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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ELKHA : Jurnal Teknik Elektro Jurnal sistem informasi, Teknologi informasi dan komputer Jurnal Informatika dan Teknik Elektro Terapan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Eksplora Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) DoubleClick : Journal of Computer and Information Technology Informatik : Jurnal Ilmu Komputer Kurawal - Jurnal Teknologi, Informasi dan Industri JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Informatika Global EDUMATIC: Jurnal Pendidikan Informatika Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) JUKI : Jurnal Komputer dan Informatika TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Pendidikan dan Teknologi Indonesia Jumat Informatika: Jurnal Pengabdian Masyarakat Bulletin of Computer Science Research Jurnal Abdi Masyarakat Indonesia Jurnal Pengabdian Masyarakat IPTEK Brilliance: Research of Artificial Intelligence Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Nasional Teknologi Komputer Arcitech: Journal of Computer Science and Artificial Intelligence Jurnal Informatika Progres The Indonesian Journal of Computer Science Research Mestaka: Jurnal Pengabdian Kepada Masyarakat Innovative: Journal Of Social Science Research MDP Student Conference Journal of Embedded Systems, Security and Intelligent Systems JRIIN :Jurnal Riset Informatika dan Inovasi Jurnal Rekayasa Sistem Informasi dan Teknologi Jurnal Software Engineering and Computational Intelligence Scientific Journal of Informatics LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics Applied Information Technology and Computer Science (AICOMS) Welfare: Jurnal Pengabdian Masyarakat Jurnal Nasional Teknologi Informasi dan Aplikasinya Jurnal Nasional Komputasi dan Teknologi Informasi
Muhammad Rizky Pribadi
Universitas Multi Data Palembang
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Perancangan UI/UX dan Evaluasi Usability Sistem Cerdas Prediksi Titik Api Sumatera Selatan Hotspot Monitor Muhammad Rizky Pribadi; Dedy Hermanto; Hafiz Irsyad
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 4 No 2 (2024): April 2024 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v4i2.17243

Abstract

South Sumatra faces recurring forest and land fires, yet hotspot information remains difficult for lay users to interpret. This study designs and evaluates the user interface (UI/UX) of Sumsel Hotspot Monitor, accommodating a representation of AI-based wildfire hotspot prediction via the Design Thinking method (Empathize, Define, Ideate, Prototype, Test). Interviews with residents, disaster officers, and meteorological operators revealed a need for plain language and color-coded indicators, translated into a map prototype displaying illustrative output from LightGBM (spread probability) and ConvLSTM (movement direction) models, adopted as design references; their training and quantitative validation against real historical data are planned for future research. Usability testing (SUS) with 24 respondents yielded an average score of 78.54 (Grade B+, "Good"), indicating the prototype is acceptable for use. This research bridges AI-based hotspot prediction with user-centered UI/UX design, offering practical recommendations for an accessible mitigation application; empirical validation of the AI component remains necessary before full adoption by disaster agencies.
Pengembangan Model Matematika Penyebaran Api Berbasis Vektor dan Filter Titik Panas Industri untuk Sistem Peringatan Dini Karhutla Muhammad Rizky Pribadi; Dedy Hermanto; Hafiz Irsyad
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i2.17244

Abstract

Forest and land fires (karhutla) in the tropical peatland ecosystem of South Sumatra pose recurring ecological threats and transboundary haze disasters during every dry season. Existing early warning systems generally rely on satellite hotspot detections without accounting for the direction and rate of fire spread, and remain vulnerable to false alarms caused by persistent industrial heat sources such as refineries, palm oil mill flare stacks, and power plants. This study develops a deterministic, vector-based mathematical model to predict the direction, rate, and hazard-zone geometry of fire spread in near real-time, complemented by a spatial-temporal filtering algorithm that eliminates industrial heat sources. The model derives a propagation bearing from wind direction, a base rate of spread from four environmental factors, and constructs three risk zones as cone-shaped polygons in geospatial coordinates. The model was implemented in the Sumsel Hotspot Monitor system, processing VIIRS and MODIS data from NASA FIRMS. Evaluation using Intersection over Union (IoU) and Dice Similarity Coefficient against real satellite ground truth shows that model performance degrades as the prediction time horizon increases. These results confirm that the model can run at low computational cost and is suitable as an early prediction baseline, although its accuracy still requires further parameter calibration before full adoption by the regional disaster management agency.
Analisis Sentimen Terhadap Aplikasi Mitra Darat Menggunakan Algoritma Naive Bayes Classifier dan K-Nearest Neighbor Ananda Wijaya; Mario Rivaldo; Muhammad Rizky Pribadi
Informatik : Jurnal Ilmu Komputer Vol 20 No 3 (2024): Desember 2024
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v20i3.7967

Abstract

Industri transportasi sekarang menjadi elemen penting seiring dengan berkembangnya jaman terutama untuk generasi muda sekarang. Mitra Darat sendiri juga salah satu dari industri tersebut. Aplikasi yang memungkinkan untuk pengguna dengan mudah mengetahui jadwal keberangkatan bus yang akan mereka tumpangi dimana pun dan kapan pun di perangkat seluler mereka. Ulasan pasti diberikan untuk setiap aplikasi yang tersedia baik positif dan negatif. Dengan ini, kami mencoba melakukan penelitian analisis sentimen untuk aplikasi Mitra Darat melalui ulasan komentar dari google play store agar kami dapat mengidentifikasi sentimen yang terkait dengan penggunaan aplikasi Mitra Darat, serta memberikan wawasan beharga kepada penyedia layanan transportasi darat untuk memahami pandangan pengguna dan meningkatkan pelayanan pengguna dari hasil analisis sentimen kami. Algoritma yang digunakan kami ialah KNN dan NBC. Kedua algoritma ini sudah umum digunakan oleh banyak orang karena keahlian dalam mengklasifikasi data analisis sentimen dan juga popular di kalangan peneliti. Bedasarkan hasil pengujian kami bisa disimpulkan untuk model analisis sentimen kami yang dirancang menggunakan algoritma NB menampilkan performa akurasi lebih tinggi dibandingkan KNN. Akurasi model NB mencapai 99,28%, sedangkan KNN mendapatkan akurasi sebesar 80%. Ini menunjukkan bahwa algoritma naïve bayes lebih cocok digunakan untuk mendapatkan keakuratan yang maksimal dibandingkan menggunakan k-nearest neighbor.
Analysis of Optimal Epoch Selection for YOLO26 Model in Detecting Graves and Free Slots Using UAV Photogrammetry Hafiz Irsyad; Muhammad Rizky Pribadi; Dedy Hermanto; Dina Lestari Putri
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 2 (2026): June 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i2.11573

Abstract

Purpose – This study investigates the optimal number of training epochs for the YOLO26 model in detecting graves and free burial slots from UAV photogrammetry imagery, with particular attention to model convergence, generalization, and detection performance. Design/methods/approach – A publicly available cemetery dataset containing two object classes, namely graves and free burial slots, was preprocessed using auto-orientation, 2×2 tiling, resizing to 640 × 640 pixels, grayscale conversion, and data augmentation. The YOLO26 model was trained using transfer learning under six epoch configurations: 50, 100, 150, 200, 250, and 300 epochs. Performance was evaluated using precision, recall, F1-score, mAP@50, mAP@50–95, confusion matrices, and training and validation loss curves. Findings – Model performance improved substantially as training progressed and began to stabilize after approximately 200 epochs. The highest observed performance occurred at epoch 289, with a precision of 98.78%, recall of 98.26%, F1-score of 99%, mAP@50 of 99.40%, and mAP@50–95 of 90.94%. Although the 300-epoch configuration produced similarly strong results, the additional gains were marginal, indicating diminishing returns after convergence. Research implications/limitations – The findings provide practical guidance for selecting an appropriate training duration in UAV-based cemetery mapping and small-object detection. However, the study relies on a relatively small, single-source dataset, which may limit generalizability across different cemetery layouts, environmental conditions, and UAV imaging configurations. Originality/value – This study provides a domain-specific multi-epoch benchmark for YOLO26 and demonstrates the importance of metric- and convergence-based checkpoint selection rather than relying solely on the maximum predefined number of epochs.
Pelatihan Pemrograman Dasar Python Dengan Memanfaatkan ChatGPT pada SMK Methodist 2 Palembang: Pelatihan pemrograman dasar menggunakan bahasa Python kepada para siswa kelas 10 SMK Methodist 2 Palembang Steven Tribethran; Daniel Daniel; Rio Ferdynand; Andreas Saputra; Hansen Hansen; Muhammad Rizky Pribadi
Jumat Informatika: Jurnal Pengabdian Masyarakat Vol. 4 No. 2 (2023): Agustus
Publisher : LPPM Universitas KH. A. Wahab Hasbullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32764/abdimasif.v4i2.3709

Abstract

Teknologi menjadi hal yang semakin penting dalam kehidupan kita saat ini. Terutama dalam era digital, dimana hampir semua aspek kehidupan berhubungan dengan teknologi. Hal ini juga sangat mempengaruhi bidang pendidikan yang menyangkut kebutuhan pelaksanaan akademik para siswa di segala jenjang pendidikan. Oleh karena itu, salah satu keterampilan yang dirasakan penting untuk dipelajari adalah pemrograman. Namun, belajar pemrograman bagi orang awam seringkali menimbulkan kendala dan kesulitan saat ingin memulainya. Pengabdian ini bertujuan untuk mengatasi masalah tersebut dengan memanfaatkan ChatGPT, sebuah model bahasa Artificial Intelligence yang dikembangkan oleh OpenAI dalam pelatihan pemrograman dasar menggunakan bahasa pemrograman Python dengan memanfaatkan platform cloud Jupyter Notebook dari Google Colab yang memungkinkan siapapun untuk menjalankan kode pemrograman Python langsung dari perangkat yang memiliki browser dan akses internet dimanapun itu. Metode praktik langsung atau drill method digunakan dalam pengabdian ini, dimana para peserta akan mengikuti instruksi dari pemateri dan juga dapat bereksperimen sendiri untuk menambah pengetahuan para peserta. Pelatihan dilakukan pada bulan April 2023 dengan para peserta merupakan para siswa kelas 10 jurusan Teknik Komputer Jaringan dari Sekolah Menengah Kejuruan Methodist 2 Palembang. Kegiatan ini memberikan pengetahuan langsung kepada para siswa mengenai dasar pemrograman menggunakan bahasa Python untuk menunjang minat para siswa untuk mendalami bidang yang berkaitan dengan pelatihan tersebut. Hasil pengabdian menunjukkan sebagian besar peserta (lebih dari 60%) dari total 23 peserta yang terlibat dalam pelatihan dapat mengikuti pelatihan dengan baik dan paham mengenai konsep pemrograman dasar yang disampaikan.
UAV Based Automated Surveillance of Ganoderma boninense in Oil Palm Canopies Using YOLO26 Architecture Muhammad Rizky Pribadi; Hafiz Irsyad; Eka Puji Widiyanto; Muhammad Tri Setianto; Safeti Intan Pratiwi
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.11502

Abstract

Purpose – This study develops and evaluates a UAV-based automated surveillance approach using the YOLO26 architecture to detect visible symptoms associated with Ganoderma boninense infection in oil palm canopies. The study addresses the limitations of conventional manual inspection and multi-stage detection systems by applying a unified single-stage object-detection framework. Design/methods/approach – The model was developed using a publicly available dataset containing 1,133 annotated UAV images of oil palm canopies. Images were preprocessed, augmented, and partitioned using a plantation-block-aware strategy to reduce spatial data leakage. YOLO26s was trained using the Ultralytics framework on an NVIDIA Tesla T4 GPU. Model performance was evaluated using precision, recall, mAP@50, mAP@50–95, confidence-threshold sensitivity analysis, precision–recall curves, and five-fold block-aware cross-validation. Findings – On the independent block-aware test set of 118 images, the model achieved an mAP@50 of 77.14%, mAP@50–95 of 41.98%, precision of 66.03%, and recall of 76.32%. Five-fold block-aware cross-validation produced a mean mAP@50 of 74.12% ± 5.51% and a mean mAP@50–95 of 41.31% ± 4.00%. The relatively high recall indicates that the model can identify most visible infection instances, although its moderate precision shows that false-positive detections remain a practical concern. Research implications/limitations – The findings demonstrate the potential of YOLO26 to support UAV-based oil palm disease surveillance and targeted field inspection. However, the study relies on a single public dataset with inherited annotation procedures, lacks geographically independent external validation, and does not include direct benchmarking on onboard UAV or embedded edge devices. Originality/value – This study provides an early empirical evaluation of YOLO26 for UAV-based detection of visible Ganoderma symptoms in oil palm canopies. Its contribution lies in combining a single-stage detection architecture with plantation-block-aware evaluation, threshold-sensitivity analysis, and cross-validation to provide a more leakage-controlled assessment of model performance.
ANALISIS OPINI PUBLIK TWITTER TERHADAP ISU ENERGI NASIONAL MENGGUNAKAN PENDEKATAN INDOBERT DAN NAÏVE BAYES Jennifer Verty; Femmy Johan; Muhammad Rizky Pribadi
PROGRESS Vol 18 No 2 (2026): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i2.474

Abstract

The increase in fuel oil prices and electric vehicles has become an issue that continues to develop on Twitter. This study aims to analyze public sentiment regarding fuel oil price increases and Public Electric Vehicle Charging Stations (SPKLU) in Indonesia, comparing the Naive Bayes and IndoBERT methods in sentiment classification. The research method includes tweet data collection, data preprocessing, public sentiment labeling, model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The preprocessing stage consists of case folding, tokenization, stopword removal, and stemming of Indonesian-language text. The data consist of 700 tweets collected from Twitter using the keywords related to fuel oil, electric vehicles, and Public Electric Vehicle Charging Stations (SPKLU). The results show that the IndoBERT method has better performance than Naive Bayes in sentiment classification because it is able to understand the context of the Indonesian language. This study is expected to contribute to the development of Indonesian-language sentiment analysis.
Co-Authors -, Felicia Adi Saputra Aditya Al Assad Aditya Ali Kusuma Adrian Chen Adrian Suparto Adrian Suparto Ahmad Dumyati Ahmad Zaky Nadimsyah Albert Cahayadi Alvin Hujaya Alwin Marcellino Amarullah, Rendy Ampu Syura Ananda Wijaya Andreas Andreas Andreas Danny Agus W Andreas Saputra Andrian Wijaya Angel Kelly Asyraq, Cerwyn Bakti Ananda Fernando Bautista, Christian Bebin Paula Bella Jenni Ourelia Boy Putra Brilliant Chandra Pratama Calvin Bertnas Valentino Calvin Saputra Carissa Maharani Chandra Chandra Saputra Christian Richie Wijaya Clara Meyhazlinda Putri Clement, Michael Joy Daffa Yudha Musyaffa Daniel Daniel Daniel Johan Daniel Udjulawa Daniel Wijaya Darwin Saputra David Sebastian Dedy Hermanto Desta Rahman Theja Desy Iba Ricoida Dicky Ryanto Fernandes Dina Lestari Putri Diva Putri Kynta Dwi Apriyanti Sastika Dwi Cahyadi, Ambrosius Effendi pratama, Samuel Egi Fransisco Saputra Eka Puji Widiyanto Evangs Mailoa Evi Maria Fadhel Muhammad Fadhil Sa'adat Farisi, Ahmad Farisi, Ahmad Fathimah Azzahra Felicia Felicia Felix Gunawan Fellyca Effendi Fellycia Caroline Femmy Johan Feriyanto Feriyanto Ferliansyah, Fernando Fernandi Indi Nizar G Fernando Feliansyah Fernando Fernando Fernando Namas Fionna Caroline Florence Renaldo Frans Bachtiar Fransiskus Daniel Chandra Frisky Wijaya Genisshanda Nabila Matari Geraldo Wilson Gerry Christian Pilipus Gunawan, Michael Hafidz Irsyad Hafiz Irsyad Hafizh Pebrian Hansen Hansen Hendrawan, Malvin Hendry Hindriyanto Dwi Purnomo Ilham Indra Hidayat Imelia Dwinora Cahyati Indi Nizar G, Fernandi Ivan Luthfi Laksono Jackie Wijaya Jasen Jonathan Jaysen Stephanus Ja`Far Ja`Far Jelvin Krisna Putra Jennifer Verty Jerin, Nathaniel Jesen Ong Jonathan Jason Constantine Jonathan Tanujaya Jonathan Wijaya Joseph Eduard Uly Loni Jovansa Putra Laksana Kasanova, Sinyo Kelvin Dwi Wahyudi Kevin agustria zahri Kevin Andreas KGS M Ammar Yazid Klaudius Audie Irsansaputra Kurniawan, Ricky Arie Laksono, Ivan Luthfi Laurentius Ricardo Wijaya Leo Chandra Leonardo Yahya Liem, Steven Lin, Valen Julyo Armando Davincy Lipi Amanda Putra Lucretia, Jolyn M Lazuardi Ferdillian M. Dhafa Adjie Saputra Marcelino Marcelino Mario Rivaldo Michael Michael Joy Clement michael Wijaya Migel Orvin Febryan Millenia Mudita Chandra Muhammad Abdul Azizul Hakim Muhammad Alfa Rizi Muhammad Azril Fahrezi Muhammad Dafhi Mayrizkiy Muhammad Dody Muhammad Fadli Muhammad Fajar Ariansyah Muhammad Hamdandi Muhammad Naufal Anugrah Muhammad Radja Juang Jamemiko Muhammad Redho Saputra Muhammad Reyza Nirwana Muhammad Robi, Muhammad Muhammad Tri Setianto Nabila Syiva Altarisa Nabilah Dayanah Nathacia Lais Naufal Akbar Neilsen Nicholas Komah Nicolas Jacky Pratama Hasan Nova Ariansyah Opita Purwasih Pambudi, Readysna Krisna Peter Reynard Susanto Pibriana, Desi Prasetyo, Zavier Billy Pratama, Brilliant Chandra Putra Laksana, Jovansa Putri, Agnes Anastasia Raphael Lee Regian batistuta, Putra Reza Satria Rika Maulina Riki Chandra Rio Ferdynand Riska Fajriati Rivaldo Therino Elevan Rivaldo, Mario Riza Umami Rizky Kurniawan Rizvi Roshan, Muhamad Roby Julian Romi Laxi Ronaldo Putra Rusbandi rusbandi rusbandi, rusbandi Safeti Intan Pratiwi Salwa Fakhira Imletta San Gabriel Vanness Kenrick Erwi Sanila Maharani Santoso, Fian Julio Saputra Edika, Nelson Sardika, Ricky Putra Se, Abd Rosyiid Serenity Devina Suryanto Setiawan, Thomas Shela, Shela Sherdian Djunaidi Sinshevan Viswanatan Kravizt Erwi Siska Amelia Siti Fatimah Az Zahrah Sonia Sonia Sri Yulianto Joko Prasetyo Steffanie Angelica Stephanie Stephanie Stephen Setyawan Steven Tribethran Suparto, Adrian Suryasatria Trihadaru Sutarto Wijono Syahrani Nur Hakim Syalsabilla Valentisyesa Syifa Wahyuni Tad Gonsalves Tangguh Prana Welas Sukma Vannes Wijaya Vanness Bee Victoria Valensita Robert Vincent Vincent Virgiansyah, Muhammad Rifqi Wijang Widhiarso Wijang Widhiarso Wijaya, Ananda Wilcent, Wilcent William Wijaya Yennica Valentine Hagunawan Yohanes Andika Dharma Yohanes Fransisco Mardi Chandra Yohannes, Yohannes Yoko Saputra Dewa Yosefa Camilia Moniung Yunarto Yunarto, Yunarto `Adelia Anjelina