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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Media Statistika TELKOMNIKA (Telecommunication Computing Electronics and Control) Sinergi Jurnas Nasional Teknologi dan Sistem Informasi Format : Jurnal Imiah Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Ilmiah FIFO Ensiklopedia of Journal International Journal of Quantitative Research and Modeling Jurnal Ilmu Komputer Dan Informatika Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Jurnal Pengabdian Masyarakat Nasional Journal Collabits
Yaya Sudarya Triana
Universitas Mercu Buana
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Physics Social Sciences

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Prediksi Kebakaran Hutan Berdasarkan Titik Panas dan Iklim Menggunakan Algoritma Random Forest Firmansyah, Aditya; Syahidin, Muhammad Farhan; Triana, Yaya Sudarya
Jurnal Nasional Teknologi dan Sistem Informasi Vol 10 No 2 (2024): Agustus 2024
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v10i2.2024.145-155

Abstract

Kebakaran hutan dan lahan semakin sering terjadi, menyebabkan dampak lingkungan yang menyebar ke luar wilayah kebakaran. Permasalahan yang terjadi salah satunya karena musim kemarau yang panjang di wilayah Kabupaten Ogan Komering Ilir Provinsi Sumatra Selatan yang menjadi faktor utama dalam meningkatnya risiko kebakaran, sebanyak 1.111 titik kebakaran tercatat pada tahun 2023. Permasalahan lainnya juga pada titik panas yang salah mendeteksi kebakaran yang seharusnya tidak kebakaran dan kasus tidak kebakaran yang seharusnya kebakaran, hal tersebut menyebabkan kerugian lingkungan maupun kerugian dana. Oleh karena itu, dibutuhkan model klasifikasi untuk memprediksi kasus kebakaran. Penelitian ini menggunakan gabungan data titik panas dan data iklim sebanyak 4343 data menggunakan metode Random Forest. Proses yang dilakukan yaitu studi literatur dan tahapan prediksi yang terdiri dari web scraping, data pre-processing, splitting data, pemodelan, dan evaluasi. Hasil penelitian berupa laporan klasifikasi, confusion matrix, dan feature importance. Hasil pengujian menunjukkan tingkat akurasi model yang baik sebesar 85.8% yang menunjukkan model menghitung seberapa tepat kinerja yang dilakukan model. Dengan penerapan model menggunakan metode Random Forest, model prediksi ini mengidentifikasi kasus kebakaran sangat baik sehingga informasi ini dapat digunakan untuk keputusan manajemen penanggulangan kebakaran dengan tepat dan meminimalisir terjadinya kerugian.
THE ROLE OF ANALYTICAL AND VISUALIZATION DATA TO OPTIMIZE ONLINE SALES Triana, Yaya Sudarya; Kaburuan, Emil Robert; Rahmad, Khozaeni Bin; Jumaryadi, Yuwan
Jurnal Pengabdian Masyarakat Nasional Vol 5, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/pemanas.v5i2.34915

Abstract

In the digital era, data analytics is a key factor in increasing sales and competitiveness for individuals, companies, industries or governments. The use of data analytics allows us to understand market trends, customer behavior, and develop more effective marketing strategies. Studies show that companies that implement data analytics experience significant increases in revenue and better operational efficiency. The purpose of this article is to review how analytics can be used to understand audience behavior and preferences, and how this information can be used to target the right consumers with more relevant messages. By using sophisticated analytics tools, companies can gain insights from customer data, from purchasing patterns to interactions with digital platforms, enabling more informed and accurate decision making. Through a data-driven approach, companies can provide more personalized services and increase customer satisfaction. The implementation of data analytics also allows businesses to respond to market changes more quickly and precisely. With proper implementation, data analytics is not only a decision support tool, but also a key strategy in business growth. Therefore, investment in analytics technology is essential to ensure the sustainability and competitiveness of companies in the long term. In the data analytics process, there are 4 stages, namely Data collection, Data processing, Data analysis, and Data interpretation. Companies can gain competitive advantage by optimizing operations, increasing efficiency, responding to market changes, and predicting future trends through this process. Data analytics changes the way businesses operate by helping to understand customers, find opportunities, and make evidence-based decisions. It also improves operational efficiency and risk management, supporting innovation and long-term growth. The output targets of this activity are publications in international/national journals/Proceedings, IPR/HKI and publications on social media.
Perbandingan Performa Xception dan InceptionV1 untuk Pengenalan Ekspresi Wajah Delio, Ferdinand Defin; Aryani, Diah; Akbar, Habibullah; Yusuf, Mohamad; Triana, Yaya Sudarya
FORMAT Vol 15, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2026.v15.i1.003

Abstract

Penelitian ini bertujuan untuk menganalisis dan membandingkan performa dua arsitektur Convolutional Neural Network (CNN) populer, yaitu Xception dan InceptionV1, dalam tugas pengenalan ekspresi wajah (Facial Expression Recognition/FER). Penelitian ini dilakukan dengan pendekatan transfer learning dan fine-tuning menggunakan dataset FER-2013 yang berisi 35.887 citra wajah grayscale berukuran 48×48 piksel yang diklasifikasikan ke dalam tujuh emosi dasar. Setiap citra diubah ukurannya menjadi 224×224 piksel, dinormalisasi, dan diproses dengan teknik augmentasi untuk meningkatkan generalisasi model terhadap variasi ekspresi wajah, pencahayaan, dan pose. Proses pelatihan dilakukan selama 30 epoch menggunakan optimizer Adam dengan learning rate 0.0001 dan batch size 64. Strategi fine-tuning dilakukan dengan membuka 30% lapisan atas model untuk mengoptimalkan bobot fitur yang telah dipelajari sebelumnya dari dataset ImageNet. Evaluasi kinerja dilakukan berdasarkan metrik akurasi, presisi, recall, F1-score, serta efisiensi komputasi yang diukur dari waktu pelatihan dan inferensi. Hasil eksperimen menunjukkan bahwa Xception mencapai akurasi validasi 70,69% dengan waktu inferensi rata-rata 20–25 ms, sedangkan InceptionV1 mencapai 65,80% dengan waktu inferensi 43–126 ms. Arsitektur Xception terbukti lebih efisien secara komputasi karena memanfaatkan depthwise separable convolution yang mengurangi jumlah parameter tanpa menurunkan akurasi. Temuan ini menunjukkan bahwa Xception lebih sesuai untuk aplikasi FER real-time dan perangkat dengan sumber daya terbatas, serta memberikan dasar yang kuat bagi penelitian lanjutan dalam pengembangan sistem pengenalan ekspresi wajah berbasis video dan lingkungan dunia nyata.
Application of Artificial Intelligence in Modern Ecology for Detecting Plant Pests and Animal Diseases Dem Vi Sara; MDD Maharani; Hafiza Farwa Amin; Yaya Sudarya Triana
International Journal of Quantitative Research and Modeling Vol. 2 No. 2 (2021): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v2i2.149

Abstract

Climate change could lead to an increase in diseases in plants and animals. Plant pathogens have caused devastating production losses, such as in tropical countries. The development of algorithms that match the accuracy of plant and animal disease detection in predicting the toxicity of substances has continued through a massive database. Data and information from 10,000 substances from more than 800,000 animal tests have been carried out to generate the algorithms. Plant and animal disease detection using artificial intelligent in the modern ecological era is important and needed. Diseases in animals are still found in several Ruminant-Slaughterhouses. The purpose of the study is to identify the leverage attributes for using of Artificial Intelligent (AI) in detecting plant pests and animal diseases. The use of Multidimensional Scaling (MDS) produces a leverage attribute for the use of AI in detecting plant pests and animal diseases. The results showed that leverage attributes found were: Prediction of the presence of proteins structures produced by pathogens with a Root Mean Square (RMS) value of 4.5123; and Plant and Animal Disease Data will be opened with an RMS value of 4.2555. The findings of this study in the real world are to produce the development of smart agricultural applications in detecting plant pests and animal diseases as an early warning system. In addition, the application is also useful for eco-tourism managers who have a natural close relationship with plants and animals, so that ecological security in the modern ecological era, can be better maintained.
Implementation of GeoGebra Web in Geometry Learning to Improve Students' Understanding of Hybrid-Based Concepts Hendry, Hendry; Supiyandi, Supiyandi; Arifin, Dafrid Cahyadi; Triana, Yaya Sudarya; Fitriasih, Sri Hariyati
JURIBMAS : Jurnal Hasil Pengabdian Masyarakat Vol 5 No 1 (2026): Juli 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juribmas.v5i1.1010

Abstract

The limited conceptual understanding of geometry among students remains a significant challenge, particularly due to the lack of interactive learning tools and ineffective integration of digital technology in classroom practices. This community service program aims to implement web-based GeoGebra within a hybrid learning environment to enhance students’ conceptual understanding of geometry. The method employed a participatory and implementation-based approach involving 32 junior high school students and 2 mathematics teachers. The program was conducted through four stages: preliminary study, program design, implementation, and evaluation. Data were collected using pre-test and post-test assessments, questionnaires, and observation sheets. Quantitative analysis was performed using percentage gain and regression analysis, while qualitative data supported behavioral evaluation. The results show a significant improvement in students’ conceptual understanding, with an average score increase from 56.25 to 82.40 and a gain of 65.38%, categorized as moderate-to-high. Furthermore, 81% of students achieved the targeted level of improvement, and student engagement reached 87.6%, exceeding the success indicator threshold. Regression analysis indicated a strong positive relationship (R² = 0.68), suggesting the intervention's effectiveness across different ability levels. In addition, the program positively influenced students’ learning behavior and provided a cost-effective solution by using open-source technology. These findings indicate that integrating GeoGebra into a hybrid learning model is an effective and scalable approach to improving geometry learning outcomes. This program also contributes to the development of sustainable, technology-enhanced educational practices in community service contexts.
Design and Implementation of Information Systems for Efficient Big Data Processing Apriadi, Deni; Anjani, Dewi; Risal, Andi Alviadi Nur; Triana, Yaya Sudarya; Mubarak, Husni
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.1014

Abstract

The rapid growth of data volume, velocity, and variety has created significant challenges for traditional information systems, which are often unable to process large-scale data efficiently. This study aims to design and implement an efficient information system for big data processing using a distributed computing approach. The research adopts a systematic and experimental method consisting of system design, implementation, and performance evaluation. The proposed system is developed using a distributed architecture with parallel processing mechanisms to improve scalability and resource utilization. Performance evaluation is conducted using key metrics, including processing time, throughput, and efficiency improvement percentage, based on experimental testing with datasets ranging from 1 GB to 10 GB. The results show that the proposed system consistently reduces processing time and increases throughput compared to the baseline system. The system achieves efficiency improvements ranging from 33.3% to 36.9%, exceeding the predefined success indicator of 30%. These findings demonstrate that the integration of distributed computing and optimized system architecture significantly enhances big data processing performance. Therefore, the proposed system provides a scalable and practical solution for handling large-scale data processing in modern information systems.
Co-Authors Adji Pratomo Akbar, Habibullah Andi Alviadi Nur Risal Anggito Susilo Anindyka Elras Saputri Anjar Rochana Arifin, Dafrid Cahyadi Budi Susatyo Budi Susatyo Delio, Ferdinand Defin Dem Vi Sara Deni Apriadi Deris Stiawan Dewi Anjani, Dewi Diah Aryani, Diah Dwi Prasetyo Firmansyah, Aditya Fitri Anggri Maharani Fitri Herawati Fitri Herawati Fryda Farizha Adrianti Gilang Tarwandi Hadi Suprapto Hafiza Farwa Amin Helvywidya Utari Hendri Gunawan Hendry Huda, Darul Fitahul Husni Mubarak Jumaryadi, Yuwan Kaburuan, Emil Robert Krisna Pangestu L. Muhamad Safiih Lilla Chairunnisa MDD Maharani Mohamad Yusuf Mohd Azam Osman Muhammad Fermi Pasha Osman, Mohd Azam Palinggi, Owen Baan Permana, Muhamad Bagas Priyono, Kus Andi Rahmad, Khozaeni Bin Rahmat Budiarto Rahmat Budiarto Raziv Herman Diputra Retnowardhani, Astari Rinto Priambodo Septian Dwi Cahyo Siti Maesaroh Sri Hariyati Fitriasih, Sri Hariyati Sumantri, Bagas Supiyandi Supiyandi Syahidin, Muhammad Farhan Vincent Capri Wijaya