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ILKOMNIKA: Journal of Computer Science and Applied Informatics
Published by Universitas Nahdlatul Ulama Blitar
ISSN : -     EISSN : 27152731     DOI : https://doi.org/10.28926/ilkomnika
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
ILKOMNIKA: Journal of Computer and Applied Informatics is is a peer reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics of computer science and applied informatics which covers five (5) majors areas of research that includes 1) Informatics Engineering and Its Application 2) Computer Science 3) Software Engineering 4) Computer Engineering 5) Information System. This journal is published 3 issues a year, in April, August, and December.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 15 Documents
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Search results for , issue "Vol 7 No 1 (2025): Volume 7, Number 1, April 2025" : 15 Documents clear
Optimization of LightGBM Model with Bayesian Optimization for Malware Detection Kasim, Afrianto Pratama; Nasib, Salmun K.; Hasan, Isran K.; Wungguli, Djihad; Yahya, Nisky Imansyah
ILKOMNIKA Vol 7 No 1 (2025): Volume 7, Number 1, April 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i1.722

Abstract

Cyberattacks through malware on Android devices continue to rise, making accurate detection crucial. This research optimizes the LightGBM model using Bayesian Optimization to enhance accuracy and efficiency in detecting Android malware. A feature selection mechanism based on Attention Mechanism is applied to select the most relevant features for classification. The dataset used comes from the Canadian Institute for Cybersecurity (CIC) and consists of 17,804 Android applications, with a balanced distribution between malware and normal applications. The dataset is split into ratios of 80%:20%, 75%:25%, and 70%:30%. Feature selection reduces the number of features from 9503 to 300, 500, and 1000. The LightGBM model is then optimized with Bayesian Optimization to fine-tune parameters such as learning rate, number of iterations, and maximum number of leaves. The model's performance is evaluated using accuracy, precision, and recall metrics. Experimental results show that the model achieves 96,99% accuracy, 97,30% precision, and 96,70% recall with an 80%:20% dataset split and 1000 features. The combination of Attention Mechanism and Bayesian Optimization effectively improves processing efficiency without compromising performance.
Goat Farm Management Application at Golden Star Farm Based on Android Romadhon, Nur Aditya; Ardiani, Farida
ILKOMNIKA Vol 7 No 1 (2025): Volume 7, Number 1, April 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i1.725

Abstract

This study designs and implements an Android-based goat farm management application at Golden Star Farm, Cilacap. The main issues addressed are the disorganized sales data recording and the lack of socialization regarding goat care procedures among employees. The system was developed using the Rapid Application Development (RAD) method to ensure speed and accuracy in meeting user needs. This application improves the accuracy of sales data recording, facilitates access to goat care information, and provides a user authentication system. Testing using the Black Box Testing method shows that all features function properly. The research results prove that this application reduces errors in data recording, enhances employee understanding of goat care, and positively impacts livestock health and the sustainability of the farming business.
Peningkatan Identifikasi PCOS dengan KELM melalui Seleksi Fitur LDA dan Deteksi Outlier LOF Ambadar, Panreshma Rizkha; Novitasari, Dian C Rini; Hamid, Abdulloh
ILKOMNIKA Vol 7 No 1 (2025): Volume 7, Number 1, April 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i1.727

Abstract

Polycystic Ovary Syndrome (PCOS) merupakan kelainan yang terjadi pada organ reproduksi perempuan. Kelainan ini mempunyai dampak implikasi klinis yang beragam dan serius, diantaranya pada bagian reproduksi, metabolisme, hingga gangguan psikologis. Identifikasi yang tepat sangat penting untuk meningkatkan penanganan. Penelitian ini bertujuan untuk menguji efektivitas metode Kernel Extreme Learning Machine (KELM) dalam mengidentifikasi PCOS setelah penghapusan outlier dengan Local Outlier Factor (LOF) dan seleksi fitur menggunakan Linear Discriminant Analysis (LDA). Dalam penelitian ini, metode KELM mengidentifikasi kelainan PCOS dengan klasifikasi berdasarkan data rekam medis pasien. Penelitian ini juga melibatkan pengolahan data dengan LOF untuk menangani data outlier dan seleksi fitur terbaik menggunakan LDA guna meningkatkan akurasi identifikasi kelainan PCOS. Berbagai uji coba dilakukan, untuk mengoptimalkan hasil identifikasi kelainan PCOS. Hasil penelitian menunjukkan bahwa ketiga kombinasi dari metode LOF, LDA, dan KELM memperoleh nilai akurasi sebesar 100% dengan eliminasi 10% data outlier dan 10 fitur utama. Hal ini yang menunjukkan kombinasi ketiga metode ini mampu meningkatkan kualitas deteksi dan identifikasi kelainan PCOS.
Sentiment Analysis of Disney+ Hotstar App User Reviews on Google Playstore Using the Naïve Bayes Method Octaviany, Dinda Nur; Rahim, Abdul; Verdikha, Naufal Azmi
ILKOMNIKA Vol 7 No 1 (2025): Volume 7, Number 1, April 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i1.729

Abstract

User reviews of the Disney+ Hotstar application on the Google Play Store present a variety of sentiments, particularly concerning the paid subscription feature. This study aims to analyze these sentiments using the Naïve Bayes classification method, categorizing user opinions into positive, negative, and neutral classes. A total of 30,571 Indonesian- language reviews were collected through web scraping, followed by a preprocessing phase that included case folding, stopword removal, and stemming. The Term Frequency-Inverse Document Frequency (TF- IDF) technique was applied to weight the significance of words. The dataset was split into 80% training and 20% testing portions. The classification model achieved an accuracy of 78%, showing reliable performance in identifying sentiment patterns. However, performance on the neutral class was lower, indicating room for improvement through better preprocessing or class balancing. The findings provide insights for Disney+ Hotstar to better understand user perceptions and guide enhancements to the subscription service.
Klasifikasi Irama Bacaan Al-Qur’an menggunakan Algoritma CNN Utama, Shoffin Nahwa; Prakasa, Johan Ericka Wahyu; Hariyanto, Wahyu
ILKOMNIKA Vol 7 No 1 (2025): Volume 7, Number 1, April 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i1.731

Abstract

Klasifikasi nada bacaan Al-Qur’an sangat penting untuk mendukung pembelajaran tajwid, tartil, serta tilawah yang sesuai dengan aturan. Tantangan utama dalam klasifikasi ini terletak pada keberagaman gaya bacaan qari dan kemiripan akustik antar maqam. Penelitian ini bertujuan untuk mengembangkan model klasifikasi otomatis irama bacaan Al-Qur’an menggunakan pendekatan berbasis CNN dengan 8 kelas maqam bacaan. Model CNN dalam penelitian ini memiliki tiga jalur konvolusi dengan ukuran kernel berbeda. Variasi bentuk masukan berupa data audio yang diubah ke dalam representasi spektrogram dan mel-frequency cepstral coefficients (MFCC). Evaluasi kinerja model pada dataset bacaan Al-Qur’an yang terdiri dari 8 kelas tilawah yaitu Ajam, Bayat, Hijaz, Kurd, Nahawand, Rast, Saba, dan Seka. Hasil pelatihan menunjukkan bahwa metode yang diusulkan mencapai akurasi 92,6%, sedangkan pada proses pengujian didapatkan akurasi sebesar 82,04%. Hasil confusion matric didapatkan nilai akurasi yang diperoleh dalam proses validasi mencapai 80,88%. Nilai presisi, recall dan F1-score masing-masing adalah 0,82, 0,80, dan 0,81. Dengan hasil ini, pendekatan CNN yang diusulkan terbukti efektif untuk mendukung otomatisasi dan peningkatan akurasi dalam klasifikasi nada bacaan Al-Qur’an.

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