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All Journal SMARTICS Journal Indonesian Journal of Information System Jurnal Nasional Komputasi dan Teknologi Informasi Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Reswara: Jurnal Pengabdian Kepada Masyarakat Teknik: Jurnal Ilmu Teknik dan Informatika Jurnal Komputer dan Teknik Informatika Jurnal Komputer, Informasi dan Teknologi
Rudi Heriansyah
Universitas Indo Global Mandiri
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Neuroscience Social Sciences Transportation

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Machine Learning for Clustering Regencies-Cities Based on Inflation and Poverty Rates in Indonesia Rendra Gustriansyah; Juhaini Alie; Ahmad Sanmorino; Rudi Heriansyah; Megat Norulazmi Megat Mohamed Noor
Indonesian Journal of Information Systems Vol. 5 No. 1 (2022): August 2022
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v5i1.5682

Abstract

The COVID-19 pandemic has increased inflation and poverty rates in many cities, thus requiring considerable attention from the government as a policymaker. Therefore, this study aims to cluster regencies/cities that need mitigation priorities from the Indonesian government based on inflation and poverty rates in 2021. Four machine learning methods, namely k-Means (KM), Partitioning around medoids (PAM), Ward, and Divisive analysis (Diana) are utilized and compared to achieve that purpose. Clustering 90 regencies/cities in Indonesia produced five optimal clusters. Furthermore, the clustering results were validated using the Silhouette width (SW) and Dunn index (DI). The results showed that the k-means method produced the most compact cluster. Hence, this study's results can be utilized as a reference for the government in determining the steps and priorities of economic policy in Indonesia.
Pemanfaatan Canva untuk Guru Sekolah Dasar sebagai Media Penyusunan Materi. Indah Permatasari; Dewi Sartika; Rudi Heriansyah; Imelda Saluza
RESWARA: Jurnal Pengabdian Kepada Masyarakat Vol 5, No 2 (2024)
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/rjpkm.v5i2.4347

Abstract

Di sekolah, saat ini telah memanfaatkan teknologi untuk menyampaikan informasi pembelajaran kepada peserta didik. Selain faktor infrastuktur dan pemahaman penggunaan internet, peningkatan pada kemampuan para pendidik dalam memberikan materi juga perlu diperhatikan. Pengembangan materi yang menyesuaikan pemanfaatan teknologi dirasa akan menjadi modal penting untuk tetap menarik perhatian peserta didik agar tetap berminat belajar secara mandiri. Modal materi inilah yang saat ini sering dibuat bervariasi karena harus bersaing dengan era generasi peserta didik yang lebih tertarik dengan teknologi. Dengan tujuan untuk memberikan pemahaman kepada para pendidik/Guru agar dapat membuat materi dengan tampilan yang lebih menarik hanya dengan memanfaatkan aplikasi Canva, tim PkM mengusulkan diperlukan kegiatan pelatihan. Metode workshop dipilih sebagai solusi agar dapat berinteraksi langsung dengan para pendidik selain pemberian pelatihan. Hasil dari kegiatan menunjukkan sebanyak 13 peserta belum pernah menggunakan aplikasi Canva serta sebanyak 22 peserta merasa tertarik dengan aplikasi Canva. kegiatan workshop ini sebagai bagian dari pengabdian masyarakat (PkM) telah diselesaikan dengan baik dan dapat disimpulkan bahwa saat ini kegiatan dianggap penting dilakukan karena masih rendahnya jangkauan pemahaman peserta/Guru terkait beberapa aplikasi pendukung penyusunan materi pembelajaran
Deteksi Pelanggaran Lalu Lintas Pengendara Sepeda Motor Metik dan Bebek Menggunakan Framework You Only Look Once (YOLO) Andri Febrian; Rudi Heriansyah; Zaid Romegar Mair

Publisher :

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i5.9673

Abstract

Abstrak − Penelitian ini mengembangkan sistem deteksi otomatis pelanggaran lalu lintas pengendara sepeda motor metik dan bebek menggunakan You Only Look Once (YOLOv11n). Sistem menargetkan pelanggaran seperti tanpa helm, tanpa plat nomor, tanpa spion, dan berbonceng lebih dari dua orang. Dataset sebanyak 739 gambar, diaugmentasi melalui rotasi dan flipping, dilatih hingga 500 epoch. Performa terbaik dicapai pada epoch 350 dengan mAP@0.5 0,572 dan mAP@0.5:0.95 0,397. Ketidakseimbangan data memengaruhi deteksi kelas minor. Sistem ini menjanjikan untuk pengawasan lalu lintas otomatis, dengan rekomendasi untuk meningkatkan kualitas data dan eksplorasi YOLO varian terbaru.Kata Kunci: YOLOv11n; pelanggaran lalu lintas; deteksi objek; sepeda motor; mAP;Abstract — This study develops an automatic traffic violation detection system for riders of moped and underbone motorcycles using You Only Look Once (YOLOv11n). The system targets violations such as riding without a helmet, without a license plate, without mirrors, and carrying more than two passengers. A dataset of 739 images was augmented through rotation and flipping and trained for up to 500 epochs. The best performance was achieved at epoch 350 with mAP@0.5 of 0.572 and mAP@0.5:0.95 of 0.397. Data imbalance affected the detection of minor classes. This system shows promise for automatic traffic surveillance, with recommendations to improve data quality and further explore newer YOLO variants.Keywords: YOLOv11n; traffic violation; object detection; motorcycle; mAP;
Classification of Coconut Fruit Ripeness Level Using Convolutional Neural Network (CNN) Method Muhammad Rizki; Rudi Heriansyah; Dwi Verano
Jurnal Komputer, Informasi dan Teknologi Vol. 6 No. 1 (2026): June
Publisher : Penerbit Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/jkomitek.v6i1.3888

Abstract

Manual assessment of coconut ripeness is often subjective and causes post-harvest losses of up to 25% in Indonesia, the world's largest coconut producer. This study aims to develop a CNN VGG-19 model for automatic classification of three ripeness levels (immature, medium, mature) with accuracy >95%. The quantitative experimental method uses supervised learning with a dataset of 900 original images (300/class) from local plantations in South Sumatra, augmented to 3000 images. Instruments include Python/TensorFlow on Google Colab, preprocessing (rembg background removal, resizing 224x224), training 10 epochs of the Adam optimizer. Analysis uses a confusion matrix, accuracy, precision, recall, and F1-score. The results show a progressive accuracy from 14% (40 test data/class) to 98% (200 test data/class). Conclusion: VGG-19 transfer learning with data augmentation is effective for local coconut ripeness classification, potentially integrating into mobile applications for the processing industry.
Analisis Sentimen Komentar Netizen Terhadap 17+8 Tuntutan Rakyat Pada X Menggunakan Naive Bayes Classifier Fransisco Lucky Halawa; Rudi Heriansyah; Indah Permatasari
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 6 No. 1 (2026): Mei : Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v6i1.1228

Abstract

This study analyzes netizen sentiment concerning the 17+8 public aspirations circulating the digital platform X spanning the period from August 18 through October 31, 2025. 1,837 comments obtained through scraping method. Classification Research stages include data preprocessing, sentiment weighting based on lexicon, and feature extraction using TF-IDF. Data 80% used for learning purposes and the remaining 20% utilized for validation. The findings reveal that the majority of comments, amounting to 81.14%, contained negative sentiment, while the remaining 18.86% were positive. The outcomes demonstrate that community reactions toward the 17+8 People's Demands were dominated by unsupportive views. From a theoretical standpoint this scholarly work offers to enriching knowledge concerning public opinion classification on political issues through a computational approach, while also serving as a reference for future research focused on improving the accuracy of sentiment analysis related to political dynamics and the behavior of state institutions.
Penguatan Kompetensi Guru SMK PGRI Kota Palembang Melalui Pemanfaatan Artificial Intelligence Dalam Perencanaan Pembelajaran Ahmad Sanmorino; Hendra Di Kesuma; Indah Pratiwi Putri; Lastri Widya Astuti; Imelda Saluza; Tasmi; Nining Ariati; Dhamayanti; Faradillah; Fery Antony; Dona Marcelina; Rudi Heriansyah
Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Vol. 9 No. 1 (2026): Januari 2026
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurdimas.v9i1.4131

Abstract

Abstract: The development of artificial intelligence (AI) technology presents new opportunities in education, particularly in lesson planning. However, most vocational high school teachers in Palembang City, including those at SMK PGRI 2, still have limited knowledge and skills in utilizing AI. This problem is the background to the implementation of community service activities (PkM) with the aim of improving teacher competency in using Large Language Models (LLM) such as Gemini and ChatGPT to develop Lesson Implementation Plans (RPP). The methods used included needs surveys, interactive workshops, hands-on practice, and evaluation through post-tests and participant feedback. The results of the activity showed a significant increase, where teacher knowledge increased from 20% to 80% and the application of AI in lesson plans increased from 10% to 65%. The contribution of this activity lies in improving teachers' ability to utilize AI to develop lesson plans more effectively and providing a scientific basis for the application of LLM in lesson planning in vocational education. Keywords: artificial intelligence, lesson planning, vocational school teachers Abstrak: Perkembangan teknologi kecerdasan buatan (Artificial Intelligence) menghadirkan peluang baru dalam dunia pendidikan, khususnya dalam perencanaan pembelajaran. Namun, sebagian besar guru SMK di Kota Palembang, termasuk di SMK PGRI 2, masih memiliki keterbatasan dalam pengetahuan dan keterampilan pemanfaatan AI. Permasalahan ini melatarbelakangi dilaksanakannya kegiatan pengabdian kepada masyarakat (PkM) dengan tujuan meningkatkan kompetensi guru dalam menggunakan Large Language Models (LLM) seperti Gemini dan ChatGPT untuk menyusun Rencana Pelaksanaan Pembelajaran (RPP). Metode yang digunakan meliputi survei kebutuhan, workshop interaktif, praktik langsung, serta evaluasi melalui post-test dan umpan balik peserta. Hasil kegiatan menunjukkan adanya peningkatan signifikan, di mana pengetahuan guru meningkat dari 20% menjadi 80% dan penerapan AI dalam RPP naik dari 10% menjadi 65%. Kontribusi kegiatan ini terletak pada peningkatan kemampuan guru dalam memanfaatkan AI untuk menyusun RPP secara lebih efektif serta penyediaan dasar ilmiah bagi penerapan LLM dalam perencanaan pembelajaran di pendidikan vokasi. Kata kunci: artificial intelligence, guru SMK, perencanaan pembelajaran
Analisis Struktur Tulisan Tangan melalui Deteksi Zona Spasi Antarkata Menggunakan CNN Mendalam Nabilah Mufti; Rudi Heriansyah; Muhammad Haviz Irfani
SMARTICS Journal Vol 12 No 1 (2026): Journal SMARTICS (April 2026)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v12i1.13937

Abstract

Advances in image processing and deep learning technology enable more accurate handwriting analysis, including the detection of interword spacing, which exhibits high complexity due to variations in writing styles. This study aims to implement a Convolutional Neural Network (CNN) algorithm using the You Only Look Once version 11 (YOLOv11) framework to detect and classify interword spacing zones into three classes: Narrow Word Spacing (NWS), Medium Word Spacing (MWS), and Wide Word Spacing (WWS). The dataset comprises 150 handwritten images with a total of 4.117 annotated interword spacing objects. The research methodology involves testing the model across variations of learning rates (0.1, 0.01, 0.001, and 0.0001) and data split ratios (70:30, 80:20, and 90:10). Model performance was evaluated using Precision, Recall, F1-Score, and mean Average Precision (mAP) metrics. Based on 12 experimental trials, the best configuration was achieved with a learning rate of 0.001 and a 90:10 data split. This configuration produced an mAP@50 of 0.455, an mAP@50–95 of 0.261, and an F1-Score of 0.49. These results indicate that the YOLOv11 model is capable of detecting interword spacing zones with reasonably good performance, despite remaining classification errors due to visual similarities between classes.
Implementasi YOLO Framework pada Deteksi Otomatis Sepatu Badminton Pemain di Lapangan Ida Bagus Bisma; Rudi Heriansyah; Muhammad Haviz Irfani
Jurnal Komputer dan Teknik Informatika Vol. 1 No. 2 (2026): Edisi: Februari-April
Publisher : Pustaka Bangsa Indonesia

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

Abstract

Penggunaan sepatu yang tidak sesuai saat bermain badminton dapat meningkatkan risiko cedera serta merusak flooring lapangan. Oleh karena itu, diperlukan sistem deteksi otomatis untuk mengidentifikasi jenis sepatu yang digunakan pemain guna meningkatkan kepatuhan terhadap peraturan lapangan. Penelitian ini mengusulkan sistem deteksi sepatu badminton menggunakan algoritma Convolutional Neural Network (CNN) dengan framework You Only Look Once (YOLO). Dataset yang digunakan terdiri dari tiga kelas, yaitu Badminton Shoes, Warning Shoes, dan Foot, yang diperoleh dari situs resmi produk sepatu serta platform Roboflow. Proses pelatihan model meliputi tahap anotasi, preprocessing, dan augmentasi data. Evaluasi kinerja model dilakukan menggunakan confusion matrix dengan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa model mampu mendeteksi dan mengklasifikasikan jenis sepatu dengan tingkat akurasi yang tinggi. Sistem ini berpotensi membantu pengelola lapangan dalam melakukan pengawasan otomatis serta mengurangi risiko cedera akibat penggunaan sepatu yang tidak sesuai.
Co-Authors Ahmad Sanmorino Andri Febrian Dhamayanti Dona Marcelina Dwi Verano Faradillah Faradillah Fery Antony Fransisco Lucky Halawa Ida Bagus Bisma Imelda Saluza, Imelda Indah Permatasari Indah Pratiwi Putri Juhaini Alie Kesuma, Hendra Di Lastri Widya Astuti Megat Norulazmi Megat Mohamed Noor Muhammad Haviz Irfani Muhammad Rizki Nabilah Mufti Nining Ariati Rendra Gustriansyah Sartika, Dewi Tasmi