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All Journal International Journal of Evaluation and Research in Education (IJERE) 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)
Afrig Aminuddin
Universitas AMIKOM Yogyakarta
Computer Science & IT Education Electrical & Electronics Engineering Engineering

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Sentiment Analysis and Topic Modeling of Kitabisa Applications using Support Vector Machine (SVM) and Smote-Tomek Links Methods Switrayana, I Nyoman; Ashadi, Diki; Hairani, Hairani; Aminuddin, Afrig
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 2 No. 2 (2023): September 2023
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v2i2.3406

Abstract

Kitabisa is an Indonesian application that functions to raise funds online. Users can easily support various types of campaigns and donate funds to various social causes through the app. User reviews of the application are very diverse, and it is not sure whether user reviews of the application tend to be positive, neutral, or negative. This research aimed to analyze the sentiment of the Kitabisa application by modeling topics using Latent Dirichlet Allocation (LDA) and classifying user reviews using a Support Vector Machine (SVM). The scrapped dataset showed imbalanced dataset problems, so the SMOTE-Tomek Links oversampling technique was proposed. The results of this study show that using LDA produces five topics often discussed in 750 reviews. Then, the performance of SVM without using SMOTE-Tomek Links was 72% accuracy, 76% precision, 72% recall, and 64% f1 score. Meanwhile, using SMOTE-Tomek Links could significantly improve the performance, namely 98% accuracy, 98% precision, 98% recall, and 98% f1 score. Based on this research, the application of SVM achieved high performance for user sentiment classification, especially when the dataset was in a balanced state. Therefore, the SMOTE-Tomek Links oversampling technique is recommended for dealing with unbalanced sentiment datasets.
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.
Analisis Komparatif Algoritma Random Forest dan Multiple Linear Regression untuk Prediksi Suhu Udara dengan Rekayasa Fitur Siklik Haryoko, Haryoko; Farida, Lilis Dwi; Aminuddin, Afrig; Ghozali, Bahrun
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
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): Juni 2026 (in progress)
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.
Co-Authors Abdullah, Khairul Hafezad Abdulloh, Ferian Fauzi Abu Tholib Acihmah Sidauruk Agastya, I Made Artha Alam Miah, Mohammad Badrul Apitiadi, Satyo Dwi Asadulloh, Bima Pramudya Ashadi, Diki Bety Wulan Sari Bety Wulan Sari, Bety Wulan Christopher Michael Lauw Dhanar Intan Surya Saputra Diki Ashadi Donni Prabowo, Donni Ghozali, Bahrun Gunawan, Puguh Hasta Hairani Hairani Hammood, Waleed A. Harianto, Fetrus Jari Haryoko, Haryoko I Nyoman Switrayana Joang Ipmawati Kusnawi Kusnawi Kusuma, Velizha S LILIS DWI FARIDA Majid Rahardi Mohammad Iskandar Dzulkurnain Mustofa, Dinar Norhikmah, - Sri Farida Utami Wiji Nurastuti, Wiji Wirasto, Anggit Yoga Pristyanto