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INDONESIA
Jiko (Jurnal Informatika dan komputer)
Published by Universitas Khairun
ISSN : 26148897     EISSN : 26561948     DOI : -
Core Subject : Science,
Computer Science & IT
Jiko (Jurnal Informatika dan Komputer) Ternate adalah jurnal ilmiah diterbitkan oleh Program Studi Teknik Informatika Universitas Khairun sebagai wadah untuk publikasi atau menyebarluaskan hasil - hasil penelitian dan kajian analisis yang berkaitan dengan bidang Informatika, Ilmu Komputer, Teknologi Informasi, Sistem Informasi dan Sistem Komputer. Jurnal Informatika dan Komputer (JIKO) Ternate terbit 2 (dua) kali dalam setahun pada bulan April dan Oktober
Arjuna Subject : -
Articles 312 Documents
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PERBANDINGAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) DAN SUPPORT VECTOR MACHINE (SVM) UNTUK KLASIFIKASI PENYAKIT KANKER TULANG BERDASARKAN DATA CITRA Bayu Angga Wijaya; Edoart Joel Pardede; Muhammad Reza; Daniel B.P Sihombing; Gian Juno Pabaha Panjaitan
JIKO (Jurnal Informatika dan Komputer) Vol 9 No 2 (2026)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v9i2.12121

Abstract

This research is motivated by the high urgency of early diagnosis in bone cancer cases to reduce patient mortality rates. This study comparatively analyzes the performance of the Convolutional Neural Network (CNN) algorithm with ResNet50 architecture, Support Vector Machine (SVM), and their integration in a Hybrid CNN-SVM model for medical image classification. The research methodology involved a dataset of 8,814 radiological images processed through normalization and augmentation stages. In single-model testing, the end-to-end ResNet50 architecture achieved an accuracy of 87%, but showed limitations in generalizing microscopic textures at the softmax classification layer. On the other hand, the SVM algorithm supported by manual Histogram of Oriented Gradients (HOG) feature extraction demonstrated significant stability with an accuracy of 93.58%, proving the superiority of the optimal margin method in handling specific feature dimensions in medical images. The crucial finding in this study shows that the Hybrid CNN-SVM model—which utilizes ResNet50 as an automatic feature extractor and SVM as the final classifier—achieved peak performance with an accuracy of 95.18%, Precision value of 0.98, Recall of 0.96, and AUC of 0.98. These results confirm that the synergy between CNN hierarchical feature extraction and SVM classification robustness can significantly minimize the risk of false negatives, making it highly recommended as a reliable Computer-Aided Diagnosis (CAD) instrument to assist medical practitioners in early detection of bone cancer.
IMPLEMENTATION OF GEOSPATIAL INTELLIGENCE FOR SENTIMENT ANALYSIS ON STUNTING POLICY IN BATANG REGENCY USING INDOBERT Dian Fitria Maharani; Bambang Agus Herlambang; Nur Latifah Dwi Mutiara Sari
JIKO (Jurnal Informatika dan Komputer) Vol 9 No 2 (2026)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v9i2.12620

Abstract

This study develops a Geospatial Artificial Intelligence (GeoAI)-based WebGIS that integrates IndoBERT sentiment classification to evaluate public percetion of stunting-management policy in Batang Regency, Central Java, Indoneia. The Cross-Industri Standart Process for Data Mining (CRISP-DM) framework was applied to the sentiment-analysis pipeline, while Rapid Application Development (RAD) governed the system construction. A total of 478 public-opinion responses were collected through questionnaires from residents of fifteen sub-districts, preprocessed through data cleaning, case folding, tokenizing, stopword removal, and stemming, then labeled and classified into positive, neutral, and negative sentiment using a fine-tuned IndoBERT model. The system was built with Python, Flask, Leaflet.js, and QGIS to visualize sentiment spatially. On a held-out test set of 96 samples, the model achieved 82.29% accuracy, 83.49% weighted precision, 82.29% weighted recall, and an 82.58% weighted F1-score (macro F1-score of 0.77), with class-weighted loss applied during fine-tuning to counter a severe class imbalance in the labeled dataset (Imbalance Ratio = 5.82). Spatial analysis showed that Bandar Sub-district recorded both the highest number of positive (37) and negative (8) responses, indicating the highest level of public engagement, Batang Sub-district recorded the highest number of neutral responses (46). System functionality was further validated through User Acceptance Testing and Black Box Testing, each covering nine functional scenarios spanning authentication, dashoard acces, sentiment-analysis display, spatial map interaction, and page navigation; all eighteen test scenarios were completed successfully (100% valid), confirming that the system operates correctly and satisfies the intended user requirements. The resulting GeoAI-based WebGIS enable policymakers to identify sub-districts requiring closer attention and design more targeted, evidence-based interventions. These findings demonstrate that integrating sentiment classification with spatial visualization provides greater insight into public perception than statistical data.

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