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All Journal Jurnal Media Infotama Sistemasi: Jurnal Sistem Informasi JOURNAL OF SCIENCE AND SOCIAL RESEARCH Menara Ilmu JATI (Jurnal Mahasiswa Teknik Informatika) Community Development Journal: Jurnal Pengabdian Masyarakat Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) 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 Indonesian Research Journal on Education Indo Green Journal Jurnal Quancom: Jurnal Quantum Komputer Journal of Information System and Education Development Jurnal Pengabdian Masyarakat Bangsa Indo Green Journal Jurnal TAM (Technology Acceptance Model) Journal of Computer Science Advancements
Hendrik, Billy
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Transformasi Sektor melalui Augmented Reality: Manfaat, Tantangan, dan Masa Depan Strategis Aswandi, Nopan; Hendrik, Billy
Journal of Education Research Vol. 6 No. 1 (2025)
Publisher : Perkumpulan Pengelola Jurnal PAUD Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37985/jer.v6i1.2152

Abstract

Augmented Reality (AR) telah berkembang pesat sebagai salah satu teknologi utama dalam era digital, dengan penerapan luas di berbagai sektor seperti pendidikan, kesehatan, industri, dan hiburan. Penelitian ini melakukan tinjauan literatur terhadap 10 jurnal terkini (2023–2024) untuk menganalisis manfaat, tantangan, dan peluang pengembangan AR. Hasil analisis menunjukkan bahwa AR memiliki dampak signifikan, khususnya dalam meningkatkan interaktivitas dan pemahaman pengguna. Di sektor pendidikan, aplikasi AR seperti media pembelajaran tumbuhan mencapai skor usability hingga 79,83%, menunjukkan efektivitasnya dalam meningkatkan keterlibatan siswa. Di bidang pemasaran, AR meningkatkan keputusan pembelian hingga 30% melalui pengalaman promosi interaktif. Di sektor budaya, AR membantu pelestarian lokal, seperti pengenalan Aksara Lampung, dengan skor learnability 4,78/5. Selain itu, dalam infrastruktur, AR meningkatkan efisiensi pekerjaan teknis melalui visualisasi interaktif. Namun, adopsi AR masih menghadapi tantangan berupa biaya pengembangan yang tinggi, keterbatasan perangkat keras, dan kebutuhan desain antarmuka yang ramah pengguna. Sebagian besar pengembangan AR dalam studi ini didukung oleh metode Multimedia Development Life Cycle (MDLC), yang memastikan efektivitas teknis dan relevansi aplikasi dengan kebutuhan pengguna. Penelitian ini merekomendasikan integrasi AR dengan teknologi seperti Artificial Intelligence (AI) dan Internet of Things (IoT) untuk memaksimalkan potensinya. Kajian ini memberikan wawasan strategis bagi peneliti dan praktisi untuk mengoptimalkan penerapan AR dalam berbagai sektor, sehingga mendorong inovasi dan transformasi digital secara global.
SOSIALISASI PEMANFAATAN DATA ANALYTICS DENGAN TEKNOLOGI BIG DATA DAN MACHINE LEARNING UNTUK MENINGKATKAN EFISIENSI DAN AKURASI PENYALURAN BANTUAN SOSIAL MASYARAKAT DI KOTO PARAK Hendrik, Billy; Masril, Mardhiah; Awal, Hasri; Firdaus, Firdaus
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 5 No. 4 (2024): Volume 5 No. 4 Tahun 2024
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v5i4.33352

Abstract

Penelitian ini bertujuan untuk memanfaatkan teknologi Big Data dan Machine Learning dalam mengoptimalkan penyelenggaraan bantuan sosial di masyarakat. Bantuan sosial menjadi penting dalam mendukung kehidupan sosial ekonomi masyarakat, namun sering kali menghadapi tantangan efisiensi dan akurasi dalam penyaluran. Teknologi Big Data memungkinkan pengumpulan dan analisis data besar secara cepat, sementara Machine Learning dapat digunakan untuk memprediksi pola penyaluran yang lebih efisien. Metode yang digunakan meliputi studi literatur, pengumpulan data, analisis data, pengembangan model Machine Learning, evaluasi, dan validasi. Diharapkan penelitian ini dapat menghasilkan sistem yang lebih efisien dalam penyaluran bantuan sosial, meningkatkan kepuasan penerima manfaat, dan menjadi rujukan untuk pengembangan kebijakan publik terkait manajemen sosial.
Segmentasi Tunggakan Pelanggan Menggunakan Algoritma K-Means Cluster pada Perusahaan Air Minum Daerah Akbar, Syifa Chairunnissa Deliva; Defit, Sarjon; Hendrik, Billy
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 2 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i2.1215

Abstract

Perusahaan Air Minum Daerah (Perumdam) Tirta Anai is a Regional Elected Business Entity providing clean water services to customers, but based on the BPKP performance report, this company is categorized as an unhealthy BUMD. One of the factors causing this is due to the high arrears of customers which have an impact on the company's revenue, while efforts in the form of late fines have not been able to provide a deterrent effect to customers. Based on this, this research was carried out with the aim of segmenting customer arrears at the Tirta Anai Regional Drinking Water Company. Segmentation is carried out using the K-Means Clustering algorithm. K-Means Clustering is a data mining algorithm used in grouping data based on its similarity in characteristics. The data in this study is sourced from the database of customers who are in arrears at the Tirta Anai Regional Drinking Water Company as of May 2025 which focuses on the Household group, with as many as 20,646 customer arrears data. From this population, samples were taken using the Slovin formula with an error rate of 5% so that 392 data were analyzed. The parameters used in analyzing this study are the number of months of customer arrears and total customer arrears. Based on the K-Means Clustering method, it is proven to be able to group customers based on their payment patterns. The results are divided into C0 (Low) containing 327 data, C1 (High) containing 6 data, and C2 (Medium) containing 59 data. The contribution of this research has an impact on companies in taking strategies for handling customer service in managing existing connections.
Diagnosa Penyakit Tuberkulosis Paru Menggunakan Metode Forward Chaining dan Certainty Factor Wirdawati, Wira; Sovia, Rini; Hendrik, Billy
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 2 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i2.1217

Abstract

Tuberculosis (TB) is an infectious disease that can affect people of all ages, including children, adolescents, and the elderly, and can cause illness and death in over one million people. The disease is spread through coughs or sneezes by people with pulmonary TB, through contaminated saliva, and inhalation by healthy people with weakened immune systems. Therefore, this study aims to develop an expert system to assist in the diagnosis of pulmonary tuberculosis using the Forward Chaining and Certainty Factor methods. This process begins by identifying symptoms reported by the user and then searching for rules in the knowledge base that match those symptoms. This method allows the system to follow a logical flow of reasoning similar to the way a doctor diagnoses a disease. This study used data from 100 patients from 2023 at the Pariaman Community Health Center. Using the Forward Chaining and Certainty Factor methods, three patient data sets with three types of tuberculosis were tested. The percentage results for each type of disease were 100% positive for pulmonary tuberculosis, 0.91% negative for pulmonary tuberculosis, and 0.92% latent for pulmonary tuberculosis, with a confidence level of Very Confident. This research contributes to increasing knowledge and understanding in the field of expert systems, particularly in the application of the Forward Chaining and Certainty Factor methods for diagnosing tuberculosis.
Evaluasi Kinerja Pelayanan Pegawai Kantor Camat Padangsidimpuan Utara Menggunakan Pendektan Fuzzy Inference System Sugeno Subarja, Roy Efendi; Hendrik, Billy
Indo Green Journal Vol. 1 No. 3 (2023): Green 2023
Publisher : Published by Institut Teknologi Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/green.v1i3.17

Abstract

Penelitian ini bertujuan untuk mengevaluasi kinerja pelayanan pegawai di Kantor Camat Padangsidimpuan Utara dengan memanfaatkan Pendekatan Fuzzy Inference System Sugeno. Metode ini memungkinkan untuk mengukur dan menganalisis berbagai aspek kinerja pelayanan, termasuk efisiensi, responsifitas, dan kepuasan masyarakat. Data evaluasi dikumpulkan melalui survei kepada stakeholder terkait dan dianalisis menggunakan model Fuzzy Inference System Sugeno yang telah dikembangkan. Penelitian ini menghasilkan hasil evaluasi yang lebih akurat dan dapat memberikan rekomendasi yang lebih terperinci untuk pengambilan keputusan terkait peningkatan kinerja pelayanan. Temuan dari penelitian ini dapat menjadi landasan bagi pihak terkait di Kantor Camat Padangsidimpuan Utara untuk mengimplementasikan strategi dan kebijakan yang bertujuan untuk meningkatkan kualitas pelayanan. Selain itu, metodologi yang digunakan dalam penelitian ini juga dapat diadaptasi dan diterapkan dalam evaluasi kinerja pelayanan di kantor-kantor Camat lainnya atau lembaga pemerintah lainnya untuk memperoleh informasi yang lebih akurat dan mendalam terkait dengan kinerja pelayanannya.
Fuzzy C-Means and K-Means Methods Comparison for the Detection of Diabetes Subarja, Roy Efendi; Hendrik, Billy
Journal of Computer Science Advancements Vol. 1 No. 5 (2023)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v1i5.620

Abstract

The necessity for precise and effective disease detection techniques has increased due to the rising incidence of diabetes. The main objective of this study is to assess how well the fuzzy C-Means and K-Means clustering algorithms detect diabetes. Based on pertinent medical data, the study attempts to examine how well these two clustering approaches identify cases of diabetes. For testing, a dataset with a variety of health and diagnostic indicator variables was used. Metrics including sensitivity, specificity, accuracy, and F1-score were used to evaluate the detection performance of the Fuzzy C-Means and K-Means algorithms that were used to cluster the dataset. Based on several evaluation criteria, the results show that both clustering approaches have promising potential for diabetes identification. However, their performance varies. This study sheds light on the advantages and disadvantages of clustering algorithms and advances our understanding of their suitability for diabetes identification. Improved diagnosis precision and early diabetes management intervention could result from more optimization and validation of these algorithms
Literature Review: Perbandingan Metode Foward Chaining, Deep Transfer Learning dan CNN pada Klasifikasi Penyakit Kulit Maharani, Dian; Hendrik, Billy
Journal of Education Research Vol. 6 No. 4 (2025)
Publisher : Perkumpulan Pengelola Jurnal PAUD Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37985/jer.v6i4.2181

Abstract

Kulit merupakan salah satu bagian tubuh manusia yang berfungsi untuk mengatur suhu tubuh pada manusia, sekaligus untuk melindungi seluruh bagian yang ada dalam tubuh manusia. Banyak sekali faktor yang mempengaruhi kondisi kesehatan kulit yang menimbulkan penyakit pada kulit. Dalam penelitian ini, metode Forward Chaining, Deep Transfer Learning dan Convulotional Neural Network digunakan untuk mengidentifikasi penyakit kulit pada manusia. Tujuan penelitian ini adalah untuk mengumpulkan, mengevaluasi, dan menyintesis data ilmiah yang relevan dengan tujuan penelitian melalui metode peninjauan literatur sistematis (SLR). SLR melibatkan proses pencarian sumber informasi, penilaian kualitas studi, dan analisis menyeluruh dari hasil. Penelitian ini menggunakan data primer melalui observasi, studi pustaka, dan dokumentasi, serta data sekunder dari jurnal yang relevan. Metodologi penelitian dilakukan dengan menggunakan Google Scholar dengan menggunakan istilah yang sesuai dengan subjek penelitian. Jurnal-jurnal yang relevan dipilih berdasarkan kriteria inklusi dan eksklusi, dan jurnal-jurnal tersebut dievaluasi berdasarkan pertanyaan evaluasi yang telah ditentukan sebelumnya.
Analisis Efektivitas dalam Menentukan Nilai Kualitas Layanan Jaringan Internet Menggunakan Metode AHP Waruwu, Kalfinus; Hendrik, Billy
Journal of Education Research Vol. 6 No. 4 (2025)
Publisher : Perkumpulan Pengelola Jurnal PAUD Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37985/jer.v6i4.2311

Abstract

Optimalisasi bandwidth dalam jaringan masih menjadi tantangan bagi lingkungan pendidikan atau industri, terutama dalam distribusi yang sesuai dengan kebutuhan yang beragam. Dengan tujuan menggunakan metode AHP ini untuk dapat memastikan distribusi yang tepat berdasarkan kebutuhan jaringan dan pengguna. Melalui analisis hierarki, AHP memungkinkan analisis multikriteria untuk menentukan faktor-faktor seperti jenis aplikasi (VoIP, streaming, transfer data), jumlah pengguna, dan durasi penggunaan jaringan. Tidak seperti penelitian sebelumnya yang hanya berfokus pada perbandingan prioritas aplikasi secara statis, penelitian ini mengusulkan model dinamis yang memungkinkan penyesuaian bobot kriteria secara adaptif sesuai dengan kondisi jaringan secara real-time. Dengan menerapkan AHP ini dibandingkan dengan teknik lain berdasarkan kemampuannya memberikan kesimpulan berdasarkan fakta yang objektif dan konsisten. Hasil analisis menunjukkan bahwa menghasilkan sistem distribusi bandwidth yang lebih presisi, memastikan alokasi optimal bagi aplikasi kritikal seperti e-learning, konferensi video, dan penelitian berbasis cloud. AHP merupakan solusi yang fleksibel dan efektif untuk mengoptimalkan bandwidth di lingkungan pendidikan atau industri.
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
Co-Authors A'yuni, Qurrata Abdi Juliantho, Dwana Abdi Julianto, Dwana Ade saputra Akbar, Syifa Chairunnissa Deliva Amin Amirul Mukminin, Andi Amri, Yassirli Andi, Muhammad Yusril Haffandi Arby, Willya Aswandi, Nopan Awal, Hasri Budiantoro, Hendro Deti Karmanita Dian Maharani, Dian Febby Olivia, Ladyka Firdaus Firdaus Gevindo, Aprilian Gunadi Widi Nurcahyo Haffandi, Muhammad Yusril Hasibuan, Elpina Sari Dewi Ikhlas, Ariza Indhira, Sonia Karseno, Doni Lubis, Fitri Amelia Sari Lubis, Siti Sahara Mardhiah Masril Muhammad, Imam Fakhri Nasution, Ayu Lestiani Nugraha, Fajri Olivia, Ladyka Febby Raharjo, Tio Doli Resnawita Ridwan, Ridwan Ritna Wahyuni Ruri Hartika Zain Rusydi, Rezki Sahara Lubis, Siti Sani, Fadhila Putri Sari, Ade Puspita Sarjon Defit Sonia Indhira Sovia, Rini Subarja, Roy Efendi Sukardi Sukardi Sumijan Supperianto, Bambang Suri, Melati Rahma Sutri, Ridwan Syafri Arlis Tamin, Zulfiqar Vidyanti, Angela Waruwu, Kalfinus Wira, M Wira Sanjaya Wirdawati, Wira Yuhandri Yuhandri, Y