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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.
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Articles 580 Documents
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The Development of Classification Algorithm Models on Spam SMS Using Feature Selection and SMOTE Chrysanti, Rachma; Wijaya, Sony Hartono; Haryanto, Toto
ILKOM Jurnal Ilmiah Vol 16, No 3 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i3.2220.356-370

Abstract

Short Message Service (SMS) is a widely used communication media. Unfortunately, the increasing usage of SMS has resulted in the emergence of SMS spam, which often disturbs the comfort of cellphone users. Developing a classification model as a solution for filtering SMS spam is very important to minimize disruption and loss to cellphone users due to SMS spam. To address this issue, utilize the Naïve Bayes algorithm and Support Vector Machine (SVM) along with Chi-square and Information Gain. This study focuses on the classification and analysis of SMS spam on a cellular operator service in a telecommunications company using machine learning techniques. This study applies and combines a combination of classification methods including Naive Bayes and Support Vector Machine (SVM). The combination is carried out with Chi-square and Information Gain feature selection to reduce irrelevant features. This study also applies a combination with data balancing techniques using the Synthetic Minority Oversampling Technique (SMOTE) to balance the number of unbalanced classes. The results show that SMOTE improves classification performance. SVM performs spam SMS classification or not spam Model 7 (SVM) achieves accuracy 98,55% and it has improved the performance when it was combined with SMOTE Model 10 (SVM + SMOTE) achieves F1-score 99,23% in performing spam SMS classification or not this outperforms all other models. These results indicate that the SVM algorithm achieved better performance in detecting spam SMS compared to Naive Bayes, which demonstrated a lower level of accuracy. These results illustrate the effectiveness of combining machine learning models to enhance classification accuracy with balanced data, emphasizing the model that exhibited the most substantial improvement in performance.
Comparison of Convolutional Neural Network Models for Feasibility of Selling Orchids Chusna, Nuke L; Khumaidi, Ali
ILKOM Jurnal Ilmiah Vol 16, No 3 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i3.2006.296-304

Abstract

Orchid flowers are one of the most popular ornamental plants, widely appreciated for their unique features and aesthetic appeal, making them highly potential for sales in the global market. While numerous studies have explored Orchid flower characteristics and disease detection, research on the classification of Orchid salability remains unexplored. This study addresses this gap by classifying Orchid flowers into three categories: saleable, potential saleable, and not saleable. Convolutional Neural Networks (CNN), known for their effectiveness in image-based classification, were employed in this study with performance enhancement through the application of transfer learning. Two prominent transfer learning architectures, VGG-16 and ResNet-50, were implemented and compared to evaluate their suitability for Orchid salability classification. The results demonstrated that the VGG-16 model significantly outperformed ResNet-50 in all evaluation metrics. The VGG-16 model achieved an accuracy of 98%, precision of 99%, recall of 97%, and an F1 score of 98%. In contrast, the ResNet-50 model yielded lower performance, with an accuracy of 69%, precision of 68%, recall of 56%, and an F1 score of 56%. The study also observed that increasing the training epochs from 25 to 50 had no significant impact on the performance of either model. This research highlights the superior performance of VGG-16 in Orchid salability classification and underscores the potential of transfer learning in advancing ornamental plant research.
Urban Traffic Volume Prediction using LSTM and Bi-LSTM: Performance Evaluation on the Metro Interstate Dataset Pranolo, Andri; Saifullah, Shoffan; Putra, Agung Bella Utama; Dreżewski, Rafał; Wibawa, Aji Prasetya
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.3001.227-240

Abstract

Urban traffic forecasting underpins the mitigation of congestion, enhancement of road safety, and reduction of emissions in intelligent transportation systems. We benchmark Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) models on the Metro Interstate Traffic Volume dataset under an identical preprocessing and training pipeline for a fair comparison. Using a 24-hour multivariate input window (temperature, rainfall, snowfall, cloud cover), LSTM delivers the best overall balance of accuracy and efficiency on the full test sequence (RMSE = 0.196, MAPE = 2.36%, R² = 0.480; 7,344 s training). Bi-LSTM achieves competitive short-window accuracy but underperforms on the full sequence (RMSE = 0.231, MAPE = 2.92%, R² = 0.280; 12,672 s training). We attribute the Bi-LSTM gap to prediction "flattening" over long horizons, i.e., over-smoothed peaks from bidirectional averaging, despite its slightly stronger short-segment fit. Compared with prior RNN/GRU/CNN baselines on the same data, LSTM improves variance explanation while remaining deployable for near-real-time use. We also examine seasonality (daily/weekly cycles), weather effects, and data imbalance (peak versus off-peak) as factors that shape model error. These results support LSTM as a practical default for city-scale forecasting and motivate future work with attention/Transformer encoders and richer exogenous signals (incidents, events). The findings inform policy by enabling proactive traffic management that can reduce delays, emissions, and crash risk through earlier, data-driven interventions.
A Deep Learning Approach for Tourism Destination Recommendation Using IndoBERT and TF-IDF Silfianti, Widya; Syah, Rama Dian; Suhendra, Adang; Isra, Ali; Darmayantie, Astie; Ohorella, Noviawan Rasyid
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.3069.241-251

Abstract

The rapid development of information technology has transformed various sectors, including tourism, where recommendation systems play a vital role in providing personalized services. Tourists are often faced with a wide range of destination choices, making decision-making increasingly complex. To address this, Artificial Intelligence (AI) and Natural Language Processing (NLP) can be leveraged to enhance recommendation accuracy through deeper analysis of destination descriptions. This study proposes a tourism destination recommendation system combining IndoBERT, SimCSE, and TF-IDF methods. IndoBERT was applied to capture semantic and contextual meaning in the Indonesian language, SimCSE improved sentence-level embeddings, and TF-IDF extracted essential keywords from descriptions. The system was implemented on a website to generate personalized recommendations based on user input. Evaluation results demonstrated that the composition of IndoBERT and TF-IDF achieved strong performance, with precision, recall, and F1-score values of 1.0 at a similarity threshold of 0.20. However, higher thresholds reduced recall and F1-score, indicating that a lower threshold provided a better balance between accuracy and coverage. The recommendation outputs matched user preferences, and functional testing showed that all website features performed successfully. These findings highlight the effectiveness of combining semantic and keyword-based methods for tourism recommendation. Future work could expand the dataset, integrate user feedback, and benchmark against other state-of-the-art models to further enhance system performance.
Prediction of Rice Production in Jember Regency Using Adaptive Neuro Fuzzy Inference System (ANFIS) Riski, Abduh; Putriana, Novia Ayu; Fadri, Firda; Kamsyakawuni, Ahmad; Pradjaningsih, Agustina; Santoso, Kiswara Agung; Sari, Merysa Puspita
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.2797.262-275

Abstract

Jember Regency is the fourth largest rice-producing regency/city in East Java, so Jember Regency dramatically contributes to increasing the agricultural sector in East Java Province. However, the level of rice production can fluctuate, which is influenced by other factors such as rainfall. A prediction system is needed to anticipate a decrease in rice production. This research aims to predict rice production in the Jember Regency using the Adaptive Neuro Fuzzy Inference System (ANFIS), highlighting the impact of key variables like rainfall, harvested area, and land productivity. This research consists of three stages: training, testing, and prediction. The input variables used in this research are rainfall (mm), harvested area (Ha.), and land productivity (Kw/Ha.), while the output variable is rice production (tons). The membership functions used are generalized Bell and Gaussian, with several combinations of many membership functions. The best model obtained from this research is a model that uses generalized bell membership functions with three membership functions for rainfall variables and two membership functions for harvest area and land productivity variables. The epoch (iteration) used to achieve minimum error is 100 epochs. The best model achieved high accuracy, producing a MAPE value of 0.080% in training and 1.525% in testing, indicating its strong potential for reliable agricultural production forecasting. The predicted amount of rice production in Jember Regency in 2024 was 922,136.8317 tons.
Detection of Curcuma and Turmeric Differences Utilizing Fuzzy Tsukamoto Android-Based CCN Model Putra, Fajar Rahardika Bahari; Setyawan, Muhammad Rizki; Ilham, Ahmad; Suseno, Dimas Adi
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.2857.276-291

Abstract

Turmeric and curcuma are herbs that are often used in medicine and cooking. However, their similar shapes and colours make it difficult for people, especially in Southwest Papua, to distinguish between them directly. According to the Central Statistics Agency (BPS) in 2023, turmeric production reached 18,302 units, far higher than turmeric, which only reached 2,950 units. Based on field interviews in Southwest Papua, more than 60% of respondents had difficulty distinguishing turmeric from turmeric. To address this issue, this research develops an Android-based classification system by integrating the Fuzzy Tsukamoto algorithm with Convolutional Neural Network (CNN) models. Five CNN models VGG16, MobileNetV2, NASNetMobile, EfficientNetB2, and EfficientNetB3 were selected based on their balance between computational efficiency (MobileNetV2, NASNetMobile), depth and proven stability (VGG16), and modern scalable architectures (EfficientNetB2 and B3). Each model was combined with fuzzy logic to enhance classification accuracy. he dataset consisted of 800 images of curcuma and turmeric obtained from Kaggle and field collections. The data were divided into training, validation, and testing sets, and augmented through a series of transformations including rescaling to a range of 0 to 1, rotation up to 40 degrees, horizontal shift of 20%, angular distortion (shear) of 20%, zoom up to 30%, horizontal flipping, and brightness adjustment. Empty areas generated during augmentation were filled using the nearest pixel value with the ‘nearest’ mode to preserve image integrity. Training was performed using the AdamW optimizer and fine-tuning. Model evaluation employed accuracy, precision, recall, F1-score, and confusion matrix metrics. The results showed that the VGG16 model performed best, achieving 97% accuracy, 98% precision, 97% recall, and 98% F1-score, as confirmed by the classification report and confusion matrix. This model was also the most stable when tested on the Android system, while EfficientNetB2 and B3 produced less satisfactory outcomes. These findings demonstrate that combining CNN and Fuzzy Tsukamoto improves the classification accuracy of images with high visual similarity. The proposed system has the potential to be applied as a direct plant identification tool in the field and can be further extended to classify other visually similar plants
ESP32-Based Sumo Robot Control System Using PlayStation 4 Controller with Semi-Autonomous Ultrasonic Features Sidehabi, Sitti Wetenriajeng; Mubarak, Muhammad Muflih; Gani, Hamdan
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.2591.292-301

Abstract

This study presents the design and implementation of a sumo robot control system integrating an ESP32 Devkit V1 microcontroller with a wireless PlayStation 4 controller and semi-autonomous features based on the HC-SR04 ultrasonic sensor and MG-995 servo motor. The system addresses challenges in sumo robots, including communication stability and control precision. Hardware integration involved DC motors, an L298N driver, and a LiPo battery, while software development used the Arduino IDE with Bluetooth connectivity. Experimental testing demonstrated stable communication with a maximum range of 36 meters, an average controller connection time of 1.998 seconds, and 100% detection accuracy within a 10 cm radius. Push performance tests showed the robot could move loads up to 1655 g with standard tires and 3340 g with sponge tires. These results highlight the advantages of combining consumer-grade game controllers with advanced microcontrollers, offering improved precision, extended range, and intuitive user interaction for competitive robotics.
Optimizing K-Means Using Greylag Goose Optimization Algorithm for Household Energy Consumption Pattern Segmentation Arini, Florentina Yuni; Heryansyah, Ahmad Rozaq; Dewanti, Rahima Ratna; Saputro, Rizky Aulia Adi; Romadhoni, Awan Saputra; Wibowo, Muhammad Lutfi; Ardiansyah, Ikhsan; Duankhan, Poomin
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.2851.302-311

Abstract

Electricity is a crucial resource in everyday life, and rising household energy demand requires smarter monitoring and management approaches. Analyzing consumption data enables the discovery of typical energy usage behaviors that support efficient resource planning. Clustering techniques are widely used to group usage profiles without predefined categories, with K-Means being one of the most popular methods because of its speed and practical implementation. However, this algorithm is highly dependent on the initial centroid selection and may generate inaccurate grouping results if trapped in local optima. To overcome these drawbacks, this research combines K-Means with the Greylag Goose Optimization (GGO) algorithm, a nature-inspired metaheuristic that simulates the adaptive navigation and social coordination of migratory grey geese. By enhancing both exploration and exploitation, GGO improves the accuracy of centroid placement and overall clustering performance. The research utilized Individual Household Electric Power Consumption dataset, which consists of minute-by-minute measurements of several electrical attributes. After preprocessing and exploratory analysis, clustering was executed using three approaches: conventional K-Means, GGO, and a hybrid K-Means–GGO model. Based on the Silhouette Score evaluation, clustering performance improved significantly from 0.6236 with standard K-Means to 0.9675 using the hybrid approach. The resulting segmentation provides deeper insights into household consumption behaviors.
Evaluation of Deep Learning Model for Detection of Banana Consumption Feasibility Using Yolov8 Method Saputra, Guntur Eka; Yanni, Revanza Raditya Putra
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.2775.213-226

Abstract

This study aims to improve the accuracy of banana edibility detection using the YOLOv8 deep learning model. A total of 346 banana images were captured using a smartphone camera and split into training (303), validation (29), and testing (14) subsets. The research framework consisted of four main stages: data collection, preprocessing, model training, and performance evaluation. Preprocessing was conducted using the Roboflow platform and included several techniques such as image annotation, resizing, automatic orientation correction, contrast adjustment, and data augmentation through rotation, mosaic, and noise addition to enrich data variation and model robustness. The YOLOv8 model was trained for 60 epochs, achieving optimal convergence in 0.173 hours. Random search was utilized for hyperparameter optimization to achieve the best model configuration. The evaluation demonstrated remarkable results with a precision of 99.7%, recall of 100%, and mean Average Precision (mAP) of 99.5%. Visualization metrics, including the Precision-Confidence, Recall-Confidence, and F1-Confidence curves, each reached 100%, and the normalized confusion matrix demonstrated flawless classification performance. Testing on unseen data further confirmed the model’s ability to accurately detect and classify bananas into Good Quality and Bad Quality classes with high confidence scores. These findings highlight the capability of YOLOv8 as a robust and reliable model for automated fruit quality assessment. The implementation of this approach offers a non-destructive, fast, and consistent method for evaluating banana edibility, reducing dependency on manual inspection and human error. In addition, this study contributes to the advancement of smart agriculture and post-harvest management by demonstrating the potential of deep learning and computer vision to support real-time quality control and decision-making in the agricultural industry.
Smart Verification of High School Student Reports Using Optical Character Recognition and BERT Models Syahyadi, Asep Indra; Afif, Nur; Yusuf, Ahmad; Setiaji, Haris; Ridwang, Ridwang; Irfan, Mohammad
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.2764.252-261

Abstract

This study proposes an intelligent framework for verifying high school report cards with diverse layouts by integrating Optical Character Recognition (OCR) and a fine-tuned BERT model. While previous works primarily address document formats with uniform structures, this research specifically tackles the heterogeneity of report cards that differ in subject arrangement, naming conventions, and grade presentation across schools. The system was trained and evaluated using 1,000 Indonesian high school report card pages encompassing 20 subjects, both core (e.g., Mathematics, Indonesian History, Religious Education) and non-core (e.g., Arts and Culture, Physical Education). OCR was employed to extract textual content from scanned or image-based report cards, while BERT handled contextual mapping between subjects and corresponding grades. The dataset was divided into 80% for training and 20% for validation, and the model was fine-tuned on the IndoBERT-base architecture. Experimental results showed that the proposed OCR–BERT pipeline achieved an average accuracy of 97.7%, with per-subject accuracies ranging from 96% to 99%. The model exhibited high robustness in handling inconsistent layouts and minimizing deviations between actual and detected grades. Comparative analysis indicated that this hybrid approach outperforms traditional OCR-only or CNN-based methods, which are typically constrained by fixed template assumptions and lack contextual understanding. The proposed system demonstrates practical relevance for large-scale admission platforms such as SPAN-PTKIN, where manual verification of thousands of report cards is laborious and error-prone. By automating the verification process, the framework reduces human workload, enhances accuracy, and supports fairer, data-driven admission decisions. Future research will explore multimodal integration of textual and visual features, expansion to broader datasets, and application to other academic documents such as transcripts and diplomas. Overall, this work contributes a scalable, accurate, and context-aware solution for educational data verification in heterogeneous document environments.

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