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All Journal CogITo Smart Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Enrico Samuel Djimesha
Universitas Klabat, Minahasa Utara
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering

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Analysis of Factors Affecting Intention in Using Google Classroom in Post-Pandemic Era with UTAUT2 Approach Enrico Samuel Djimesha; Erma Suryani
CogITo Smart Journal Vol. 10 No. 1 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i1.666.566-577

Abstract

A pandemic that happened a few years ago has forced universities around the world to adopt online learning. This was driven by government regulations that forced society to adopt health protocols and social distancing. The adoption of Google Classroom as a learning management system (LMS) has potential for universities because of its relatively lower cost than other LMSs, its ability to integrate with Google Meet, an online video conference application, and its ability to help manage learning files. XYZ University provides learning management services through Google Classroom. However, the usage of this LMS post-pandemic decreases after the social distancing regulation is lifted. This has become attention for the researcher to analyze and give recommendations to XYZ University on improving the usage of Google Classroom in the post-pandemic era to digitalize and centralize the learning process in a system. The researcher has designed the research stages, starting with problem formulation, using the UTAUT2 approach, analysis with PLS-SEM, and providing recommendations for the university. This model resulted in two factors affecting the acceptance of Google Classroom: performance expectancy and habit. Also, this model explains 56.5% of behavioral intention on using Google Classroom and 59.9% of use behavior of Google Classroom. This study recommends the institution to enforce the use of Google Classroom for every learning activity so that both faculty members and students are used to using it. This study also recommends the institution to socialize about the features and advantages of Google Classroom to help users aware of the positive impact of using Google Classroom on learning activities.
Sistem Lost and Found Berbasis Vision-Language Retrieval dengan Explainable AI untuk Lingkungan Smart Campus Enrico Samuel Djimesha; Regi Fernando Najoan; Argha Orion Silitonga; Wilsen Grivin Mokodaser; George Morris William Tangka
Journal of Information System Research (JOSH) Vol 8 No 1 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v8i1.10990

Abstract

Evidence for combined image-text retrieval in campus lost-and-found systems remains limited, and zero-shot CLIP's ability to distinguish item attributes and identities is uncertain. This study evaluates CLIP ViT-B/32 on an item-image collection covering eight categories. The controlled multimodal evaluation uses 404 images, 85 metadata-defined relevance groups as proxy ground truth, and 237 leave-one-out queries. Separate image-only validation uses 387 images from 79 provenance-based Object IDs after duplicate removal and quality control; each Object ID contains multiple images of the same physical item. In Object-ID validation, CLIP achieved Recall@1 of 0.674, Recall@10 of 0.935, and mAP of 0.419 (95% object-cluster bootstrap CI: 0.369–0.469). In multimodal evaluation, image-only achieved mAP 0.624, text-only 0.409, and image+text reached the best mAP of 0.732 at α=0.5, selected in four of five group cross-validation folds. Ablation showed that the single fused-index outperformed the three-index configuration (mAP 0.715 versus 0.692); therefore, three-index is not claimed as an accuracy improvement. Direct Indonesian input to English CLIP achieved mAP 0.163, compared with 0.363 using multilingual CLIP and 0.409 through translation. Attention Rollout is positioned only as a supporting visualization because its deletion-curve AUC of 0.871 lacks a random-occlusion baseline. These results indicate the benefit of image-text fusion under controlled evaluation but do not establish performance for free-form user descriptions or production readiness.
Klasifikasi Hierarkis dengan Kalibrasi Probabilitas untuk Penyakit Daun Singkong Menggunakan Fusi Fitur dan Random Forest Regi Fernando Najoan; Enrico Samuel Djimesha; Argha Orion Silitonga; Wilsen Grivin Mokodaser; George William Tangka
Journal of Information System Research (JOSH) Vol 8 No 1 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v8i1.11238

Abstract

Field-image classification of cassava leaf disease requires trustworthy probabilities and a mechanism for withholding uncertain decisions, rather than label accuracy alone. This study evaluates HSV, GLCM, and MobileNetV3-Small embedding fusion in a two-stage hierarchical Random Forest with probability calibration and selective prediction. A total of 7,508 CCMT images were evaluated using outer stratified 5-fold cross-validation. Model comparisons report mean ± standard deviation across five outer folds. End-to-end MobileNetV3-Small achieved 93.90±0.73% accuracy, 94.64±0.59% balanced accuracy, and 94.23±0.73% Macro F1, exceeding the Hierarchical Hybrid Random Forest at 88.28±1.12%, 89.66±0.76%, and 88.79±1.07%, respectively. The 1.03-percentage-point Macro F1 difference between the hierarchical and Flat Hybrid models was not significant at the 5% level (two-sided Wilcoxon, p=0.125). A separate calibration and selective-prediction experiment divided each outer-train fold into 70% model-fit, 15% calibrator-fit, and 15% threshold-selection partitions, all disjoint from the outer-test fold. Simplex-temperature yielded the lowest ECE (0.54%), whereas sigmoid performed better on log loss and Brier score. Thresholds selected on the held-out threshold-selection partition yielded 75.87% outer-test coverage, 95.40% accepted accuracy, and 95.58% accepted Macro F1. Selective accuracy applies only to accepted samples, not all data or guaranteed future performance; the contribution is evaluation of the coverage–risk trade-off, not improved intrinsic accuracy.
Comparing Machine Learning and BERT for Classifying Grammatical Error Types in Indonesian EFL Essays Meily Ivane Esther Neman; George Morris William Tangka; Regi Fernando Najoan; Enrico Samuel Djimesha; Daniel Semuel Warouw
Journal of Information System Research (JOSH) Vol 8 No 1 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v8i1.11296

Abstract

Automated writing feedback tools are usually trained on learner corpora from outside Indonesia, so their accuracy on Indonesian students' grammatical errors is unknown. This study compares Multinomial Naive Bayes, a linear Support Vector Machine (SVM), Random Forest, and a fine-tuned BERT model for classifying nine grammatical error types. The models were trained on 48,011 error edits from the BEA-2019 W&I+LOCNESS and FCE corpora, re-annotated with ERRANT 3, and tested in-domain on 5,129 edits and cross-corpus on 1,900 edits from 66 essays by 33 Indonesian university students in the ICNALE Edited Essays. BERT achieved the highest macro-F1 on both test sets, 0.917 in-domain and 0.849 on the Indonesian essays, and McNemar tests confirmed its advantage over each classical model. All models lost 0.035 to 0.068 macro-F1 under the corpus shift. Noun number errors formed 24% of the Indonesian errors, three times their training share, while word form and pronoun were the hardest classes. This study contributes a cross-corpus benchmark of classical and Transformer-based error-type classifiers on Indonesian students' essays, a reproducible pipeline that labels both corpora with the same ERRANT version, and a class-level reliability profile for classroom feedback. For English teachers in Indonesia, the results support explicit, contrastive practice on articles and plural marking and the use of BERT for immediate feedback on reliably recognised classes, while its word form and pronoun suggestions are confirmed by the teacher.
Co-Authors Argha Orion Silitonga Daniel Semuel Warouw Erma Suryani George Morris William Tangka George William Tangka Meily Ivane Esther Neman Regi Fernando Najoan Wilsen Grivin Mokodaser