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All Journal Bulletin of Electrical Engineering and Informatics ILKOM Jurnal Ilmiah Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Indonesian Journal of Data and Science Buletin Sistem Informasi dan Teknologi Islam Ilmu Komputer untuk Masyarakat Journal of Technology Research in Information System and Engineering JBIMA: Jurnal Pengabdian kepada Masyarakat Linier: Literatur Informatika dan Komputer
Tasrif Hasanuddin
Teknik Informatika, Universitas Muslim Indonesia, Makassar, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Mathematics Public Health Other

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Comparison of ResNet50 and ResNet101 Feature Extraction for Tea Leaf Disease Classification Using Support Vector Machine Wistiani Astuti; Erick Irawadi Alwi; Farniwati Fattah; Tasrif Hasanuddin; Julisa; Ulfa Sari; Ainur Rahma Almagfirah
Indonesian Journal of Data and Science Vol. 7 No. 2 (2026): Indonesian Journal of Data and Science
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i2.338

Abstract

Introduction: Tea leaf diseases can substantially reduce crop quality and productivity, making early and accurate diagnosis important for effective disease management. This study compares ResNet50 and ResNet101 as pretrained deep feature extractors combined with Support Vector Machine (SVM) to determine whether a deeper residual architecture can improve discrimination among visually similar tea leaf disease classes. Method: Images were obtained from the Kaggle “Identifying Disease in Tea Leaves” dataset comprising eight classes. Data augmentation increased each class to 800 images, yielding 6,400 images that were divided into training and testing sets using an 80:20 ratio. ResNet50 and ResNet101 pretrained on ImageNet were used as fixed feature extractors, and the resulting feature vectors were standardized and classified using an RBF-kernel SVM. Results and Discussion: ResNet101–SVM achieved the best performance with 97.97% accuracy and precision, recall, and F1-score of 98%, substantially outperforming ResNet50–SVM, which achieved 89.15% accuracy, 90% precision, 89% recall, and 89% F1-score. The deeper ResNet101 architecture provided more discriminative representations for visually similar disease patterns, although a small number of misclassifications remained. Conclusion: ResNet101 combined with SVM provides a more accurate and reliable framework than ResNet50–SVM for multi-class tea leaf disease classification and offers a promising foundation for automated disease diagnosis systems.
Analisis Pengukuran Kualitas Website FIKOM Dengan Menggunakan Metode WEBQUAL 4.0 Aqifah Kadir; Tasrif Hasanuddin
LINIER: Literatur Informatika dan Komputer Vol 3, No 1 (2026)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/linier.v3i1.3481

Abstract

Website menjadi sarana utama dalam penyebaran informasi akademik dan administratif di lingkungan perguruan tinggi. Penelitian ini bertujuan untuk mengukur kualitas website FIKOM.UMI.AC.ID menggunakan metode WebQual 4.0, yang menilai tiga dimensi utama, yaitu usability (kegunaan), information quality (kualitas informasi), dan service interaction quality (kualitas interaksi layanan), serta pengaruhnya terhadap kepuasan pengguna.Metode penelitian yang digunakan mencakup penyebaran kuesioner berbasis skala Likert kepada pengguna website, diikuti dengan uji validitas dan reliabilitas untuk memastikan keakuratan instrumen penelitian. Analisis data dilakukan menggunakan uji regresi linear berganda, uji t, dan uji f untuk mengidentifikasi pengaruh masing-masing variabel terhadap kepuasan pengguna.Hasil penelitian menunjukkan bahwa variabel service interaction quality memiliki pengaruh signifikan terhadap kepuasan pengguna, sedangkan usability dan information quality tidak memberikan pengaruh signifikan secara individu. Namun, secara simultan, ketiga variabel independen memiliki pengaruh signifikan terhadap kepuasan pengguna. Hal ini mengindikasikan bahwa aspek interaksi layanan merupakan faktor utama dalam meningkatkan kepuasan pengguna terhadap website
Akses Kontrol Pintu Gerbang Otomatis Berbasis Arduino UNO Agus Sukardi Nasrullah; Tasrif Hasanuddin; Huzain Azis
LINIER: Literatur Informatika dan Komputer Vol 1, No 1 (2024)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/linier.v1i1.2484

Abstract

Dunia elektronika dan kontrol saat ini berkembang sangat cepat. Kehidupan manusia juga dipengaruhi oleh teknologielektronika. berbagai macam peralatan yang diciptakan oleh manusia untuk memenuhi kebutuhan dan keinginan untukmengendalikan pintu gerbang berbasis arduino uno. Dengan menggunakan teknologi elektronika yaitu mikrokontrolleruntuk merancang Akses Kontrol Pintu Gerbang Otomatis berbasis arduino uno. Makalah ini fokus pada pengguanan SensorUltrasonik sebagai inputan data dalam alat ini. Metode yang diguanakan yaitu Deskriptif Analitif, prosesnya berupaAnalisis Masalah, Perancangan Alat, Simulasi Alat, dan Tahap Evaluasi. Alat yang digunakan dalam makalah ini yaituArduino Uno R3, Potensiometer, Servo, Sensor Ultrasonik. Hasil makalah menunjukan bahwa penerapan Kontrol Pintugerbang otomatis ini dapat membantu seseorang dapat mengendalikan Pintu Gerbang sesuai keinginan dia, tanpa perlu lagimengeluarkan tenaga yang banyak. Seseorang dapat terhubung lewat sensor ultrasonnic, dan ketika telah terhubung, orangtersebut dapat mengendalikan Pintu gerbang untuk membuka, menutup dan menyetop sesuai kontrol yang telah melaluisensor ultrasonic
An LLM-Based AI Task Agent for Academic Task Management with n8n and Telegram Nabila Widiyanti; Tasrif Hasanuddin; Huzain Azis
Indonesian Journal of Data and Science Vol. 7 No. 2 (2026): Indonesian Journal of Data and Science
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i2.412

Abstract

Introduction: Managing multiple academic tasks with overlapping deadlines remains challenging for university students, while conventional task-management applications still require substantial manual organization and prioritization. This study develops an LLM-based AI Task Agent that enables conversational academic task management through Telegram and workflow automation. Method: The proposed system integrates Telegram as the interaction interface, n8n for workflow orchestration, an LLM-based AI agent for natural-language interpretation and tool selection, Google Sheets for task-data operations, PostgreSQL for conversational memory, and scheduled workflows for automated reminders. The system supports Create, Read, Update, and Delete operations, contextual priority recommendations based on deadline, urgency, and lecturer strictness, and proactive reminders. Functional performance and response time were evaluated across the primary system functions. Results and Discussion: Create, Read, Update, and Delete operations achieved 100% functional accuracy, while priority recommendation and automated reminder functions achieved 95%. Recorded processing times ranged from 3.1 to 3.5 seconds, with an average of approximately 3.32 seconds. The results demonstrate that separating LLM-based interpretation from predefined external tool execution enables reliable conversational task management while maintaining controlled data operations. Conclusion: The proposed LLM-based AI Task Agent demonstrates the feasibility of integrating conversational interaction, executable task-management functions, contextual prioritization, memory, and proactive reminders within a unified Telegram-based academic workflow.
Co-Authors Agus Sukardi Nasrullah Ainur Rahma Almagfirah Alfian Putra Ramadhan Alwi Alwi Andi Puspitasari Andi Yudra Bramantya Aqifah Kadir Dolly Indra Erick Irawadi Alwi, Erick Irawadi Farniwati Fattah Fathurrahman Putra Syah Fitriyani Umar Furqaan Ismail Huzain Azis Julisa Kurniati, Nia Lokapitasari Belluano, Poetri Lestari Lutfi Budi Ilmawan, Lutfi Budi Manga, Abdul Rachman Mardiyyah Hasnawi Marsvendah Fariza Mahatmi Muammar Alam Sudmar Muhammad Fadhiel Muhammad Yahya Muhammad Zulfikar Mus Mulyadi Baharuddin Nabila Widiyanti Rainanda Darya Saputra Ramdaniah Ramdaniah Ridwan Suhair Anwar Siska Anraeni Suwahyudi Syarif Syahrul Mubarak Abdullah Syamsul Yakin Syamsumarlin Syamsumarlin Ulfa Sari Wistiani Astuti Yusrizal Damri Zulihsan Rahman