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All Journal International Journal of Evaluation and Research in Education (IJERE) TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Information Systems Engineering and Business Intelligence Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Information Systems and Informatics The Indonesian Journal of Computer Science Research Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Journal of Computing Theories and Applications
Afrig Aminuddin
Universitas AMIKOM Yogyakarta
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Rancang Bangun Aplikasi Smart Touring Berbasis Android Majid Rahardi; Afrig Aminuddin
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 1 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i1.1185

Abstract

Perkembangan teknologi saat ini begitu cepat. Teknologi membuat perubahan pada peradaban manusia. Telah banyak kegiatan manusia yang didukung oleh kemajuan teknologi. Tak terkecuali kegiatan touring yang dilakukan bersama-sama. Touring adalah kegiatan berkendara dari suatu tempat ke tempat lain secara bersama-sama. Saat ini komunitas touring terus meningkat, namun masih memiliki beberapa permasalahan saat melakukan aktifitasnya. Saat ini salah satu permasalahan yang ada pada aktifitas touring adalah pengendara satu dengan yang lainnya tidak bisa mengetahui lokasi semua teman touring mereka. Oleh karena itu sangat dimungkinkan ada anggota touring mereka yang tertinggal jauh atau salah jalur. Dengan teknologi smartphone yang sangat pesat, dimungkinkan dibangun sebuah sistem yang dapat mendukung kegiatan touring tersebut. Pada penelitian ini telah berhasil dibangun sistem yang dapat mendukung kelancaran aktifitas touring. Fokus penelitian ini adalah membangun sistem touring yang dapat mendeteksi keberadaan semua member touring ketika sedang melakukan kegiatan touring. Sistem yang dibangun adalah berbasis contextual awareness, yaitu sistem yang mampu memberikan informasi kepada pengguna dengan data yang didapat dari lingkungannya. Dalam hal ini adalah memberikan informasi ketika ada member touring yang berjauhan dengan member touring lainnya.
K-Nearest Neighbor Performance Optimization for Multiclass Imbalance of Intrusion Detection Data Using SMOTE and Distance Variation-Based Parameter Tuning Hairani Hairani; Christopher Michael Lauw; Sri Farida Utami; Afrig Aminuddin; Abu Tholib
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7489

Abstract

The increasing use of computer networks and internet-based services has made cybersecurity threats more complex. Intrusion Detection Systems (IDS) play a crucial role in identifying network attacks; however, conventional signature- or rule-based approaches are limited in handling novel attacks and dynamically changing attack patterns. Therefore, machine learning approaches are applied to enhance the adaptive capabilities of IDS. Nevertheless, the use of machine learning in IDS still faces a major challenge: data imbalance, where normal traffic significantly outweighs attack traffic. This condition biases models toward the majority class, leading to suboptimal detection of minority attacks. Based on this issue, this study aims to improve the performance of the K-Nearest Neighbor (KNN) method in network attack detection by applying the Synthetic Minority Over-sampling Technique (SMOTE) and parameter tuning. The study employs KNN with parameter tuning and SMOTE to address multiclass data imbalance in network attack detection. Parameter tuning is conducted to determine the optimal value of k and distance functions, including Euclidean, Manhattan, and Cosine Similarity. The results show that KNN with k = 3 and Manhattan distance on SMOTE-balanced data achieves the highest accuracy of 96.51%, outperforming Euclidean and Cosine Similarity distances. These findings conclude that applying SMOTE and appropriately selecting k and distance metrics significantly improve KNN performance in network attack detection and increase overall detection accuracy.
Conceptual model of internet banking adoption with perceived risk and trust factors Waleed A. Hammood; Afrig Aminuddin; Omar A. Hammood; Khairul Hafezad Abdullah; Davi Sofyan; Majid Rahardi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 5: October 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i5.24581

Abstract

Understanding the primary factors of internet banking (IB) acceptance is critical for both banks and users; nevertheless, our knowledge of the role of users’ perceived risk and trust in IB adoption is limited. As a result, we develop a conceptual model by incorporating perceived risk and trust into the technology acceptance model (TAM) theory toward the IB. The proper research emphasized that the most essential component in explaining IB adoption behavior is behavioral intention to use IB adoption. TAM is helpful for figuring out how elements that affect IB adoption are connected to one another. According to previous literature on IB and the use of such technology in Iraq, one has to choose a theoretical foundation that may justify the acceptance of IB from the customer’s perspective. The conceptual model was therefore constructed using the TAM as a foundation. Furthermore, perceived risk and trust were added to the TAM dimensions as external factors. The key objective of this work was to extend the TAM to construct a conceptual model for IB adoption and to get sufficient theoretical support from the existing literature for the essential elements and their relationships in order to unearth new insights about factors responsible for IB adoption.
Analisis Komparatif Algoritma Random Forest dan Multiple Linear Regression untuk Prediksi Suhu Udara dengan Rekayasa Fitur Siklik Haryoko Haryoko; Lilis Dwi Farida; Afrig Aminuddin; Bahrun Ghozali
The Indonesian Journal of Computer Science Research Vol. 5 No. 1 (2026): Januari
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v5i1.255

Abstract

Prediksi temperatur cuaca merupakan tantangan signifikan dalam bidang meteorologi dan pertanian presisi dikarenakan karakteristik data atmosfer yang sangat fluktuatif dan non-linear. Penelitian ini bertujuan untuk melakukan analisis komparatif kinerja algoritma Machine Learning dalam mengestimasi suhu udara harian, dengan membandingkan model Multiple Linear Regression sebagai baseline dan Random Forest Regressor sebagai model utama. Eksperimen melibatkan penerapan teknik rekayasa fitur (feature engineering) yang komprehensif, khususnya transformasi variabel arah angin menjadi komponen vektor kontinu serta ekstraksi fitur waktu menjadi komponen siklik untuk menangkap pola musiman. Berdasarkan hasil evaluasi menggunakan dataset historis cuaca, model Random Forest menunjukkan performa yang lebih superior dibandingkan Multiple Linear Regression, dengan capaian nilai Koefisien Determinasi ( ) sebesar 0.990, Mean Absolute Error (MAE) sebesar 0.74°C, dan Root Mean Squared Error (RMSE) sebesar 0.95. Hasil analisis membuktikan bahwa pendekatan ensemble learning dengan penanganan fitur siklik jauh lebih efektif dalam memetakan kompleksitas interaksi variabel cuaca dibandingkan metode linear konvensional
Retinal Vessel Segmentation via Morphological Refinement and Adaptive Late Fusion Afrig Aminuddin; Mohammad Badrul Alam Miah; Ahmed Adil Nafea; Hesmeralda Rojas Enriquez
Journal of Computing Theories and Applications Vol. 4 No. 1 (2026): JCTA 4(1) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.16093

Abstract

Retinal vessel segmentation is fundamental for quantitative retinal vascular analysis; however, accurate delineation remains challenging because of nonuniform illumination, low-contrast capillaries, pathological lesions, and variations in image acquisition. This study presents a rule-based unsupervised retinal vessel segmentation framework based on decision-level adaptive late fusion, in which six complementary local adaptive thresholding methods are integrated and subsequently refined through luminance validation, hysteresis reconstruction, component cleanup, elongation filtering, and boundary smoothing. In this context, unsupervised denotes that no statistical segmentation model is trained using manual vessel annotations; instead, fixed parameters and fusion weights are determined from a small development subset, while all evaluation images remain unseen during method development. The proposed framework was evaluated on 135 independent retinal fundus images from the DRIVE, STARE, CHASE_DB1, HRF, and LES-AV datasets using an eroded field-of-view protocol. It achieved an image-weighted Dice coefficient of 0.7104 (95% bootstrap confidence interval: 0.7005–0.7205), an IoU of 0.5539, a sensitivity of 0.7544, a specificity of 0.9597, a balanced accuracy of 0.8570, and a clDice score of 0.7395. Compared with fixed majority voting, the proposed adaptive late fusion strategy significantly improved segmentation performance in 128 of 135 test images, yielding a mean Dice improvement of 0.0270 (paired Wilcoxon, Holm-adjusted p = 7.67 × 10⁻²¹). Although segmentation performance remains limited for complex pathological images, particularly in the HRF dataset, the proposed framework demonstrates consistent cross-dataset performance, transparent decision-making, and strong reproducibility, providing an effective alternative when interpretable, training-free retinal vessel segmentation is required.
Enhanced Predictive Modeling for Non-Invasive Liver Disease Diagnosis Donni Prabowo; Bety Wulan Sari; Yoga Pristyanto; Afrig Aminuddin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6449

Abstract

Liver diseases (e.g. cirrhosis, hepatitis, and fatty liver disease) are globally one of the leading causes of mortality and are typically diagnosed in advanced stages due to vague symptoms and the difficulty involved in existing diagnostic techniques (e.g. biopsies). To optimize the early diagnosis of liver disease, this study proposes an enhanced, non-invasive approach using machine learning techniques. The research is enriched with a full pipeline, from exploratory data analysis and imputation of the dataset, treatment of the outlier, encoding of labels and scaling using ILPD (Indian Liver Patient Dataset). The classification models compared were RandomForest, XGBoost, LGBM, and CatBoost. The CatBoost algorithm fine-tuned with RandomizedSearchCV showed the highest performance with a test accuracy of 93%. The performance was again better than any already published methods showing that advanced ensembling and hyperparameter optimization worked. The proposed model is suitable for incorporation into clinical decision support systems and provides reliable and accurate diagnostic assistance. In addition to its high accuracy, the model is robust for missing and categorical data, which is a challenge in any real-world clinical scenario. These findings add to the growing body of evidence supporting AI-based medical diagnostics and suggest that CatBoost is a highly promising tool for facilitating timely screening and diagnosis of liver disease. Furthermore, the study stresses the need for thorough preprocessing and cross-validation, which serve to reduce biases that are present in widely applied datasets. Ongoing future efforts may involve the integration of multi-source data and implementation of explainable AI techniques to allow for wider clinical trust and use.
Co-Authors Abdullah, Khairul Hafezad Abdulloh, Ferian Fauzi Abu Tholib Acihmah Sidauruk Agastya, I Made Artha Ahmed Adil Nafea Alam Miah, Mohammad Badrul Apitiadi, Satyo Dwi Asadulloh, Bima Pramudya Ashadi, Diki Bahrun Ghozali Bety Wulan Sari Bety Wulan Sari, Bety Wulan Christopher Michael Lauw Dhanar Intan Surya Saputra Diki Ashadi Donni Prabowo Donni Prabowo, Donni Gunawan, Puguh Hasta Hairani Hairani Hammood, Waleed A. Harianto, Fetrus Jari Haryoko Haryoko Hesmeralda Rojas Enriquez I Nyoman Switrayana Joang Ipmawati Khairul Hafezad Abdullah Kusnawi Kusnawi Kusuma, Velizha S LILIS DWI FARIDA Majid Rahardi Mohammad Badrul Alam Miah Mohammad Iskandar Dzulkurnain Mustofa, Dinar Norhikmah, - Omar A. Hammood Sri Farida Utami Waleed A. Hammood Wiji Nurastuti, Wiji Wirasto, Anggit Yoga Pristyanto