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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Ilmiah Matrik JURNAL INSTEK (Informatika Sains dan Teknologi) Martabe : Jurnal Pengabdian Kepada Masyarakat The IJICS (International Journal of Informatics and Computer Science) Jurnal Teknologi Terpadu Jurnal Informasi dan Teknologi Jurnal Minfo Polgan (JMP) Indonesian Journal of Society Development (IJSD) ABDIKOM Jurnal Pengabdian Masyarakat Manage
Triwahyono, Bambang
Program Studi Sistem Informasi, Fakultas Ilmu Komputer, Universitas Pembangunan Nasional Veteran Jakarta, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Social Sciences

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Analisis Tingkat Literasi Artificial Intelligence Guru Dalam Media Pembelajaran Pada SDI Plus Darul Ulum Limo Depok Tri Rahayu; Anita Muliawati; Rio Wirawan; Tjahjanto Tjahjanto; Bambang Triwahyono
Jurnal Ilmiah Matrik Vol. 27 No. 3 (2025): Jurnal Ilmiah Matrik
Publisher : Direktorat Riset dan Pengabdian Pada Masyarakat (DRPM) Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/jm2gjf51

Abstract

The acceleration of Artificial Intelligence (AI) technology in education requires teachers to have new competencies in integrating this technology into the learning process. At the elementary school level, AI literacy is crucial to support the implementation of the innovation-based Merdeka Curriculum. As a school committed to quality, SDI Plus Darul Ulum Limo Depok faces challenges in mapping the readiness of its teaching staff amidst diverse digital competency backgrounds. This study aims to analyze the level of AI literacy of teachers at SDI Plus Darul Ulum, covering cognitive, affective, technical, and pedagogical aspects, and to identify barriers to the use of AI as a learning medium. This study uses a descriptive quantitative approach. Data were collected through a structured questionnaire consisting of 15 statements with a Likert scale. The study respondents were all 30 teaching staff at SDI Plus Darul Ulum Limo Depok. Data analysis was conducted using descriptive statistics to determine the distribution of teacher literacy categories. The research findings show that the overall level of teacher AI literacy is in the Medium category (60%), with a small portion in the High (20%) and Low (20%) categories. Based on the aspect analysis, the highest score was achieved in the Affective aspect (4.10) which indicates teachers' positive attitudes and openness towards AI. However, the lowest score was found in the Pedagogical aspect (2.80), where teachers still experience difficulties in integrating AI into lesson plans (RPP) and teaching methods in the classroom.  
ANALISIS TRADE-OFF ANTARA PERFORMA DAN STABILITAS PADA KLASIFIKASI SENTIMEN WISATA BERBAHASA INDONESIA DENGAN TEKNIK DATA BALANCING Chamidah, Nurul; Indriana, Intan Hesti; Triwahyono, Bambang; Widiyanto, Didit; Solihin, Indra Permana; Wirawan, Rio
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 11 No 1 (2026): APRIL
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v11i1.66678

Abstract

Analisis sentimen pada dataset berukuran kecil sering menghadapi masalah ketidakseimbangan kelas yang dapat menurunkan performa klasifikasi. Berbagai teknik penanganan seperti Synthetic Minority Oversampling Technique (SMOTE) dan Random Under Sampling (RUS) telah digunakan untuk meningkatkan performa, tapi aspek stabilitas model masih relatif jarang diperhatikan. Penelitian ini bertujuan untuk menganalisis performa dan stabilitas model klasifikasi sentimen pada dataset ulasan wisata berbahasa Indonesia yang berukuran kecil dan tidak seimbang. Selain itu, penelitian ini mengintegrasikan metrik stabilitas, yaitu Stability Sensitivity Index (SSI) dan Relative Robustness Score (RRS), yang masih jarang dieksplorasi dalam klasifikasi sentimen berbahasa Indonesia. Model yang digunakan adalah Logistic Regression dengan representasi fitur TF-IDF. Eksperimen dilakukan menggunakan tiga skenario, yaitu tanpa balancing, SMOTE, dan RUS, serta dievaluasi menggunakan 5-fold cross-validation. Hasil menunjukkan bahwa SMOTE menghasilkan F1-score tertinggi sebesar 0.926, sedangkan metode RUS memiliki stabilitas terbaik dengan standar deviasi terendah dan nilai RRS tertinggi. Temuan ini menunjukkan adanya trade-off antara performa dan stabilitas, di mana model dengan performa terbaik tidak selalu paling stabil. Penelitian ini memberikan kerangka evaluasi yang lebih komprehensif untuk klasifikasi sentimen pada dataset kecil dan tidak seimbang.
Co-Authors Andik Bintoro Anita Muliawati Bernadin Dwi M Br Pinem, Dahlia Didit Widiyanto Fryda Fatmayati I Wayan Rangga Pinastawa Ika Nurlaili Isnainiyah Indra Permana Solihin Intan Hesti Intan Hesti Indriana Khoerunisa, Mutia Kraugusteeliana Kraugusteeliana Kraugusteeliana Kraugusteeliana Maria Assumpta Wikantari Mohamad Bayu Wibisono Mohamad Bayu Wibisono Muhammad Ikhwani Nasution, Habibi Azka Nurhuda Maulana Nurul Chamidah Radinal Setyadinsa Ridwan Raafi'udin Rio Wirawan Samosir, Alfonso Sembiring, Bobby Rafael Sirojul Munir Sitti Nur Alam Theresiawati Tjahjanto Tjahjanto Tri Rahayu Wirawan, Rio Zalfie Ardian