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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Embedded Systems, Security and Intelligent Systems
Tatik Melinda Tallulembang
Universitas Musamus
Computer Science & IT Engineering

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Accuracy–Efficiency Trade-off Analysis of Five Lightweight CNN Architectures for Mobile-Deployable Corn Leaf Disease Classification Jarot Budiasto; Hasanudin Jayawardana; Tri Kustanti Rahayu; Tatik Melinda Tallulembang
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.12264

Abstract

Purpose – Corn leaf disease diagnosis in resource-constrained agricultural settings requires mobile-deployable models that maintain a practical balance between classification accuracy, model size, and on-device latency. This study aims to provide empirical guidance for selecting lightweight Convolutional Neural Network (CNN) architectures by systematically analyzing the accuracy–efficiency trade-offs of five models for corn leaf disease classification. Design/methods/approach – MobileNetV2, MobileNetV3-Small, MobileNetV3-Large, EfficientNetB0, and NASNetMobile were evaluated on the PlantVillage Corn dataset comprising 4,188 images across four classes under identical experimental settings. The models were trained using a two-phase strategy and converted into standard and dynamic-range quantized TensorFlow Lite formats. Evaluation covered classification accuracy, macro F1-score, model size, Android on-device inference latency, Pareto frontier and radar analyses, and pairwise McNemar's tests with Yates continuity correction. Findings – EfficientNetB0 achieved the highest accuracy (95.25%) and macro F1-score (93.77%). MobileNetV3-Small offered the strongest efficiency under the tested Android CPU setting, reaching 94.54% accuracy with a 1.18 MB dynamic-range quantized TensorFlow Lite model and 3.89 ± 0.04 ms standard inference. The top three models were statistically comparable (p = 0.6625-1.0000). Research implications/limitations – Standard TensorFlow Lite is preferable for low-latency Android CPU deployment, whereas dynamic-range quantized TensorFlow Lite supports storage-constrained offline distribution. However, the findings are limited to the PlantVillage benchmark dataset and testing on a single mid-range Android device. Originality/value – This study integrates lightweight CNN benchmarking, TensorFlow Lite deployment, real-device Android testing, accuracy–efficiency trade-off analysis, and statistical validation to support evidence-based mobile agricultural AI model selection.
Reliable Indonesian Academic Service Chatbot Using Retrieval-Augmented Generation and Adaptive Refusal Jarot Budiasto; Tatik Melinda Tallulembang; Selfina Pare; Muhammad Hasbi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 5 (2026): October 2026 (In progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i5.8018

Abstract

Academic service information is often stored in lengthy, formal, and distributed university documents, making accurate access difficult. Generative chatbots can improve access but may answer unsupported questions unless retrieval and refusal are controlled. This study proposes a reliability-oriented Retrieval-Augmented Generation (RAG) chatbot for Indonesian academic services integrating BGE-M3, FAISS, adaptive refusal thresholding, rule-based guardrails, Qwen2.5, and retry-on-refusal. A controlled experiment used 12 official documents and 160 Indonesian questions across eight categories. The final configuration achieved Precision@5 of 0.7250, Recall@5 of 0.9125, MRR of 0.8940, nDCG@5 of 0.8888, and mean semantic similarity of 0.8516. All 16 expected-refusal questions were refused and none of the 144 answerable questions was falsely refused, yielding observed refusal accuracy of 1.0000 and a false refusal rate of 0.0000. Retry-on-refusal reduced the false refusal rate from 0.0556 to 0.0000 and increased answerable-only similarity from 0.8150 to 0.8392. These results provide initial evidence that combining retrieval confidence, guardrails, and retry supports more reliable behavior within the controlled benchmark. Because the refusal subset was small and institution-specific, larger evaluations using naturally occurring student queries are required before inferring operational reliability.
Co-Authors Hasanudin Jayawardana JAROT BUDIASTO Muhammad Hasbi Selfina Pare Tri Kustanti Rahayu