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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 Innovative: Journal Of Social Science Research 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 Library & Information Science 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.
Harnessing Machine Learning for Stock Price Prediction with Random Forest and Simple Moving Average Techniques Priyatno, Arif Mudi; Ningsih, Lidya; Noor, Muhammad
Journal of Engineering and Science Application Vol. 1 No. 1 (2024): April
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v1i1.1

Abstract

This paper explores the application of machine learning in predicting stock price trends, specifically for PT Bank Central Asia Tbk (BBCA) shares, using the Random Forest Regression model and Simple Moving Average (SMA) techniques. The SMA parameters ranged from 3 to 200 days, aiding in forecasting the price trends as either rising, sideway, or declining. To achieve accurate and generalizable predictions, the data normalization process was implemented using the MinMax scaler. The methodological framework adopted a time series cross-validation (CV) approach, executed 10 times with a future test window of 40 days, ensuring the robustness and reliability of the predictive model. The model's performance was systematically evaluated based on metrics of accuracy, recall, precision, and F1-score. Results from the cross-validation series indicated varied performance, with the most notable achievements in the 9th and 10th iterations, where both demonstrated an F1-score surpassing 0.745 and 0.808 respectively, and similar levels of accuracy and recall at 0.825. These high F1-scores signify a strong harmonic balance between precision and recall, underscoring the model's capability to effectively predict the stock price movements of BBCA. The findings affirm the potential of utilizing advanced machine learning techniques like Random Forest in conjunction with SMA indicators to enhance the predictability of stock market trends, offering valuable insights for investors and financial analysts.
Comparison of Similarity Methods on New Student Admission Chatbots Using Retrieval-Based Concepts Priyatno, Arif Mudi; Prasetya, M. Riko Anshori; Cholidhazia, Putri; Sari, Resy Kumala
Journal of Engineering and Science Application Vol. 1 No. 1 (2024): April
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v1i1.2

Abstract

A college's students are an essential component. The college always opens registration for new students each year. Every year, more than 1,000 prospective new students register. Because of this, the new student admissions committee is constantly overwhelmed when responding to campus-related questions. As a result, developing a chatbot to assist new students is necessary. The best similarity method is needed for the development of a chatbot using a retrieval-model approach. The New Student Admission Chatbot and the Similarity Method are compared in this study using the Retrieval-Based Concept. The cosine, Jaccard, dice, euclidean, Manhattan, Canberra, and Chebyshev similarity methods are compared. In the context of Universitas Pahlawan Tuanku Tambusai, the data used are information about new students as well as accreditation for study program. There are 41 pieces of information used. Labels and information make up data. According to the test results, the dice and cosine similarity methods are the most effective. On all tested thresholds, dice and cosine similarity achieved an f1-score above 80%. Recall produces extremely optimal results, including 100%.Over 75% of the time, good results are reliably achieved. This demonstrates that the retrieval-model concept can be applied
Predict Students' Dropout and Academic Success with XGBoost Ridwan, Achmad; Priyatno, Arif Mudi; Ningsih, Lidya
Journal of Education and Computer Applications Vol. 1 No. 2 (2024)
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jeca.v1i2.13

Abstract

The attrition rate of students in higher education is a worldwide issue that profoundly affects both individuals and institutions. Students who fail to complete their studies often encounter economic and social difficulties, while educational institutions suffer a deterioration in reputation and operational efficacy. This paper proposes the creation of a prediction model utilizing the XGBoost algorithm to assess students' academic progress and dropout risk. The model incorporates several elements, such as academic, demographic, and socio-economic, to yield comprehensive insights into students' educational trends. This research utilizes the Predict Students' Dropout and Academic Success dataset, comprising 4,424 data points and 36 attributes. The data underwent normalization via StandardScaler and was divided into five scenarios for training and testing, ranging from a 50:50 to a 90:10 split. The evaluation of the model was conducted utilizing accuracy, precision, recall, and F1-Score criteria. The findings indicate that the model attains peak performance in the 80:20 scenario, exhibiting 88% precision and an 81% F1-Score, signifying an ideal equilibrium between predictive accuracy and risk identification capability. This study demonstrates that XGBoost can serve as a dependable predictive instrument to aid decision-making in the education sector. These findings establish a foundation for formulating targeted interventions aimed at enhancing student retention. Subsequent study may investigate the use of real-time data and sophisticated models to enhance predictive accuracy.
Pengaruh Inovasi Produk dan Harga terhadap Minat Pembelian Sepeda Motor Listrik di Bangkinang Kota Yadi, Hebry Andri; Librianty, Nany Librianty; Priyatno, Arif Mudi
Innovative: Journal Of Social Science Research Vol. 4 No. 4 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i4.13416

Abstract

Penelitian ini bertujuan untuk mengetahui pengaruh inovasi produk dan harga terhadap minat pembelian sepeda motor listrik di Bangkinang Kota. Metode penelitian yang digunakan kuantitatif, melibatkan 100 orang masyarakat Kecamatan Bangkinang Kota sebagai sampel penelitian, dengan teknik pengumpulan data menggunakan kuesioner. Data yang didapat dianalisis melalui analisis regresi linier berganda dengan program SPSS. Hasil penelitian menunjukkan; Terdapat pengaruh yang signifikan antara inovasi produk terhadap minat pembelian sepeda motor listrik di Bangkinang Kota. Terdapat pengaruh yang signifikan antara harga terhadap minat pembelian sepeda motor listrik di Bangkinang Kota. Terdapat pengaruh yang signifikan antara inovasi produk dan harga terhadap minat pembelian sepeda motor listrik di Bangkinang Kota. Besar pengaruh inovasi produk dan harga terhadap minat pembelian sebesar 73% sedangkan sisanya dipengaruhi oleh variabel lain yang tidak diteliti dalam penelitian ini.
Pelatihan Desain Kemasan Produk yang Menarik Pada PT Mond Nature Lestari Sudirman, Wahyu Febri Ramadhan; Priyatno, Arif Mudi; Lasepa, Wanda; Afrinis, Nur; Rizqi, Eka Roshifita
Journal of Community Service and Empowerment Vol. 1 No. 1 (2024)
Publisher : Global Sustainability Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jcse.v1i1.23

Abstract

Product packaging design training for PT Mond Nature Lestari using Canva aims to improve the design skills of the internal team in creating attractive and professional packaging for moringa leaf-based products, such as moringa floss, moringa chocolate, moringa tea, and moringa coffee. Through this training, participants were introduced to the Canva interface and various design elements, and learned to apply good graphic design principles in packaging creation. The results of the training showed that the updated packaging design not only improved the visual appeal of the product but also supported the company's goals in promoting sustainability and innovation. With the use of Canva, the team was able to create designs quickly and efficiently, saving time and costs, and increasing efforts in responding to market needs. This training also made a significant contribution to increasing the company's competitiveness in the market, strengthening brand identity, and meeting consumer expectations for high-quality products.
Comparison Random Forest Regression and Linear Regression For Forecasting BBCA Stock Price Priyatno, Arif Mudi; Tanjung, Lailatul Syifa; Ramadhan, Wahyu Febri; Cholidhazia, Putri; Jati, Putri Zulia; Firmananda, Fahmi Iqbal
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 6 No. 3 (2023): July 2023
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v6i3.16933

Abstract

Stock trading is a popular financial instrument worldwide. In Indonesia, the stock market is known as the Indonesia Stock Exchange (BEI), and one actively traded stock is PT Bank Central Asia (BBCA). However, predicting stock price movements is challenging due to various influencing factors. Investors use fundamental and technical analyses for decision-making, but results often vary. Machine learning, particularly random forest regression and linear regression algorithms, can be used for stock price forecasting. In this paper, we compares these two machine learning methods to forecast BBCA stock prices, aiming to provide more accurate and effective solutions for investor's investment and trading decisions. The evaluation results of cross-validation mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) for linear regression were 0.12848, 0.35807, 0.29570, and 0.0036%, respectively, while for random forest regression were 27473.76, 158.04, 142.70, and 1.7153%. These findings indicate that linear regression outperforms in forecasting performance.
Co-Authors Achmad Noerkhaerin Putra Achmad Ridwan, Achmad Afrinis, Nur Agus Zainal Arifin Amalia, Fazilla Arnes Yuli Vandika Baskoro Nugroho Cholidhazia, Putri Dini Adni Navastara, Dini Adni Eha Renwi Astuti Evan Tanuwijaya Fahmi Iqbal Firmananda Fahmi Syuhada Febi Yanto Firmananda, Fahmi Iqbal Firmanda, Fahmi Iqbal Hermawati, Sela Hidayat Hidayat Iis Afrianty Jati, Putri Zulia Joni Joni Lasepa, Wanda Librianty, Nany Librianty Lidya Ningsih 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 Musridho, R. Joko Musrodho, R. Joko Muttaqi, Muhammad Mirza Ningsih, Lidya Ningsih, Yunia Nur Afrinis Nurhaeni Nurhaeni Nurnasrina Nurnasrina Prasetya, M. Riko Anshori Prasetya, Riko Anshori Putri, Elen Sri Ega Radityo Anggoro, Radityo Raja Joko Musridho Rarasmaya Indraswari Rizki, Syafnur Muhammad Rizqi, Eka Roshifita Rizqon Jamil Farhas Rusnedy, Hidayati Sari, Resy Kumala Saru Reza Subhannur Rahman, Subhannur Sudirman, Wahyu Febri Suwanto Sanjaya Syaipudin, Muhamamd Syuhada, Fahmi Tanjung, Lailatul Syifa Triyanna Widiyaningtyas Wahyu Febri Ramadhan Sudirman Wahyuni, Ulfia Wiratmo, Agung Yadi, Hebry Andri Zahara, Alfiatun Zubaidah Assyifa