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All Journal Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Social Responsibility Projects by Higher Education Forum Journal of Computing and Informatics Research Bulletin of Artificial Intelligence Bulletin of Information System Research
Mesran Mesran
Sekolah Tinggi Ilmu Manajemen Sukma, Medan
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Social Sciences

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Deep Learning-Based Early Detection Optimization for Rice Leaf Diseases to Support Sustainable Local Agriculture Putrama Alkhairi; Agus Perdana Windarto; Mesran Mesran; Roznim Roznim
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9338

Abstract

Rice leaf diseases such as Bacterial Blight and Blast are major threats to rice productivity that directly impact food security and the sustainability of local agriculture. This study aims to develop and optimize a deep learning-based early detection system for rice leaf diseases using a Convolutional Neural Network (CNN) architecture, specifically the Inception_v3 model. The research method includes five main stages, namely collecting rice leaf image datasets, data pre-processing (resize, normalization, and augmentation), CNN model design, model training and evaluation, and performance optimization through the application of different optimizer algorithms. Two model variants were tested and compared, namely Inception_v3 Basics with the RMSprop optimizer and Inception_v3 Optimization with the Adam optimizer. Experimental results showed that the Inception_v3 Optimization model provided the best performance, with a Precision value of 0.9672, Recall of 0.8939, F1-score of 0.9291, Balanced Accuracy of 0.9297, Matthews Correlation Coefficient (MCC) of 0.8578, Cohen's Kappa of 0.8573, and AUC ROC of 0.98. These results indicate that the Adam optimizer is able to accelerate convergence and improve model accuracy compared to RMSprop, while producing a more stable and efficient classification system. Thus, this study successfully demonstrated that the optimized Inception_v3 architecture can be used effectively for early detection of rice leaf diseases and has high potential for integration into smart farming systems to support sustainable, technology-based local agricultural practices.
Intervensi Terfase Model IN-ON-IN untuk Mengatasi Gap Implementasi Literasi Koding dan AI Guru SD di Sumatera Utara Akhyar Lubis; Fatma Sari Hutagalung; Riah Ukur Ginting; Mesran Mesran; Juanda Hakim Lubis; Fajrul Malik Aminullah Napitupulu
Journal of Social Responsibility Projects by Higher Education Forum Vol 6 No 3 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jrespro.v6i3.9752

Abstract

This community service program aimed to address the gap between conceptual knowledge and practical implementation ability of elementary school (SD) teachers in coding and artificial intelligence (AI) instruction in Medan City and Deli Serdang Regency. Initial needs analysis revealed that while many teachers possessed basic familiarity with coding and AI terminology, the majority had not independently designed lesson plans incorporating coding activities nor systematically used coding platforms in classroom settings. Partner schools also faced uneven device availability and internet connectivity, alongside weak post-training support structures that hindered the transfer of learning to classroom practice. The intervention applied the IN-ON-IN model with contextual coaching: In Service Training 1 (conceptual and practical workshops using Scratch 3.0, Code.org, and Teachable Machine), On-the-Job Training (classroom implementation with field coaching and telementoring), and In Service Training 2 (reflection and consolidation). The program involved 175 elementary school teachers from both regions, implemented October 2025 through February 2026. Evaluation used an explanatory sequential mixed-methods design: pretest–posttest, classroom observation rubrics, learning artifact analysis, and in-depth interviews and focus groups. Results showed knowledge score improvement from 81% to 95% with reduced standard deviation (3.11 to 1.09), alongside qualitative findings confirming gains in lesson plan design competency and project-based coding instruction for the majority of participants. Persistent barriers included limited devices, intermittent internet access, and time constraints for lesson planning. Recommendations emphasize low-resource module development, expansion of telementoring, and cross-stakeholder collaboration to strengthen school infrastructure.
Mapping the Research Landscape of Multi-Objective Optimization by Ratio Analysis (MOORA) within Multi-Criteria Decision-Making: A Comprehensive Bibliometric and Science Mapping Analysis from 2012 to 2024 Juni Ismail; Alfry Aristo Jansen Sinlae; Zulfikar Zulfikar; Yanto Saputra; Elsy Rahajeng; Mesran Mesran
Bulletin of Information System Research Vol 3 No 2 (2025): April 2025
Publisher : Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/bios.v3i2.191

Abstract

The Multi-Objective Optimization by Ratio Analysis (MOORA) method has become an increasingly prominent technique within the multi-criteria decision-making (MCDM) family due to its computational simplicity, mathematical stability, and strong capacity to rank alternatives under conflicting criteria, yet the intellectual structure of this rapidly expanding field remains fragmented and insufficiently mapped. This study aims to systematically chart the global research landscape of MOORA within the MCDM domain and to identify its leading contributors, foundational works, dominant publication outlets, and prevailing thematic structures. A bibliometric research design guided by the PRISMA protocol was adopted, drawing on 275 English-language documents retrieved from the Scopus database for the period 2012 to 2024. The data were analysed using VOSviewer and Scopus analytical tools to examine annual publication trends, subject-area distribution, leading sources, co-citation networks, and keyword co-occurrence patterns. The results reveal a field that has accelerated sharply since 2018 and again after 2020, reaching a peak of seventy-one documents in 2024, with output concentrated in Engineering and Computer Science and disseminated through a heterogeneous ecosystem of mechanical-engineering, cleaner-production, and intelligent-systems outlets. Co-citation analysis confirms a theoretical base anchored in the canonical contributions of Brauers and Zavadskas, while keyword mapping shows MOORA functioning as a central decision-making nucleus closely tied to ratio analysis, optimisation, and surface-roughness applications, with TOPSIS, AHP, and WASPAS emerging as salient companion techniques. The novelty of this study lies in its focused mapping of the MOORA intersection rather than MCDM in general, exposing a loosely integrated thematic structure and a reliance on a narrow citation canon dominated by methodological pioneers. Its principal contribution is a consolidated knowledge map that clarifies the field's foundations and directs future methodological, fuzzy-extension, and interdisciplinary innovation
Random Forest, LSTM, and IndoBERT Comparison for TikTok App Sentiment Analysis Imam Saputra; Mesran Mesran; Ruziana Mohamad Rasli
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9714

Abstract

The rapid growth of social media platforms like TikTok has generated a massive volume of user reviews on the Google Play Store, serving as a critical indicator of application service quality. However, the unstructured nature of Indonesian social media text and the significant imbalance between sentiment classes pose substantial challenges for automated classification systems. Addressing this class imbalance is highly crucial for application developers, as critical negative and neutral feedback containing essential feature complaints is easily marginalized by the overwhelming majority of positive reviews, leading to biased operational insights. This research conducted a comprehensive comparative study of three distinct computational paradigms: Random Forest, Long Short-Term Memory (LSTM), and IndoBERT, to identify the most effective model for sentiment analysis. A dataset of 5,000 TikTok reviews was meticulously processed using a negation-aware preprocessing pipeline to preserve semantic integrity. To address class imbalance, architecture-specific techniques were deployed, including SMOTE for Random Forest, Class Weighting for LSTM, and Random OverSampling for IndoBERT. The experimental results demonstrate that IndoBERT significantly outperforms other models, achieving the highest global accuracy of 81% and a Macro F1-Score of 0.56. While Random Forest and LSTM yielded lower accuracies of 75% and 71%, respectively, they exhibited stability in predicting the majority class but struggled with the inherent ambiguity of neutral sentiments. The study concludes that IndoBERT’s bidirectional self-attention mechanism provides superior contextual understanding of Indonesian slang and non-formal syntax. This research contributes a robust framework for application developers to monitor public opinion objectively. Furthermore, the findings highlight that despite advanced balancing techniques, the "neutrality bottleneck" remains a challenge, suggesting that future research should explore aspect-based sentiment analysis to enhance classification granularity in the Indonesian NLP domain.
Sistem Pendukung Keputusan Pendataan Warga Penerima Bantuan Raskin dengan Menerapkan Metode Weight Aggregated Sum Product Assesment (WASPAS) Mesran Mesran; Rosmita Sari; Ridha Maya Faza Lubis; Muhammad Syahrizal
Journal of Computing and Informatics Research Vol 5 No 2 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v5i2.2642

Abstract

Raskin (rice for the poor) is a rice program for the poor. The Raskin program is one of the government's efforts to reduce the burden of expenditure on poor families. However, in practice, decision-making for determining the criteria for rice recipients usually does not refer to the criteria of poor families, resulting in misdirected rice distribution. To address this issue, a decision support system will be developed to assist in the targeted distribution of Raskin using the Weighted Aggregated Sum Product Assessment (WASPAS) method. This research was conducted by finding the weight value for each attribute, then a ranking process was carried out to determine the best alternative. The criteria used were: Type of Employment, Income, House Condition, Family Size, Age. The results of the study recommend that alternative 4, with the highest score of 0.676, be selected to receive Raskin assistance
Mapping Research Trends of Entropy-Based Weighting and AHP Integration in Multi-Criteria Decision Analysis for Sustainable Development Applications Zulfikar Zulfikar; Juni Ismail; Alfry Aristo Jansen Sinlae; Yanto Saputra; Raja Anan Nasution; Elsy Rahajeng; Mesran Mesran
Bulletin of Information System Research Vol 4 No 1 (2025): December 2025
Publisher : Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/bios.v4i1.230

Abstract

The integration of entropy-based weighting and the Analytic Hierarchy Process (AHP) has become an increasingly important strategy for balancing objective and subjective criterion weights in multi-criteria decision-making (MCDM), yet the intellectual structure of this hybrid field remains fragmented and insufficiently mapped. This study aims to systematically chart the global research landscape of entropy-AHP integration in MCDM and to identify its leading contributors, foundational works, and dominant thematic structures. A bibliometric research design guided by the PRISMA protocol was adopted, drawing on 160 English-language documents retrieved from the Scopus database for the period 2001 to 2025. The data were analysed using VOSviewer and Scopus analytical tools to examine annual publication trends, subject-area distribution, leading sources, co-citation networks, and keyword co-occurrence patterns. The results reveal a field that has accelerated sharply since 2021, reaching a peak of thirty-four documents in 2025, with output concentrated in Engineering and Computer Science and disseminated through a diverse ecosystem of energy-oriented journals and conference outlets. Co-citation analysis confirms a theoretical base anchored in the canonical works of Saaty and Zeleny, while keyword mapping shows entropy functioning as a conceptual bridge between expert judgment and data-driven weighting, with TOPSIS emerging as a salient companion technique. The novelty of this study lies in its focused mapping of the entropy–AHP intersection rather than MCDM in general, exposing a loosely integrated thematic structure and a reliance on a narrow citation canon. Its principal contribution is a consolidated knowledge map that clarifies the field's foundations and directs future methodological and interdisciplinary innovation.
Utilization of Hybrid Digital Technologies for Optimizing Waste Bank Management and Elevating Community Literacy Imam Saputra; Mesran Mesran; Dian Purnama Sari; Dito Putro Utomo
Journal of Social Responsibility Projects by Higher Education Forum Vol 7 No 1 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jrespro.v7i1.10793

Abstract

Grassroots community waste banks play a pivotal role in urban environmental management; however, they frequently face operational bottlenecks due to reliance on conventional paper-based record-keeping. The community partner faced severe challenges, including administrative processing delays, accounting errors in customer balances, and limited market reach for upcycled products. To address these problems, this community service activity aimed to optimize waste bank administration and elevate digital marketing literacy through an integrated hybrid capacity-building framework. The contribution of this initiative lay in deploying a low-latency hybrid learning setup—combining dual-WAN bonding, multi-camera switching, and cloud-accessible digital ledger tools—to deliver interactive training across physical and synchronous online cohorts (). Methodologically, a mixed-methods approach evaluated participant progress using pre- and post-test diagnostic questionnaires and post-event usability surveys. The results of the community service demonstrated a statistically significant increase in participant digital literacy (), with composite cognitive scores improving from a baseline of to , achieving a high normalized Hake gain (). Field execution successfully digitized operational transaction logs, eliminated calculation discrepancies, and enabled digital cataloging on social media platforms for waste-derived products. Overall participant evaluation indicated outstanding satisfaction (), confirming the practical utility and technical reliability of the hybrid delivery system. This activity successfully transformed the partner's operational workflows from manual ledgers to transparent digital management while offering a scalable model for circular economy empowerment.
Sistem Pendukung Keputusan Pemilihan Laptop dengan Menerapkan Metode Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) Kelik Sussolaikah; Juanda Hakim Lubis; Achmad Fikri Sallaby; Ega Yuliani; Mesran Mesran
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6525

Abstract

The lack of knowledge and information regarding laptop specifications makes ordinary people who want to buy a laptop feel confused about determining which laptop suits their needs. As a tool that can be used to select a laptop, a decision support system is needed. In decision support systems there are several methods, one of which can be used is the MOORA method (Multi-Objective Optimization on the basis of Ratio Analysis). In this research, the author will raise a case to find the best alternative from predetermined criteria to determine comparisons by rating existing alternatives using the MOORA (Multi-Objective Optimization on the basis of Ratio Analysis)method. Based on the relative performance scores, Asus Rog GL552JX (A9) obtained the highest score of 0.217 and ranked first. This was followed by Asus A455LD (A4) with a score of 0.21585 and second place, and Acer Aspire E5-551 (A2) with a score of 0.19785 and third place. Acer One 10 S100X (A10) received the lowest score of 0.1042 and ranked last. Thus, Asus Rog GL552JX (A9) can be considered as the best laptop based on the established criteria in this study.
Mapping the Evolution of Multi-Criteria Decision-Making and Simple Additive Weighting Research: A Comprehensive Bibliometric and Science Mapping Analysis from 2000 to 2025 Alfry Aristo Jansen Sinlae; Zulfikar Zulfikar; Juni Ismail; Yanto Saputra; Raja Anan Nasution; Elsy Rahajeng; Mesran Mesran
Bulletin of Artificial Intelligence Vol 4 No 2 (2025): October 2025
Publisher : Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/buai.v4i2.204

Abstract

The increasing complexity of decision-making environments driven by digital transformation, sustainability challenges, and technological advancements has significantly accelerated the adoption of Multi-Criteria Decision-Making (MCDM) methods across diverse scientific and practical domains. Among these approaches, the Simple Additive Weighting (SAW) method has gained substantial attention due to its simplicity, transparency, and effectiveness in evaluating alternatives based on multiple criteria. Despite the rapid growth of MCDM-SAW studies, the existing body of knowledge remains fragmented across disciplines, institutions, and application areas, creating a need for a comprehensive assessment of its intellectual and thematic development. Therefore, this study aims to systematically map the evolution, intellectual structure, and emerging research trends of MCDM and SAW research from 2000 to 2025. A bibliometric research design combined with science mapping techniques was employed using data retrieved from the Scopus database. A total of 381 English-language publications were selected through a PRISMA-based screening process. Data analysis was conducted using the Scopus Analysis Tool for performance analysis and VOSviewer for network visualization, including publication trend analysis, subject area distribution, co-citation analysis, and keyword co-occurrence mapping. The findings reveal a substantial increase in scientific production, particularly after 2015, indicating the growing relevance of MCDM and SAW in contemporary decision-support research. Engineering and Computer Science emerged as the most dominant subject areas, while leading publication sources included Expert Systems with Applications, Mathematics, Sustainability, and IEEE Access. Co-citation analysis identified influential scholars and foundational theories that shape the field, whereas keyword co-occurrence analysis highlighted the growing integration of sustainability, optimization, artificial intelligence, and hybrid MCDM frameworks. The novelty of this study lies in its integrated examination of publication performance, intellectual structure, and thematic evolution within the MCDM-SAW domain. The study contributes by providing a comprehensive knowledge map that supports future theoretical development, interdisciplinary collaboration, and methodological innovation in decision-support research
Co-Authors Agus Perdana Windarto Akhyar Lubis Alfry Aristo Jansen Sinlae Dian Purnama Sari Dito Putro Utomo Ega Yuliani Elsy Rahajeng Fajrul Malik Aminullah Napitupulu Fatma Sari Hutagalung Imam Saputra Juanda Hakim Lubis Juni Ismail Juni Ismail Kelik Sussolaikah Muhammad Syahrizal Putrama Alkhairi Raja Anan Nasution Riah Ukur Ginting Ridha Maya Faza Lubis Rosmita Sari Roznim Roznim Ruziana Mohamad Rasli Sallaby, Achmad Fikri Yanto Saputra Zulfikar Zulfikar