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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Jurnal TIMES CESS (Journal of Computer Engineering, System and Science) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi JurTI (JURNAL TEKNOLOGI INFORMASI) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Query : Jurnal Sistem Informasi METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi JURIKOM (Jurnal Riset Komputer) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jambura Journal of Electrical and Electronics Engineering JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA JPM: JURNAL PENGABDIAN MASYARAKAT International Journal of Engineering, Science and Information Technology Yayasan Cita Cendikiawan Al Khwarizmi JIKEM: Jurnal Ilmu Komputer, Ekonomi dan Manajemen Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) INCODING: Journal of Informatics and Computer Science Engineering EXPLORER Prosiding Snastikom Jurnal ABDIMAS Budi Darma Journal of Practical Computer Science (JPCS) Jurnal Informatika Teknologi dan Sains (Jinteks) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Prioritas : Jurnal Pengabdian Kepada Masyarakat Jurnal Indonesia Sosial Teknologi Jurnal Ilmu Komputer dan Sistem Informasi Jurnal Pengabdian Masyarakat (Jubdimas) CompTech : Jurnal Ilmu Komputer dan Teknologi
Rahman, Sayuti
Universitas Medan Area, Medan, Indonesia
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Pemanfaatan Teknologi Digital untuk Meningkatkan Produktivitas dan Kemandirian Peternak Ikan Desa Mariendal II Bayu Aditya Pratama; Nasaruddin Nur Hasibuan; Sayuti Rahman; Dadan Ramdan; Rahmad Syah; Hartono; M. Khahfi Zuhanda; Arnes Sembiring; Asmah Indrawati; Habib Satria; Iqbal Giffari Ritonga
Dedikasi Sains dan Teknologi (DST) Vol. 5 No. 2 (2025): Artikel Pengabdian Nopember 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dst.v5i2.7875

Abstract

Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan untuk meningkatkan produktivitas dan kemandirian peternak ikan air tawar di Desa Mariendal II melalui penerapan sistem feeder dan monitoring pakan ikan berbasis Internet of Things (IoT). Permasalahan utama mitra meliputi pemberian pakan yang masih dilakukan secara manual, ketidakteraturan jadwal pakan, pemborosan pakan, serta keterbatasan literasi teknologi. Metode pelaksanaan kegiatan menggunakan pendekatan partisipatif yang meliputi tahap persiapan, pelatihan dan implementasi sistem IoT, pendampingan, serta evaluasi. Evaluasi dilakukan menggunakan pre-test dan post-test untuk mengukur peningkatan pemahaman dan keterampilan peserta. Hasil kegiatan menunjukkan peningkatan rata-rata kompetensi peserta sebesar 47%, dengan peningkatan tertinggi pada keterampilan instalasi dan pengoperasian perangkat serta kemampuan membaca hasil monitoring pakan. Nilai effect size yang sangat besar menunjukkan bahwa peningkatan kompetensi dipengaruhi secara signifikan oleh intervensi kegiatan. Selain itu, penerapan sistem feeder otomatis memberikan dampak operasional berupa peningkatan keteraturan pemberian pakan, pengurangan pemborosan pakan, serta efisiensi waktu dan tenaga kerja peternak. Tingginya tingkat keberterimaan teknologi tercermin dari konsistensi penggunaan sistem oleh sebagian besar peserta setelah kegiatan berakhir. Dengan demikian, kegiatan PkM ini membuktikan bahwa penerapan teknologi IoT yang tepat guna dan disertai pendampingan berkelanjutan mampu meningkatkan efisiensi budidaya ikan sekaligus mendorong kemandirian peternak secara berkelanjutan.
Evaluasi Recursive Feature Elimination Untuk Klasifikasi Kanker Payudara Menggunakan Berbagai Algoritma Machine Learning Syarifah Yusnaini Putri; Sayuti Rahman; Nia Ramadani; Novalia Aprianti Ginting; Layla Syalsyadilla; Dedi Agustriaman Zebua
Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Vol. 8 No. 1 (2026): Edisi April
Publisher : Universitas Harapan Medan

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Abstract

Early detection of breast cancer requires classification models that are not only accurate but also efficient and interpretable. This study evaluates the effect of Recursive Feature Elimination (RFE) on the performance of several machine learning algorithms for breast cancer classification. The dataset used is the Wisconsin Diagnostic Breast Cancer (WDBC) dataset from the UCI Machine Learning Repository, consisting of 569 samples and 30 numerical features. The research stages include data preprocessing, removal of non-informative attributes, feature standardization using StandardScaler, train-test splitting with an 80:20 ratio, feature selection using Logistic Regression-based RFE, and training and testing of 11 classification algorithms. Model performance was evaluated using accuracy, precision, recall, F1-score, confusion matrix, and Receiver Operating Characteristic (ROC) curve. The results show that before feature selection, Support Vector Machine, Logistic Regression, and Voting Classifier achieved the highest accuracy of 98.25%. After applying RFE, the accuracy of these models decreased slightly to 97.37%, while the number of features was reduced from 30 to 15. Several algorithms, including Nearest Centroid, Naïve Bayes, and AdaBoost, showed improved accuracy after RFE. These findings indicate that RFE does not always improve the best model accuracy, but it can produce a more compact, efficient, and interpretable classification model.
The Influence of Population Size on the Computational Time of Genetic Algorithms in Course Scheduling Rudi Salman; Arwadi Sinuraya; Irfandi Irfandi; Eswanto Eswanto; Sayuti Rahman; Herdianto Herdianto; Olnes Yosefa Hutajulu; Agung Y S Halawa
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 1 (2026): Januari - Juni 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i1.33508

Abstract

Course scheduling is a complex problem in higher education because it must satisfy multiple constraints involving courses, instructors, rooms, and time slots. This study examines the impact of population size variation on the computational efficiency of a Genetic Algorithm (GA) applied to a medium-scale instance consisting of 35 courses, 15 instructors, 12 rooms, and 20 time slots. Simulations were conducted in MATLAB using population sizes ranging from 20 to 1000, while all other GA parameters were held constant to isolate the effect of population size. Solution quality was evaluated using a conflict-based fitness function, and all configurations yielded valid timetables with zero hard-constraint violations. Experimental results reveal a consistent non-linear relationship between population size and computation time. Statistical findings in Table 1—including mean values, standard deviations, and 95% confidence intervals—show that both very small and very large populations produce higher and more variable execution times. In contrast, population sizes of 300–400 achieve the lowest and most stable computation times, indicated by the smallest mean values and narrow confidence intervals. For the instance and configuration used in this study, this range serves as an effective starting point for population size tuning. Overall, the findings highlight the importance of empirical parameter selection to balance computational efficiency and solution quality in academic timetabling systems.
Pengembangan Convolutional Neural Network untuk Klasifikasi Ketersediaan Ruang Parkir Sayuti Rahman; Haida Dafitri
Explorer Vol 2 No 1 (2022): Januari 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v2i1.148

Abstract

Information on the availability of parking spaces is needed for drivers. Drivers walking around looking for parking spaces have negative impacts, including traffic jams, waste of fuel, increasing pollution and even causing driver panic. Classification of parking spaces properly and quickly becomes a solution to present information on the availability of parking spaces. Based on the technology used, parking space classification usually uses sensors or computer vision. However, computer vision is lower in cost usage because a single camera can classify multiple parking spaces simultaneously. Convolutional Neural Network (CNN) is a popular method in dealing with vision problems. mAlexnet is one of the CNN architectures that has succeeded in classifying parking spaces well, but its accuracy still needs to be improved. A better architecture of mAlexnet needs to be made to improve classification accuracy and speed. In this study, we designed a CNN architecture named ParkingNet. Based on testing using sub-dataset camera B from the CNRPark dataset, ParkingNet is better than mAlexnet, both in terms of accuracy, the number of parameters, and FLOPs. ParkingNet managed to outperform mAlexnet's accuracy by 0.68%. Although not significant, ParkingNet is faster in classification due to the smaller number of parameters and FLOPs. The number of ParkingNet parameters is 4/5 mAlexnet parameters and the number of ParkingNet FLOPs is 2/5 mAlexnet. ParkingNet can be implemented in a smart parking system to classify parking spaces with lower computational costs.
Analisis Klasifikasi Mobil Pada Gardu Tol Otomatis (GTO) Menggunakan Convolutional Neural Network (CNN) Sayuti Rahman; Adinda Titania; Arnes Sembiring; Mufida Khairani; Yessi Fitri Annisah Lubis
Explorer Vol 2 No 2 (2022): July 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v2i2.286

Abstract

The concept of a smart city is the most important issue in the development aspect of big cities in the world. Where the city must promise a more comfortable, organized, healthy and efficient life. Smart transportation is part of a smart city that is useful for improving better urban planning. Smart transportation also applies to toll roads, such as automating toll road retribution payments. Automatic Toll Gate (GTO) in Indonesia still uses sensors. However, sensors often misclassify trailers. In addition, the use of sensors also requires additional costs in installation and maintenance. Currently, every toll gate is equipped with cameras for various purposes. By utilizing the camera for vehicle type classification, the cost of the GTO will be reduced. For this reason, utilizing a digital camera with computer vision for vehicle type classification is the solution. Convolutional Neural Networks (CNN) is the most popular technique today in solving computer vision problems. Exploit the existing CNN by replacing the last fully connected output according to the number of vehicle classes. The test results show that mobilenet V2 is better in the classification of vehicle types, the best accuracy is Alexnet 93.81% and Mobilenet 96.19%. Computer vision by utilizing CNN is expected to replace the use of sensors so that implementation costs are cheaper.
A Hybrid GDHS and GBDT Approach for Handling Multi-Class Imbalanced Data Classification Hartono Hartono; Muhammad Khahfi Zuhanda; Rahmad Syah; Sayuti Rahman; Erianto Ongko
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.894

Abstract

Multiclass imbalanced classification remains a significant challenge in machine learning, particularly when datasets exhibit high Imbalance Ratios (IR) and overlapping feature distributions. Traditional classifiers often fail to accurately represent minority classes, leading to biased models and suboptimal performance. This study proposes a hybrid approach combining Generalization potential and learning Difficulty-based Hybrid Sampling (GDHS) as a preprocessing technique with Gradient Boosting Decision Tree (GBDT) as the classifier. GDHS enhances minority class representation through intelligent oversampling while cleaning majority classes to reduce noise and class overlap. GBDT is then applied to the resampled dataset, leveraging its adaptive learning capabilities. The performance of the proposed GDHS+GBDT model was evaluated across six benchmark datasets with varying IR levels, using metrics such as Matthews Correlation Coefficient (MCC), Precision, Recall, and F-Value. Results show that GDHS+GBDT consistently outperforms other methods, including SMOTE+XGBoost, CatBoost, and Select-SMOTE+LightGBM, particularly on high-IR datasets like Red Wine Quality (IR = 68.10) and Page-Blocks (IR = 188.72). The method improves classification performance, especially in detecting minority classes, while maintaining high accuracy.
Peningkatan Profit Melalui Strategi Diskon Dinamis Menggunakan Reinforcement Learning pada Data Transaksi Ritel Daring Andre Hasudungan Lubis; Rizki Muliono; Nanda Novita; Susilawati Susilawati; Muhathir Muhathir; Sayuti Rahman
Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Vol. 8 No. 2 (2026): Edisi Oktober
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Discount strategies play a critical role in e-commerce profitability, yet most platforms continue to rely on fixed discount rates that fail to account for heterogeneous customer behavior. This study proposes a dynamic discount optimization framework based on the Q-Learning algorithm, in which discount decisions are modeled as a Markov Decision Process (MDP) with a state space constructed from two customer behavioral indicators: transaction revenue and purchase frequency. Each customer transaction is mapped to one of six discrete states formed by combining three revenue categories (Low, Medium, High) and two frequency categories (Low, High), and the agent selects from five discount levels (0%, 5%, 10%, 15%, 20%) to maximize cumulative profit. A price elasticity mechanism is incorporated into the reward function to simulate demand responsiveness to price reductions, enabling the agent to learn non-trivial discount preferences. The framework is evaluated on the Online Retail II dataset comprising 397,885 cleaned transactions. Experimental results show that the proposed method achieves a total profit of 10,586,828.77, outperforming four of five fixed discount baselines with profit improvements of +18.80%, +11.16%, +5.60%, and +1.65% over the 0%, 5%, 10%, and 15% uniform discount scenarios, respectively. The learned policy consistently assigns 15% discounts to Low-frequency customers and 20% discounts to High-frequency customers, demonstrating that purchase frequency is the primary driver of optimal discount differentiation. These findings confirm that RL offers a more targeted and profit-competitive alternative to static promotional strategies
Co-Authors Abdul Malik Adam Adinda Titania Aditya Pratama, Bayu Agung Y S Halawa Alfyanang Fattulah Andi Marwan Elhanafi Andre Hasudungan Lubis Ari Usman Arnes Sembiring Arnes Sembiring Arnes Sembiring Arnes Sembiring Arnes Sembiring Arwadi Sinuraya Asih, Munjiat Setiani Asmah Indrawati Bayu Aditya Pratama Bayu Aditya Pratama Bayu Syah, Rahmad Beby Suryani Fithri Billiam Zealtiel Budi Santoso Budi Santoso Chairul Rizal Chiuloto, Kalvin Citra Rahmadhani Dadan Ramdan Daffa, Daffa Zain Shahriza Dedi Agustriaman Zebua Deseari Baeha Desi Yanti Dodi Siregar Dodi Siregar Emil Fitranshah Aliff S Erianto Ongko Erianto Ongko Eswanto Eswanto Fera Damayanti Finta Aramita Fiqi Arfian Habib Satria Hafifah, Febri Haida Dafitri Haida Dafitri Haida Dafitri, Haida Harahap, Herlina Hartono Hartono Hartono Hasibuan, Ade Zulkarnain Hasibuan, Muhammad Ridwan Herdianto Herdianto Herlina Andriani Simamora Ilham Faisal Ilham Faisal Iqbal Giffari Ritonga Irfandi Irfandi Irwan Irwan Isnaini Kharunnisa Kharunnisa Layla Syalsyadilla Lili Suryati Liza, Risko Lubis, Husni lubis, ihsan M F Verri Anggriawan M. Khahfi Zuhanda Manurung, Dionikxon Marischa Elveny, Marischa Martini, Dewi Marwan Ramli Marwan Ramli Mendarissan Aritonang Muchzakhir Bustari Mufida Khairani Mufida Khairani Muhammad Khahfi Zuhanda Muhammad Rizky Irwansyah Muhammad Zen Muhammad Zen, Muhammad Muhathir, Muhathir Munadi Munadi Muzdalifah Ulfayani Nanda Novita Nasaruddin Nur Hasibuan Nia Ramadani Novalia Aprianti Ginting Olnes Y. Hutajulu Prana Ugi Putra, Andre Kurnia Rachmat Aulia Rachmat Aulia, Rachmat Rafiqi Rahmad B.Y Syah Retna Astuti Kuswardani Riki Agusetiawan Risko Liza Rizki Muliono Robby Darwis Rudi Salman Sembiring, Arnes Shabila Shaharani Tanjung Siregar, Rosyidah Siti Sundari Sri Eka Riyani Harahap Sultan Shidqi Sumi Khairani Suriati Suriati Suriati Suriati Suriati, Suriati Susilawati Susilawati Suswati Suswati Suswati suswati suswati Syarifah Yusnaini Putri Tanjung, Rino Nurcahyo Fauzi Taufik Siregar Tengku Mhd Diansyah Tengku Mohd Diansyah, Tengku Mohd Ulfa Sahira Winanda, Icha Windy Sri Wahyuni Wiraswan Duha Yasir, Amru Yessi Fitri Annisah Lubis Yuni Syahputri