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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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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.
Workflow reproducibility and observability layer for Tomofast-x gravity inversion experiments I Wayan Pio Pratama
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.v6i3.374

Abstract

Tomofast-x is an open-source parallel platform for gravity and magnetic inversion; however, reproducible execution, runtime observability, and workflow traceability are commonly managed outside the inversion software itself. This study presented a provenance-aware and telemetry-aware experimentation environment for reproducible Tomofast-x workflows and evaluated it using four publicly available reference scenarios archived on Zenodo. Each scenario was reproduced three times, resulting in 12 platform-managed runs executed through isolated workspaces with structured provenance and telemetry collection. The reproduced solutions showed close agreement with the reference outputs, with mean absolute root mean square error differences ranging from approximately 2.47 × 10-11 to 4.72 × 10-9. Runtime telemetry revealed substantial operational differences between scenarios. The uncompressed baseline required approximately 10.9 GB peak memory, whereas compressed scenarios required approximately 202 – 247 MB. Runtime decreased from approximately 436 s in the baseline case to approximately 300 – 340 s in compressed executions. Telemetry was successfully collected for all runs, including processor utilization, observed process-level RAM, runtime progression, and message passing interface (MPI) worker activity. Workflow robustness was further evaluated using injected failure cases involving corrupted parameter files, missing data grids, invalid execution paths, and simulated MPI failures. The results demonstrated that the proposed platform provided reproducible, provenance-aware, and telemetry-aware experimentation support for workstation-scale Tomofast-x workflows.
YOLOv11-based detection and classification of diseases in Siamese orange fruit using digital images Rehan Khairuno; Anggi Hadi Wijaya
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.v6i3.375

Abstract

Diseases in Siamese orange fruit are one of the factors that can reduce the quality and yield of agricultural production. Manual disease identification requires considerable time and depends heavily on human observation skills; therefore, an automated system capable of detecting diseases quickly and accurately is needed. This study aims to implement the YOLOv11 model for detecting and classifying diseases in Siamese orange fruit based on digital images. The dataset used consisted of four classes, namely anthracnose, citrus canker, scab, and healthy, with a total of 627 images divided into training, validation, and testing datasets. The study utilized 20 variations of data augmentation, and DataV18 produced the best performance. The training process was conducted using the YOLOv11s architecture with 200 epochs and various data augmentation techniques. Based on the testing results, the model achieved a precision of 70.5%, recall of 61.2%, F1-score of 65.5%, mAP@0.5 of 61.8%, and mAP@0.5:0.95 of 44.8%. The results indicate that the YOLOv11 model has a fairly good capability in detecting diseases in Siamese orange fruit based on digital images and has the potential to be applied in the development of artificial intelligence-based plant disease detection systems.
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.
Antibacterial activity of a fraction of Senna alata L. leaves against Staphylococcus aureus: A comparative study of solvent types and concentrations Eldya Mossfika; Dwi Saputra; M Saka Abeiasa; Azuxetullatif Azuxetullatif
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.v6i3.379

Abstract

Skin infections occur when microorganisms enter the bloodstream, spread to other organs, multiply, and cause severe illness. One of the common causative agents is Staphylococcus aureus. Thank to their phytochemical content, the Senna alata L. leaf have been used traditionally for antibacterial properties. Herein, we report the utilize of Senna alata L. leaf extract as an antibacterial agent against the Staphylococcus aureus grow up using a two-solvent fractionation method i.e. water and 96% alcohol at concentrations of 40%, 50%, 60%, and 70%. The phytochemical screening and disc diffusion tests were adopted to determine the phytochemical compounds and antibacterial efficacy of the fraction, respectively. Further, the statistical analysis including normality and homogeneity tests followed by a two-way ANOVA to evaluate the effects of solvent and concentration. The phytochemical screening detected the presence of alkaloids, flavonoids, tannins, and saponins in both fractions. The antibacterial efficacy results shows that the water and alcohol fractinations at a concentration of 70% produced the greatest antibacterial effect, with an inhibition zone diameter of 18 ± 0.18 mm and 18.90 ± 0.32 mm, respectively. A two-way ANOVA revealed a significant interaction (p = 0) between solvent type and concentration, indicating that antibacterial potency depends on both factors. These results confirm that the Senna alata L. leaf extract has a good potential for use as an antimicrobial agent.
Identification of tenera and dura variety of oil palm fresh fruit bunches based on RGB color and fruit firmness Melisa Zuliana; Minarni Shiddiq; Herman Syahdan; Farid Amanullah; Tiya Novita Sari; Mita Virdina; Ola Noviza; Vicky Vernando Dasta
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.v6i3.381

Abstract

Sorting and grading oil palm fresh fruit bunches (FFBs) by variety in oil palm mills is destructive and inefficient, necessitating a more accurate, rapid, non-destructive approach. This preliminary study aims to develop a computer vision system to identify oil palm FFBs varieties (dura and tenera) using RGB intensities and fruit firmness levels. The study used 20 dura FFBs and 20 tenera FFBs, each with 10 ripe and 10 unripe FFBs, while fruit firmness was measured with a GY-3 needle-type penetrometer. Analysis of RGB intensities showed that ripe tenera had the highest values, while unripe dura had the lowest. In addition to RGB intensity analysis, this study also used principal component analysis (PCA) to visualize the separation patterns of varieties and ripeness levels based on RGB values. The PCA results showed that RGB intensity values clearly distinguished the dura and tenera groups in both ripe and unripe conditions. In terms of firmness, unripe fruits of both varieties had significantly higher firmness values than ripe fruits, with unripe dura showing the highest value of 12.5 kg/cm2 and ripe tenera showing the lowest value of 7.23 kg/cm2, indicating an inverse relationship between ripeness and fruit firmness. This study demonstrates that RGB intensities and fruit firmness levels can serve as potential parameters for distinguishing between the dura and tenera varieties in a computer-vision-based oil palm FFBs sorting system.
Adsorption of lead (II) ions using NaOH-activated matoa fruit shell (Pometia pinnata): Characterization and adsorption kinetics Fadhil Maulana Harahap; T Abu Hanifah; Sofia Anita
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.v6i3.382

Abstract

This study focuses on the utilization of matoa fruit shell waste, which contains cellulose, as a potential biosorbent for binding heavy metals in solution. The study aims to examine the ability of matoa fruit shell powder (Pometia pinnata) as a biosorbent in removing lead (II) ions from solution and to analyze adsorption characteristics through kinetic studies. The research methods included biosorbent activation using NaOH at activation ratios of 1:1, 1:2, 1:3, 1:4, and 1:5 (w/v). The adsorption process was conducted with variations in parameters, including biosorbent dose, pH, and contact time. Characterization was performed using FTIR to determine functional groups, SEM-EDS to examine surface morphology and elemental composition, and ICP-OES to determine lead concentration in the solution. Kinetic analysis employed first-order pseudo-kinetic, second-order pseudo-kinetic, and intraparticle diffusion models. FTIR analysis results indicated the involvement of hydroxyl (-OH) and carboxyl (-COO-) groups in the lead (II) ion binding process. The results of the study indicate that optimal adsorption conditions were achieved at a dose of 0.05 grams, a pH of 6, and a contact time of 60 minutes, with an adsorption efficiency of 90.76% and an adsorption capacity of 27.59 mg/g. The most suitable kinetic model was the pseudo-second-order model (R2 = 0.9999), indicating a chemisorption mechanism.
Enhancing Indonesian hadith classification through multi-word embedding and support vector machine Mila Hastati; Junadhi Junadhi; Susi Erlinda; Agustin Agustin
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.v6i3.384

Abstract

Hadith classification plays an important role in supporting the organization and retrieval of Islamic knowledge in digital environments. However, the increasing volume of digital hadith collections presents challenges for manual classification, making automated approaches increasingly necessary. This study proposes a hadith text classification framework based on support vector machine (SVM) and a Multi-Word Embedding approach. The dataset used in this study was obtained from the Kaggle hadith dataset repository and consists of 34,441 hadith records. The textual data were preprocessed through case folding, noise removal, stopword removal, and stemming before feature extraction. Three embedding strategies were evaluated, namely Word2Vec, FastText, and the proposed multi-word embedding, which combines Word2Vec and FastText representations through vector concatenation. The generated feature vectors were subsequently classified using SVM and evaluated using accuracy, precision, recall, and F1-score. Experimental results show that the proposed multi-word embedding approach achieved the best performance, obtaining an accuracy of 75.58%, precision of 75.68%, recall of 75.58%, and F1-score of 75.46%. These results outperform Word2Vec + SVM and FastText + SVM, demonstrating that the integration of contextual semantic and subword-level information produces richer feature representations and improves classification effectiveness. The findings indicate that multi-word embedding is a promising approach for automated hadith text classification and can contribute to the development of intelligent Islamic information systems.
Implementation of U-Net as EfficientNet encoder for brain tumour type classification Rahma Aulia; Junadhi Junadhi; Lusiana Efrizoni; Rini Yanti
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.v6i3.387

Abstract

Brain tumor is one of the most dangerous diseases that requires fast and accurate diagnosis to support patient diagnose. The application of deep learning on magnetic resonance imaging (MRI) images has been widely used to assist automatic brain tumor classification. This study aims to implement a hybrid U-Net encoder-EfficientNet architecture for brain tumor classification using MRI images. In this study, the U-Net encoder was utilized to extract spatial features and generate an attention mask to highlight important regions before the classification process was performed by EfficientNet-B0. The dataset used was BRISC 2025, consisting of 6,000 MRI images divided into four classes: glioma, meningioma, pituitary, and no tumor. The experiments were conducted using three data splitting scenarios, namely 60:20:20, 70:15:15, and 80:10:10. The results showed that the proposed model achieved good classification performance across all testing scenarios. In the 60:20:20 scenario, the model achieved an accuracy of 82%, precision of 0.83, recall of 0.82, and F1-score of 0.81. In the 70:15:15 scenario, the model achieved an accuracy of 84%, precision of 0.85, recall of 0.84, and F1-score of 0.83. Meanwhile, the 80:10:10 scenario produced the best performance with an accuracy of 85%, precision of 0.86, recall of 0.85, and F1-score of 0.84. These results indicate that the use of the U-Net encoder was able to help the model focus on tumor regions, thereby improving the effectiveness of the classification process.
The application of visual programming technology to classify mango leaves using a convolutional neural network Silfia Andini; Teuku Radillah; Sumijan Sumijan
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.v6i3.388

Abstract

Leaf images are representations of leaves captured using digital cameras, which visually display the morphology and structure of the leaves. The classification of mango fruit can be identified from the type of leaves by using a convolutional neural network (CNN). The aim of this research is to obtain information on the type of mango fruit based on leaf type using image processing. The experimental setup involved the application of the test data set and the training data set to the classification of mango leaves, utilising iterations on both the training and testing data sets. This process involved the use of images exhibiting slight variations in shape, both in terms of image position and leaf type, to facilitate a comparison with images of the intended leaf type and leaves not included in the training classification. The experimental results yielded an accuracy level of 80.76%, validating the efficacy of the approach.

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