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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Komputer dan Informasi Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Manajemen Teori dan Terapan Register: Jurnal Ilmiah Teknologi Sistem Informasi Sistemasi: Jurnal Sistem Informasi Seminar Nasional Teknologi Informasi Komunikasi dan Industri JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Informasi dan Teknologi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknik Industri Terintegrasi (JUTIN) International Journal of Science, Engineering and Information Technology International Journal on Social Science, Economics and Art Journal of Artificial Intelligence and Digital Business Indonesian Journal of Multidisciplinary on Social and Technology Jurnal Rekayasa elektrika CARE: Jurnal Pengabdian Multi Disiplin Dedikasi: Jurnal Pengabdian Pendidikan dan Teknologi Masyarakat Journal of Digital Community Services Journal Of Community Sustainability Journal of Engineering and Science Application Journal of Education and Computer Applications Journal of Community Service and Empowerment Dedikasi: Jurnal Pengabdian Pendidikan dan Teknologi Masyarakat JES-TM Social and Community Service
Arif Mudi Priyatno
Universitas Pahlawan Tuanku Tambusai
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences Other

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EduMood: Sistem Deteksi Sentimen Berbasis Web Menggunakan Metode Machine Learning untuk Identifikasi Awal Gejala Stres Mahasiswa Prasetya, Riko Anshori; Rahman, Subhannur; Priyatno, Arif Mudi; Mera, Mera; Wahyuni, Ulfia
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): Juli 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.8042

Abstract

Students' mental health is an important issue that needs serious attention, especially in the era of social media which is full of psychological pressure. This research aims to develop EduMood, a web-based sentiment analysis system to monitor college students' mental health issues by analyzing tweets on Twitter. The tweet data is collected using relevant keywords and goes through preprocessing stages such as text cleaning, bilingual lexicon-based initial labeling, and balancing the amount of data between sentiment classes. The system uses two machine learning algorithms, Support Vector Machine (SVM) and Naive Bayes with Term Frequency-Inverse Document Frequency (TF-IDF) feature representation. The evaluation results show that SVM has a higher accuracy of 99.3% compared to Naive Bayes which reaches 96.5% with f1 scores for all classes above 0.99 for SVM. EduMood is implemented as a web-based application using Flask and Bootstrap 5, which presents the analysis results through an interactive dashboard. The dashboard displays the aggregate sentiment distribution in the form of diagrams, wordclouds, monitoring tables, and text manual predictions. The results of this study show that EduMood not only provides excellent model performance, but also offers a practical solution for the campus to monitor the psychological condition of students in a fast, real data-based, and easily accessible manner. This system is expected to support efforts to improve student mental health in a sustainable manner.
Evaluating Imputation Approaches and Support Vector Regression Parameters in Weather Forecasting Priyatno, Arif Mudi; Ningsih, Yunia
Journal of Engineering and Science Application Vol. 2 No. 2 (2025): October
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v2i2.34

Abstract

Rainfall plays a vital role in various sectors such as transportation, agriculture, and industry. Having accurate rainfall information enables stakeholders in these fields to take proper measures and minimize potential losses caused by inaccurate data. This study focuses on identifying an effective method for rainfall forecasting by examining imputation techniques in data preprocessing and parameter settings within Support Vector Regression (SVR). The experimental findings indicate that the most effective imputation method for SVR is determined using the Mean Squared Error (MSE) and Mean Absolute Error (MAE) evaluation metrics. Based on MSE, the k-nearest neighbor method proves to be the most reliable approach for data imputation preprocessing. The preprocessing results were then applied to Polynomial SVR with parameters C = 1000, tolerance = 0.001, epsilon = 0.01, and unlimited iterations. Conversely, MAE results highlight Artificial Neural Network (ANN) as the optimal imputation method. ANN, when combined with a radial basis function kernel, gamma = 0.001, C = 1000, tolerance = 0.001, and unlimited iterations, was further tested using RBF SVR under the same parameter settings.
A Robust Hybrid Approach for Malware Detection: Leveraging CNN and LSTM for Encrypted Traffic Analysis Priyatno, Arif Mudi; Ningsih, Yunia; Vandika, Arnes Yuli; Muhammadong, Muhammadong
Journal of Engineering and Science Application Vol. 1 No. 2 (2024): October
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v1i2.10

Abstract

The rapid growth in Internet usage and advancements in network technologies have escalated the risk of network attacks. As the adoption of encryption protocols increases, so does the difficulty in identifying malware within encrypted traffic. Malware represents a significant danger in cyberspace, as it compromises personal data and harms computer systems. Network attacks involve unauthorized access to networks, often aiming to disrupt or damage them, with potentially severe consequences. To counter these threats, researchers, developers, and security experts are constantly innovating new malware detection techniques. Recently, deep learning has gained traction in network security and intrusion detection systems (IDSs), with models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) showing promise in detecting malicious traffic. Despite these advancements, extracting relevant features from diverse malware types remains a challenge. Current solutions demand substantial computational resources and are often inefficient for large datasets. Additionally, existing image-based feature extraction methods consume significant resources. This study tackles these issues by employing a 1D CNN alongside LSTM for the detection and classification of encrypted malicious traffic. Using the Malware Analysis benchmark dataset, which consists of 42,797 malware and 1,079 goodware API call sequences, the proposed model achieved an accuracy of 99.2%, surpassing other state-of-the-art models
Penggunaan Aplikasi Pendeteksi Olahraga berbasis Global Positioning System (GPS) untuk Meningkatkan Aktivitas Fisik Masyarakat Musridho, Raja Joko; Priyatno, Arif Mudi; Ramadhan, Wahyu Febri
Journal of Social and Community Service Vol. 3 No. 3 (2024): November 2024
Publisher : Faculty of Engineering University of Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jestmc.v3i3.200

Abstract

The lack of physical activity in society has become one of the main factors contributing to the increased risk of non-communicable diseases. GPS-based technology has rapidly developed and can be utilized to enhance motivation for exercising. This community service program aims to educate and assist the public in using GPS-based exercise tracking applications to increase their physical activity. The methods used include socialization, training, monitoring of application usage, and evaluation of its effectiveness. The results indicate that the application helps raise awareness and motivation for exercising, as evidenced by the increased frequency and duration of physical activity. Thus, the use of GPS-based exercise tracking applications can be an innovative solution for promoting a healthier lifestyle in society.
Co-Authors Achmad Noerkhaerin Putra Achmad Ridwan Agus Zainal Arifin Amalia, Fazilla Arnes Yuli Vandika Baskoro Nugroho Cholidhazia, Putri Dini Adni Navastara, Dini Adni Eha Renwi Astuti Eka Roshifita Rizqi Evan Tanuwijaya Fahmi Iqbal Firmananda Fahmi Iqbal Firmananda Fahmi Iqbal Firmanda Fahmi Syuhada Fazilla Amalia Fazilla Amalia Febi Yanto Firmananda, Fahmi Iqbal Firmanda, Fahmi Iqbal Hermawati, Sela Hidayat Hidayat Iis Afrianty Jati, Putri Zulia Joni Joni Lailatul Syifa Tanjung Lasepa, Wanda Lidya Ningsih M. Riko Anshori Prasetya Mardiyah, Suci Marwa, Safni Melvi Lesmana Alim Mera, Mera Mifta Hasda Mohd. Winario Muhammad Mirza Muttaqi Muhammad Noor Muhammad Riko Anshori Prasetya Muhammad Riko Anshori Prasetya Muhammad Syaipudin Muhammadong Muhsin Bayu Aji Fadhillah Musrodho, R. Joko Muttaqi, Muhammad Mirza Ningsih, Yunia Nur Afrinis Nurhaeni Nurhaeni Nurnasrina Nurnasrina Prasetya, Riko Anshori Putri Zulia Jati Putri, Elen Sri Ega Radityo Anggoro, Radityo Raja Joko Musridho Rarasmaya Indraswari Resy Kumala Sari Rizki, Syafnur Muhammad Rizqi, Eka Roshifita Rizqon Jamil Farhas Rizqon Jamil Farhas Rusnedy, Hidayati Saru Reza Subhannur Rahman, Subhannur Suwanto Sanjaya Syaipudin, Muhamamd Syuhada, Fahmi Tanjung, Lailatul Syifa Triyanna Widiyaningtyas Wahyu Febri Ramadhan Sudirman Wahyuni, Ulfia Wanda Lasepa Wiratmo, Agung Zahara, Alfiatun Zubaidah Assyifa