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Multitek Indonesia : Jurnal Ilmiah
Published by Universitas Muhammadiyah Ponorogo
ISSN : 19076223     EISSN : 25793497     DOI : -
Core Subject : Science, Engineering,
Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering
Multitek Indonesia : Jurnal Ilmiah is a journal published by the Technic Faculty, Universitas Muhammadiyah Ponorogo (Unmuh Ponorogo) in collaboration with Universitas Muhammadiyah Ponorogo Research and Community Service. Published twice a year (June and Desember), contains six to ten articles and receive articles in the field of technic review studies with research methodologies that meet the standards set for publication. Manuscript articles can come from researchers, academics, practitioners, and other technic observers who are interested in research in the field of tehnic.
Arjuna Subject : -
Articles 207 Documents
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Thermal Analysis And Performance Of An Active Solar Dryer System Based On Solar Panels For Improving The Quality Of Cocoa Bean Drying Nely Ana Mufarida; Sofia Ariyani; Nurhalim; Sutikno; Ulya Anisatur Rosydah
MULTITEK INDONESIA Vol 20 No 1 (2026): July (On Progress)
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/mtkind.v20i1.12863

Abstract

Cocoa is one of Indonesia’s leading agricultural commodities and plays a strategic role in increasing national foreign exchange earnings. However, the low quality of Indonesian cocoa beans remains a major obstacle in penetrating the export market. This issue is largely caused by post-harvest drying processes that still rely on traditional sun-drying methods. Dependence on weather conditions often results in cocoa beans failing to reach the ideal moisture content (6–8%), making them susceptible to mold growth, deterioration, and reduced quality. These conditions ultimately lower the market value and competitiveness of Indonesian cocoa in the global market.This study aims to develop a direct-type active solar dryer technology powered by solar energy to improve drying efficiency and preserve the quality of cocoa beans. The system is designed using a combination of solar panels, heating elements, and exhaust fans controlled to regulate temperature, humidity, and airflow optimally throughout the drying process. The methodology includes design engineering, thermal simulation, and experimental testing to determine the best operating conditions that ensure fast drying, energy efficiency, and maintained physical and chemical quality of the cocoa beans. This research is expected to serve as an innovative step in the application of renewable energy in the agricultural sector, contributing to improved productivity and sustainable competitiveness for Indonesian cocoa farmers.
Syllable-Based Detection and Recognition of Braille Cells Using Faster R-CNN Risky Aswi Ramadhani; Made Ayu Dusea Widyadara; Wahyu Cahyo Utomo; Marga Asta Jaya Mulya
MULTITEK INDONESIA Vol 20 No 1 (2026): July (On Progress)
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/mtkind.v17i1.13429

Abstract

Braille digitization plays an important role in improving access to written information for visually impaired individuals. However, automatic recognition of Braille text remains challenging due to the small size, dense spatial arrangement, and subtle variations of Braille dot patterns. This study proposes a syllable-based Braille cell detection and recognition framework using a customized Faster R-CNN architecture. The system employs a Region Proposal Network (RPN) to localize individual Braille cells, followed by an AlexNet-based classifica-tion network to recognize syllable-level patterns. The proposed method is evaluated on a syllable-level Braille dataset covering 50 syllable classes, and a two-stage training strategy is adopted to improve bounding box localization accuracy. Experimental results show stable training convergence and consistent classification performance across syllable categories. Confusion matrix analysis indicates that most misclassifications occur among syllables with visually similar dot configurations. Despite sensitivity to variations in physical Braille quality, the proposed framework indicates potential applicability in accessibility-oriented Braille digitization systems
PERANCANGAN SISTEM INFORMASI PENGARSIPAN DOKUMEN BERBASIS WEB DI BIDANG SUMBER DAYA KESEHATAN DINAS KESEHATAN KABUPATEN PATI Nurul Baiah; Sigit Sugiharto
MULTITEK INDONESIA Vol 20 No 1 (2026): July (On Progress)
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/mtkind.v20i1.13443

Abstract

Advances in information technology have led government agencies to adopt digital changes in administrative management, including document archiving. Manual archive management is still widely used and causes various problems, such as difficulty in searching for documents, the risk of archive destruction, and a lack of work efficiency. The Health Resources Division of the Pati District Health Office has a high volume of documents, requiring an integrated, digital-based archiving system. This study aims to design and develop a Web-Based Document Archiving Information System to increase the effectiveness of archive management, speed up the document search process, and support orderly administration. The system was developed using the Waterfall method, which includes the stages of requirements, analysis, design, implementation, testing, and maintenance. The system was refined using the PHP programming language and a database as a centralized archive storage medium. The main features of the system include user authentication, document uploading by category, and document management in the form of viewing, downloading, renaming, and deleting documents. System testing was conducted using the Black Box method on 26 functional components, and the test results indicated that all system functions worked well as required. Thus the developed information system is capable of improving archive management capabilities and supporting the improvement of administrative service quality in the Health Resources Division of the Pati District Health Office.
Effect Of Palm Shell Waste As A Filler In AC-WC Using Local Aggregate Louise Elizabeth; Celvin Stevianus Mangago Jati; Elizabeth
MULTITEK INDONESIA Vol 20 No 1 (2026): July (On Progress)
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/mtkind.v20i1.13643

Abstract

The rise in palm oil production in Indonesia generates large amounts of palm shell waste, which can pollute the environment if not properly managed. This study analyzes the characteristics of So'do River aggregate and the effect of using palm shell waste as a filler on Asphalt Concrete – Wearing Course (AC-WC) mixtures. Laboratory experiments used 60/70 penetration asphalt with 6.50% asphalt content. Cement filler was partially replaced with palm shells at 0%, 25%, 50%, 75%, and 100%. Marshall tests were conducted following the 2018 General Bina Marga Specifications Revision 2 to measure stability, flow, VIM, VMA, and VFB. Results indicate that So'do River aggregate meets technical standards, and palm shell filler affects AC-WC mixture properties, particularly stability and volumetric parameters. The best stability occurred at a 50% palm shell–50% cement ratio. Some substitution levels still comply with specifications, showing palm shell waste has potential as a sustainable alternative filler in AC-WC mixtures.
Automated Synthesis Of Product Return Recommendations Via Groq And Large Language Models Dian Hanifudin Subhi; usman nurhasan; Ibnu Tsalis Assalam
MULTITEK INDONESIA Vol 20 No 1 (2026): July (On Progress)
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/mtkind.v20i1.13659

Abstract

Industrial economic resilience depends on the efficiency of after-sales service provisioning, which is often hindered by semantic ambiguity in customer reports and latency constraints of conventional computing infrastructures. This study examines the integration of a Language Processing Unit (LPU) with a Large Language Model (LLM) under a Deterministic Reasoning Architecture (DRA) framework to address these limitations. Experiments were conducted on a heterogeneous dataset (N = 27.500) consisting of operational service records from PT Rekaindo Global Jasa and a Southeast Asian manufacturing entity over the period 2021–2025. Semantic complexity analysis based on Shannon Entropy indicates that the Repair category exhibits the highest information density (5.2 bits), corresponding to an increased risk of logical failure. Performance benchmarking demonstrates that the proposed LPU-based architecture achieves deterministic inference with a Risk Priority Number (RPN) of 42 significantly lower than stochastic GPU-based baselines (RPN > 120). Predictive integrity evaluation yields an AUC–ROC of 0.988 and an inter-rater agreement of 0.81 (Fleiss Kappa), indicating substantial alignment between automated recommendations and expert assessments. Economic robustness is validated through Monte Carlo simulations, showing a 94.2 % probability of achieving Return on Investment within 20 months, even under high-volatility scenarios. Furthermore, the framework complies with ISO/IEC 42001:2023 and the EU AI Act, achieving a Fairness Ratio above 0.94. Overall, the results demonstrate that the LPU–LLM synergy enables fast, reliable, and responsible generative AI deployment in industrial settings.
Bahasa Inggris Usman Nurhasan; Dian Hanifudin Subhi; Anugrah Nur Rahmanto; Endah Septa Sintiya; Deddy Kusbianto Purwoko Aji
MULTITEK INDONESIA Vol 20 No 1 (2026): July (On Progress)
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/mtkind.v20i1.13681

Abstract

Automated evaluation of flowchart representations is essential for the facilitation of the acquisition of basic programming concepts. Nevertheless, traditional evaluation systems that rely exclusively on structural matching demonstrate some of their most fundamental limitations. The false negative misclassification rates of such systems are frequently high when students create visually distinct structures for algorithmic logic that are semantically equivalent. A hybrid assessment framework is introduced in this study to improve the reliability and efficacy of code evaluation in order to address this challenge. The model that has been proposed combines the probabilistic feature extraction capabilities of Graph Convolutional Networks (GCNs) with mathematical logic verification through symbolic execution of an SMT Solver. While the SMT Solver deterministically establishes functional equivalence, the GCN module adaptively manages graph topological variations. Use of a real-world dataset consisting of 3.600 flowcharts generated by novice students was implemented to assess the hybrid system's functionality. According to quantitative experimental results, the proposed framework obtained a peak F1 Score of 0.88, which is a substantial improvement over conventional Abstract Syntax Tree (AST) methods (F1 Score 0.75). Additionally, the 77.4% reduction in false negative rates was achieved by incorporating the SMT Solver in comparison to a pure GCN configuration. Finally, the semantic equivalence and structural divergence issues that arise during algorithm assessment are effectively resolved by this dual architectural integration. By implementing the proposed system, higher education institutions are equipped with a more dependable mechanism for reducing human error, thereby improving the impartiality, accuracy, and efficiency of the evaluation process.
DETEKSI DAN KLASIFIKASI KUALITAS PASCAPANEN MENGGUNAKAN MODEL HYBRID SSD-EFFICIENTNETV2 BERBASIS TRANSFER LEARNING PADA BUAH TOMAT Edga Sukma Pratama; Danar Putra Pamungkas; Made Ayu Dusea Widyadara; Boyang (Tony) Yu
MULTITEK INDONESIA Vol 20 No 1 (2026): July (On Progress)
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/mtkind.v20i1.13819

Abstract

Manual post-harvest sorting of tomato fruit is prone to subjectivity and inconsistency, necessitating an automated quality assessment approach. Single Shot Detector (SSD) and EfficientNetV2 are both included in the Deep Learning architecture for efficient object detection and classification. This research develops a hybrid model that processes SSD data through a single direct detection, making it lighter than other methods, while EfficientNetV2 serves as the backbone model, capable of producing deep features efficiently. The design of the hybrid SSD-EfficientNetV2 model for the automatic detection and classification of tomato fruit quality (Solanum lycopersicum) into two classes, namely Grade A with fresh and marketable fruit conditions and Grade B with damaged or rotten conditions, is expected to replace the manual sorting process, which is prone to inconsistencies. The data was directly collected from the sales centers and local tomato farms in Nganjuk Regency. The obtained data underwent preprocessing, including resizing, normalization, and augmentation in the form of brightness adjustment, contrast, and hue saturation manipulation. The data is divided into 60% training data, 15% validation data, and 25% testing data. The model was trained for 32 epochs using the AdamW optimizer with a learning rate warm-up and cosine decay scheme. The final evaluation resulted in a classification accuracy of 95.12%, a macro F1 Score of 95.11%, and a Mean Average Precision (mAP) of 85.70% with a precision of Grade A at 94.87% and Grade B at 95.35%. The proposed model offers a reliable contribution as a foundation for an artificial intelligence-based sorting system in the post-harvest tomato industry.

Page 21 of 21 | Total Record : 207

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