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Jurnal Ilmiah Multidisiplin Indonesia
Published by SEAN INSTITUTE
ISSN : -     EISSN : 28289463     DOI : -
Core Subject : Science, Agriculture, Engineering,
Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Engineering Physics
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) is a peer-reviewed journal regularly published by the SEAN Institute every three months. namely, several research publications to publish multi-disciplinary articles with general topics on engineering, science, agriculture, plantations, forestry and marine.
Arjuna Subject : Umum - Umum
Articles 288 Documents
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Implementation of Organic and Inorganic Waste Detection Modeling in School Waste Bins Using Yolov11 Feriyanto, Feriyanto; Eka Puji Widiyanto
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 5 No. 02 (2026): Jurnal Ilmiah Multidisplin Indonesia (JIM-ID), February 2026
Publisher : Sean Institute

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Abstract

Waste management in the school environment poses a significant challenge due to the high volume of mixed organic and inorganic waste, which hinders the recycling process. The utilization of object detection technology can offer a solution. However, previous studies employed older YOLO architectures, which still have room for improvement. This research aims to implement a detection model to differentiate between organic and inorganic waste within the school environment, with a focus on the implementation of the YOLOv11 architecture. The method used is a Convolutional Neural Network (CNN) featuring the YOLOv11 architecture, utilizing a public dataset from Kaggle that is divided into 7 waste classes. The research stages include image preprocessing, image augmentation, and dataset partitioning using Stratified K-Fold Cross Validation. The model’s performance will be evaluated using mean Average Precision (mAP), precision, recall, and F1-score metrics. Subsequently, the model will be developed into a desktop-based system application. The result of this study are expected to provide an accurate and efficient waste detection model to assist in recognizing the types of waste present in the school environment.
The Influence of Digitalization in Improving the Integrity and Ethics of the Notary Profession Aulia Syifa Sjamhars; Pujiyono, Pujiyono
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 5 No. 02 (2026): Jurnal Ilmiah Multidisplin Indonesia (JIM-ID), February 2026
Publisher : Sean Institute

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Abstract

Digitalization has become a strategic step to enhance the integrity and ethics of the notary profession in the globalization era. Technologies such as electronic signatures and cloud-based document management systems improve security, transparency, and accountability. Using a normative juridical approach, this study analyzes the impact of digitalization on the notary profession and identifies challenges such as technological infrastructure, digital literacy, and regulatory gaps. Proposed solutions include improving infrastructure, digital training, regulatory updates, and data security. The findings show that digitalization strengthens public trust through safer, more efficient, and transparent document management.
Professional Ethics of Notaries in Promotion on Social Media the Impact and Implications of Fake Testimonials St Syarifah; Pujiyono, Pujiyono
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 5 No. 02 (2026): Jurnal Ilmiah Multidisplin Indonesia (JIM-ID), February 2026
Publisher : Sean Institute

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Abstract

The professional ethics of notaries serve as a moral and legal foundation to maintain public trust in the notarial profession. In the digital era, social media is frequently used for promotion, but this practice often conflicts with ethical codes, particularly regarding the use of fake testimonials. Such promotions can harm public trust and the reputation of the notarial profession. This study employs a qualitative approach with a descriptive-analytical method to examine the impact of fake testimonials in notary promotions on social media. The analysis reveals that fake testimonials potentially violate ethics and laws, leading to administrative sanctions or license revocation. To safeguard professional integrity, strict supervision by the Notary Honorary Council (MKN) and a comprehensive understanding of ethical promotion boundaries are required.
The Influence of the Corporate Life Cycle and Sales Growth on Cash Holding with Geographic Diversification as a Moderating Variable (An Empirical Study of Industrial Sector Companies Listed on the Indonesia Stock Exchange) Rachmawan, Mochamad Aditia; Sugiyanto, Sugiyanto; Nofryanti, Nofryanti
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 5 No. 02 (2026): Jurnal Ilmiah Multidisplin Indonesia (JIM-ID), February 2026
Publisher : Sean Institute

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Abstract

This study investigates the influence of the firm life cycle and sales growth on corporate cash holdings, with geographic diversification examined as a moderating variable. Cash holding policy is a strategic financial decision that reflects firms’ responses to operational uncertainty, investment opportunities, and financial constraints, particularly in the context of dynamic industrial sector conditions. This research employs a quantitative approach using panel data from industrial sector companies listed on the Indonesia Stock Exchange (IDX) during the 2020–2024 period. The empirical analysis is conducted using Moderated Regression Analysis (MRA) to examine both the direct effects of firm life cycle and sales growth on cash holdings and the potential moderating role of geographic diversification. The results indicate that firm life cycle and sales growth have a significant influence on corporate cash holdings, suggesting that firms’ stages of development and growth dynamics shape their liquidity management strategies. However, geographic diversification does not significantly moderate the relationship between firm life cycle and sales growth on cash holdings. These findings imply that corporate cash holding policies in Indonesian industrial firms are primarily driven by internal firm characteristics rather than by the extent of geographic diversification. The study contributes to the literature on corporate liquidity management by providing empirical evidence from an emerging market context and offers practical implications for managers in formulating cash management strategies aligned with firms’ life cycle stages and growth conditions.
Implementation of Convolutional Neural Networks for Eyeglass Product Image Retrieval: A Comparative Study of ResNet-50 and MobileNetV2 Taufik, Handri; Anggai, Sajarwo; Taryo, Taswanda
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 5 No. 02 (2026): Jurnal Ilmiah Multidisplin Indonesia (JIM-ID), February 2026
Publisher : Sean Institute

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Abstract

The increasing similarity among eyewear product designs poses significant challenges for conventional text-based search systems, highlighting the need for effective Content-Based Image Retrieval (CBIR) approaches. This study proposes a CNN-based CBIR system for eyeglass frame and sunglasses retrieval, employing a comparative analysis of ResNet50 and MobileNetV2 as feature extractors. The dataset comprises 4,500 gallery images and 300 query images, with feature similarity measured using cosine similarity and accelerated through FAISS indexing. Experimental results indicate that ResNet50 achieves higher recall (0.0622), demonstrating its ability to capture more complex visual features. In contrast, MobileNetV2 provides superior ranking performance, achieving an mAP of 0.6091 and an MRR of 0.1427, outperforming ResNet50 (mAP of 0.5019 and MRR of 0.0713), while also reducing feature extraction time (0.1348 s versus 0.2023 s). These findings suggest that ResNet50 is more suitable for accuracy-oriented retrieval tasks, whereas MobileNetV2 is better suited for real-time and resource-constrained applications.
Dataset Protocol and Evidence Preservation for Detecting Cyber Incident Screenshot Manipulation: Data Structure, Tamper Recipes, and Chain of Custody Abdussalam, Muhamad Haikal; Sadewa, Rahes Restu
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 5 No. 02 (2026): Jurnal Ilmiah Multidisplin Indonesia (JIM-ID), February 2026
Publisher : Sean Institute

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Abstract

Screenshots are widely used in cybersecurity and digital forensics as preliminary evidence of incidents such as phishing pages, website defacement, and SIEM/IDS dashboard captures; however, their ease of manipulation through overlay, cropping, splicing, copy-move, and recompression undermines evidentiary reliability and complicates investigation triage. This study aims to design a standardized dataset protocol for cyber incident screenshots that strengthens digital evidence preservation and supports reproducible analysis workflows. The proposed protocol defines acquisition documentation, SHA-256 hashing, and chain-of-custody recording, alongside a structured folder hierarchy, evidence naming conventions, labeling schemes for binary and multi-class classification tasks, acquisition metadata, documented manipulation procedures via a tamper_recipe, and case_id-based data splitting to prevent leakage of derived manipulations across dataset partitions. As an implementation reference for triage modules, a lightweight analytical framework using GLCM texture features and classical classifiers is specified to demonstrate practical integration without positioning the work as a performance benchmark. The resulting outputs include a comprehensive, auditable protocol specification, standardized metadata and labeling templates, and a reproducible data management workflow tailored for cyber incident screenshots. The study concludes that formalizing acquisition, provenance, and splitting practices improves evidentiary integrity, reduces contamination risk across data partitions, and enhances the utility of screenshots for early-stage forensic triage while remaining compatible with resource-constrained operational settings.
Optimization of RNN and Tree-Based Models with Imbalance Handling for Fraud Detection in Digital Banking Transactions Darmawan, Rizki Ahmad; Musyafa, Ahmad; Handayani, Murni
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 5 No. 02 (2026): Jurnal Ilmiah Multidisplin Indonesia (JIM-ID), February 2026
Publisher : Sean Institute

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Abstract

This study focuses on addressing the growing challenge of fraud detection in digital banking transactions, which has intensified alongside the rapid expansion of digital financial services. Fraud detection is particularly complex due to the highly imbalanced nature of transaction data, large data volumes, and intricate transaction patterns that make fraudulent activities difficult to identify accurately. Although previous research has applied a wide range of methods, from conventional machine learning techniques to advanced deep learning models, many approaches still face limitations in balancing high detection accuracy with computational efficiency. The main objective of this research is to compare the performance of Recurrent Neural Network (RNN)–based models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM), with tree-based ensemble models such as XGBoost and LightGBM in detecting fraudulent banking transactions. To enhance model effectiveness, the study implements a comprehensive data preprocessing framework that includes data cleaning, feature engineering, and techniques for handling class imbalance, particularly the use of Synthetic Minority Over-sampling Technique (SMOTE). Furthermore, model performance is optimized through systematic hyperparameter tuning using Optuna, Hyperopt, and Keras Tuner. Evaluation is conducted using metrics suitable for imbalanced datasets, such as precision, recall, F1-score, and AUC-ROC. The expected outcome is the identification of a robust and efficient fraud detection model that improves detection accuracy and sensitivity, while offering valuable insights for both academic research and practical banking applications.
Design and Development of a Web-Based Information System for the Management and Maintenance of Primary Operational Equipment (ALOPTAMA) at the North Lampung Geophysical Station Sifa Mis Ayu; Yulina, Yulina
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 5 No. 02 (2026): Jurnal Ilmiah Multidisplin Indonesia (JIM-ID), February 2026
Publisher : Sean Institute

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Abstract

The management and maintenance of primary equipment (ALOPTAMA) at geophysical stations play a crucial role in ensuring the reliability of earthquake observation data and other geophysical phenomena. However, maintenance recording and reporting processes at the Geophysical Station of North Lampung are still conducted using conventional and separated files, which may lead to data duplication, information inconsistency, and delays in report generation. This study aims to design and develop a web-based information system to support the integrated management and maintenance of ALOPTAMA. The system development adopts the Waterfall method, consisting of requirements analysis, system design, implementation, and testing stages. The system is developed using PHP as the programming language, MySQL as the database management system, and AdminLTE as the user interface framework. The results indicate that the developed system effectively facilitates ALOPTAMA data management, inbound and outbound inventory recording, stock monitoring, and automated, structured maintenance reporting. The implementation of the system improves operational efficiency, reduces recording errors, and accelerates the reporting process. Therefore, the proposed information system serves as a digital solution to support ALOPTAMA maintenance operations within the BMKG environment.
Automating Cloud-Based Device Log Processing Using Google Apps Script Muhammad Hasbi Asshidiqi; Muhammad Hilman Naufal; Muhamad Fikri; Oky Tria Saputra
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 5 No. 02 (2026): Jurnal Ilmiah Multidisplin Indonesia (JIM-ID), February 2026
Publisher : Sean Institute

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Abstract

Device log management is often a challenge for system administrators due to large data volumes and unstructured formats. Manual processing is time-consuming and prone to human error. This research aims to implement an automated device log processing system utilizing Google Apps Script (GAS) as a cloud-based processing engine. The research method used is Research and Development (R&D), consisting of log data collection, automation script design, integration with Google Sheets as a database, and functional testing. The results show that the developed system is capable of parsing log data in real-time, categorizing log types based on urgency levels, and presenting them in structured reports. Efficiency testing demonstrates a reduction in data processing time by [X]% compared to conventional methods. The conclusion of this study is that Google Apps Script provides a cost-effective and efficient solution for managing device logs for medium-scale institutions.
Analysis and Evaluation of Qur’an Translation Topics Using Classical, Neural, and Transformer-Based Topic Modelling Kurnia, Akhmad Rinaldy; Anggai, Sajarwo; Handayani, Murni
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 5 No. 02 (2026): Jurnal Ilmiah Multidisplin Indonesia (JIM-ID), February 2026
Publisher : Sean Institute

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Abstract

Topic modelling is an important approach for extracting latent thematic structures from text corpora, including religious texts that are characterized by dense semantics and short documents. This study aims to compare the performance of several topic modelling methods Latent Dirichlet Allocation (LDA), Biterm Topic Model (BTM), Combined Topic Model (CombinedTM), and BERTopic in extracting topics from the Indonesian translation of the Qur’an. The dataset consists of 6,236 verses, with each verse treated as a single document. Topic quality is evaluated using two main metrics: coherence score (C_v) and topic diversity. The experimental results show that CombinedTM achieves the highest coherence score, with a maximum value of approximately 0.52 at K = 10 topics, followed by BTM, which demonstrates relatively high and stable coherence scores (around 0.50) across certain topic number variations. LDA yields the highest topic diversity, exceeding 0.90, but with lower coherence scores compared to the other models, indicating its limitations in preserving semantic coherence in short texts. Meanwhile, BERTopic exhibits consistently high topic diversity (0.85–0.88) across different numbers of topics, although its bag-of-words–based coherence scores do not always increase significantly. These findings highlight that the choice of topic modelling method should be aligned with the characteristics of the corpus and the objectives of thematic analysis, particularly in the context of short-form religious texts.

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