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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Simantec Jurnal Edukasi dan Penelitian Informatika (JEPIN) Edukasi: Jurnal Pendidikan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal GERVASI: Jurnal Pengabdian kepada Masyarakat JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal of Applied Business and Technology Reflection: Education and Pedagogical Insights PROCEEDING OF INTERNATIONAL CONFERENCE ON BUSINESS MANAGEMENT AND ACCOUNTING Luxury: Landscape of Business Administration Jurnal Pustaka Robot Sister INOVTEK Polbeng - Seri Informatika
Marlim, Yulvia Nora
Institut Bisnis Dan Teknologi Pelita Indonesia
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Social Sciences Other

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Learning Smart Farming through IoT Prototypes, Educational Impacts of Smart Goat Housing Systems in Vocational Education Achmad Tavip Junaedi; Nicholas Renaldo; Wilda Susanti; Rangga Rahmadian Yuliendi; Wahyu Joni Kurniawan; Yulvia Nora Marlim; Kristy Veronica; Harry Patuan Panjaitan; Umar Faruq; Jahrizal Jahrizal
Reflection: Education and Pedagogical Insights Vol. 3 No. 1 (2026): Reflection: Education and Pedagogical Insights
Publisher : First Ciera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61230/reflection.v3i1.141

Abstract

This study investigates the educational impacts of learning smart farming through Internet of Things (IoT)-based smart goat housing systems in vocational education. The rapid digital transformation of agriculture has created a growing demand for graduates with strong technological and applied competencies; however, the integration of real smart farming technologies into vocational curricula remains limited. To address this gap, this research employed a quasi-experimental design with a pretest–posttest non-equivalent control group to compare IoT prototype-based learning with conventional instructional approaches. The study involved vocational students enrolled in agriculture-related programs, where the experimental group engaged in project-based learning using an operational IoT-enabled smart goat housing system, while the control group received traditional instruction. The findings indicate that students exposed to IoT prototype-based learning demonstrated significantly higher improvements in digital competence, applied learning outcomes, and learning engagement compared to those in the control group. Qualitative insights further revealed that authentic interaction with real-time data and automated systems enhanced students’ understanding, motivation, and confidence in using digital technologies. These results highlight the pedagogical value of integrating real IoT prototypes into vocational education and confirm the effectiveness of experiential and technology-enhanced learning approaches in developing workforce-relevant competencies. This study contributes to vocational education literature by positioning livestock-based smart farming systems as effective learning media for digital agriculture education.
Enhancement of Supervised Learning Models for Intrusion Detection Through Mutual Information and Hyperparameter Tuning Deny Jollyta; Yoakhina Nicole Makaruku; Alyauma Hajjah; Yulvia Nora Marlim
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.5760

Abstract

Enhancing the performance of supervised learning algorithms through feature and hyperparameter testing remains challenging for users, particularly when detecting computer network intrusions. There are opportunities to assess whether a supervised learning algorithm performs optimally, depending on the number of features and the choice of hyperparameters. The purpose of this research is to enhance the network intrusion detection performance of three supervised learning algorithms, namely Support Vector Machine (SVM), eXtreme Gradient Boosting, and Random Forest, by using the Mutual Information feature selection approach and hyperparameter tuning. Mutual Information measures the dependency of features on the target. Features with high values are the most informative. Hyperparameters are not learned from the data; they are set before training begins. Hyperparameters are selected in accordance with the requirements of the three algorithms via iterative training and testing on the NSL-KDD dataset. The dataset was split into 80:20, 70:30, and 60:40. The results showed that the fifteen features with the highest mutual information were identified and trained on the data using appropriate hyperparameters. By splitting the data in an 80:20 ratio, the accuracy of Support Vector Machine reached its maximum, increasing from 90% to 98%. In contrast, eXtreme Gradient Boosting and Random Forest reached their maximum, increasing from 97% and 98% to 100%, respectively. The study’s findings advance our understanding of how algorithm performance depends on feature and hyperparameter selection.
Framework Smart Mushroom Farming Berbasis IoT dan AI Menggunakan Pendekatan Multimodal untuk Budidaya Jamur Merang Desnelita, Yenny; Gustientiedina, Gustientiedina; Noratama Putri, Ramalia; Hajjah, Alyauma; Nora Marlim, Yulvia; Irwan, Irwan
Jurnal Pustaka Robot Sister (Jurnal Pusat Akses Kajian Robotika, Sistem Tertanam, dan Sistem Terdistribusi) Vol 4 No 2 (2026): Jurnal Pustaka Robot Sister (Pusat Akses Kajian Robotika, Sistem Tertanam, dan Si
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakarobotsister.v4i2.2451

Abstract

Budidaya jamur merang (Volvariella volvacea) sangat bergantung pada kestabilan mikroklimat yang selaras dengan tahapan pertumbuhannya, sehingga data lingkungan dan perkembangan visual jamur perlu dianalisis secara terpadu agar pengelolaan budidaya dapat dilakukan secara lebih presisi. Penelitian ini mengusulkan sebuah framework Smart Mushroom Farming berbasis Internet of Things (IoT) dan Artificial Intelligence (AI) dengan pendekatan multimodal untuk mendukung pemantauan lingkungan sekaligus analisis fase pertumbuhan jamur merang. Framework disusun melalui pendekatan Design Science Research (DSR) dan diwujudkan dalam arsitektur empat lapisan, yaitu sensing, communication, processing, dan application layer. Pada lapisan akuisisi data, parameter suhu, kelembapan relatif, konsentrasi CO₂, intensitas cahaya, dan kelembapan media dipadukan dengan citra RGB perkembangan jamur sebagai representasi kondisi visual. Seluruh data tersebut ditransmisikan melalui protokol MQTT dan dikelola dalam basis data berbasis cloud. Pada lapisan pemrosesan, pendekatan multimodal dirancang untuk menggabungkan fitur mikroklimat dengan fitur visual yang diekstraksi menggunakan Convolutional Neural Network (CNN), sebagai dasar konseptual bagi identifikasi fase pertumbuhan mulai dari miselium, primordia (tiny button), button (egg), elongation, hingga fase matang/siap panen. Framework yang diusulkan menghubungkan tahapan akuisisi data, komunikasi IoT, integrasi data multimodal, analisis berbasis AI, visualisasi, hingga keluaran pengendalian mikroklimat dalam satu arsitektur yang saling terhubung. Kontribusi utama penelitian ini terletak pada penyediaan dasar konseptual dan teknis bagi pengembangan sistem budidaya jamur merang yang adaptif dan berbasis data, yang selanjutnya dapat diuji lebih lanjut melalui tahap implementasi dan validasi eksperimental.
Disrupting the Veterinary Value Chain through AI-Based Goat Health Management Achmad Tavip Junaedi; Nicholas Renaldo; Nyoto Nyoto; Erlin Erlin; Gustientiedina Gustientiedina; Yulvia Nora Marlim; Tito Suprayoga; Jahrizal Jahrizal; Umar Faruq; Kristy Veronica
Journal of Applied Business and Technology Vol. 7 No. 2 (2026): Jounal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/9x4hae67

Abstract

The digital transformation of livestock production is creating new opportunities to redesign conventional veterinary service delivery. However, veterinary services in goat farming remain constrained by limited personnel, geographic accessibility, delayed disease detection, and relatively high service costs. This study examines how artificial intelligence can disrupt the veterinary value chain through Kambing Light Vision, an AI-based goat health management platform that integrates Computer Vision, disease detection, prediction confidence, and AI-assisted veterinary care recommendations. An exploratory qualitative case study approach was employed by analyzing the technology development process, veterinary service workflow, stakeholder roles, commercialization pathways, and potential business value. The results indicate that AI can shift preliminary health assessment from a predominantly veterinarian-dependent process toward a digitally assisted and farmer-accessible service. This transformation can shorten information flows, reduce service and transaction bottlenecks, improve veterinary triage, and create new value propositions for farmers, veterinary clinics, cooperatives, and agribusiness organizations. The analysis further identifies subscription-based Software as a Service, technology licensing, and managed veterinary services as potential mechanisms for converting AI capabilities into scalable business value. The study argues that the disruptive potential of AI does not lie solely in disease-classification accuracy but in its ability to reconfigure the veterinary value chain and transform how veterinary services are accessed, delivered, and commercialized. Kambing Light Vision therefore represents a transition from AI as a diagnostic technology toward AI-enabled veterinary service infrastructure with potential implications for digital agriculture and sustainable livestock business.
Co-Authors Achmad Tavip Junaedi Adiya, M. Hasmil ahmad kamal, ahmad Alyauma Hajjah Cecilia, Cecilia Chandra Putra Dinata Dadang Priyanto Deny Jollyta Deny Jollyta Desnelita, Yenny Dewi Nasien Dwi Oktarina Erlin Erlin Erlin Erlin Fadrul Fadrul Fauzan Azim Gusrianty Gusrianty Gusrio Tendra Gustientiedina Harry Patuan Panjaitan Hendri Hartono, Hendri Jahrizal Johan Johan Johan Johan Junadhi Junadhi Koto, Jaswar Kristy Veronica Laili Suryati Leonardo Yang M Dalil Marcellino, Marcellino Muhammad Zarlis Mukhsin Mukhsin Musa, Sulaiman Nabila Wahid Nicholas Renaldo Noratama Putri, Ramalia Nova Agustina Nyoto Prihandoko . Rahmadian Yuliendi, Rangga Rahman, Sarli Ramalia Noratama Putri Rangga Rahmadian Yuliendi Rizqon Jamil Farhas Saputra, Fandri Sulaiman Musa Tito Suprayoga Umar Faruq Wahid, Nabila Wahyu Joni Kurniawan Wahyu Joni Kurniawan Wilda Susanti William William Yoakhina Nicole Makaruku