculation, and division result preparation. The system performance is evaluated for conceptual questions using BERTScore and weighted scoring model (WSM) for inheritance calculation questions. Experimental results show that the conceptual question-answering mode achieves a pass rate of 91.3% on questions in that domain. For inheritance calculation, the RAG-based approach achieves an average score of 44%, while the rule-based expert system achieves 100% in all evaluation categories. These findings indicate that the proposed hybrid architecture effectively combines the contextual reasoning capabilities of RAG with the deterministic accuracy of rule-based calculation, making it a reliable solution for mawaris consultation and inheritance distribution assistance.
Co-Authors Abdul Aziz Sulton Elvia Budianita Fitri Insani Irpan Afrizal Putra Eriani Iwan Iskandar Lestari Handayani Muhammad Irsyad Nur Delifah Okfalisa Okfalisa Surya Agustian Suwanto Sanjaya