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All Journal Gorga : Jurnal Seni Rupa JTSL (Jurnal Tanah dan Sumberdaya Lahan) Jurnal Ampere The Indonesian Biomedical Journal Biomedical Journal of Indonesia Jurnal Pertanian Terpadu Martabe : Jurnal Pengabdian Kepada Masyarakat JURNAL PENDIDIKAN TAMBUSAI Teorema: Teori dan Riset Matematika IJISTECH (International Journal Of Information System & Technology) JOURNAL OF SCIENCE AND SOCIAL RESEARCH SRIWIJAYA JOURNAL OF MEDICINE JSI (Jurnal sistem Informasi) Universitas Suryadarma Psikostudia : Jurnal Psikologi Agroekoteknologi Tropika Lembab Poltekita : Jurnal Ilmu Kesehatan JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Community Development Journal: Jurnal Pengabdian Masyarakat TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal REKSA: Rekayasa Keuangan, Syariah dan Audit Ekonomi Bisnis Manajemen dan Akuntansi (EBMA) IJISTECH Jurnal Pengabdian Masyarakat: Humanity and Medicine Hukum Responsif : Jurnal Ilmiah Fakultas Hukum Universitas Swadaya Gunung Jati Cirebon Journal of Community Development Jurnal Teknik Elektro Jurnal Teknik Sipil Dinamis Jurnal Minfo Polgan (JMP) SENTRI: Jurnal Riset Ilmiah Digital Transformation Technology (Digitech) Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Innovative: Journal Of Social Science Research Jurnal Pengmas Kestra (JPK) Local History & Heritage Jurnal Informatika Ekonomi Bisnis Jurnal Pustaka Dianmas Tarbiyah: Jurnal Ilmu Pendidikan dan Pengajaran Jurnal Inovatif : Inovasi Teknologi Informasi dan Informatika Jurnal Literasi Pengabdian dan Pemberdayaan Masyarakat Jurnal Indonesia Mengabdi (JIM) jle Journal of Technology and Computer (JOTECHCOM) PUBLIDIMAS Edukatif: Jurnal Pengabdian Masyarakat Economic Development Progress
Surya Darma
Program Studi Pendidikan Biologi FKIP Untan, Pontianak
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Penilaian Kesesuaian Lahan Untuk Pengembangan Tanaman Padi Sawah Irigasi (Oryza Sativa L.) di Kota Samarinda Panjaitan, Pranata Halasan; Darma, Surya; Sulistioadi, Yohanes Budi; Zulkarnain, Zulkarnain; Fahrunsyah, Fahrunsyah; Ibrahim, Ibrahim
Jurnal Agroekoteknologi Tropika Lembab Vol 8, No 2 (2026): Agroekoteknologi Tropika Lembab Volume 8 Nomor 2 Februari 2026
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jatl.8.2.2026.21945.76-82

Abstract

Sektor pertanian pada negara-negara berkembang termasuk Indonesia memiliki pengaruh besar dalam meningkatkan pertumbuhan ekonomi dan ketahanan pangan. Namun, pada saat yang sama lahan pertanian semakin tergerus akibat terjadinya alih fungsi lahan. Perubahan ini menyebabkan lahan-lahan sawah produktif berubah yang menuntut penggunaan lahan sawah eksisting digunakan secara intens untuk memenuhi kebutuhan pangan masyarakat yang menyebabkan menurunnya kondisi kesesuaian lahan dan  bervariasinya kemampuan produksi lahan sawah. Penelitian ini bertujuan untuk mengetahui kesesuaian lahan aktual dan potensial tanaman padi sawah di Kota Samarinda dan memberikan arahan untuk mengatasi faktor-faktor pembatas. Penelitian ini menggunakan metode metode matching yaitu mencocokkan karakteristik lahan dengan persyaratan tumbuh tanaman berdasarkan Petunjuk Teknis Evaluasi Lahan untuk Komoditas Pertanian. Hasil evaluasi kesesuaian lahan aktual komoditas padi di Kota Samarinda secara keseluruhan tergolong kelas S2 (cukup sesuai) berada pada subkelas S2nr,na dan S2nr dengan faktor pembatas KTK, P-Tersedia, KB dan pH tanah. Perbaikan yang dapat dilakukan untuk meningkatkan kesesuaian lahan yaitu dengan pengapuran tanah, penambahan bahan organik dan pemupukan yang akan meningkatkan kelas kesesuaian lahan potensialnya menjadi S1 (sangat sesuai).
IMPLEMENTATION OF THE INTERNET OF THINGS IN CREATING SMART CLASSROOMS Ginting, Subhan Hafiz Nanda; Wahyuni, Dewi; Sridewi, Nurmala; Wayahdi, M. Rhifky; Darma, Surya
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 9, No 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5806

Abstract

Abstract: The development of digital technology has driven the transformation of educational services through the implementation of data-based learning systems and smart devices. One of the emerging approaches is the use of the Internet of Things (IoT) in building smart classrooms, which are classrooms capable of integrating physical devices, sensors, and information systems to improve learning efficiency and learning environment management. This study aims to analyze the implementation of IoT in the formation of smart classrooms, covering aspects of system design, device integration, and evaluation of its effectiveness in supporting the teaching and learning process. The research method used is a quantitative and experimental approach, by designing an IoT-based smart classroom prototype that integrates temperature, humidity, light intensity, and presence detection sensors, as well as device control such as lights, air conditioning, and projectors through an automatic system and remote control. Data was collected through device performance measurements, network stability tests, and questionnaires distributed to users (teachers and students) to assess the system's ease of use and usefulness. The results showed that the implementation of IoT in classrooms can improve the efficiency of facility management through device automation, facilitate real-time monitoring of classroom conditions, and provide a more comfortable and responsive learning environment. In addition, the developed system demonstrated stable data communication performance with low latency within acceptable operational limits. These findings indicate that the application of IoT in smart classrooms has the potential to contribute significantly to improving the quality of learning and classroom management, particularly in supporting a technology-based education ecosystem. This study recommends further development in the areas of data security, device interoperability, and integration with Learning Management Systems (LMS) to strengthen the sustainable implementation of smart classrooms. Keywords: Internet of Things, Smart Classroom, Classroom Automation, Sensors, Technology Based Education. Abstrak: Perkembangan teknologi digital mendorong transformasi layanan pendidikan melalui penerapan sistem pembelajaran berbasis data dan perangkat cerdas. Salah satu pendekatan yang berkembang adalah pemanfaatan Internet of Things (IoT) dalam membangun smart classroom, yaitu ruang kelas yang mampu mengintegrasikan perangkat fisik, sensor, serta sistem informasi untuk meningkatkan efisiensi pembelajaran dan pengelolaan lingkungan belajar. Penelitian ini bertujuan untuk menganalisis implementasi IoT dalam pembentukan smart classroom, mencakup aspek desain sistem, integrasi perangkat, serta evaluasi efektivitasnya dalam mendukung proses belajar mengajar. Metode penelitian yang digunakan adalah pendekatan kuantitatif dan eksperimental, dengan merancang prototipe smart classroom berbasis IoT yang mengintegrasikan sensor suhu, kelembapan, intensitas cahaya, deteksi kehadiran, serta pengendalian perangkat seperti lampu, pendingin ruangan, dan proyektor melalui sistem otomatis maupun kendali jarak jauh. Data dikumpulkan melalui pengukuran kinerja perangkat, uji stabilitas jaringan, serta penyebaran kuesioner kepada pengguna (guru dan siswa) untuk menilai tingkat kemudahan penggunaan dan kebermanfaatan sistem. Hasil penelitian menunjukkan bahwa implementasi IoT pada ruang kelas mampu meningkatkan efisiensi pengelolaan fasilitas melalui otomasi perangkat, mempermudah monitoring kondisi kelas secara real-time, serta memberikan dukungan lingkungan belajar yang lebih nyaman dan responsif. Selain itu, sistem yang dikembangkan menunjukkan performa komunikasi data yang stabil dengan tingkat keterlambatan (latency) yang rendah dalam batas operasional yang dapat diterima. Temuan ini mengindikasikan bahwa penerapan IoT dalam smart classroom berpotensi memberikan kontribusi signifikan terhadap peningkatan kualitas pembelajaran dan manajemen kelas, khususnya dalam mendukung ekosistem pendidikan berbasis teknologi. Penelitian ini merekomendasikan pengembangan lanjutan pada aspek keamanan data, interoperabilitas perangkat, serta integrasi dengan Learning Management System (LMS) untuk memperkuat implementasi smart classroom secara berkelanjutan. Kata Kunci: Internet Of Things, Smart Classroom, Otomasi Ruang Kelas, Sensor, Pendidikan Berbasis Teknologi.
Predictive Analysis of Flood Risk Factors Based on a Machine Learning Approach: Comparative Study of SVM and XGBoost Algorithms Darma, Surya; Al Fayed, Ahmad Jihad; P Pardede, Surya Maruli; Aqsha, Muhammad Hizbul; Novelan, Muhammad Syahputra
Journal of Technology and Computer Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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

Abstract

Flood events in Indonesia continue to increase in frequency and impact due to high rainfall variability, land-use change, and complex hydrological conditions. Accurate predictive modeling is therefore essential to support flood risk assessment and mitigation planning. This study evaluates the predictive performance of two supervised machine learning algorithms, Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost), for flood risk classification. The analysis is conducted using a publicly available dataset comprising 500 samples that represent multiple environmental and spatial factors related to flood occurrence. Data preprocessing includes cleaning, normalization, and feature consistency adjustment prior to model implementation. Both algorithms are trained and tested using the same dataset configuration to ensure objective comparison. Model performance is assessed using accuracy, precision, recall, and F1-score metrics. Experimental results indicate that XGBoost achieves higher accuracy and precision, demonstrating stronger capability in reducing false-positive predictions, while SVM shows relatively higher recall, reflecting better sensitivity in identifying flood-prone cases. Overall, XGBoost provides more reliable predictive performance for flood risk modeling on the dataset used. The findings confirm the effectiveness of machine learning-based approaches for flood risk prediction and highlight the importance of algorithm selection in disaster risk analysis.
Aplikasi Penentuan Jumlah Pinjaman USP (Usaha Simpan Pinjam) Bantan Jaya Berbasis Web Darma, Surya; Kurniati, Rezki
SENTRI: Jurnal Riset Ilmiah Vol. 5 No. 2 (2026): SENTRI : Jurnal Riset Ilmiah, Februari 2026
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v5i2.5683

Abstract

The rapid development of information technology has encouraged digital transformation in microfinance institutions, including Savings and Loan Enterprises (USP); however, most existing systems primarily focus on transaction recording and administrative efficiency without integrating structured financial feasibility analysis into loan amount determination. This study aims to design and develop a web-based loan determination system at USP Bantan Jaya that integrates field survey data and the Utilization Business Plan (RUP) into an automated calculation model incorporating net income analysis, installment capacity (set at 50% of net income), and risk adjustment factors based on historical arrears ratios. The novelty of this research lies in the integration of measurable financial parameters and risk-based adjustment into a structured decision-support mechanism supported by Role-Based Access Control (RBAC) to ensure data security and access control. Functional testing using the black box method demonstrated a 100% success rate across nine core system features, while simulation results showed that the system standardized loan eligibility calculations and adjusted loan disbursement up to 60% of the initial application based on risk assessment. These results indicate that the proposed system enhances decision consistency, transparency, and accuracy in determining loan amounts, providing a practical and replicable model for more professional and sustainable USP management.
PERBANDINGAN KINERJA ALGORITMA MACHINE LEARNING DALAM MEMPREDIKSI TINGKAT STRES MAHASISWA BERDASARKAN FAKTOR AKADEMIK DAN NON-AKADEMIK Fayed, Ahmad Jihad Al; Darma, Surya; Aqsha, Muhammad Hizbul; Pardede, Surya Maruli P; Amin, Muhammad
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 9, No 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5805

Abstract

Abstract: Stress among students is a growing phenomenon due to high academic demands, changes in the social environment, and various non-academic pressures faced during their studies. Stress that is not managed properly can have a negative impact on students' mental health, motivation to study, and academic achievement. Therefore, an approach is needed that can identify and predict students' stress levels objectively and based on data. This study aims to analyze and compare the performance of several machine learning algorithms in predicting student stress levels based on academic and non-academic factors. The dataset used in this study is Student Stress Factors, which includes various variables such as Sleep Quality, Academic Achievement, Study Load, Frequency of Headaches, Extracurricular Activities, Level of Social Support, Screen Time, etc. The algorithms applied are Support Vector Machine and Naive Bayes. This research is expected to contribute to the development of a decision support system for early detection of student stress levels, as well as serve as a reference for educational institutions in designing strategies for the prevention and management of mental health issues in higher education environments. Keywords: Machine Learning, Support Vector Machine, Naive Bayes, Stress, Student Abstrak: Stres pada mahasiswa merupakan fenomena yang semakin meningkat seiring dengan tuntutan akademik yang tinggi, perubahan lingkungan sosial, serta berbagai tekanan non-akademik yang dihadapi selama masa studi. Kondisi stres yang tidak dikelola dengan baik dapat berdampak negatif terhadap kesehatan mental, motivasi belajar, serta capaian akademik mahasiswa. Oleh karena itu, diperlukan suatu pendekatan yang mampu mengidentifikasi dan memprediksi tingkat stres mahasiswa secara objektif dan berbasis data. Penelitian ini bertujuan untuk menganalisis serta membandingkan kinerja algoritma machine learning dalam memprediksi tingkat stres mahasiswa berdasarkan faktor akademik dan non-akademik. Dataset yang digunakan pada penelitian ini adalah Student Stress Factors, yang mencakup berbagai variabel seperti Kualitas Tidur, Prestasi Akademik, Beban Studi, Frekuensi Sakit Kepala, Kegiatan Ekstrakurikuler, Tingkat Dukungan Sosial, Jam Waktu Layar, dll. Algoritma yang diterapkan yaitu Support Vector Machine dan Naive Bayes dengan akurasi tertinggi dihasilkan oleh Algoritma SVM dengan akurasi 85% sedangkan NV memiliki akurasi 83%. Penelitian ini diharapkan dapat memberikan kontribusi dalam pengembangan sistem pendukung keputusan untuk deteksi dini tingkat stres mahasiswa, serta menjadi referensi bagi institusi pendidikan dalam merancang strategi pencegahan dan penanganan masalah kesehatan mental di lingkungan perguruan tinggi. Kata kunci: Machine Learning, Support Vector Machine, Naive Bayes, Stres, Mahasiswa
Bilangan Kromatik-Total Hasil Operasi SHACKLE pada Graf Lintasan Darma, Surya; Mujib, Abdul
Teorema: Teori dan Riset Matematika Vol 11, No 1 (2026): Maret
Publisher : Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/teorema.v11i1.19050

Abstract

Pewarnaan total graf merupakan salah satu topik penting dalam teori graf yang berfokus pada penentuan jumlah warna minimum untuk mewarnai titik dan sisi sehingga elemen yang saling bertetangga atau berinsiden memiliki warna berbeda, namun kajian pada graf hasil operasi shackle masih relatif terbatas. Penelitian ini bertujuan untuk menentukan bilangan kromatik dan bilangan kromatik-total pada graf hasil operasi shackle dari graf lintasan yang dinotasikan dengan . Penelitian ini menggunakan pendekatan deduktif melalui pemanfaatan teorema yang telah ada serta analisis pola melalui konstruksi kasus-kasus khusus untuk memperoleh bentuk umum. Hasil penelitian menunjukkan bahwa bilangan kromatik graf tersebut adalah , sedangkan bilangan kromatik-totalnya adalah . Hasil ini diperoleh melalui konstruksi pewarnaan total yang valid serta pembuktian minimalitas jumlah warna yang digunakan. Temuan ini memberikan kontribusi teoretis dalam pengembangan kajian pewarnaan total graf hasil operasi, khususnya operasi shackle, serta memperluas pemahaman mengenai karakteristik bilangan kromatik-total pada kelas graf lintasan.
Co-Authors . Apriansyah ., Berliyanto ., Wibisono ABDUL MUJIB Abdullah, Ilmi Adinata, Triawan Agustria, Amanda Al Fayed, Ahmad Jihad Aldian, Muhammad Yudi Alfathoni, Muhammad Ali Mursid Alferon, Hazalin Algifari, M. Ridho Novtriawan Al’amudi, Ivana Supi Angie, Evelyn Annisa Fithria Annisa, Rifka Anriyana, Sri Sukma Aprian, Rara Baitul Yuli Apriansyah Aqsha, Muhammad Hizbul Arda, Dina Yulia Argiyanti, Anitya Arikha Saputra Asrar, Leni Devera Asrori, Muhamad Amin Asrullah Asrullah, Asrullah Azmi Ginting, Naufal Bagus Adi Nugroho Bahar Bahar, Bahar Bangun, Sri Melda Berliyanto, Berliyanto Damanik, Abdi Rahim Desi, Efani Dewi Wahyuni Dhonanto, Donny Eddy Mart Salim, Eddy Mart Eka Irawan Elhias Nst, Mas Ayoe Ellanda Purwawijaya Fachrezi, Harahap Adrie Fadhila, Adinda Khairunnisyah Fahrunsyah, Fahrunsyah Fayed, Ahmad Jihad Al FIKRI HAIKAL Firzada, Fahmi fithry tahel, fithry Ginting, Erwin Ginting, Rosita Ginting, Subhan Hafiz Nanda Hadi Pranoto Harahap, Muhammad Farhan Harianto Harianto Harianto Helena, Shifa Hendrawan, Jodi Hendri Budi Kurniyanto Herman, Naina Azzahra Hermansyah Hermansyah Hermayeni, Hermayeni Hertanto, Rusdian Heru Satria Tambunan, Heru Satria Hery Widijanto Hidayat, Wahyu Handoyono Husna, Auliya Ul Ibni Aura Sasabela Ibrahim Ibrahim Idris, Suria Darma Ikha Safitri Ilham Syahputra Saragih IMANG, NDAN Indra Gunawan Indrajaya, Taufik Indrawan, Yudhistira Fauzy Irmayani Irmayani John Juliandra, Obie Khairul Amri Kriswiastiny, Rina kurniati, nova Kushadiwijayanto, Arie Antasari Liliana Puspa Sari Lubis, Yuliani Mardiati M. Rhifky Wayahdi Manesah, Dani Mardiati Mardiati MARIA BINTANG Matra, Ikahariya Pratiwi Mediarty, Mediarty Mega Sari Juane Sofiana Mikhael, Rodry Minsas, Sukal Monang, Sori Muchsin, Kasron Muhammad Amin Muhammad Reagan Muhammad Syahputra Novelan Muharraran, Firdha Mulya, Mhd. Ade Mulyadi Mulyadi Mulyadi, Asri Muthia, Putri Muthia, Putri Mutiar Mutiar, Mutiar Nasution, Muhammad Iqbal Nefi Darmayanti Nora Idiawati Nur Riviati, Nur Nurdiansyah, Sy. Irwan Nurdiansyah, Syarif Irwan Nurhartanto, Nurhartanto Nurhasanah Nurhasanah Nurrohman, Aji Okprana, Harly Overnandes, Overnandes P Pardede, Surya Maruli Panjaitan, Pranata Halasan Paranoan, Ria Rachel Pardede, Surya Maruli P Parinduri, Anggi Isnani Perbata, Rangga Wira Polem, Haga Putra Arza Prayitno, Dwi Imam Purnomo, Aji Putra, Dian Eka RACHMAT HIDAYAT Radiyati Umi Partan, Radiyati Umi Rahim Damanik, Abdi Rahman, Dedy Rahman, Nopijal Rizki Rahmayanti, Putri Intan Ramadani, Alpin Ramadhan, Noor Muhammad Rani, Nesa Mutia Rezeki, Rezeki Rezki Kurniati, Rezki Rizky Khairunnisa Sormin Rizqa Amelia, Rizqa Robiansyah, Wendi Roro Kesumaningwati Rudiyanto Rudiyanto, Rudiyanto ruziq, fahmi S, Budiman Sagala, Very Edward Charles Sahara, Natasha Taqwa Salsabila, Ghaisani Samosir, Marsiti Jalianti Samura, Jul Asdar Saputra, Rizky Nanda Saputra, Sardinal Sarwoto Sarwoto, Sarwoto Setiawan, Candra Budi Siadari, Eva Nava Sari Sigit Wibisono Silitonga, Aira Priamas Sirait, Abadihon Fazli Siregar, firman Goliath Ardyna Siregar, Sri Hilma Siti Aliyah Sri Wahyuni Sridewi, Nurmala Suhairi Suhairi Suherman, R.M. Edy Suherman, Suherman Sukal Minsas Sumarno . Syahputri, Aprilia Taufan Purwokusumaning Daru, Taufan Purwokusumaning Tia Nuraya Triandy, Okta Triyono Budi Santoso Umi Partan, Radiyati Usman, Khairul Veithzal Rivai Zainal Warsidah, Warsidah Wibisono Wibisono Widodo Saputra Yulianto kusnadi Yuniza, Yuniza Yusman, Yanti Yusuf Arief Nurrahman Zahari, Cut Latifah Zahrawani, Anggie Zulkarnain Zulkarnain