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All Journal JUITA : Jurnal Informatika CESS (Journal of Computer Engineering, System and Science) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Pilar Nusa Mandiri ILKOM Jurnal Ilmiah Jurnal Manajemen Informatika MULTINETICS EVOLUSI : Jurnal Sains dan Manajemen IMTechno: Journal of Industrial Management and Technology Simpatik: Jurnal sistem Informasi dan Informatika J-Intech (Journal of Information and Technology) J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat) Ladang Artikel Ilmu Komputer Journal of Information Technology
Satia Suhada, Satia
Program Studi Sistem Informasi, Universitas Bina Sarana Informatika, Sukabumi, Indonesia
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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Sistem Rekomendasi Produk Menggunakan Metode User-Based Collaborative Filtering Pada Digital Marketing Satia Suhada; Saeful Bahri; Setyo Bagus Nugraha; Taufik Hidayatulloh; Dede Wintana
J-INTECH ( Journal of Information and Technology) Vol 11 No 1 (2023): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v11i1.866

Abstract

The recommendation system has been implemented in digital marketing used in marketing products and services. The recommendation system is used to provide offers of goods and services in accordance with customer habits and interests in the proposed products and services, but in practice the right product offering for customers leads to the idea of developing a product recommendation system. Purchase data obtained from customers can be used to analyze customer needs and product preferences. In the recommendation system, Collaborative Filtering is one of the most commonly used algorithms. The purpose of this study is to find out how accurate the recommendation system is based on the purchase of similar goods between consumers using User-based Collaborative Filtering. Based on the results of the study, User-based Collaborative Filtering using Cosine Similarity calculations can be applied and produce 10 product recommendations with an RMSE value of 0.9.
Penerapan Metode CNN Berbasis Arsitektur Mobilenet Pada Klasifikasi Citra Bunga (Famili Asteraceae) Pangestu, Agis; Pribadi, Denny; Bahri, Saeful; Suhada, Satia
Swabumi Vol 13, No 2 (2025): Volume 13 Nomor 2 Tahun 2025
Publisher : Universitas Bina Sarana Informatika Kota Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/swabumi.v13i2.25638

Abstract

Penelitian ini bertujuan untuk menerapkan model klasifikasi citra bunga dari famili Asteraceae menggunakan metode Convolutional Neural Network (CNN) berbasis arsitektur MobileNet. Latar belakang penelitian ini adalah keanekaragaman bunga dalam famili Asteraceae yang menyulitkan proses klasifikasi manual. Dengan menggunakan teknologi pengolahan citra digital, klasifikasi otomatis diharapkan dapat memberikan hasil yang lebih akurat dan efisien. Dataset yang digunakan terdiri dari 2.600 citra bunga yang dikumpulkan dari berbagai genus dalam famili Asteraceae seperti Ageratum, Aster, Chrysanthemum, Cornflower, Cosmos, Dahlia, Daisy, Marigold, dan Sunflower. Penelitian ini menggunakan metode CNN dengan arsitektur MobileNet yang dikenal memiliki kinerja baik dalam klasifikasi citra dengan ukuran model yang lebih kecil dan efisien. Hasil penelitian menunjukkan bahwa metode CNN berbasis arsitektur MobileNet mampu mengklasifikasikan citra bunga dari famili Asteraceae dengan akurasi yang tinggi. Implementasi model ini diharapkan dapat berkontribusi dalam pengembangan teknologi pengenalan tanaman yang berguna untuk keperluan budidaya, penelitian, dan edukasi. This study aims to apply an image classification model for flowers from the Asteraceae family using the Convolutional Neural Network (CNN) method based on the MobileNet architecture. The background of this study is the diversity of flowers in the Asteraceae family, which makes manual classification difficult. By using digital image processing technology, automatic classification is expected to provide more accurate and efficient results. The dataset used consists of 2,600 flower images collected from various genera in the Asteraceae family, such as Ageratum, Aster, Chrysanthemum, Cornflower, Cosmos, Dahlia, Daisy, Marigold, and Sunflower. This study uses the CNN method with the MobileNet architecture, which is known to perform well in image classification with a smaller and more efficient model size. The results show that the MobileNet-based CNN method is capable of classifying images of flowers from the Asteraceae family with an accuracy of 90.51%. The implementation of this model is expected to contribute to the development of plant recognition technology that is useful for cultivation, research, and education purposes.
Optimization of Android Malware Detection Based on Permissions Using LightGBM with Hyperparameter Tuning and Chi-Square Feature Selection Dede Wintana; Satia Suhada; Gunawan
J-INTECH ( Journal of Information and Technology) Vol 14 No 02 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i02.2306

Abstract

This study aims to develop an efficient Android malware detection framework using the LightGBM algorithm optimized through Chi-Square feature selection and hyperparameter tuning. Although machine learning techniques have been widely applied in Android malware detection, many previous studies primarily focus on classification accuracy while paying limited attention to feature reduction efficiency, computational complexity, and permission-based feature interpretability. Therefore, this study proposes a lightweight permission-based detection framework that integrates Chi-Square feature selection with optimized LightGBM classification to improve detection performance while reducing irrelevant features. The dataset used was obtained from Kaggle and consists of 29,300 Android applications, including 14,630 benign applications and 14,700 malware samples. Each application is represented using 86 binary permission-based features extracted from the AndroidManifest.xml file. The research stages include preprocessing, feature selection, training-testing data splitting, hyperparameter optimization, and model evaluation. Experimental results show that the proposed model achieves an accuracy of 0.96, precision of 0.95, recall of 0.96, F1-score of 0.96, and ROC-AUC of 0.9885. These findings indicate that the proposed framework effectively distinguishes malware from benign applications while maintaining computational efficiency and improving permission-based malware detection interpretability. The novelty of this study lies in the integration of Chi-Square feature selection and Grid Search-optimized LightGBM to develop a lightweight permission-based Android malware detection framework that reduces feature dimensionality while maintaining high detection performance and interpretability.
Co-Authors A.Gunawan Abidin Abidin Acep Suherman Ami Rahmawati Andi Riyanto Arif Purnama Daniel Ratag Dasya Arif Firmansyah Dede Wintana Desi Susilawati Dicky Prayudi Dwiza Riana Eva Marsusanti Gunawan Gunawan Gunawan Hamdun Sulaiman Heryadi Putra, Panji Febrian Iskandar, Ardy Jamal Maulana Hudin Jasisca Marleftan Lestari Yusuf Muhamad Reza Vahlefi Mukhlisani, Vina Mustakimm, Robi Nugraha, Setyo Bagus Nursyifa, Eva Pangestu, Agis Pribadi, Denny Purwaningtias, Deasy Rahmawati, Ami Ramdhan Saepul Rohman, Ramdhan Saepul Resti Yulistria Rifa Nurafifah Syabaniah Rina Riniawati Risdiansyah, Deni Ruhimat, Dede Rusda Wajhillah Rusli Nugraha Saeful Bahri Saeful Bahri Saeful Bahri Saghifa Fitriana Selvia Septi Palupi Setyo Bagus Nugraha Siskawati Siskawati Siti Fatimah Susilawati Susilawati Syifa Nur Fitria Taufik Hidayatulloh Taufik Hidayatulloh Veithzal Rivai Zainal Wahyu Nugraha Winardi, Ardi Yahya Iskandar Yulhan Wahyudin