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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Riset Informatika JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Proceeding of International Conference Health, Science And Technology (ICOHETECH)
Lubna
Universitas Duta Bangsa Surakarta
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology

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ANALYSIS OF CLASSIFICATION ALGORITHM IN UNBALANCED DIABETES DATASET Ahmad Rifa'i; Herin Dwibima Aprianto; Lubna
Jurnal Riset Informatika Vol. 8 No. 1 (2025): Desember 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i1.458

Abstract

Diabetes mellitus is a metabolic disease that is spreading rapidly and has the potential to be life-threatening worldwide. This condition occurs when the body experiences a decline in its ability to process glucose, triggering metabolic disorders. The use of machine learning algorithms is one effective approach to predicting or detecting diabetes based on the severity of a patient's symptoms. This study uses the Diabetes dataset from Kaggle and compares the performance of several classification algorithms in unbalanced data conditions and after data balancing using the SMOTE, Random Under Sampling, Random Over Sampling, and Near Miss resampling techniques. The results show that model performance is greatly influenced by data balance conditions and the resampling method used. In the original unbalanced data condition, Artificial Neural Network (ANN) provided the best results with the highest accuracy of 96.98%, indicating that ANN is the most adaptive to class imbalance. After resampling, the performance pattern changed: with SMOTE, Random Under Sampling, and Random Over Sampling, the Random Forest algorithm consistently produced the highest accuracy of 96.52%, 89.84%, and 96.26%, respectively, demonstrating its superiority in utilizing balanced data. Meanwhile, in the Near Miss method, the best performance was achieved by Logistic Regression with an accuracy of 94.41%, indicating that minority sample selection based on proximity is more suitable for linear models. Therefore, selecting the right combination of resampling methods and machine learning algorithms is an important factor in obtaining optimal diabetes predictions.
PENGEMBANGAN MEDIA PEMBELAJARAN PERAKITAN KOMPUTER BERBASIS AUGMENTED REALITY DENGAN MDLC-SUS Lubna Lubna; Buyut Khoirul Umri; Sahrul Amir Romadhon
Journal of Information System Management (JOISM) Vol. 8 No. 1 (2026): Juni
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/joism.2026v8i1.2749

Abstract

Pemahaman komponen dan prosedur perakitan komputer merupakan kompetensi dasar mahasiswa teknologi informasi, namun pembelajaran konvensional berbasis teks, gambar dua dimensi, dan demonstrasi terbatas belum mampu memvisualisasikan bentuk, posisi, serta urutan pemasangan komponen secara interaktif. Praktik langsung juga berisiko merusak perangkat keras dan membutuhkan biaya tinggi. Penelitian ini bertujuan mengembangkan media pembelajaran perakitan komputer berbasis Augmented Reality pada Android menggunakan metode Multimedia Development Life Cycle, meliputi concept, design, material collecting, assembly, testing, dan distribution. Aplikasi menyediakan fitur Play AR, scan marker, model 3D komponen, deskripsi, animasi tutorial, dan kuis interaktif. Pengujian blackbox menunjukkan seluruh fitur utama berjalan sesuai rancangan. Evaluasi usability menggunakan System Usability Scale dilakukan terhadap 25 responden dan menghasilkan total skor 2.025 dengan rata-rata 81,00, termasuk kategori Sangat Baik/Excellent. Hasil ini menunjukkan aplikasi layak digunakan sebagai media pembelajaran interaktif.
Analisis Kebutuhan Pengembangan Framework Game-Based Learning Berbasis AI–IoT untuk Peningkatan Kualitas Pembelajaran dan Otomatisasi Asesmen di Pendidikan Menengah Herin Dwibima Aprianto; Lubna Lubna; Anindhiasti Ayu Kususma Asri; Anip Moniva
Techno.Com Vol. 25 No. 3 (2026): August 2026
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v25i3.16811

Abstract

Perkembangan teknologi digital mendorong sekolah untuk mengadopsi pendekatan pembelajaran yang lebih inovatif, interaktif, dan berpusat pada peserta didik. Meskipun pemanfaatan Game-Based Learning (GBL), Artificial Intelligence (AI), dan Internet of Things (IoT) dalam pendidikan terus berkembang, penelitian yang mengkaji kebutuhan pengguna sebagai dasar pengembangan framework terintegrasi masih relatif terbatas. Penelitian ini bertujuan untuk menganalisis kebutuhan guru dan peserta didik terhadap pengembangan Framework Game-Based Learning berbasis AI–IoT untuk mendukung peningkatan kualitas pembelajaran dan otomatisasi asesmen di pendidikan menengah. Penelitian menggunakan pendekatan mixed-methods dengan desain analisis kebutuhan. Data dikumpulkan melalui kuesioner daring yang melibatkan 51 peserta didik dan 12 guru, serta wawancara semi-terstruktur terhadap lima guru. Hasil penelitian menunjukkan bahwa peserta didik memiliki tingkat kebutuhan yang tinggi (M = 3,95), sedangkan guru menunjukkan tingkat kebutuhan yang sangat tinggi (M = 4,58) terhadap framework yang diusulkan. Dimensi Learning Analytics, Automated Assessment, dan AI Feedback & Recommendation menjadi kebutuhan utama bagi kedua kelompok responden. Temuan wawancara mengungkapkan bahwa guru masih menghadapi kendala dalam proses asesmen manual, pemberian umpan balik, dan pemantauan perkembangan belajar siswa. Berdasarkan hasil analisis kebutuhan, penelitian ini mengusulkan framework yang terdiri atas enam komponen utama, yaitu Learning Content, Game-Based Learning, Automated Assessment, AI Feedback and Recommendation, Learning Analytics, dan IoT Integration. Framework tersebut diharapkan menjadi landasan bagi pengembangan sistem pembelajaran yang lebih adaptif, efisien, dan berkelanjutan. Keywords - Game-Based Learning; Artificial Intelligence; Internet of Things; Analisis Kebutuhan; Pendidikan Menengah
Analisis Pemanfaatan Multimedia dan Artificial Intelligence dalam Mendukung Kesiapan Digital UMKM Pangan Nibras Faiq Muhammad; Putri Intan Prastiwi; Lubna Lubna
Techno.Com Vol. 25 No. 3 (2026): August 2026
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v25i3.17105

Abstract

UMKM pangan berperan strategis dalam ketahanan ekonomi lokal, tetapi transformasi digitalnya sering terkendala oleh kemampuan produksi konten dan kesiapan teknologi. Penelitian ini menganalisis pemanfaatan multimedia dan Artificial Intelligence (AI) dalam mendukung kesiapan digital UMKM pangan dalam perspektif Technology Readiness. Data dari 136 UMKM pangan diproses melalui pembersihan data, pemeriksaan duplikasi, transformasi data, perhi-tungan skor kesiapan (scoring), dan analisis keterkaitan eksploratif untuk memetakan adopsi pada alur kerja promosi digital. Hasil menunjukkan bahwa 87,5% responden telah menggunakan media digital, 64,7% pernah membuat video promosi, 87,5% pernah mendengar AI, tetapi hanya 49,3% yang menggunakan AI untuk promosi dan 26,5% yang menggunakan AI khusus untuk video promosi. AI paling banyak dimanfaatkan pada tahap pra-produksi, yaitu ide konten, penyusunan script, dan caption, bukan untuk produksi video penuh. Berdasarkan temuan tersebut, penelitian menyusun model konseptual berbasis data yang memetakan profil UMKM, tingkat kesiapan, tahapan promosi digital, rekomendasi fitur AI, dan kebutuhan platform distribusi. Segmentasi kesiapan dengan algoritma K-Means (k=3, silhouette 0,451) menghasilkan tiga klaster UMKM, dan rancangan arsitektur sistem berpola input-process-output disusun untuk menggambarkan alur pemrosesan komputasional dari data profil UMKM menjadi rekomendasi fitur AI, sebagai dasar pengembangan sistem rekomendasi pada penelitian lanjutan.   Kata Kunci - UMKM pangan; adopsi multimedia; artificial intelligence; promosi digital; technology readiness
AN INTELLIGENT GAME-BASED LEARNING ARCHITECTURE BASED ON AN AI–IOT FRAMEWORK FOR SMART LEARNING ENVIRONMENTS Herin Dwibima Aprianto; Anindhiasti Ayu Kusuma Asri; Lubna
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2026: Proceeding of the 7th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/fgjkjy50

Abstract

The integration of Artificial Intelligence (AI), the Internet ofThings (IoT), and Game-Based Learning (GBL) has thepotential to create adaptive learning environments, yetexisting studies rarely combine these technologies within aunified operational architecture. This study proposes theIntelligent Game-Based Learning (IGBL) Architecture as theoperational realization of a previously developed AI–IoTGame-Based Learning framework. Using the Design ScienceResearch methodology, a five-layer adaptive architecturewas developed and evaluated through expert review andsimulation-based assessment using 80 predefined studentprofiles. Two architectural configurations were compared:gameplay analytics only and gameplay analytics enriched with contextual learning information obtained through IoT-enabled connected learning technologies. The results showed that the proposed architecture improved learning-state classification accuracy (82.4% to 89.8%), adaptive decision accuracy (79.3% to 86.5%), and successfuladaptation rate (80.7% to 88.7%) while maintaining nearreal-time processing performance. These findingsdemonstrate that the proposed architecture is conceptuallyvalid and operationally feasible, providing a structuredimplementation model for integrating AI, IoT, and GBL incontext-aware adaptive learning environments.
AUGMENTED REALITY LEARNING MEDIA FOR COMPUTER ASSEMBLY: SUPPORTING SDG 4 QUALITY EDUCATION Lubna Lubna; Buyut Khoirul Umri; Sahrul Amir Romadhon
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2026: Proceeding of the 7th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/d7by8q88

Abstract

Understanding computer components and assembly procedures is a fundamental competency for information technology students, yet conventional two-dimensional media cannot interactively visualize the shape, position, and correct sequence of component installation. Hands-on practice also carries the risk of hardware damage and requires substantial cost, limiting equitable access to quality technical education. This study develops an Augmented Reality (AR)-based interactive learning platform for computer assembly on the Android platform as a digital innovation supporting SDG 4 (Quality Education). The platform was developed using the Multimedia Development Life Cycle (MDLC) method, comprising concept, design, material collecting, assembly, testing, and distribution stages. Key features include Play AR, marker scanning, 3D component models, component descriptions, tutorial animations, and an interactive quiz. Blackbox testing confirmed that all core features function as designed. A usability evaluation using the System Usability Scale with 25 respondents produced an average score of 81.00, placing the application in the Excellent category and exceeding the score of an earlier VR-based simulation. These results indicate that the platform is feasible as an interactive, low-risk, and cost-effective learning medium that widens access to quality technical education in the digital era.
Co-Authors Ahmad Rifa’i Anindhiasti Ayu Kusuma Asri Anip Moniva Buyut Khoirul Umri Herin Dwibima Aprianto Nibras Faiq Muhammad Putri Intan Prastiwi Sahrul Amir Romadhon