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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING JOURNAL OF SCIENCE AND SOCIAL RESEARCH Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) ARSY : Jurnal Aplikasi Riset kepada Masyarakat JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Journal Software, Hardware and Information Technology Journal of Mathematics and Scientific Computing With Applications Jurnal Sains dan Teknologi Jurnal Masyarakat Indonesia Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Jurnal Ilmu Komputer dan Sistem Informasi Jurnal Tiyasadarma: Jurnal Pengabdian Kepada Masyarakat Management of Information System Journal Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Kemitraan dan Pengabdian
Lubis, Arif Ridho
Politeknik Negeri Medan
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Optimization Performance of Extreme Gradient Boosting and Random Forest for Child Stunting Classification Based on Economic Factors Lase, Yuyun Yusnida; Putra, Purwa Hasan; Lubis, Arif Ridho; Prayudani, Santi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5864

Abstract

Stunting remains a major health concern in Indonesia due to its impact on children’s physical growth and cognitive development. One of the factors influencing the incidence of stunting is family economic status, which is linked to access to nutrition, sanitation, and a healthy environment. This study aims to optimize the performance of the XGBoost and Random Forest algorithms in classifying stunting in children based on economic factors and to compare the performance of the two models. The methods used in this study involve a machine learning approach, including data preprocessing, model training, hyperparameter optimization, and performance evaluation using a confusion matrix, accuracy, precision, recall, F1-score, and ROC-AUC curves. The results indicate that both algorithms perform well in classification, with an accuracy rate of approximately 70%. The Random Forest model demonstrated better performance than XGBoost with an AUC value of 0.7655, while XGBoost had an AUC value of 0.75. Additionally, the feature importance results indicated that economic and environmental factors, such as housing conditions and sanitation, have a significant influence on the incidence of stunting.
Multi-Detection System Using Faster R-CNN for Fish Species Classification and Quality Assessment on Android Sharfina Faza; Arif Ridho Lubis; Meryatul Husna; Rina Anugrahwaty; Muhammad Rafif Rasyidi; Romi Fadillah Rahmat
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16374

Abstract

Species identification and quality assessment of fish in trade still rely on manual visual observation, which is subjective and requires specialized expertise. This method's limitations make it difficult for consumers to distinguish species with similar morphology and accurately assess fish quality, which can lead to inappropriate purchasing decisions. This research develops a multi-detection system based on Faster R-CNN with VGG16 backbone for fish species classification and quality assessment simultaneously on Android platform. The system uses convolutional layers to extract visual features from input images, Region Proposal Network for fish object detection and localization, and fully connected layers for simultaneous classification of species and quality levels. The research dataset consists of 3,000 images of five fish species (gourami, tilapia, nile tilapia, snapper, and pomfret) with four quality levels, divided into 2,400 training images and 600 testing images. The trained model is converted to TensorFlow Lite format for implementation on Android devices. Test results show the multi-detection system achieves 92% accuracy in fish species classification and quality assessment, demonstrating the effectiveness of the Faster R-CNN approach for multi-detection applications in the Android-based fisheries sector.
Analisis Deteksi Penyakit Daun Pisang Menggunakan Ekstraksi Fitur CNN (MobileNetV2) dan Klasifikasi SVM Yuyun Yusnida Lase; Lampson Pindahaman Purba; Santi Prayudani; Arif Ridho Lubis; Hikmah Adwin Adam
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 6 (2025): Desember 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i6.6590

Abstract

Banana plants (Musa spp.) are one of the leading horticultural commodities in Indonesia that have high economic value and play an important role in national food security. However, banana productivity often decreases due to attacks by various diseases such as Sigatoka, Cordana, and Pestalotiopsis infections that can spread quickly. Early detection of these diseases is crucial to prevent greater losses. This study aims to develop a banana plant disease detection system based on digital image processing with the Support Vector Machine (SVM) algorithm. The research method includes the stages of banana leaf image acquisition, pre-processing using color segmentation, color and texture feature extraction, and disease type classification with the SVM algorithm. The test results show that the developed system is able to recognize banana leaf diseases with an accuracy of 97.8%, precision of 97%, and recall of 98%. These findings prove that the application of digital image processing and the SVM algorithm is effective in detecting banana plant diseases. This system is expected to be a fast, efficient, and accurate diagnostic tool for farmers to increase the productivity and quality of banana harvests.
Fuzzy Mamdani-Based Vegetable Crop Recommendation System with Historical Climate Pattern Analysis in Deli Serdang Regency Meryatul Husna; Mhd Ikhsan P Siregar; Fachry Ferdiansyah Sembiring; Arif Ridho Lubis
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.11923

Abstract

Climate variability poses significant challenges to short-cycle vegetable farming, leading to crop failure and economic losses. This study develops a Decision Support System (DSS) to recommend suitable vegetable crops based on historical climate pattern analysis in Deli Serdang Regency. The system utilizes meteorological data from BMKG spanning January 2022 to December 2024, including average temperature, rainfall, and humidity. Historical pattern analysis employs a three-month rolling mean to predict climate conditions for the upcoming planting period. The Fuzzy Mamdani method is implemented as the inference engine to determine crop suitability scores by processing uncertainty in growing requirements. The system was tested across four planting periods (January, April, July, and October) and successfully generated differentiated recommendations with fuzzy scores ranging from 50% to 88%. Results demonstrate that the system effectively adapts recommendations to seasonal climate variations, providing farmers with data-driven decision support to reduce planting risks and improve crop success rates. Future enhancements include real-time climate data integration and expansion of input variables such as soil type and solar radiation intensity.
Perancangan dan Implementasi Website Masjid Baitul Ilmi Politeknik Negeri Medan sebagai Media Informasi dan Transparansi Keuangan BKM Rian Syahputra; Donny Sanjaya; Weno Syechu; Ferry Fachrizal; Arif Ridho Lubis; Gabriel Ardi Hutagalung; Efori Bu'ulolo; Bister Purba; Arif Hamied Nababan; Indri Sulistianingsih
Jurnal Kemitraan dan Pengabdian Vol. 2 No. 1 (2026): JUNI
Publisher : PT Arfa Digital Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65358/jurnamitra.v2i1.206

Abstract

Badan Kemakmuran Masjid (BKM) Baitul Ilmi Politeknik Negeri Medan mengelola informasi kegiatan dan keuangan secara konvensional melalui pengumuman fisik dan grup pesan, sehingga jangkauan informasi terbatas dan transparansi keuangan belum optimal. Kegiatan pengabdian ini bertujuan merancang dan mengimplementasikan website berbasis Laravel sebagai media informasi digital dan sarana transparansi keuangan BKM. Metode yang digunakan adalah perancangan dan implementasi langsung melalui tahapan analisis kebutuhan, perancangan, pengembangan, pengujian, dan serah terima. Website mencakup fitur manajemen berita, jadwal sholat otomatis berbasis API, laporan keuangan transparan, donasi online, galeri dokumentasi, dan dashboard administrasi. Hasil implementasi menunjukkan website berhasil diakses publik dan mampu meningkatkan jangkauan informasi serta mewujudkan akuntabilitas pengelolaan keuangan masjid secara digital.
Co-Authors A, Azanuddin Achmad Yani Adam, Hikmah Adwin Adha, Lilis Tiara Al Khowarizmi Ali Basrah Pulungan Alif Noorachmad Muttaqin Alkhowarizmi Arif Hamied Nababan Ariyani, Tika Azhar, Muhammad Fauzan Bister Purba Dini Oktarina Dwi Handayani Donny Sanjaya Efori Bu'ulolo Elviawaty Muisa Zamzami Fachry Ferdiansyah Sembiring Fahdi Saidi Lubis Fatmi, Yulia Fawwaz, Mohammad Faris Faza, Sharfina Ferry Fachrizal - Firjatullah, Muhammad Gabriel Ardi Hutagalung Gunawan Gunawan Habibi Ramdani Safitri Harefa, Hafid Rahman Haryadi - Hidayatullah, Rafly Artha Hikmah Adwin Adam Husna, Meryatul Ilham Ramadhan Nasution Imani, Muhammad Rayyan Indri Sulistianingsih Irvan, Irvan Julham Julham Julham Julham Kamil, Idham Lampson Pindahaman Purba Luckyhasnita, Andam M.Pd, Akrim Mahyuddin K. M Nasution Mardianto, Willy Mayang Mughnyanti Mhd Faris Pratama Mhd Ikhsan P Siregar Michael J Watts Mughnyanti, Mayang Muhammad Basri Muhammad Luthfi Hamzah Muhammad Rafif Rasyidi Muharman Lubis Nadi, Farhad Nst, Fifi Anggiani Br Nurhaflah Soraya Nurlinda Opim Salim Sitompul Prayudani, Santi Purba, Lampson Pindahaman Purnamawati, Sarah Putra, Purwa Hasan Raditiansyah, Farhan Rahmadani Rahmadani Rahmadani Rahmadani Rian Syahputra Rina Anugrahwaty Rinaldy, Muhammad Eri Riza Sulaiman Rizki Syahputra Romi Fadillah Rahmat Salam, Azrizal Sarah Purnamawati Selvida, Desilia Sembiring, Boni Oktaviani Sibarani, Yous Syafli, Sekar Arini Syamsul Arifin Tasril, Virdyra Tessya Fakhta Tri Nasution Tomi Mulhartono Virdyra Tasril Weno Syechu Yulia Fatmi Yulia Fatmi Yusuf, Kadri Yuyun Yusnida Lase