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All Journal Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Computer and Technology Explore
Supardianto
Universitas Teknologi Mataram
Computer Science & IT Control & Systems Engineering

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PENERAPAN KNNIMPUTER DALAM MENGOLAH DATA MISSING VALUE UNTUK MEMBANTU MENINGKATKAN AKURASI SUPPORT VECTOR MACHINE KLASIFIKASI PENYAKIT TIROID Supardianto; Lalu Mutawalli; Wafiah Murniati
Jurnal Informatika Teknologi dan Sains Vol 4 No 4 (2022): EDISI 14
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (463.007 KB) | DOI: 10.51401/jinteks.v4i4.2077

Abstract

Thyroid is a condition of abnormalities in a person due to thyroid disorders. Based on data from the ministry of health in the world, the prevalence of thyroid is still relatively high, if five million babies are born each year, then there are one thousand six hundred babies with hyperthyroidism. The algorithm used for data processing and modeled into knowledge is a support vector machine (SVM), SVM is used for classification. After exploring the datasets of the 23 attributes contained in the datasets, there are 9 attributes that have missing values, including age 4 lines, sex 307 lines, TSH 804 lines, T3 2604 lines, TT4 442 lines, T4U 809 lines, FTI 802 lines, TBG 8823 lines, and target 1626 lines. Based on the evaluation results on the model that has been tested for precision 94%, recall 100%, F1-score 97% with an accumulated accuracy of 93%. The overall total evaluation on the model is 93%.
Sistem Informasi Apotek Berbasis Website Menggunakan Framework Codeigniter dan Bootstrap Versi 4 Rudi Muslim; Beni Ari Hidayatullah; Supardianto; Ihza Mahendra
Explore Vol 13 No 1 (2023): Januari 2023
Publisher : Universitas Teknologi Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35200/ex.v13i1.35

Abstract

Perkembangan teknologi informasi terkhusus pada sistem informasi website mengharuskan pelaku bisnis untuk mengubah stategi bisnisnya dari konvensional menjadi lebih fleksibel dengan menggunakan sistem informasi untuk menuju perubahan yang lebih baik.Pencatatan data secara konvensional menghabiskan banyak waktu dan kemungkinan besar akan terjadi kesalahan dan kurang konsisten pada saat pencatatan. Terlebih ketika pembuatan laporan sangatlah menyita waktu. Tujuan penelitian ini membangun sistem informasi apotek berbasis website sebagai pengolahan data terkait dengan manajemen apotek. Hasil penelitian ini yaitu memperoleh sistem informasi apotek yang dibangun menggunakan famework codeigniter dan bootstrap versi 4 dengan mengikuti tahapan pada metode pengembangan perangkat lunak yaitu metode Waterfall. Adanya sistem informasi apotek ini, memberikan kemudahan dalam pengolahan dan pencatatan data dan dapat mengurangi kesalahan dan laporan apotek mudah dibuat, serta membangun sistem informasi menggunakan codeigniter dan bootstrap versi 4 memberikan keamanan yang lebih baik daripada tidak menggunakan framework dan juga konten website lebih interaktif.
A Dual-Pipeline Imbalance-Robust Framework for SMS Spam Detection: Achieving Flawless Precision via SMOTE-Augmented Ensembles with Rigorous Statistical Validation Zulpan Hadi; Selamet Riadi; Supardianto; Aulia Riswanti Naya; Liana Trihardianingsih
Journal Computer and Technology Vol. 4 No. 1 (2026): July 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v4i1.510

Abstract

The rapid proliferation of digital communication has exponentially increased the volume of Short Message Service (SMS) spam, exposing mobile users to systemic convenience disruptions, productivity drops, and severe financial losses through sophisticated fraudulent schemes. To construct a highly dependable filtering mechanism, this study presents a rigorous dual-pipeline machine learning framework that systematically addresses the challenges of class imbalance in statistical text mining. Utilizing a verified dataset of 5,572 Indonesian-context short messages, the raw textual corpus is subjected to uniform case normalization, structural URL extraction, and character filtering before feature projection via Term Frequency–Inverse Document Frequency (TF-IDF) vectorization. To overcome the inherent accuracy paradox of skewed class distributions, the experimental design evaluates a baseline pipeline (imbalanced data) against a synthetic data augmentation pipeline leveraging the Synthetic Minority Oversampling Technique (SMOTE) across four distinct classifiers: Logistic Regression, Naive Bayes, Linear Support Vector Machine (Linear SVM), and Random Forest. Empirical results demonstrate that while the baseline Linear SVM serves as the optimal standalone model for overall balance, achieving a peak accuracy of 98.11% and a dominant F1-Score of 92.83%, the SMOTE-augmented Random Forest configuration yields an exceptional high-security alternative by securing a flawless 100.00% precision envelope alongside an 83.89% recall rate. Advanced post-hoc evaluations including McNemar's statistical significance tests (,  for Random Forest), qualitative error analyses of semantic edge cases, and runtime profiling confirm that the developed architecture establishes a highly scalable, mathematically verified, and low-latency solution suitable for integration into real-time telecom filtering gateways.
Co-Authors Aulia Riswanti Naya Beni Ari Hidayatullah Ihza Mahendra Lalu Mutawalli Liana Trihardianingsih Rudi Muslim Selamet Riadi Wafiah Murniati Zulpan Hadi