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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING BAREKENG: Jurnal Ilmu Matematika dan Terapan 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 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 Advance Sustainable Science, Engineering and Technology (ASSET)
Lubis, Arif Ridho
Politeknik Negeri Medan
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Veterinary

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PKM Pendampingan Laporan Keuangan Berbasis Digital dan Pemasaran Digital pada Kelompok Desa Sirumbia, Kecamatan Simpang Empat, Kabupaten Karo, Sumatera Utara Rizki Syahputra; Luckyhasnita, Andam; Ridho Lubis, Arif; Ardi Hutagalung, Gabriel
Jurnal Tiyasadarma Vol. 3 No. 1 (2025): Juli 2025 | Jurnal Tiyasadarma
Publisher : LPPM ITEBA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62375/jta.v3i1.389

Abstract

Desa Sirumbia in Karo Regency has high economic potential, particularly in the promising coffee cultivation business; however, traditional financial recording methods and limited market reach hinder its development. Despite the availability of internet access, coffee sales from this village remain limited and are not widely recognized. The goal of this activity is to maximize the economic potential of Desa Sirumbia by modernizing financial management and utilizing digital technology to enhance operational efficiency and expand market reach and branding of the coffee products. The partners in this initiative are community groups in Desa Sirumbia engaged in coffee production. The implementation method involves observations and surveys to understand the issues, followed by socialization, training, and assistance in managing digital financial records and marketing strategies through digital media. The results show that participants successfully grasped the basics of accounting based on SAK EMKM to prepare financial reports and effectively utilized the Canva application to create promotional designs in the form of flyers. Evaluation of the activities was conducted through written questionnaires completed by the participants. This community service program has successfully improved the partners' skills in digital financial recording and digital marketing. The training provided solutions to the main challenges faced by the partners, namely limitations in financial management and product marketing. Consequently, the partners can now independently create simple digital financial reports and design digital promotions.
Prediction of Cyberbullying in Social Media on Twitter Using Logistic Regression Prayudani, Santi; Adha, Lilis Tiara; Ariyani, Tika; Lubis, Arif Ridho
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9842

Abstract

As cases of cyberbullying on social media increase, there is a need for efficient measures to detect the vice. This research aims to establish the application of machine learning algorithms in analyzing text on social media to determine potentially harmful comments using logistic regression. The first and most important research question of this study is to assess the extent to which the model is capable of correctly identifying the comments that contain features of cyberbullying and those that do not. The data set included comments from different social media sites and was preprocessed before further analysis was conducted on it. Exploratory Data Analysis was applied in the study to establish relationships and textual features with bullying behavior. As with any other model, after training and testing the model, the results were analyzed using parameters like precision, precision, gain, and F1 statistics. The outcomes of this study revealed that the use of logistic regression models can give a fairly satisfactory level of accuracy in identifying cyberbullying. In light of this, this study underscores the need to use machine learning algorithms to minimize negative actions in cyberspace.
Quantifying the Causal Impact of Employment Trends on Academic Performance Using Time-Series and Public Interest Data in Indonesia Muttaqin, Alif Noorachmad; Lubis, Muharman; Mulhartono, Tomi; Lubis, Arif Ridho
Advance Sustainable Science Engineering and Technology Vol. 7 No. 4 (2025): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i4.2358

Abstract

This study quantifies the causal impact of employment trends on academic performance using a hybrid model of survey data and time-series public interest data from Google Trends in Indonesia. Employing Granger causality and regression analysis, the research investigates eight determinants of GPA and their relationship to labor indicators. A purposive sample of 40 respondents and secondary data from 2011–2019 were analyzed. Granger tests reveal significant one-way causality from employment to GPA indicators, particularly in parental monitoring (F = 7.06; p < 0.05) and learning motivation (F = 9.68; p < 0.05). Regression analysis supports these findings with R² values above 0.50. Results highlight the potential of integrating behavioral data into educational analytics. This research contributes methodological innovation by incorporating public interest data to explain academic outcomes, with implications for predictive modeling in education policy and planning.
Penerapan Aplikasi Pintar Tani Untuk Peningkatan Pemasaran Pertanian Pada Eco Farm di Desa Kelambir V Hasan Putra, Purwa; Julham, Julham; Lubis, Arif Ridho; Azanuddin, Azanuddin; Selvida, Desilia
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 5 No. 4 (2024): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN) Edisi September - Desembe
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v5i4.4328

Abstract

Program Pengabdian Penerapan Aplikasi Pintar Tani bertujuan untuk membantu membuka wawasan petani agar semakin leluasa dalam mengolah lahan dan hasil pertanianya. Aplikasi Pintar Tani diharapkan dapat digunakan untuk berbagi ilmu tentang pertanian dan perternakan serta dapat mengoptimalkan pemasaran dari berbagai hasil pertanian. Belum adanya pemanfaatan teknologi informasi dengan maksimal dan masih minimnya pengetahuan masyarakat terhadap digitalisasi pemasaran produk pertanian dan perternakan. Sedangkan target khususnya adalah untuk pemberian aplikasi Pintar Tani, pemberian pelatihan penggunaan aplikasi, dan mengoptimalkan pemasaran hasil dari pertanian dan perternakan. Metode yang digunakan terdiri dari 4 tahapan yaitu: dimulai dari Tim Pengabdian Pintar Tani memahami permasalahan mitra, dari hasil analisis data, Menyusun solusi-solusi yang akan dilakukan untuk mengatasi permasalahan mitra, melaksanakan solusi-solusi yang ditawarkan dan terakhir melaukan publikasi pada media massa cetak,online, artikel ilmiah pada jurnal nasional sebagai luaran wajib, dan HKI (hak cipta) sebagai luaran tambahan.
WEB-BASED MANAGEMENT INFORMATION SYSTEM WITH CODEIGNITER FRAMEWORK Nst, Fifi Anggiani Br; Lubis , Arif Ridho; Sembiring, Boni Oktaviani
Journal of Mathematics and Scientific Computing With Applications Vol. 3 No. 1 (2022)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/jmscowa.v3i1.58

Abstract

The need for shelter becomes very needed at this time, especially people from outside the city who want to work or continue their education to other cities. So that the need for boarding houses to increase and demand by many people. The system that is running on the full boarding house is still done manually where, there is no system that can help in managing his boarding house, where people who want boarding must come directly to see the facilities they have, room status and costs. And there is no system that can help the owner in managing boarding house payments. The development of this system uses the waterfall method. Web-based full boarding management information system can manage boarding payment data and tenant data management.
PKM DIGITALISASI SISTEM PRESENSI SISWA MELALUI APLIKASI DI MADRASAH ALIYAH SUNGGAL DESA TANJUNG GUSTA KEC SUNGGAL KAB DELI SERDANG SUMATERA UTARA Putra, Purwa Hasan; Julham, Julham; Lubis, Arif Ridho; Tasril, Virdyra; Mughnyanti, Mayang; Selvida, Desilia
Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Vol 5, No 2 (2025): Desember 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jpstm.v5i2.5385

Abstract

Abstract: The Community Service Activity of Digitalizing Student Attendance System through Application was carried out at Sunggal Islamic High School, Tanjung Gusta Village, Sunggal District, Deli Serdang Regency, North Sumatra as an effort to improve the efficiency and accuracy of student attendance recording which was previously still done manually. The web-based digital attendance system developed by the PKM team from Medan State Polytechnic allows teachers and administrative staff to record student attendance quickly, securely, and can be accessed in real-time. The recording process which previously took 7 minutes per class can be cut to 2 minutes per class. The implementation method of the activity includes observing school needs, designing and developing the application, training for teachers and staff, and mentoring during implementation. The results of the activity show that this system not only improves work efficiency but also helps improve the digital literacy of teachers and school staff. Keyword: Digitalization, Attendance, Students, MAS Aliyah Sunggal Abstrak: Kegiatan Pengabdian Kepada Masyarakat Digitalisasi Sistem Absensi Siswa Melalui Aplikasi dilaksanakan di SMA Islam Sunggal, Desa Tanjung Gusta, Kecamatan Sunggal, Kabupaten Deli Serdang, Sumatera Utara sebagai upaya untuk meningkatkan efisiensi dan akurasi pencatatan kehadiran siswa yang sebelumnya masih dilakukan secara manual. Sistem absensi digital berbasis web yang dikembangkan oleh tim PKM dari Politeknik Negeri Medan ini memungkinkan guru dan tenaga administrasi untuk mencatat kehadiran siswa secara cepat, aman, dan dapat diakses secara real-time. Proses pencatatan yang sebelumnya membutuhkan waktu 7 menit per kelas dapat dipangkas menjadi 2 menit per kelas. Metode pelaksanaan kegiatan meliputi observasi kebutuhan sekolah, perancangan dan pengembangan aplikasi, pelatihan bagi guru dan tenaga kependidikan, serta pendampingan selama pelaksanaan. Hasil kegiatan menunjukkan bahwa sistem ini tidak hanya meningkatkan efisiensi kerja tetapi juga membantu meningkatkan literasi digital guru dan tenaga kependidikan sekolah. Kata kunci: Digitalisasi, Absensi, Siswa, MAS Aliyah Sunggal 
Optimization of Convolutional Neural Network for Classification of Hydroponic Vegetable Cultivation Using Machine Learning Lubis, Arif Ridho; Prayudani, Santi; Putra, Purwa Hasan; Lase, Yuyun Yusnida
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i1.7231

Abstract

In an effort to apply applied product innovation and support the improvement of hydroponic vegetable cultivation, it is based on several things. Among them are changes in the texture of the year, stems and vegetable quality. At this time the problems faced by hydroponic vegetable pickers, especially banyumas village youth organizations who have UMKM hydroponic vegetable cultivation. This situation will have an impact on problems and losses that result in a lack of yield and quality of harvested vegetables if not resolved quickly. The results of this study resulted in optimal accuracy performance in the classification of hydroponic vegetables with CNN, this study also successfully classified normal vegetables with vegetables affected by disease. This research produces accuracy in the first test 73% and the second test 92%.
A COMPARATIVE STUDY OF PIPELINE-VALIDATED MACHINE LEARNING CLASSIFIERS FOR PERMISSION-BASED ANDROID MALWARE DETECTION Lubis, Arif Ridho; Wulandari, Dewi; Adha, Lilis Tiara; Ariyani, Tika; Lase, Yuyun; Lubis, Fahdi Saidi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1675-1692

Abstract

The growing prevalence of Android malware distributed through third-party APK sideloading poses a significant security threat to users and developers. This study aims to evaluate the effectiveness of three machine learning algorithms—Logistic Regression (LR), Random Forests (RF), and Gradient Boosting Machine (GBM)—for static Android malware detection based on permission features. The experiment employs the publicly available Android Malware Prediction Dataset (Kaggle, accessed 2025), containing 4,464 application samples with 328 binary permission attributes. A leakage-free CRISP-DM workflow was implemented, integrating data cleaning, automated feature selection via SelectKBest (Mutual Information), and hyperparameter optimisation using GridSearchCV with stratified 5-fold cross-validation. Results on the unseen hold-out test set show that GBM achieved the best performance, with 96.05% accuracy and 0.9924 ROC-AUC, outperforming LR and RF. In addition, GBM exhibited superior probability calibration (Brier Score = 0.0344) and interpretability, as confirmed through SHAP analysis. The ablation study further validated that optimal model performance saturates at 30–40 selected features. This research contributes a reproducible and pipeline-validated comparative framework for static Android malware detection, addressing prior studies’ limitations regarding feature selection bias and data leakage. Nevertheless, the study is limited by its reliance on static permission features and the absence of dynamic behavioural data, which may restrict generalisation to evolving malware families.
Multi-Detection System Using Faster R-CNN for Fish Species Classification and Quality Assessment on Android Faza, Sharfina; Lubis, Arif Ridho; 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.
Co-Authors A, Azanuddin Achmad Yani Adam, Hikmah Adwin Adha, Lilis Tiara Al Khowarizmi Ardi Hutagalung, Gabriel Ariyani, Tika Azhar, Muhammad Fauzan Dewi Wulandari Dini Oktarina Dwi Handayani Elviawaty Muisa Zamzami Fatmi, Yulia Fawwaz, Mohammad Faris Faza, Sharfina Firjatullah, Muhammad Gabriel Ardi Hutagalung Gunawan Gunawan Habibi Ramdani Safitri Harefa, Hafid Rahman Haryadi - Hidayatullah, Rafly Artha Hikmah Adwin Adam Hikmah Adwin Adam Husna, Meryatul Ilham Ramadhan Nasution Imani, Muhammad Rayyan Irvan, Irvan Julham Julham Julham Julham Kamil, Idham Lampson Pindahaman Purba Lase, Yuyun Lubis, Fahdi Saidi Luckyhasnita, Andam M.Pd, Akrim Mahyuddin K. M Nasution Mardianto, Willy Mhd Faris Pratama Michael J Watts Mughnyanti, Mayang Muhammad Basri Muhammad Luthfi Hamzah Muhammad Rafif Rasyidi Muharman Lubis Mulhartono, Tomi Muttaqin, Alif Noorachmad 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 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 Yulia Fatmi Yulia Fatmi Yusuf, Kadri Yuyun Yusnida Lase