cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Ramdan Satra
Contact Email
Ramdan Satra
Phone
-
Journal Mail Official
ramdan@umi.ac.id
Editorial Address
-
Location
Kota makassar,
Sulawesi selatan
INDONESIA
ILKOM Jurnal Ilmiah
Published by Universitas Muslim Indonesia
ISSN : 20871716     EISSN : 25487779     DOI : -
Core Subject : Science,
Computer Science & IT
ILKOM Jurnal Ilmiah is an Indonesian scientific journal published by the Department of Information Technology, Faculty of Computer Science, Universitas Muslim Indonesia. ILKOM Jurnal Ilmiah covers all aspects of the latest outstanding research and developments in the field of Computer science, including Artificial intelligence, Computer architecture and engineering, Computer performance analysis, Computer graphics and visualization, Computer security and cryptography, Computational science, Computer networks, Concurrent, parallel and distributed systems, Databases, Human-computer interaction, Embedded system, and Software engineering.
Arjuna Subject : -
Articles 15 Documents
 1   2 
Search results for , issue "Vol 18, No 1 (2026)" : 15 Documents clear
Blockchain-Based Diploma Authentication System: A Design Science Approach Using Smart Contracts and Ganache Haryati, Haryati; Vernanda, Dwi
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.3224.69-84

Abstract

Academic credential fraud poses a critical challenge to Indonesian higher education, with approximately 30% of job applicants providing false academic qualifications while conventional verification processes require 2–4 weeks with significant administrative costs. This research addresses the gap where 77% of blockchain education research remains conceptual by proposing and evaluating a four-layer blockchain system architecture for academic diploma authentication. Using Design Science Research Methodology (DSRM), the study designs and implements a layered architecture comprising a Presentation Layer (React 18.2.0 with client-side SHA-256 hashing), Application Layer (Node.js 18.20.8 with Web3.js), Data Layer (PostgreSQL 14.5 for off-chain metadata), and Blockchain Layer (DiplomaValidator smart contract in Solidity 0.8.19 on Ganache 2.7.1). The architectural design enforces separation of concerns, enabling tamper-evident credential storage through immutable on-chain hash registration and trustless public verification through zero-gas view functions. Comprehensive evaluation through 38 functional tests, performance benchmarking, security auditing, and integration testing demonstrates 100% pass rate across all categories. Performance metrics show registration in 15.23 ms (240,082 gas units) and verification in 9.47 ms at zero gas cost, achieving 51.81 TPS throughput. Security audit yields 95/100 with zero high or medium vulnerabilities. The primary contribution of this research is a formally documented four-layer blockchain architecture for academic credential authentication validated through DSRM providing a replicable architectural model and quantified performance baselines for the Computer Science community and Indonesian higher education institutions considering blockchain adoption
Optimization of Text Emotion Classification through the Combination of ITC Smoothed and Linear Models Garonga, Melki; Rangga Punne, Mc Rore; Damayanti, Irene Devi
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.2954.1-16

Abstract

This research investigates four feature extraction techniques TF-IDF, Smoothed TF-IDF, Inverse Term Counting (ITC), and ITC Smoothed to determine how effectively they enhance text-based emotion classification when working with imbalanced datasets. The study also seeks to pinpoint the most effective pairing between feature extraction methods and classification algorithms. Its key contributions include a methodical side-by-side comparison of these lesser-examined TF-IDF variations and demonstrating empirically that linear models handle class imbalances with considerable resilience. The analysis drew upon an Indonesian Twitter dataset comprising 4,132 tweets, categorized into six unequally distributed emotional states: anger, fear, joy, love, sadness, and neutrality. These four feature extraction approaches were assessed using five distinct classifiers: Naive Bayes, Logistic Regression, SVM, Random Forest, and KNN. Performance was measured through accuracy, precision, recall, and F1-score. Findings indicate that linear classifiers, specifically Logistic Regression and SVM, delivered superior performance, achieving accuracy rates between 93.71% and 94.44%. These models consistently outperformed both probabilistic and distance-based algorithms regardless of the feature extraction method applied. Interestingly, the impact of smoothing proved context-dependent. While applying smoothing to both TF-IDF and ITC boosted the performance of linear models over their unsmoothed counterparts, it paradoxically reduced accuracy for the standard ITC method. This outcome questions the widely held belief that smoothing universally enhances model performance. The combination of Logistic Regression with the unITC Smoothed method yielded the peak accuracy of 94.44%. The study offers actionable guidance, suggesting the pairing of Logistic Regression with ITC as a highly effective strategy for text-based emotion classification. It also contributes theoretically by underscoring the particular aptitude of linear models for managing high-dimensional text data within imbalanced class contexts
A Hybrid Deep Learning–Machine Learning Approach for the Identification of Active Compounds in Blumea balsamifera (Sembung Leaves) Kusnaeni, Kusnaeni; Prihatin, Prihatin; Rahmatullah, Rahmatullah; Hafid, Mega Sartika; Nisardi, Muhammad Rifki; Nurmalasari, Nurmalasari; Andy B, Afif Budi
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.3195.165-179

Abstract

Blumea balsamifera (sembung) is a medicinal plant with well-documented antibacterial, anti-inflammatory, and analgesic properties. However, the systematic identification of its bioactive compounds remains a significant challenge due to the complexity and high dimensionality of LC–MS (Liquid Chromatography–Mass Spectrometry) data. This study aims to develop a robust computational framework for automated compound identification using a hybrid modeling approach.A hybrid model integrating Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) was employed to enhance feature extraction and classification performance. The LSTM component was utilized to capture sequential dependencies in spectral data, while XGBoost performed optimized classification through gradient boosting. This integration enables efficient handling of complex spectral patterns and improves predictive accuracy.The proposed model achieved an accuracy of 91%, demonstrating strong performance in classifying and identifying bioactive compounds. Feature importance analysis identified several key compounds contributing to the model predictions, including Luteolin-7-methyl-ether, Umbelliferone, Blumeatin, Dihydroquercetin-7,4′-dimethylether, Chrysosplenol C, Blumealactone B, and Blumeaene E. These compounds are associated with known pharmacological activities, supporting the therapeutic relevance of B. balsamifera.The proposed hybrid LSTM–XGBoost framework provides an effective and scalable approach for LC–MS-based compound identification. This method reduces analytical complexity, enhances classification reliability, and offers a data-driven strategy for accelerating phytochemical research and bioactive compound validation
Comparative Performance Analysis of Modified VGG16 and Slim-CNN for Arabica Coffee Bean Defect Classification Ardian, Yusriel; Astawa, I Nyoman gede Arya; Irawan, Novta Danyel; Pradnyana, I Putu Bagus Arya; Sulistyo, Agung
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.3244.85-96

Abstract

Defect detection in Arabica coffee beans is a critical aspect of quality control, particularly for export-oriented commodities that require consistent visual standards and uniform quality across production batches. Black and partial-black defects are known to significantly affect market value, quality perception, and sensory characteristics. Meanwhile, manual inspection processes remain vulnerable to evaluator subjectivity and inter-operator inconsistency.This study aims to conduct a comparative analysis between a Modified VGG16 architecture and Slim-CNN for detecting these two defect categories using a deep learning-based Convolutional Neural Network (CNN) approach. The dataset consists of 4,080 high-resolution images of Arabica green coffee beans captured using a 24.2 MP macro camera under controlled lighting conditions to minimize shadows and visual distortion. To preserve the natural characteristics of the defects, minimal data augmentation was applied using cropping and 15-degree rotation techniques. The Modified VGG16 architecture was simplified by reducing the complexity of the fully connected layers, integrating batch normalization, and applying dropout to enhance training stability and computational efficiency. Slim-CNN was employed as a lightweight comparative model with fewer parameters and lower memory requirements, making it suitable for resource-constrained deployment scenarios. Four training schemes were evaluated using variations in learning rate and epoch number to assess configuration impacts on performance. Experimental results show that Modified VGG16 achieved the highest test accuracy of 86.7% at a learning rate of 0.001 with 3 epochs, demonstrating a strong balance between training and validation accuracy. Slim-CNN exhibited shorter training time and lower computational complexity, although with slightly lower classification accuracy compared to Modified VGG16. These findings highlight a trade-off between classification performance and computational efficiency in selecting CNN architectures for coffee bean defect detection. Although the results demonstrate potential for industrial automatic classification systems, further validation using larger datasets and more comprehensive evaluation schemes is required to improve model generalization. This study contributes to the development of a more measurable, adaptive, and efficient deep learning-based coffee quality inspection system to support agro-export industry requirements.
Development of an Intelligent Catch the Stick System for Measuring Human Motor Coordination and Reaction Speed Wirawan, Nanda Tommy; Ernes, Risa Nadia
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.2887.126-137

Abstract

Conventional clinical methods for assessing sensorimotor coordination, such as the Fugl Meyer Assessment (FMA) and Action Research Arm Test (ARAT), often lack the objectivity and high-resolution sensitivity required to detect subtle micro improvements in motor performance. This study presents the design, development, and validation of an intelligent "Catch the Stick" system aimed at accurately and quantitatively assessing human sensorimotor coordination and reaction speed. The proposed multi-metric system integrates 9 axis inertial measurement units (IMUs), a 60 fps computer vision tracking system, and algorithmic classification to evaluate real-time temporal and spatial responses during random stick-dropping tasks. An experimental study was conducted involving fifteen participants (10 healthy individuals and 5 clinical patients with mild to moderate sensorimotor deficits) tested under varying stimulus loads ranging from 1 to 10 sticks. The system demonstrated strong to excellent test-retest reliability (ICC 0.75) and high detection precision (±15 ms temporal error, 2 mm spatial error). Experimental results revealed that increased stick quantity directly correlated with prolonged reaction times, thereby objectively quantifying cognitive motor overload. Furthermore, the system exhibited strong concurrent validity with conventional tools, showing significant positive correlations with FMA (r = 0.78) and ARAT (r = 0.74) scores. Notably, the intelligent system proved more sensitive to micro improvements in 72% of participants compared to traditional clinical scales, although ceiling effects were observed in low difficulty tasks among healthy users. Overall, the intelligent Catch the Stick platform offers a robust, scalable, and highly sensitive solution for quantifying sensorimotor performance in clinical settings, laying the foundation for future robotic automation and autonomous training protocols
Isolation Forest-Based Anomaly Detection in IoT Smart Home Network Traffic Luthfi, Ahmad; Emigawaty, Emigawaty
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.3156.43-57

Abstract

The convergence of the Internet of Things (IoT) and Society 5.0 has successfully led to a human-centered and data-driven life ecosystem. IoT has become the backbone for infrastructure implemented in various domains, ranging from smart homes and smart farming to smart industrial environments. Nevertheless, as IoT devices become more connected and integrated into the ecosystem, the attack surface expands and network security becomes more challenging. The massive convergence and connectivity of IoT devices have a high potential for attacks on network infrastructure, such as Denial of Service (DoS), port scanning, exfiltration, brute force, and man-in-the-middle attacks. This study aims to detect anomalies in IoT network traffic by applying the Isolation Forest (IF) algorithm. The dataset was obtained from an IoT gateway connected to smart home devices and includes features such as data packet size, connection duration, source and destination capacity, attack protocols used, and the connection status of each device. The experimental results of this study indicate that the IF method can identify smart home device attacks with a competitive level of accuracy. The results of the anomaly analysis are then presented through a confusion matrix, classification report, and analytical visualizations such as 2D PCA, t-SNE, heatmap, and temporal distribution of anomalies. This study declares that the IF method contributes effectively to the analysis of Intrusion Detection Systems (IDS) in IoT environments such as smart homes that are heterogeneous and dynamic
An Exploration of the Work Performance of Educators in Transformative Schools: Leveraging Machine Learning for Performance Insights Maulidi, Rakhmad; Palandi, Jozua Ferjanus; Kristanto, Bagus Kristomoyo; Isyriyah, Laila; Rahmatullah, Rizky; Adi, Puput Dani Prasetyo; Kitagawa, Akio
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.2358.109-125

Abstract

Education has gone through various phases, and entered the transformative school mode which can be said to change the existing order of the previous schooling system or procedures, because many modes can be done in the transformative school, students can learn in school buildings or classes, or in the field or real industry or the real world of work, with the introduction of a wider and more complex world, this is one of them. This research tries to create and analyze transformative schools in 3 algorithms, namely regression algorithms, classification algorithms, and clustering algorithms that provide a detailed analysis of the results of the analysis of transformative schools currently promoted by the government. from the results of the analysis raises performance conclusions, and in this phase a conclusion can be drawn whether the Transformative school is able to provide answers about the performance of teachers, students, teacher education levels, school locations, number of students, learning methods, or any paramaters that can provide detailed and detailed answers to get performance analysis from Machine Learning, and Work Performance of teachers in Transformative schools with precision. Quantitatively, the value of performance is determined by innovation by 43.2%, followed by technological capabilities and collaboration, 27.9% and 17.2% respectively. and based on cluster level, cluster 3 is the best with 118 educators, cluster 0, 127 educators with high innovators, and cluster 2, 126 educators, and cluster 1 with 129 educators. and from the paradox of transformative practices 30.6% are high Adopters
Evaluating the Effectiveness of TBaWI for Imputation of Missing Rainfall Data Syafie, Lukman; Awangga, Narendra; Salim, Yulita
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.3273.97-108

Abstract

Daily rainfall data plays an important role in hydrological and climatological analysis, especially in tropical regions characterised by high rainfall variability and sharp seasonal changes. However, observational data often has gaps, which can reduce model accuracy and obscure relevant climatological signals. This study addresses these issues by applying the Trend-Based Adaptive Window Imputation (TBaWI) method, an adaptive imputation approach that considers local temporal trends and seasonal dynamics in estimating missing rainfall values. This method was tested using CHIRPS data for the Makassar region for the period 2014–2023 with synthetic data loss scenarios of 10%, 15%, 20%, and 25%. The results show that TBaWI consistently provides a lower Mean Absolute Error (MAE) value, namely 6.14–7.65 mm, compared to linear interpolation, which produces 6.46–7.75 mm. The SMAPE value of TBaWI is also lower, for example 33.16% in the 15% data loss scenario, compared to interpolation at 35.06%. In addition, this method showed an improvement in the ability to identify dry days through the Zero Hit Rate (ZHR), which reached 60.08% in the 20% data loss scenario, higher than the interpolation of 58.32%, while the Rainy Hit Rate (RHR) remained in a stable range of 79–88%. These findings indicate that TBaWI is more effective in maintaining climatological consistency and numerical accuracy of tropical rainfall data. Further research is expected to integrate spatial aspects and optimise machine learning-based parameters to improve the generalisation of the method under various climatic conditions.
A Hybrid BERT–RAG Model for Developing Knowledge-Validated Conversational Systems Anggreani, Desi; Ismawati, Ismawati; Auliyah, A. Inayah; Lukman, Lukman; Rahman, Aedah Abd; Nurmisba, Nurmisba; Akbar, Muh Ilham
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.3126.30-42

Abstract

The transition of freshmen into the university environment requires adaptive and responsive information support. This study develops a chatbot system based on a hybrid BERT–RAG architecture integrated with the FAISS Index to provide automated consultation services for new students. The novelty of this research lies in the implementation of a faculty-based hierarchical knowledge structure and an adaptive multi-domain context mechanism—an approach not previously found in studies involving BERT–RAG for university onboarding services. This design enables the chatbot to deliver more relevant, personalized, and faculty-specific responses. The dataset was derived from three primary sources of information: the Faculty of Economics and Business (FEB), the Faculty of Teacher Training and Education (FKIP), and the Faculty of Engineering (FT), which were structured into a validated knowledge base in documents.json format. System evaluation was conducted across ten interaction scenarios using performance metrics including BERT Similarity, BLEU Score, ROUGE-1, ROUGE-2, and ROUGE-L. The system achieved excellent results, with average scores of 0.905 (BERT Similarity), 0.844 (BLEU), 0.876 (ROUGE-1), 0.820 (ROUGE-2), and 0.871 (ROUGE-L) and standard deviations below 0.1 across all metrics. Strong metric correlations (0.85–0.99) further indicate consistency between semantic understanding and generated text quality. Furthermore, the system effectively minimizes hallucination through validated knowledge integration and faculty-based reranking strategies. Overall, this research provides a significant contribution to the development of institutionally contextual educational chatbots capable of delivering accurate, natural, and responsive communication to support new student orientation in higher education
Optimization-Based Geospatial Clustering Using Fuzzy Geographically Weighted Clustering and Flower Pollination Algorithm for Stunting Risk Mapping Ngatimin, Ngatimin; Istiawan, Deden; Ustyannie, Windyaning; Riansyah, Rahmat; Sholicah, Ameliatus
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.3130.151-164

Abstract

Stunting remains a major public health challenge in Indonesia, characterized by significant regional disparities and complex multidimensional determinants. Effective intervention strategies therefore require analytical approaches that are capable of capturing spatial heterogeneity and identifying region-specific vulnerability patterns. This study applies Fuzzy Geographically Weighted Clustering (FGWC) optimized using the Flower Pollination Algorithm (FPA) to map district-level stunting vulnerability and identify priority intervention areas. The analysis covers 514 districts using 21 multidimensional indicators representing health, nutrition, housing conditions, food security, social protection, and demographic characteristics derived from the Central Statistics Agency. The integration of FGWC with FPA enhances clustering performance by incorporating spatial dependence and metaheuristic optimization, enabling the algorithm to produce more stable and geographically sensitive clusters. Cluster validity indices confirm that a four-cluster solution provides the most optimal segmentation of stunting vulnerability. The results reveal distinct regional structures, socioeconomic-driven vulnerability associated with limited asset ownership, high dependence on social assistance and large household size, multidimensional deprivation concentrated primarily in eastern Indonesia, and nutrition-related vulnerability linked to breastfeeding duration and food security. These findings demonstrate that stunting patterns in Indonesia are spatially heterogeneous and influenced by diverse structural factors. The proposed FGWC–FPA framework offers a robust geospatial optimization approach that can support more precise, evidence-based, and region-specific strategies for accelerating stunting reduction.

Page 1 of 2 | Total Record : 15

 1   2 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol 18, No 1 (2026) Vol 17, No 3 (2025) Vol 17, No 2 (2025) Vol 17, No 1 (2025) Vol 16, No 3 (2024) Vol 16, No 2 (2024) Vol 16, No 1 (2024) Vol 15, No 3 (2023) Vol 15, No 2 (2023) Vol 15, No 1 (2023) Vol 14, No 3 (2022) Vol 14, No 2 (2022) Vol 14, No 1 (2022) Vol 13, No 3 (2021) Vol 13, No 2 (2021) Vol 13, No 1 (2021) Vol 12, No 3 (2020) Vol 12, No 2 (2020) Vol 12, No 1 (2020) Vol 11, No 3 (2019) Vol 11, No 2 (2019) Vol 11, No 1 (2019) Vol 11, No 1 (2019) Vol 10, No 3 (2018) Vol 10, No 3 (2018) Vol 10, No 2 (2018) Vol 10, No 2 (2018) Vol 10, No 1 (2018) Vol 10, No 1 (2018) Vol 9, No 3 (2017) Vol 9, No 3 (2017) Vol 9, No 2 (2017) Vol 9, No 2 (2017) Vol 9, No 1 (2017) Vol 9, No 1 (2017) Vol 8, No 3 (2016) Vol 8, No 3 (2016) Vol 8, No 2 (2016) Vol 8, No 2 (2016) Vol 8, No 1 (2016) Vol 8, No 1 (2016) More Issue