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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) Journal of Applied Data Sciences JPM: JURNAL PENGABDIAN MASYARAKAT Bulletin of Computer Science Research Journal of Social Responsibility Projects by Higher Education Forum KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) EXPLORER Jurnal Pengabdian Masyarakat Gemilang (JPMG) Jurnal Pengabdian Masyarakat Inovasi Jurnal Bangun Abdimas Jurnal Informatika dan Teknologi Informasi Journal of Computing and Informatics Research Buletin Ilmiah Informatika Teknologi DUNIA KEPERAWATAN: JURNAL KEPERAWATAN DAN KESEHATAN Jurnal Pengabdian Harapan Bangsa Jurnal Teknologi Ilmu Komputer Jurnal INFOTEL Journal Global Technology Computer Management of Information System Journal Portal Riset dan Inovasi Sistem Perangkat Lunak Journal of Decision Support System Research Jurnal Mitra Pengabdian Farmasi
Abdul Karim
Universitas Labuhanbatu
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Transportation Other

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Pemanfaatan Teknologi AI untuk Mengembangkan Strategi Digital Marketing Berbasis Data bagi UMKM Desa Karim, Abdul; Syahrizal, Muhammad; Diansyah, Tengku Mohd.
Jurnal Pengabdian Masyarakat Inovasi Vol. 5 No. 1 (2026): February 2026
Publisher : Sekolah Tinggi Ilmu Manajemen Sukma Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35126/jpmi.v5i1.999

Abstract

This community service program aims to enhance the capacity of micro, small, and medium enterprises (MSMEs) in Aek Pamingke Village in utilizing artificial intelligence (AI) for data-driven digital marketing strategies. The activities were conducted through several stages, including needs analysis, intensive training, practical implementation, and evaluation. The initial analysis revealed that most MSME participants had limited knowledge and skills in digital marketing, with 60% categorized as having low understanding before the program. After the training, significant improvement was recorded, with 50% of participants reporting being very satisfied and 35% satisfied. The program’s impact was evident in the participants’ improved ability to design more effective data-driven marketing strategies. The main limitations of this program were the relatively small number of participants and the limited implementation time, indicating the need for extended programs with broader coverage in the future.
Peningkatan Pengarahan Beam dan Estimasi Sudut Kedatangan Berbasis CNN untuk Sistem Antena MIMO Cerdas Karim, Abdul; Purnama, Iwan; Ernawati, Andi
Explorer Vol 6 No 1 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i1.2592

Abstract

This study proposes a Convolutional Neural Network (CNN)–based approach to enhance the intelligence of MIMO antenna systems in Internet of Things (IoT) environments, particularly for modeling the relationship between wireless channel characteristics and achievable communication capacity. Modern MIMO systems face complex challenges due to dynamic channel conditions such as noise, path loss, and multipath fading, which significantly affect data transmission quality. In this research, channel-related features are processed through a structured preprocessing stage before being fed into a CNN model to learn nonlinear relationships among channel parameters. The developed model is designed to predict achievable channel capacity accurately as part of an adaptive and intelligent wireless communication framework. Experimental results show that the proposed CNN model achieves a Test Loss of 0.0317 and a Mean Absolute Error (MAE) of 0.1267 on unseen test data. Visualization of actual versus predicted values indicates that the model demonstrates good generalization across most data ranges, although some deviations remain at extremely high capacity values. Compared to conventional approaches, the CNN-based method shows superior capability in capturing complex correlations among MIMO channel parameters. Therefore, this approach contributes to the development of adaptive and efficient intelligent antenna systems, supporting the growing demands of next-generation IoT communication networks.
Sistem Pendukung Keputusan Pemilihan Kepala Desa Terbaik Menerapkan Metodethe Extended Promethee II (EXPROM II) Nurlela Nurlela; Muhammad Syahrizal; Fadlina Fadlina; Abdul Karim
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 1 No. 3 (2020): Mei 2020
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v1i3.2151

Abstract

Decision Support System is a system that can help management in making the right decision, which is needed at a management level. Likewise at the Lubuk Pakam Sub-District Office in selecting the best village head. So far, the Camat Office has never determined the best village head in Lubuk Pakam Subdistrict, so that it encounters obstacles in choosing the village head election. SPK is able to provide alternative solutions to semi / unstructured problems for individuals or groups and in a variety of decision making processes and styles, SPK uses data, databases and analyzes of decision models. Seeing this, researchers are interested in conducting research by applying the Extended Promethee II (EXPROM II) method to elect the best village head in a decision support system. It is expected that the results of the research can help the Lubuk Pakam sub-district
Enhanced InceptionV3 Transfer Learning with Augmentation Strategy for Multi-Class Fruit Classification Abdul Karim; Fakhri Lambardo; Rizqi Elmuna Hidayah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7531

Abstract

Fruit variety recognition using digital images is an important component in developing automated systems for agricultural handling and food-industry processing. The task is difficult because different fruit types may present nearly identical visual patterns, while image acquisition factors such as illumination, viewing position, and image quality can reduce classification reliability. To address this issue, this study designed a deep learning model for 131 fruit classes by adapting InceptionV3 through a transfer learning scheme. The pre-trained feature extraction layers were retained without retraining, while the original output structure was replaced with task-specific layers consisting of global average pooling, a 1024-unit dense layer with ReLU activation, and a softmax classifier. The image data were standardized to 224 × 224 pixels, augmented to increase visual variation, and divided into training, validation, and testing subsets using an 80:10:10 ratio. The proposed model produced an accuracy of 99.80%, with precision, recall, and F1-score values of 0.9900. These results exceeded the performance of GoogLeNet, ResNet, and VGGNet, showing that the use of pre-trained InceptionV3 features, customized classification layers, and augmentation can improve prediction consistency and reduce classification errors. Further evaluation on unconstrained real-world images and optimization for real-time use are recommended for future development.
Comparison of Random Forest and XGBoost Methods Based on Hyperparameter Tuning for Classification of Customer Churn Rate of Telecommunication Providers Abdul Karim; Muhammad Hidayatullah; Nora Dery Sofya; Erwin Mardinata; Shinta Esabella
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.v8i1.9918

Abstract

Customer churn represents one of the most critical challenges in the telecommunications industry, as the cost of acquiring new customers significantly outweighs the expense of retaining existing ones. High churn rates directly impact corporate revenue stability and market competitiveness, necessitating the development of precise predictive systems. This study presents a comprehensive comparative analysis of two prominent ensemble learning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), to establish a robust predictive framework for identifying potential churners using a large-scale Telco subscriber dataset. To ensure the reliability and scientific validity of the comparison, the research methodology incorporates the Synthetic Minority Over-sampling Technique (SMOTE) to rigorously address the inherent class imbalance within the dataset, ensuring that the minority churn class is adequately represented during the training phase to avoid model bias. Furthermore, a systematic hyperparameter tuning process was executed via GridSearchCV, exploring multiple combinations of estimators, depth, and learning rates to identify the optimal configurations for both algorithms. The experimental results reveal that while both models are highly effective, Random Forest slightly outperformed XGBoost, achieving an overall accuracy of 77.54% and a balanced F1-score of 0.616, compared to XGBoost’s accuracy of 76.54% and F1-score of 0.605. Notably, although both models demonstrated an identical recall rate of 67.64%, Random Forest exhibited superior precision (56.47% vs. 54.76%), which is vital for minimizing false positives and ensuring cost-effective retention campaigns. Feature importance analysis, conducted through Gini impurity and gain metrics, further identified tenure, total charges, and month-to-month contract types as the primary drivers of customer attrition. This study concludes that an optimized Random Forest model provides a more stable and accurate framework for telecommunication providers to proactively mitigate customer turnover. The findings offer valuable business intelligence, allowing stakeholders to transition from reactive measures to proactive, data-driven loyalty programs that enhance long-term business sustainability.
Estimasi Sudut Kedatangan yang Ditingkatkan dengan CNN pada Array Antena MIMO Menggunakan Data Sinyal IoT Dunia Nyata Abdul Karim; Andi Ernawati
Management of Information System Journal Vol 4 No 2: Maret 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i2.2564

Abstract

− This study proposes the application of a Convolutional Neural Network (CNN)–based approach to analyze signals in Internet of Things (IoT)–based MIMO antenna systems, with the aim of enhancing the understanding of system performance characteristics, particularly in predicting latency parameters. The CNN model is trained using real-world IoT signal data that have undergone comprehensive preprocessing stages, including data normalization, missing value handling, and feature engineering to ensure compatibility with the model input format. Experimental results on previously unseen test data demonstrate that the proposed model achieves a test loss of 1.4410, represented by the Mean Squared Error (MSE), and a Mean Absolute Error (MAE) of 0.9395. These results indicate that the model attains a relatively low prediction error and effectively captures the nonlinear relationships between signal features and system responses. Visualization of the testing results reveals a strong correlation between actual and predicted latency values, although some dispersion remains due to channel complexity and the inherent variability of IoT signals. The distribution of prediction errors is centered around zero, indicating the absence of significant systematic bias in the model. Overall, the findings confirm the potential of CNN as a reliable approach for modeling and performance analysis of IoT-based MIMO antenna systems, while also highlighting opportunities for further development in spatial parameter estimation and intelligent wireless communication system optimization.
Analisa Perbandingan Algoritma Shannon Fano Dan Algoritma Stout Code Pada Kompresi File Teks Dito Putro Utomo; Abdul Karim; Muhammad Syahrizal
Management of Information System Journal Vol 4 No 2: Maret 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i2.2568

Abstract

The rapid development of technology today attracts a lot of attention from the wider community. The dynamic development of computers is accompanied by the ability to get information very quickly. Data compression is a technique to reduce the amount of data in the original data. Data compression is usually applied to computer machines. This happens because each symbol displayed on the computer has a different bit value. The large size of text files will be a problem for storage space. Because the need for text files is very important, we tend to collect data in the form of text files, and often without realizing it, we store it in large sizes. This causes the need for storage media to be large. To overcome this problem, text files that have a larger size are used by compressing text files. Large data will be compressed into a small size, which will reduce storage. After applying the comparison of the Shannon Fano Algorithm and the Stout Code algorithm, compressing the text file has proven that the text file has been successfully compressed. After performing the text file compression process, the author can conclude that the Shannon Fano algorithm is better at performing the compression process.
Enhancing Lung Cancer Detection: Optimizing CNN Architectures through Hyperparameter Tuning Sundari Retno Andani; Poningsih; Abdul Karim
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6357

Abstract

TThis study aimed to compare the performance of various Convolutional Neural Network (CNN) architectures, including LeNet, ResNet, AlexNet, GoogleNet, VGGNet, and the proposed model, in medical image classification for disease detection. The proposed model was developed by adding additional layers and fine-tuning the hyperparameters in the ResNet architecture to enhance its ability to extract complex features. The training and testing processes were conducted using an augmented X-ray image dataset to increase the data diversity. The results indicate that the proposed model achieved the highest testing accuracy of 76.33%, surpassing other models in terms of accuracy, precision, recall, and F1-score. Although there are some limitations in specificity and the Matthews Correlation Coefficient (MCC), the proposed model still demonstrates better generalization ability, with an AUC-ROC score approaching an optimal value. These findings suggest that the proposed model has advantages in medical image classification and holds potential for further development to enhance disease classification accuracy.
Comparison Of Machine Learning Algorithms For Rice Production Prediction Abdul Karim; Yuwaldi Away; Syahrial; Roslidar; Jeperson Hutahaean; William Ramdhan; Yessica Siagian
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7453

Abstract

Rice production forecasting plays an important role in supporting future agricultural planning, food supply management, and food security. Accurate yield prediction allows governments and farmers to estimate production outcomes and develop appropriate strategies to maintain stable food availability.This study addresses this gap by comparing four regression-based machine learning models: Random Forest, XGBoost, Support Vector Regression (SVR), and Artificial Neural Network (ANN). All models were trained and tested using the same dataset to ensure a fair evaluation. Model performance was measured using the coefficient of determination (R²). The results show that Random Forest achieved the best performance (R² = 0.963), followed by XGBoost (R² = 0.959). In contrast, SVR (R² = -0.064) and ANN (R² = -2.417) performed poorly, indicating limited predictive capability. Overall, these findings suggest that ensemble-based methods, particularly Random Forest and XGBoost, are more reliable and effective for rice production forecasting compared to SVR and ANN.
Optimizing Agricultural Commodity Price Forecasts Using an Ensemble Stacking Method Based on Market and Product Characteristics Abdul Karim; Kusmanto Kusmanto
Journal Global Technology Computer Vol 5 No 3 (2026): Agustus 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jogtc.v5i3.11033

Abstract

Commodity price prediction is a vital aspect of supporting decision-making because price fluctuations can create uncertainty for businesses and stakeholders. This study aims to compare the performance of several machine learning algorithms for commodity price prediction, namely Random Forest, XGBoost, Support Vector Regression (SVR), Gradient Boosting, and Stacking Ensemble. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The experimental results show that Gradient Boosting achieved the best overall performance, with an RMSE of 219.07 and an R² of 0.9968, while Random Forest had the lowest MAE of 65.98. The Stacking Ensemble also demonstrated competitive performance, achieving an RMSE of 253.07 and an R² of 0.9958. In contrast, SVR produced the lowest performance, with an RMSE of 1,919.65 and an R² of 0.7555. Based on these results, Gradient Boosting was selected as the most optimal model because it provided the lowest prediction error and the highest ability to explain data variation. These findings demonstrate that selecting an appropriate machine learning algorithm plays a crucial role in improving commodity price prediction performance.
Co-Authors . Roslidar Afrendi, Mohammad Agus Perdana Windarto Agustina Sidabutar Agustina, Asri Widya Ahyuna Ahyuna, Ahyuna Aldiansyah, Ferry Alfarisi Pasaribu, Ahmad Ambiyar, Ambiyar Andi Ernawati Andi Ernawati Andi Ernawati Andriani, Titi Aritonang, Putri Armasari, Selly Arridha Zikra Syah Asyahri Hadi Nasyuha Awfa, Qifari Bangun, Budianto Bernadus Gunawan Sudarsono Bobbi Kurniawan Nasution, Muhammad Chairul Rizal Cheylani Lukito, Salwa Christiorenfa Br Haloho, Agatha Daulay, Nelly Khairani Dayu Sari, Arini Dhea Ananda, Tasya Dito Putro Utomo Dwika Asrani Dwika Assrani Efendi Hutagalung, Jhonson Efendi, Safri Erlin Windia Ambarsari Erwin Mardinata Fadli, Muhammad Bagus Fadlina Fahmi Rizal Febriani, Budi Fifto Nugroho Garuda Ginting Guidio Leonarde Ginting Harahap, Armyka Pratama Hasibuan, Awaludin Heni Pujiastuti Hersatoto Listiyono Hidayatullah, Muhammad I Wayan Sugianta Nirawana Imam Saputra Indah Sari, Leni Indrayani, Puput Iwan Purnama Jahril Jeperson Hutahaean Jeperson Hutahaean Kraugusteeliana Kraugusteeliana Kurniawan Nasution, Muhammad Bobbi Kusmanto Kusmanto Kusmanto Kusmanto Kusmanto Kusmanto Lambardo, Fakhri M. Rafi Mardinata, Erwin Marha As, Pawa Niassa Meryance Viorentina Siagian Mesran, Mesran Mhd Ali Hanafiah Mhd Bobbi Kurniawan Nasution Moustafa H. Aly Muhammad Bobbi Kurniawan Nasution Muhammad Hamka Muhammad Hidayatullah Muhammad Syahrizal Muhammad Syahrizal Nababan, Dosmaida Nasution, Mhd Bobbi Kurniawan Nasution, Muhammad Bobbi Kurniawan Natalia Silalahi Nona Oktari Nurlela Nurlela Nurliadi Pane, Rahmadani Pane, Siddik Pohan, Tatang Hidayat Poningsih Pratama, Armyka Prayetno, Sugeng Prayetno, Sugeng Prayetno Purba, Elvitrianim Purba, Elvitrianim Putra Juledi, Angga Putri, Nathania Rahman, Ben Rizqi Elmuna Hidayah Rohani Rohani Roslidar Saidi Ramadan Siregar Saludin Muis Sartika Br Siregar, Amanda Sempurna, Teguh Shinta Esabella Siagian, Yessica Siddik Siregar, Anwar Sinulingga, Raja Ingata Siregar, Feby Khairunnisya Siti Sahara Nasution Soeb Aripin Sofya, Nora Dery Suha Alvita Suhada, Karya Sundari Retno Andani Supiyandi Supiyandi Suryadi, Sudi Sutrino Dwi Raharjo Syahputra Harahap, Hasmi Syahrial Syahrial, Syahrial Tengku Mohd Diansyah, Tengku Mohd Triana, Dewi Trianovie, Sri Trianovie, Sri Unung Verawardina Uswatun Hasanah Vita S. Siregar, Siony William Ramdhan Wilson, Eric Yessica Siagian Yessica Siagian Yulizar, Isma Ahmad Yuwaldi Away Zebua, Yuniman Zulham Sitorus Zulkifli Zulkifli Zuly Budiarso