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Firdaus, Iqbal
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Classification of Banana Leaf and Ornamental Plant Diseases Using Gray Level Co-occurrence Matrix (GLCM) and Hybrid Random Forest–Support Vector Machine (SVM) Urfiyati, Novia; Rijati, Nova; Pujiono, Pujiono; Soeleman, Arief; Firdaus, Iqbal; Nurhuda, Yeni Agus
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.4966

Abstract

Leaf diseases in banana plants and ornamental crops can significantly reduce productivity and product quality, highlighting the need for accurate early detection methods. This study proposes an image-based classification approach utilizing texture features extracted from the Gray Level Co-occurrence Matrix (GLCM) combined with a Hybrid Stacking model that integrates Random Forest (RF) and Support Vector Machine (SVM). The preprocessing stage involves image resizing and noise reduction, followed by feature extraction using energy, contrast, homogeneity, and correlation parameters. The dataset consists of eight classes of healthy and diseased leaves, collected from both field documentation and secondary sources. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics under a cross-validation scheme. Experimental results show that SVM achieved 89.2% accuracy, RF 88.5%, while the stacking model yielded the best performance with 91.7% accuracy, effectively reducing misclassification among visually similar disease classes. This study demonstrates the effectiveness of combining GLCM features and hybrid stacking models for leaf disease classification, with potential applications in automated plant monitoring systems to support precision agriculture.
Improving EFL Students’ Listening and Critical Thinking Skills through Higher-Order Thinking Questions Ramadhana, Diba; Firdaus, Iqbal
Journal of Language Intelligence and Culture Vol. 7 No. 2 (2025): Journal of Language Intelligence and Culture
Publisher : Faculty of Tarbiyah and Teacher Training, IAIN Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35719/jlic.v7i2.653

Abstract

This study explores the effectiveness of higher-order thinking skills (HOTS) questions in fostering critical thinking among EFL learners in a Listening for Academic Purposes (LAP) class. While academic listening inherently involves cognitive processes such as inference, evaluation, and analysis, EFL instruction often focuses on lower-order comprehension tasks. To address this gap, the study implements HOTS-based questioning strategies within a Classroom Action Research (CAR) framework, promoting reflective teaching and continuous improvement. The research was conducted with six intermediate EFL students over two cycles, incorporating academic audio materials, structured HOTS questions, and student-centered discussions. Data were collected through observation checklists, student reflection sheets, and pre- and post-tests. Findings indicate that students initially struggled with higher-order questions but showed significant improvement in both critical thinking and listening performance after receiving appropriate scaffolding and practice. The post-test results and classroom observations revealed increased engagement, deeper analysis, and more thoughtful responses. This study concludes that integrating HOTS questions into LAP instruction can effectively enhance critical thinking skills in EFL learners. It also highlights the value of reflective teaching practices in adapting instructional strategies to meet learner needs. Implications are offered for language teachers, curriculum designers, and future researchers aiming to enrich listening pedagogy through critical thinking development.
Pengaruh Sikap Pada Artificial Intelligence (AI) Terhadap Kinerja Pegawai Aparatur Sipil Negara Di Kalimantan Selatan Fuad Arifin, Falah; Arifin, Zainal; Nastiti, Rizky; Firdaus, Iqbal
Jurnal GeoEkonomi Vol. 17 No. 2 (2026)
Publisher : Program Studi Manajemen Fakultas Ekonomi dan Bisnis Universitas Balikpapan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36277/geoekonomi.v17i2.742

Abstract

This study aims to analyze the influence of attitude components toward Artificial Intelligence (AI)—namely cognition, affection, and conation—on the performance of civil servants (ASN) in South Kalimantan Province. The research employed a quantitative approach with a causal associative design. The sample size was determined using the Slovin formula, resulting in 100 respondents selected through purposive sampling. Data were collected using questionnaires and analyzed through validity testing, reliability testing, classical assumption testing, and multiple linear regression analysis.The results indicate that cognition has a positive and significant effect on ASN performance, while affection shows no significant effect. Conation demonstrates the strongest and most significant influence, revealing that the intention to use AI is a key factor in improving employee performance. Simultaneously, cognition, affection, and conation significantly affect ASN performance. The study concludes that knowledge and intention to adopt AI among civil servants are crucial for successful technology implementation in public administration. Recommendations include strengthening AI-related training, improving digital infrastructure, and providing technical assistance to enhance AI adoption in government institutions.  
Co-Authors Abdul Kadir Abdurrahman, Ahmad Faruq Agung Putra Wijaya Boedi, Soelistijono Budi Dermawan Diba Ramadhana Dwi Asmi Eliasta Ketaren, Eliasta Firdaus, Gusti Aditya Aromatica Fuad Arifin, Falah Henny Septiana Amalia Jacob, Louris Maisarah, Hj. Melya Riniarti Muhammad Maladi Nastiti, Rizky Nova Rijati Nurhuda, Yeni Agus Pohan, Sry Dhina Posman Manurung Pujiono Pujiono Raffa, Muhammad Rio, Avilla Riswan Ludfi Siska Ayu Widiana Soeleman, Arief Soeleman, M. Arief Sumardi . Supriyanto Urfiyati, Novia Wijaya, Mario Eka Zaharani, Rethanita Zainal Arifin