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All Journal Jurnal Media Infotama LEX ET SOCIETATIS Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Sistemasi: Jurnal Sistem Informasi Information System for Educators and Professionals : Journal of Information System Sebatik JOURNAL OF SCIENCE AND SOCIAL RESEARCH EDUKATIF : JURNAL ILMU PENDIDIKAN Menara Ilmu JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Community Development Journal: Jurnal Pengabdian Masyarakat Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research Journal of Education Research Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Sains dan Teknologi Jurnal Sains Informatika Terapan (JSIT) Jurnal Komtekinfo Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Populer: Jurnal Penelitian Mahasiswa INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Jurnal Ilmiah Teknik Informatika dan Komunikasi Indonesian Research Journal on Education Jurnal Ilmiah Dan Karya Mahasiswa Jurnal Satya Informatika Jurnal Sistem Informasi dan Ilmu Komputer Indo Green Journal Jurnal Elektronika dan Teknik Informatika Terapan Jurnal Penelitian Teknologi Informasi dan Sains Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Jurnal Quancom: Jurnal Quantum Komputer Jurnal Pengabdian Masyarakat dan Riset Pendidikan Journal of Information System and Education Development Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal Pengabdian Masyarakat Bangsa Jurnal Ilmu Komputer dan Informatika Indo Green Journal Jurnal TAM (Technology Acceptance Model) Journal of Computer Science Advancements Edumaspul: Jurnal Pendidikan
Billy Hendrik
Universitas Putra Indonesia YPTK Padang
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences 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 Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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Flipped Classroom: A Digital Revolution Learning Strategy for Curriculum 2013 Mardhiah Masril; Billy Hendrik; Harry Theozard Fikri; Firdaus Firdaus; Hasri Awal
Edumaspul: Jurnal Pendidikan Vol 6 No 2 (2022): Edumaspul: Jurnal Pendidikan
Publisher : Universitas Muhammadiyah Enrekang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33487/edumaspul.v6i2.5348

Abstract

This study aims to analyze the suitability of flipped classrooms, a 21st-century learning method, with the curriculum implemented in Indonesia in 2013. The methodology used in this research is qualitative, using a literature study as the data collection method. Data collection was conducted with the main object of surveying relevant literature. Data analysis in this research is descriptive qualitative. The results of this study show that flipped classroom learning follows the principles of learning in the 2013 curriculum. Evidenced by the implementation process, which is more student-centered. In addition, activities such as observing, questioning, gathering information, associating, analyzing, and communicating can be implemented in a technology-enhanced classroom to develop skills appropriate for the 21st century.
Sosialisasi Dan Penerapan Sistem Informasi Mitigasi Bencana Alam di Kabupaten Agam Berbasis Web untuk Meningkatkan Kesadaran dan Kesiapsiagaan Masyarakat Ritna Wahyuni; Firdaus , Firdaus; Mardhiah Masril; Billy Hendrik
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4209

Abstract

Bencana alam merupakan ancaman yang dapat menimbulkan kerugian materiil maupun korban jiwa. Kesadaran dan kesiapsiagaan masyarakat menjadi faktor penting dalam mengurangi dampak bencana. Penelitian ini bertujuan untuk mengembangkan dan mensosialisasikan Sistem Informasi Mitigasi Bencana Alam berbasis web di Kabupaten Agam, sebagai media edukasi dan koordinasi bagi masyarakat. Sistem ini menyajikan peta rawan bencana, panduan evakuasi, informasi kontak darurat, serta statistik kejadian bencana secara real-time. Metode pengembangan menggunakan pendekatan waterfall, dimulai dari analisis kebutuhan, perancangan, implementasi, hingga pengujian sistem. Hasil implementasi menunjukkan bahwa sistem informasi ini mampu meningkatkan pemahaman masyarakat terhadap risiko bencana, mempermudah akses informasi terkait mitigasi, dan memperkuat kesiapsiagaan dalam menghadapi bencana alam. Dengan demikian, penerapan sistem berbasis web ini menjadi salah satu solusi efektif dalam mendukung mitigasi bencana dan meningkatkan kesadaran masyarakat.
Optimization of LPG Gas Distribution Routes with a Combination of the Saving Matrix Method and Nearest Neighbor Amin Amirul Mukminin, Andi; Hendrik, Billy; Sovia, Rini
Jurnal KomtekInfo Vol. 12 No. 4 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i4.656

Abstract

Distribution is an important process in economic activities, which involves the delivery of goods or products from producers to end consumers. Efficiency in the distribution system highly depends on the selection of optimal routes, which can affect costs, time, and the quality of service provided. PT Amartha Anugrah Mandiri, which operates in the distribution of 3 kg LPG, faces significant challenges in terms of inefficient distribution route selection, limited fleet capacity, and unstructured variations in LPG demand. The distribution routes currently used do not consider the aspects of distance, time, and cost efficiency, resulting in the wastage of resources such as fuel and time. This research aims to optimize LPG distribution routes. The methods used in this study are the Saving Matrix and Nearest Neighbor. The Saving Matrix method is used to reduce distribution distance and costs by combining existing delivery routes, while the Nearest Neighbor is applied to determine the order of visits to the nearest bases gradually. Both methods are designed to produce distribution routes that are efficient in terms of time, distance, and cost, as well as to maximize the use of the existing fleet. The data in this study were obtained thru direct observation at PT. Amartha Anugrah Mandiri. The data collected included base locations, LPG demand, vehicle capacity, and operational costs. There are 22 bases served with a total delivery reaching 1120 LPG 3 kg cylinders spread across various sub-districts of Batam City. Deliveries are carried out using trucks with a maximum capacity of 560 cylinders, so in one day, distribution requires more than one trip. Using this data, the distance matrix and savings matrix were calculated to design a more efficient distribution system. The research results show that the application of these two methods successfully reduced the total distance traveled, delivery time, and operational costs significantly, as well as improved the efficiency of LPG distribution. This research is expected to contribute to the company so that the 3 kg LPG delivery process can run optimally.
Combination of Support Vector Machine and Artificial Neural Network Methods in Negative Content Filtering System Wira, M Wira Sanjaya; Yuhandri, Y; Hendrik, Billy
Jurnal KomtekInfo Vol. 12 No. 4 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i4.660

Abstract

Local Wi-Fi network access has become a common necessity in everyday digital activities, but it is vulnerable to misuse to access negative content. This content includes pornographic material, hate speech, and violent content that can adversely affect users, especially in educational settings. For this reason, a system that is able to filter malicious content automatically and efficiently is needed. This research aims to design an artificial intelligence-based negative content filtering system that can be run on local network devices. The methods used include image classification using Convolutional Neural Network (CNN) and Artificial Neural Network (ANN), as well as text classification with DistilBERT and Support Vector Machine (SVM). To maintain user privacy, the model is trained using a federated learning approach that allows for decentralized learning. Knowledge distillation is also applied to produce lightweight models that can be run on edge devices such as routers. The datasets used include NSFW Image Dataset, OpenPornSet, as well as a collection of toxic comments from Reddit and Twitter. The evaluation was carried out in a simulation of a local network with 50 active devices. The test results showed an ANN accuracy rate of 93.4% in recognizing visual content, and SVM accuracy of 91.7% in detecting text-based hate speech. This research can be a reference in the application of AI-based content filtering systems for safe and responsible digital access protection
PREDIKSI PRODUKTIVITAS LAHAN KELAPA SAWIT PASCA PEREMAJAAN MENGGUNAKAN ALGORITMA MACHINE LEARNING Sukardi, Sukardi; Hendrik, Billy
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 2 (2025): May 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i2.3279

Abstract

Abstract: Oil palm land rejuvenation is an essential step in maintaining long-term productivity. However, uncertainty about post-rejuvenation productivity poses a significant challenge for farmers and plantation managers. This study aims to develop a predictive model for oil palm land productivity after rejuvenation using machine learning algorithms. The methods used include historical production data collection, environmental factors, and plant characteristics. The tested algorithms include Linear Regression, Random Forest, and Artificial Neural Network (ANN). The results indicate that the Random Forest model has the highest accuracy in predicting productivity with an R-squared value of 0.87. Implementing this model is expected to support more accurate decision-making in oil palm plantation management. Keyword: Oil Palm; Rejuvenation; Productivity Prediction; Machine Learning Abstrak: Peremajaan lahan kelapa sawit merupakan langkah penting dalam mempertahankan produktivitas jangka panjang. Namun, ketidakpastian mengenai produktivitas pasca peremajaan menjadi tantangan utama bagi para petani dan pengelola perkebunan. Studi ini bertujuan untuk mengembangkan model prediksi produktivitas lahan kelapa sawit pasca peremajaan dengan menggunakan algoritma machine learning. Metode yang digunakan mencakup pengumpulan data historis produksi, faktor lingkungan, serta karakteristik tanaman. Algoritma yang diuji meliputi Regresi Linear, Random Forest, dan Artificial Neural Network (ANN). Hasil penelitian menunjukkan bahwa model Random Forest memiliki akurasi tertinggi dalam memprediksi produktivitas dengan nilai R-squared sebesar 0,87. Implementasi model ini diharapkan dapat membantu pengambilan keputusan yang lebih akurat dalam manajemen perkebunan kelapa sawit. Kata kunci: Kelapa Sawit; Peremajaan; Prediksi Produktivitas; Machine Learning
Rancang Bangun Alat Ukur Kadar Protein Pada Makanan Pokok Berbasis Iot Dengan Kendali BOT Telegram Billy Hendrik; Ruri Hartika Zain; Afifah Syahidah Nahda
Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026 Vol. 2 No. 4 (2026): April - Juni
Publisher : GLOBAL SCIENTS PUBLISHER

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

Abstract

Protein is one of the macronutrients that is very important and essential for the human body. This substance functions in various biological processes, including as a builder of muscle tissue, skin, enzymes, hormones, and the body's immune system. Although important, monitoring daily protein consumption is often not carried out accurately by the general public. Most people only rely on rough estimates based on the type of food consumed, or nutritional information printed on the packaging label. This is a challenge, especially for staple foods or home-made foods that do not have nutritional labels. One technology that supports the development of this system is a load cell, which is able to measure the mass or weight of an object precisely and stably. This sensor is widely used in digital scales and industrial systems. In the context of protein measurement, the weight of a food ingredient can be converted to an estimate of its protein constent, based on the average protein content data of each type of food that has been determined. With the help of a microcontroller such as Arduino, ESP32, or other types, this system can regulate the logic flow of the tool's operation, calculate protein levels based on input from the load sensor, and display the measurement results to the user via an output device such as a 20x4 LCD. In addition, the use of infrared (IR) sensors as object presence detectors allows the system to recognize when food has been placed on the device, so that the measurement process can be carried out automatically and responsively.
Deep Learning Analysis for Predicting the Approval Time of Clinical Practice Guidelines (CPG) Based on Historical Administrative Data Yuliana Pertiwi; Musli Yanto; Billy Hendrik
Sebatik Vol. 30 No. 1 (2026): June 2026
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/bfapvs23

Abstract

This study aims to predict the processing time of the approval of Clinical Practice Guidelines (CPG), which exhibits considerable variation in duration and is difficult to predict accurately. In addition, the utilization of historical hospital administrative data to build effective predictive models for estimating the duration of the CPG approval process has not yet been optimized. Therefore, this research seeks to develop a predictive model to estimate the processing time of the CPG approval process.The proposed approach employs deep learning techniques by leveraging historical administrative data as the basis for modeling. The methods applied include K-Means Clustering, Decision Tree, and Long Short-Term Memory (LSTM). K-Means Clustering is used to group CPG data based on similar administrative characteristics, enabling the identification of approval time patterns. Subsequently, the Decision Tree method is utilized to analyze the relationships among variables and to generate classification rules that explain the factors influencing the duration of the CPG approval process. Meanwhile, LSTM serves as the primary model for predicting the processing time of CPG approval.This study uses 487 CPG records collected over the period from 2020 to 2024. The evaluation results indicate that the K-Means Clustering method achieves an accuracy rate of 87,36%. This level of accuracy reflects strong clustering performance and a high degree of conformity with actual conditions, indicating that the results are suitable to be used as a foundation for further analysis in the classification and prediction stages of the CPG approval process.
Sosialisasi Dan Pelatihan Pemanfaatan CHATGPT Dan AI Tools Sebagai Media Pembelajaran Dan Produktivitas Akademik: Pengabdian Ritna Wahyuni; Firdaus; Mardhiah Masril; Billy Hendrik
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 4 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 4 Tahun 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i4.6549

Abstract

Perkembangan teknologi Artificial Intelligence (AI) telah memberikan dampak signifikan dalam dunia pendidikan, khususnya dalam mendukung proses pembelajaran dan peningkatan produktivitas akademik. Namun, masih banyak mahasiswa dan masyarakat akademik yang belum memahami pemanfaatan teknologi AI secara optimal, efektif, dan etis. Kegiatan Pengabdian kepada Masyarakat (PKM) ini bertujuan untuk meningkatkan pemahaman dan keterampilan peserta dalam memanfaatkan ChatGPT dan berbagai AI tools sebagai media pembelajaran serta pendukung produktivitas akademik. Metode pelaksanaan kegiatan dilakukan melalui sosialisasi, pelatihan praktik langsung, diskusi, dan pendampingan penggunaan AI tools dalam berbagai aktivitas akademik seperti pencarian ide, penyusunan materi, pembuatan presentasi, penulisan akademik, serta manajemen waktu belajar. Kegiatan ini melibatkan peserta dari kalangan mahasiswa dan generasi muda yang memiliki kebutuhan terhadap peningkatan literasi digital dan teknologi AI. Hasil kegiatan menunjukkan bahwa peserta memiliki antusiasme yang tinggi dan mengalami peningkatan pemahaman terkait penggunaan AI tools secara produktif, kreatif, dan bertanggung jawab. Selain itu, peserta juga mampu memanfaatkan teknologi AI untuk membantu proses belajar secara lebih efektif dan efisien. Dengan adanya kegiatan ini, diharapkan pemanfaatan teknologi AI dapat mendukung peningkatan kualitas pembelajaran dan kompetensi digital masyarakat akademik dalam menghadapi era transformasi digital.
Simulasi dan Analisis Strategi Hybrid Teaming Menggunakan Algoritma Naive Bayes dalam Deteksi Serangan Distributed Denial of Service (DDoS) Aprilian Gevindo; Yuhandri Yuhandri; Billy Hendrik
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i3.9323

Abstract

Cyber attacks, particularly Distributed Denial of Service (DDoS), have become a serious threat to the availability of servers and other network infrastructure. These attacks can paralyze services on large-scale networks by flooding the target system with extremely high traffic. Based on this, the objective of this research is to simulate and analyze a Hybrid Teaming strategy using the Naïve Bayes algorithm. This strategy simulates structured collaboration between the Red Team (attackers), Blue Team (defenders), and Purple Team (evaluators) to test resilience while comprehensively strengthening the security posture. The Naïve Bayes algorithm is one of the best algorithms in Machine Learning and excels at performing data classification processes. The performance of the Naïve Bayes algorithm combined with the Hybrid Teaming strategy is developed into an intelligent detection system. This system is trained using 10,000 data points from a public dataset and 1,688 data points from the network logs of the Tapan Regional General Hospital (RSUD). Based on the data analysis results, the model training outcomes fall into the perfect category, with accuracy, precision, recall, and F1-score achieving a result of 100%. The model was then implemented on a server and a MikroTik router within a simulation environment that replicates the Tapan RSUD network. The test results on these two components show that the system successfully detected various Flooding attack patterns with a detection accuracy of 100%. The system is capable of responding automatically by blocking the attacker's IP (Internet Protocol) address at both layers, as well as sending real-time notifications via WhatsApp and Email. The contribution of this research results in a comprehensive and effective cybersecurity defense framework.
Penerapan K-Means dan Algoritma C4.5 dalam Klasifikasi Ulasan Pengguna Aplikasi Mitsubishi SFID Dzil Hidayati; Sarjon Defit; Billy Hendrik
INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Vol 11 No 1 (2026): INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS (Juni 2026)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/isbi.v11i1.3831

Abstract

Pada penelitian ini dilatarbelakang oleh dimana banyaknya ulasan pengguna aplikasi SFID yang berisi keluhan dan penilaian dengan bahasa yang beragam sehingga sulit dianalisis secara manual, cepat dan objektif. Kondisi tersebut menyebabkan informasi penting terkait pengalaman pengguna, kendala teknis, dan kualitas layanan aplikasi belum dapat dimanfaatkan secara optimal sebagai dasar pengambilan keputusan pengembangan aplikasi. Dengan demikian, penelitian ini bertujuan untuk mengelompokkan dan mengklasifikasikan ulasan pengguna aplikasi Mitsubishi SFID secara sistematis guna mendukung proses pengambilan keputusan. Dalam penelitian ini, Metode K-Means Clustering digunakan untuk mengelompokkan ulasan berdasarkan tingkat kemiripan karakteristik dan tema, sedangkan algoritma C4.5 diterapkan untuk mengklasifikasikan ulasan pada setiap kelompok ke dalam kategori tertentu berdasarkan atribut yang relevan. Data yang dianalisis berasal dari ulasan pengguna aplikasi Mitsubishi SFID yang diperoleh melalui platform Google Play Store. Penelitian ini menunjukkan bahwa model klasifikasi yang dibuat berhasil memberikan tingkat akurasi yang baik serta hasil validasi menunjukkan bahwa model memiliki kinerja yang stabil dan dapat diandalkan dalam mengklasifikasikan ulasan pengguna dengan nilai akurasi 88,89%. Dampak dari penelitian ini adalah tersedianya informasi terstruktur mengenai permasalahan dan kebutuhan pengguna yang dapat dimanfaatkan sebagai dasar penentuan prioritas perbaikan fitur, peningkatan kualitas layanan aplikasi, serta membantu mengambil keputusan yang lebih tepat dan didasarkan pada data
Co-Authors A'yuni, Qurrata Abdi Juliantho, Dwana Abdi Julianto, Dwana Ade Saputra Ade saputra Afifah Syahidah Nahda Akbar, Syifa Chairunnissa Deliva Akhiruddin Pulungan Amin Amirul Mukminin, Andi Amin, Andi Amir Salim Khairul Rijal Amir Salim Khairul Rijal Amri, Yassirli Andi, Muhammad Yusril Haffandi Aprilian Gevindo Arby, Willya Aswandi, Nopan Awal, Hasri Budiantoro, Hendro Deti Karmanita Dewi, Apriandini Sri Dian Maharani, Dian Diffri Solihin Siregar Doni Karseno Dwana Abdi Juliantho Dzil Hidayati Fadil Idensia Febby Olivia, Ladyka Firdaus Firdaus Firdaus , Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Fitri Amelia Lubis Fungki Wahyu Gevindo, Aprilian Ghinaa Fadhiilah Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo Haffandi, Muhammad Yusril Harry Theozard Fikri Hasibuan, Elpina Sari Dewi Hasri Awal Hasri Awal Hasri Awal hidayat, Ilsa Ikhlas, Ariza Indhira, Sonia Juliantho, Dwana Abdi Karseno, Doni Ladyka Febby Olivia Lubis, Fitri Amelia Sari Lubis, Fitri Sari Lubis, Siti Sahara Mardhiah Masril Muhammad, Imam Fakhri Nasution, Ayu Lestiani Nella Novrina Doni Nugraha, Fajri Olivia, Ladyka Febby Ondra Eka Putra Permana, Nabilah Putri Putri Ramadani Raharjo, Tio Doli Resnawita Resnawita Resnawita Rico Anggara Ridwan, Ridwan Riris Agustin Riris Agustin Ritna Wahyuni Rizqi Nusabbih Hidayatullah Gaja Roy Efendi Subarja Roy Efendi Subarja Ruri Hartika Zain Ruri Hartika Zain Ruri Hartika Zain Rusydi, Rezki S Sumijan Sahara Lubis, Siti Sani, Fadhila Putri Santriawan, Aji Sari, Ade Puspita Sarjon Defit Satria Satria, Satria Septiawan, Edo Siti Sahara Lubis Siti Sahara Lubis Sonia Indhira Sonia Indhira, Sonia Sovia, Rini Sri Mulya Subarja, Roy Efendi Sukardi Sukardi Sumijan Supperianto, Bambang Suri, Melati Rahma Sutri, Ridwan Syafri Arlis Tamin, Zulfiqar Uthama, Rayhan Vidyanti, Angela Waruwu, Kalfinus Wira, M Wira Sanjaya Wirdawati, Wira Yanto, Musli Yuhandri Yuhandri, Yuhandri Yuliana Pertiwi