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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Intech : Information Technology Journal of UMUS JAMU : Jurnal Abdi Masyarakat UMUS
Rama Aria Megantara
Universitas Dian Nuswantoro
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Other

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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 Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

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.
PENCAPAIAN KLASIFIKASI TERBAIK BERBASIS PERBAIKAN CITRA CLAHE DAN DARK CHANNEL PRIOR PADA SPESIES IKAN Dewi Pergiwati; Ricardus Anggi Pramunendar; Dwi Puji Prabowo; Farrikh Alzami; Rama Aria Megantara
Jurnal Teknik Informatika UMUS Vol 7 No 2 (2025): November
Publisher : Universitas Muhadi Setiabudi

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Abstract

Ikan merupakan bahan pangan lauk-pauk utama yang dikonsumsi manusia untuk menunjang protein hewani dan zat-zat lain yang diperlukan tubuh. Ikan merupakan lauk-pauk pilihan utama yang memiliki harga relative murah dan mudah didapat. Namun pada nyatanya konsumsi ikan di Indonesia sangat rendah dibandingkan dengan negara-negara yang memiliki potensi sumberdaya perikanan yang jauh lebih rendah seperti negara Jepang, Korea Selatan, serta negara-negara di Asia lainnya. Di sisi lain, salah satu kekayaan Indonesia yang sangat berlimpah pada sector perairan adalah biota ikan. Dengan kondisi demikian, upaya peningkatan konsumsi ikan akan memberikan multiflier effect dalam lingkungan masyarakat. Selain meningkatkan tingkat kesehatan serta kecerdasan, juga semakin menggairahkan sektor perikanan untuk dapat mendorong peningkatan penyerapan tenaga kerja, meningkatkan pendapatan serta kesejahteraan pada masyarakat khususnya profesi nelayan, pembudidaya ikan, pengolah hasil ikan serta pihak terkait lainnya. Maka, perlu ditingkatkan kemampuan pengenalan ikan secara otomatis dengan bantuan computer untuk mengenali jenis-jenis ikan yang sangat beragam guna mempermudah proses pengelolaan dan distribusi ikan. Oleh karena itu dalam penelitian ini, peneliti ini mengusulkan untuk melakukan analisis dampak pre-processing dari kombinasi algoritma CLAHE dan DCP yang diterapkan dalam klasifikasi ikan dengan Random Forest.
Pemanfaatan Artificial Intelligence untuk Meningkatkan Efisiensi Layanan Birokrasi pada Organisasi Perangkat Daerah Pemerintah Provinsi Jawa Tengah: Utilization of Artificial Intelligence to Improve the Efficiency of Bureaucratic Services in Regional Government Organizations of Central Java Province Farrikh Alzami; Muhammad Naufal; Dewi Agustini Santoso; Dewi Pergiwati; Heni Indrayani; Karis Widyatmoko; Rama Aria Megantara
JAMU : Jurnal Abdi Masyarakat UMUS Vol. 6 No. 02 (2026): Februari
Publisher : LPPM Universitas Muhadi Setiabudi

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Abstract

Transformasi digital birokrasi menuntut pemerintah daerah untuk meningkatkan efisiensi dan kualitas layanan publik. Artificial intelligence (AI) merupakan salah satu teknologi yang memiliki potensi besar dalam mendukung otomasi administrasi, pengolahan data, serta peningkatan responsivitas layanan pemerintahan. Namun, tingkat pemahaman dan kesiapan aparatur sipil negara (ASN) dalam memanfaatkan AI masih belum merata, terutama terkait aspek etika dan pelindungan data. Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan pemahaman dan kapasitas ASN Organisasi Perangkat Daerah (OPD) Pemerintah Provinsi Jawa Tengah dalam memanfaatkan AI secara tepat, aman, dan bertanggung jawab guna mendukung efisiensi layanan birokrasi. Metode pelaksanaan kegiatan berupa workshop tatap muka yang meliputi penyampaian materi konseptual, studi kasus pemanfaatan AI di sektor publik, diskusi interaktif, serta praktik penggunaan AI dalam konteks administrasi pemerintahan. Hasil kegiatan menunjukkan peningkatan pemahaman peserta terhadap konsep AI, kemampuan mengidentifikasi potensi penerapan AI dalam tugas birokrasi, serta meningkatnya kesadaran terhadap aspek etika dan keamanan data. Kegiatan ini menunjukkan bahwa pendampingan akademik melalui workshop praktis mampu memberikan kontribusi nyata dalam mendukung transformasi digital birokrasi di tingkat pemerintah daerah.
Comparative Evaluation of Machine Learning Algorithms for Intrusion Detection Systems Reza Nismara; Rama Aria Megantara
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16227

Abstract

Performance estimates of intrusion detection models may vary depending on how the training and testing data are separated. Although random split is commonly used in IDS experiments, network traffic often follows time-dependent patterns that differ from one day to another. This study compares random split, single temporal split, and rolling temporal split to examine whether random evaluation produces overly optimistic performance estimates. The CIC-IDS2017 dataset was used because it contains network traffic collected across several days and includes benign as well as malicious activities. The evaluation involved five classical learning models: Decision Tree, Random Forest, Logistic Regression, K-Nearest Neighbors, and Linear Support Vector Machine. The dataset was prepared by combining daily traffic files, removing irrelevant and invalid features, converting labels into binary classes, and applying consistent preprocessing for all models. Performance was measured using accuracy, precision, recall, F1-score, ROC-AUC, PR-AUC, training time, and prediction time. The results show that random split produced very high scores, with several models reaching F1-scores close to 1.0. In contrast, temporal evaluation caused a clear performance decrease, with single temporal F1-scores ranging from approximately 0.60 to 0.71, while rolling temporal validation showed that model performance varied across different chronological testing periods. These findings indicate that random split may overestimate IDS model performance because similar traffic patterns can appear in both training and testing data. Therefore, time-aware evaluation provides a more realistic strategy for assessing IDS model generalization.
Co-Authors Alzami, Farrikh Chaerul Umam Danny Oka Ratmana Dewi Agustini Santoso Dewi Pergiwati Dibyo Adi Wibowo Dwi Puji Prabowo, Dwi Puji Fikri Firdaus Tananto Hasan Aminda Syafrudin Heni Indrayani L. Budi Handoko Mayra Anggraini Mira Nabila Moch. Sjamsul Hidajat Muhammad Naufal Muhammad Syaifur Rohman Reza Nismara Ricardus Anggi Pramunendar Widyatmoko Karis Yuslena Sari, Yuslena