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All Journal JURIKOM (Jurnal Riset Komputer) Jurnal Pengabdian kepada Masyarakat
Dwi Issadari Hastuti
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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PEMANFAATAN ARTIFICIAL INTELLIGENCE DALAM PEMBELAJARAN DASAR: HASIL PENGABDIAN DI MI PLUS AL-FATIMAH BOJONEGORO Ita Aristia Sa’ida; Guruh Putro Dirgantoro; Dwi Issadari Hastuti; Niken Sukmawati; Elsa Azia Ulhaq
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 5 No. 6 (2025): Nopember 2025
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/jabdi.v5i6.11805

Abstract

The rapid development of Artificial Intelligence (AI) offers significant opportunities to enhance the quality of learning, particularly at the elementary and madrasah levels. However, teachers’limited digital literacy and lack of experience in utilizing AI-based tools often hinder the integration of technology into classroom practices. This community service program aims to strengthen teachers’ competencies in implementing AI to support teaching and learning at MI Plus Al-Fatimah, Bojonegoro. The program was conducted through four stages: socialization, training, classroom implementation with mentoring, and evaluation. Various AI-based teaching products were produced, including digital learning materials, interactive assessments, infographics, and instructional videos. Evaluation results show a substantial improvement in teachers’ digital literacy and confidence, with an average increase of 65% between pre-test and post-test scores. Teachers reported that AI tools improved efficiency in preparing teaching materials, enhanced classroom engagement, and helped diversify instructional strategies. This program demonstrates that AI integration can serve as an effective capacity-building mechanism for teachers, promoting innovative, adaptive, and technology-driven learning environments. The findings suggest that continuous training, infrastructural support, and clear implementation guidelines are necessary to sustain AI adoption in schools and broaden its impact across educational settings.
Perbandingan Algoritma Machine Learning untuk Klasifikasi Kopi Menggunakan Data Sensor Electronic Nose dan Tongue Dwi Issadari Hastuti; Mula Agung Barata; Ifnu Wisma Dwi Prastya
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9349

Abstract

Coffee is a leading Indonesian commodity with a diversity of aromas and flavors influenced by variety and region of origin. However, the process of identifying and classifying coffee types is still often carried out conventionally through sensory testing, which is subjective, time-consuming, and dependent on panelist expertise. This situation encourages the need for a more objective and consistent automated approach based on sensor technology and machine learning. This study aims to compare the performance of several machine learning algorithms, namely Logistic Regression, Support Vector Classifier (SVC), and Random Forest, in classifying Indonesian coffee types using multisensor Electronic Nose and Electronic Tongue data. The data used comes from gas, temperature, and pH sensors with a total of 1,503 samples representing ten coffee classes. The preprocessing stage includes data cleaning using the Interquartile Range (IQR) method to remove outliers and noise reduction using the Moving Average method. The results show that the application of data cleaning and noise reduction significantly improves the performance of all classification models. Among the algorithms tested, Random Forest showed the most stable and superior performance in classifying coffee types. These findings confirm that the combination of appropriate data preprocessing and appropriate algorithm selection plays a crucial role in improving the accuracy of machine learning-based coffee classification systems.
Co-Authors Elsa Azia Ulhaq Guruh Putro Dirgantoro Ifnu Wisma Dwi Prastya Ita Aristia Sa'ida Mula Agung Barata Niken Sukmawati