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Rahmad Abdillah
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Science, Technology, and Communication Journal
Published by Lembaga Studi Pendidikan and Rekayasa Alam Riau
ISSN : -     EISSN : 27748782     DOI : https://doi.org/10.59190/stc
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology
Sintechcom is a periodical publication that publishes scientific articles on research results in the fields of Basic Science, Engineering, and Telecommunications. Scopes of journal are: Chemistry and Chemical Engineering; Physics, Material Sciences, and Mechanical Engineering; Biology, Biological and Bio System Engineering; Food and Agriculture Engineering; Statistics and Mathematics; Computer Science and Computational Science; Earth Science and Engineering; Space Engineering; Electrical Engineering; Environmental Science and Soil Science; Telecommunication; Electronic and Optic Communication; Image Processing, Computer Vision and Pattern Recognition ; Energy Conservation and Renewable Energy; Information System and Artificial Intelligence.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 122 Documents
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Application of recursive feature elimination for sex classification of skull bones using random forest Zam Afryan; Iwan Iskandar; Iis Afrianty; Benny Sukma Negara; Fadhilah Syafria
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.v6i3.390

Abstract

In forensic anthropology, sex estimation from the skull was a crucial initial step when visual identification of a body was not possible. Conventional methods relied on morphological assessment by experts, which was inherently subjective and dependent on the observer's experience. To address this limitation, this study implemented a computational approach using the random forest algorithm combined with the Recursive Feature Elimination feature-selection technique. The approach was evaluated using craniometric measurements from 2,524 individuals, comprising 1,368 males and 1,156 females, sourced from the Howell's craniometric dataset. The main challenge was the high dimensionality of the data, comprising 85 measurement features after non-biological attributes were removed. Using an excessive number of variables simultaneously introduced irrelevant information that lowered the model's ability to recognize true patterns, so the feature-selection technique was used to iteratively select the most informative measurements. The results showed that the model using all 82 features achieved an accuracy of 86.49 percent, while the optimized model using only 20 selected features achieved a higher accuracy of 86.85 percent. This indicated that by reducing the feature set by 75 percent, the model became lighter while remaining more accurate. The selection process further identified that cheekbone width and the height of the posterior ear protrusion were the most discriminative measurements between male and female crania, consistent with established biological evidence. In conclusion, the combination of random forest and recursive feature elimination produced an efficient and accurate sex-identification model, opening opportunities for its development as an objective forensic identification tool in the future.
Data-driven segmentation of sharia-based SMEs digital readiness: Comparing K-means and fuzzy C-means for strategic transformation planning Ananda Vermiansyah; Okfalisa Okfalisa; Rahmad Abdillah; Surya Agustian
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.v6i3.391

Abstract

Quired to adopt digital technologies while preserving Islamic business principles such as transparency, fairness, trustworthiness, halal integrity, and ethical value creation. However, many sharia-based SMEs still lack clear diagnostic information regarding their digital readinesslevel, making it difficult for policymakers, business associations, and SMEs managers to design targeted digitaltransformation interventions. Prior studies on SMEs digitalisation have largely focused on technology adoption, digital marketing, or general readiness assessment, while limited attention has been given to data-driven segmentation models that can classify sharia-based SMEs into actionable readiness groups. Addressing this gap, this study compares K-means and Fuzzy C-means clustering to identify digital readiness patterns among sharia-based SMEs. The dataset consists of 314 SMES records collected through questionnaires and structured into two main perspectives, includes economic/business readiness and technological/digital readiness. The variables include business activity, transaction capability, management capability, market interaction, macro-environmental readiness, digital culture, digital education, financial resources, and technical infrastructure. Prior to clustering, the data were normalised to ensure comparability across indicators. K-means was used as a hard clustering benchmark because of its computational simplicity and ability to produce clear readiness groups, while Fuzzy C-means was applied as a soft clustering method because SMEs readiness boundaries are often overlapping and gradual rather than strictly separated. The clustering process was designed to generate three readiness categories viz., low, moderate, and high digital readiness. Model evaluation was conducted using silhouette index, Davies–Bouldin index, accuracy, F1-score, computational stability, and principal component analysis-based visualisation. The results show a trade-off between cluster separation quality and classification-oriented performance. Fuzzy C-means achieved a higher silhouette index of 0.1362 and a lower Davies–Bouldin index of 2.6126, indicating better internal cluster quality and stronger ability to represent overlapping readiness characteristics. In contrast, K-means produced higher accuracy of 0.6033 and F1-score of 0.3202, and more clearly formed three practical readiness categories. These findings suggest that Fuzzy C-means is more suitable for exploratory readiness profiling where SMEs may belong partially to more than one readiness stage, whereas K-means is more useful for managerial decision-making requiring crisp classification into low, moderate, and high readiness groups. This study contributes to SMEs digital transformation literature by demonstrating that sharia-based digital readiness should be analysed not only through aggregate scores, but also through segmentation models that reveal heterogeneous readiness patterns. Practically, the proposed comparative clustering framework provides a diagnostic basis for policymakers, Islamic business institutions, and SMEs development agencies to design differentiated digital capability-building programmes.
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.
Nutri-score classification of snack products using word embedding and random forest Onky Wanda Darmawan; Junadhi Junadhi; Lusiana Efrizoni; Nurjayadi Nurjayadi
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.v6i3.393

Abstract

The increasing consumption of packaged snack products has raised concerns regarding their nutritional quality and potential health impacts. Although nutritional information is commonly provided on food packaging, many consumers experience difficulties in interpreting ingredient descriptions and nutritional labels, making it challenging to identify whether a product is healthy or unhealthy. Therefore, an automated classification system is needed to assist consumers in understanding nutritional information more effectively. This study proposes a text-based classification framework for categorizing snack products into healthy and unhealthy classes using Natural Language Processing (NLP), word embedding techniques, and the Random Forest algorithm. The dataset was obtained from the Open Food Facts database and filtered to include snack products only. After preprocessing and class balancing, a total of 465 samples were used for model development and evaluation. The preprocessing stage consisted of case folding, tokenization, stopword removal, and stemming. Three word embedding techniques, namely Word2Vec, GloVe, and FastText, were employed to transform textual ingredient descriptions into numerical feature representations. Subsequently, Random Forest was utilized as the classification algorithm, and its performance was evaluated using Accuracy, Balanced Accuracy, Precision, Recall, F1-score, and Macro F1-score. The experimental results show that GloVe achieved the best performance among the evaluated embedding methods, obtaining an accuracy of 86.02%, balanced accuracy of 84.72%, precision of 85.98%, recall of 86.02%, F1-score of 85.91%, and macro F1-score of 85.19%. The findings indicate that GloVe provides a more effective semantic representation of food-related textual information compared to Word2Vec and FastText. Overall, the proposed framework demonstrates the potential of NLP-based approaches for automated nutritional assessment and healthy food classification.
A web-based decision support system for e-wallet selection using AHP-TOPSIS with integrated economic and technical values Rahmat Zuhri Hafidz; Suwanto Sanjaya; Yelfi Vitriani; Fitra Kurnia; Febi Yanto
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.v6i3.394

Abstract

The rapid growth of financial technology in Indonesia has introduced a diverse range of digital wallet (e-wallet) options including OVO, GoPay, DANA, and ShopeePay. While this abundance of choice benefits consumers, it also creates decision-making challenges, particularly since most prior studies have neglected the balanced integration of economic and technical criteria in e-wallet evaluation. This study addresses that gap by developing a web-based decision support system for e-wallet selection using the AHP-TOPSIS method with integrated economic and technical criteria. Nine criteria were applied, comprising three economic criteria (cost structure, financial incentives, additional fees) and six technical criteria (data security, ease of use, merchant coverage, transaction speed, customer service, additional features). Primary data were collected from 85 active e-wallet users through a Likert scale 1 – 5 questionnaire. Results indicate that OVO ranked first with a TOPSIS preference score of 0.5835, followed by DANA (0.5802), GoPay (0.4654), and ShopeePay (0.3994). The developed system demonstrated the ability to produce objective, adaptive, and user-friendly recommendations to empower users with an interactive, data-driven decision support tool.
Parametric optimization of an axial wind turbine blade for a hybrid renewable energy system integrating solar PV and micro-hydro Rufinus Nainggolan; Idham Kamil; Aprima A Matondang; Nobert Sitorus; Suadi Suadi; Baringin Sibarani; Soni Hestukoro
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.v6i3.395

Abstract

The growing demand for sustainable energy has driven the development of hybrid renewable energy systems that combine multiple sources to enhance reliability and efficiency. Solar photovoltaic (PV) and micro-hydro power are two widely adopted renewable sources, yet their performance is often limited by intermittency and seasonal variability. This research focuses on optimizing an axial wind turbine blade designed to harness wind energy from moving vehicles as a supplementary power source for a hybrid PV-micro-hydro system. The primary objective is to perform parametric optimization of the turbine blade to determine the optimal angle of attack that yields the highest lift-to-drag ratio, thereby maximizing aerodynamic efficiency. A computational fluid dynamics (CFD) approach using the FLUENT package was employed to simulate blade performance at various angles of attack (0°, 5°, 10°, 15°, and 20°), while maintaining constant wind speed, air pressure, and other parameters. The blade design features a diameter of 0.35 m, six blades, and a linear taper form, with testing conducted at a wind speed of 22 m/s, equivalent to the average speed of a moving truck on a highway. The simulation results demonstrate that an angle of attack of 10° produces the highest lift-to-drag ratio, indicating superior aerodynamic performance compared to other tested angles. Contours of velocity magnitude further confirm that the 10° angle yields the most favorable airflow distribution across the blade surface. The optimized blade design is now validated for integration as a supplementary wind energy component in a hybrid PV-micro-hydro system, contributing to increased overall energy output and improved system reliability. This research successfully achieves its parametric optimization goals, and the resulting blade design is ready for prototype assembly and further field testing.
Predicting consumer loyalty from e-commerce reviews using emotion loyalty index with machine learning Midrawati Hasibuan; Jeni Sukmal; Selamat Subagio
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.v6i3.396

Abstract

Customer reviews provide ratings and affective text, yet conventional sentiment classification and rating prediction do not offer a transparent multidimensional measure of consumer loyalty across e-commerce platforms. This study aimed to construct and evaluate a review-based emotion-loyalty index (ELI) for Shopee, Tokopedia, Lazada, Blibli, and Bukalapak. A quantitative computational design combined six formative components: normalized rating, lexicon sentiment, positive emotion, negative emotion, recommendation signals, and seller responses within a bounded 0-1 score. Reviews were preprocessed and represented using TF-IDF; corpus size was not reported in the supplied documents. Platform differences were examined descriptively, while Naive Bayes, Random Forest, Linear SVM, and Logistic Regression were evaluated on holdout data using accuracy and class-level F1. Blibli achieved the highest mean ELI of 0.363 and high-loyalty share of 53.8%, whereas Bukalapak recorded 0.254 and 24.9%, producing gaps of 0.109 and 28.9 percentage points. Tokopedia, Shopee, and Lazada obtained mean ELI values of 0.359, 0.336, and 0.317. Linear SVM reached 94.3% accuracy for emotion polarity, 93.6% for purchase intention, and 90.6% for lexicon sentiment, while Logistic Regression achieved 68.5% for loyalty tertiles. The proposed ELI contributes an interpretable framework that integrates evaluation, affect, advocacy, and seller engagement while separating platform comparison from leakage-controlled prediction. The framework can support platform diagnostics and targeted loyalty interventions across competitive digital marketplaces in Indonesia and comparable emerging economies, although future validation requires a timestamped corpus, complete inferential statistics, alternative weighting schemes, and longitudinal behavioral outcomes.
Application of ensemble methods on transformer sequence classification of BERT base uncased and RoBERTa-base models for hate speech detection Reza Mahendra Sardi; Surya Agustian; Rahmad Abdillah; Febi Yanto
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.v6i3.397

Abstract

The rapid growth of social media platforms has brought a significant impact on the volume of digital interactions, which unfortunately is accompanied by a dramatic increase in the spread of hate speech and offensive language. Manual identification of negative content is highly inefficient and unscalable, thereby necessitating the development of state-of-the-art natural language processing (NLP) based automated detection systems. This study proposes the application of ensemble methods on Transformer architectures by combining two leading pre-trained language models, namely BERT (bidirectional encoder representations from transformers) base uncased and RoBERTa (robustly optimized BERT approach) base. The main focus of this research is to evaluate the performance of combining both models through a weighted average ensemble approach based on raw prediction probabilities (logits) with an even weighting ratio (50:50). Experiments were conducted using the public hate speech and offensive content identification (HASOC) 2021 dataset, covering two main scenarios: binary classification to distinguish NOT (normal) and HOF (hate/offensive) classes, and multi-class classification to categorize samples into HATE, OFFN (offensive), PRFN (profane), and NONE classes. To address the inherent challenge of significant class imbalance in the training data, this study implemented a custom class weighting function in the trainer module during the fine-tuning process. Empirical evaluation results demonstrate that the integration of the ensemble method effectively optimizes linguistic representation, suppresses prediction bias in minority classes, and improves performance stability. The ensemble model successfully achieved a macro F1-score of 0.8186 with 83.37% accuracy in the binary scenario, and a macro F1 score of 0.6570 with 68.93% accuracy in multi-class classification. This superior performance surpasses the capabilities of each baseline model individually, making it a robust hybrid architecture in tackling the variation of foul language in contemporary social media ecosystems.
Application of the smote and backpropagation neural network (BPNN) techniques in the classification of non-alcoholic fatty liver disease (NAFLD) Arif Utama Rambe; Reski Mai Candra; Fitri Insani; Rahmad Abdillah; Siska Kurnia Gusti
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.v6i3.399

Abstract

Non-alcoholic fatty liver disease (NAFLD) is a liver disorder with a high global prevalence and a significant mortality risk. However, clinical NAFLD datasets often exhibit severe class imbalance, causing machine learning models to become biased toward the majority class. This study aims to classify the mortality risk of NAFLD patients using the backpropagation neural network (BPNN) algorithm combined with the synthetic minority over-sampling technique (SMOTE). To ensure model validity, the follow-up time variable (futime) was excluded to prevent data leakage. The experiments were conducted by comparing different data split ratios (70:30, 80:20, and 90:10) as well as various hidden layer configurations and learning rates. The experimental results indicate that, without SMOTE, the model was trapped in the illusion of high accuracy (92%) while failing to detect mortality cases effectively (recall < 15%). In contrast, the application of SMOTE significantly improved the recall value, reaching 79.85% under the 80:20 data split scenario. These findings demonstrate that the integration of SMOTE and BPNN is highly effective in minimizing missed diagnoses (false negatives) in imbalanced medical datasets.
Comparative evaluation of PCA-based feature extraction and chi-square feature selection for student burnout classification using support vector machine Mira Salmira; Junadhi Junadhi; Rahmiati Rahmiati; Triyani Arita Fitri
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.v6i3.401

Abstract

Student depression has emerged as a critical mental health issue that adversely affects academic performance, psychological well-being, and quality of life. Early identification of depression risk is essential to enable timely intervention and effective mental health management. This study compares the effectiveness of two feature engineering techniques, principal component analysis (PCA)-based feature extraction and chi-square feature selection, for student depression classification using support vector machine (SVM). The experiments employed the student depression dataset from Kaggle, containing demographic, academic, lifestyle, and psychological attributes. Data preprocessing included data cleaning, label encoding, and feature scaling before feature engineering and classification. PCA was applied to reduce feature dimensionality while preserving the maximum data variance, whereas chi-square selected the most relevant features based on statistical significance. Model performance was evaluated using accuracy, precision, recall, and F1-score. The results demonstrate that PCA consistently outperformed chi-square feature selection. The PCA–SVM model achieved an accuracy of 84.09%, precision of 84.24%, recall of 84.09%, and F1-score of 84.14%, compared with 83.61%, 83.75%, 83.61%, and 83.65%, respectively, for the chi-square–SVM model. These findings indicate that PCA is more effective in reducing feature redundancy while preserving informative patterns, resulting in improved classification performance. Therefore, PCA-based feature extraction is a more suitable feature engineering approach for SVM-based student depression classification and offers a promising solution for intelligent early mental health screening in higher education.

Page 12 of 13 | Total Record : 122

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