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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Sistemasi: Jurnal Sistem Informasi JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Abdimasku : Jurnal Pengabdian Masyarakat
Rama Aria Megantara
Universitas Dian Nuswantoro
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

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DIABETES MELLITUS ATTRIBUTE CLASSIFICATION USING THE NAIVE BAYES ALGORITHM BASED ON FORWARD SELECTION Dwi Puji Prabowo; Rama Aria Megantara; Ricardus Anggi Pramunendar; Yuslena Sari
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 7 No. 2 (2022)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v7i2.146

Abstract

Diabetes Mellitus is a chronic condition that frequently results in death. Almost every nation has experienced and contributed to this rise in mortality. Consequently, several researchers are motivated to determine this disease's source and prevent the increase in mortality rates. The research was conducted in the field of informatics in partnership with health professionals to determine the causes of this condition. Many informatics researchers employ machine learning techniques to aid in analyzing existing data. This study suggests feature selection based on forward selection and the naive Bayes classification approach to determine this disease's primary aetiology. The results demonstrate that our proposed strategy can increase the classification accuracy of patients. The performance outcomes improved by 169%. According to this theory, it is also known that the primary cause of this disease is its dependence on body mass index and age. Therefore, additional research must explore these two variables' impact on various other disorders.
Implementation of a Supply chain Management System Blockchain-Based in Red Onion Farming Mira Nabila; Farrikh Alzami; Rama Aria Megantara; Fikri Firdaus Tananto; Hasan Aminda Syafrudin; L. Budi Handoko; Chaerul Umam
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol 11 No 1 (2023): Vol. 11, No. 1, April 2023
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2023.v11.i01.p02

Abstract

Red Onions are a horticultural commodity belonging to the spice vegetable group and an important role for economy of the Indonesian people. In red onion farming there have a problem of price fluctuations which result in an uneven and less transparent distribution of red onions yields, thus affecting both consumers and producers. To answer these problems, we designed a system to maintain and store red onion harvest data for farmers, collectors, distributors, and retailers in the form of a blockchain-based supply chain system. This system can maintain the validity of transactions in the supply chain of red onion farming with a private blockchain with Hyperledger Fabric. Then the data on the blockchain system will be displayed through the Hyperledger Explorer website. This system already passed the Black Box Testing system. From the research and testing of the system that has been made, this system can help the red onion farming to maintain the validity of transactions in the supply chain management.
Pelatihan Implementasi Artificial Intelligence Menggunakan Teachable Machine berbasis Project-Based Learning bagi Siswa SMA/SMK Dibyo Adi Wibowo; Moch. Sjamsul Hidajat; Ricardus Anggi Pramunendar; Muhammad Syaifur Rohman; Danny Oka Ratmana; Rama Aria Megantara
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 9, No 1 (2026): JANUARI 2026
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v9i1.3226

Abstract

Artificial Intelligence (AI) merupakan teknologi yang berkembang pesat dan penting untuk dikenalkan sejak jenjang pendidikan menengah. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pemahaman siswa SMA/SMK di Kota dan Kabupaten Kediri terhadap konsep dasar Artificial Intelligence dan machine learning melalui pelatihan implementasi AI menggunakan Teachable Machine berbasis Project-Based Learning (PjBL). Metode pelaksanaan kegiatan mengombinasikan pendekatan PjBL dan experiential learning, di mana peserta dilibatkan secara aktif dalam pengembangan proyek AI sederhana berbasis gambar, suara, dan pose tubuh. Evaluasi pembelajaran dilakukan menggunakan pre-test dan post-test untuk mengukur peningkatan pemahaman peserta. Hasil kegiatan menunjukkan adanya peningkatan yang signifikan pada seluruh kategori materi, termasuk konsep dasar AI, computational thinking, machine learning, penggunaan Teachable Machine, serta implementasi dan evaluasi model AI. Temuan ini menunjukkan bahwa penggunaan Teachable Machine yang dipadukan dengan pendekatan PjBL efektif dalam meningkatkan literasi Artificial Intelligence siswa SMA/SMK serta membantu peserta memahami konsep AI secara lebih konkret dan aplikatif.
Hybrid CNN and Autoencoder Deep Learning Model for Network Malware Detection Mayra Anggraini; Rama Aria Megantara
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6382

Abstract

Malware remains one of the primary threats to network security, continuously evolving with increasingly complex attack patterns that are difficult to detect using conventional methods. Data imbalance and high feature dimensionality are major challenges in improving the performance of malware detection models. This study aims to develop a deep learning-based malware detection model using a hybrid approach that combines Convolutional Neural Networks (CNN) and Autoencoders. The dataset used in this study was the improved version of the CICIDS2017 dataset, consisting of more than 2 million records and 91 features. The research stages included data collection, exploratory data analysis (EDA), data preprocessing, feature selection, and data balancing using SMOTE, followed by model design and evaluation. The Autoencoder was employed for dimensionality reduction, generating a compressed representation of 32 features, which was subsequently used as input for the CNN model in multi-class classification. The results demonstrate that the proposed model achieved high accuracy, along with strong precision, recall, and F1-score values across most classes. However, performance on minority classes still exhibited limitations due to data imbalance. Therefore, the hybrid CNN–Autoencoder approach proved effective in improving network malware detection performance.
Co-Authors Alzami, Farrikh Chaerul Umam Danny Oka Ratmana Dibyo Adi Wibowo Dwi Puji Prabowo, Dwi Puji Fikri Firdaus Tananto Hasan Aminda Syafrudin L. Budi Handoko Mayra Anggraini Mira Nabila Moch. Sjamsul Hidajat Muhammad Syaifur Rohman Ricardus Anggi Pramunendar Yuslena Sari, Yuslena