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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 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 Dedikasi: Jurnal Pengabdian Pendidikan dan Teknologi Masyarakat Jurnal Cendekia Ilmiah 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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Artificial Intelligence (AI) dan Penerapan dalam Dunia Bisnis Joko Musridho, Raja; Mudi Priyatno, Arif
Journal of Social and Community Service Vol. 2 No. 1 (2023): Maret 2023
Publisher : Faculty of Engineering University of Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jestmc.v2i1.87

Abstract

Artificial Intelligence (AI) is something that allows machine to be able to do what humans can do. Intelligent needs knowledge, experience and capability in decision-making and act. Based on the data acquired from academicians whose expertise are not in AI, there is assumption that AI is things that related to robots and always have physical shape. Therefore, re-explanation of basics of AI needs to be done in order to direct the AI development plan in Universitas Pahlawan to focus not only to the robots that have physical shape. Furthermore, applications of AI in the business world was explained to strengthen the basics of AI that have been delivered. The result of the webinar got one question about the impact of AI to the jobs availability.
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.
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.
Impurity-Based Important Features for feature selection in Recursive Feature Elimination for Stock Price Forecasting: Fitur Penting Berbasis Impurity untuk pemilihan fitur dalam Recursive Feature Elimination untuk Peramalan Harga Saham Priyatno, Arif Mudi; Sudirman, Wahyu Febri; Musridho, R. Joko; Amalia, Fazilla
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 6 No. 4 (2023): Oktober
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v6i4.17726

Abstract

Stock investors perform stock price forecasting based on technical indicators and historical stock prices. The large number of technical indicators and historical data often leads to overfitting and ambiguity in forecasting using machine learning. In this paper, we proposed a feature selection approach using impurity-based important features in recursive feature elimination for stock price forecasting. The data utilized includes historical data and various moving averages. Feature selection is employed to reduce the number of features and obtain important and relevant features. The recursive feature elimination with impurity-based important features is utilized as the feature selection method. The machine learning methods employed are linear regression, support vector regression, multi-layer perceptron regression, and random forest regression. The measurement results of mean squared error (mse), root mean squared error (rmse), mean absolute error (mae), and mean absolute percentage error (mape) show that the optimal feature selection and machine learning method is achieved with six features and linear regression. The average mse, rmse, mae, and mape values are 0.000279, 0.016577, 0.012843, and 1.42236%, respectively. These results validate the effectiveness of impurity-based important features for feature selection in recursive feature elimination using historical data and various moving averages in stock price forecasting.
The Effect of Overconfidence Bias on Investment Decision: Sharia Stock Considerations Sudirman, Wahyu Febri Ramadhan; Nurnasrina, Nurnasrina; Syaipudin, Muhammad; Priyatno, Arif Mudi
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 7 No. 2 (2024): April
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v7i2.26091

Abstract

Investment decisions are a complex process involving risk evaluation, market analysis, and investment return projections. In the decision-making process, investors sometimes show irrational behavior because they have cognitive limitations and previous investment experience so investors are exposed to overconfident behavior. This research used 178 samples consisting of investors who had investment experience of at least 1 year. The research carried out instrument testing and used the common method bias (CMB) testing procedure. The analytical method in the research uses simple linear regression. The results of testing the research hypothesis obtained positive and significant results of overconfidence bias towards irrational investment decisions The moderating role of sharia sharia considerations on the relationship between overconfidence bias and unsupported investment decisions. This research reveals that overconfidence can have a positive influence on irrational investment decision-making. Investors who tend to have excess confidence in their knowledge and skills in analyzing the market tend to make investment decisions that are more impulsive, less rational and sometimes ignore risks significantly. Future research is recommended to further investigate the mechanisms behind the relationship between overconfidence and irrational investment decision-making, as well as involving a wider sample to obtain stronger generalizations.
Pembekalan dan Pemahaman Pemanfaatan Pengunaan Teknologi Informasi Dalam Organisasi Pengurus Cabang Muslimat NU Kabupaten Bengkalis di Era Digitalisasi Firmananda, Fahmi Iqbal; Priyatno, Arif Mudi; Farhas, Rizqon Jamil; Jati, Putri Zulia
Dedikasi: Jurnal Pengabdian Pendidikan dan Teknologi Masyarakat Vol. 1 No. 1 (2023): Dedikasi 2023
Publisher : Institut Teknologi Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/dedikasi.v1i1.8

Abstract

Perkembangan Teknologi Informasi akan keterbukaan pengetahuan percepatan dalam memberikan Informasi dan komunikasi sangat diharapkan setiap kelompok dan kalangan organisasi. Muslimat Nahdlatul Ulama merupakan organisasi perempuan. Tingkatan organisasi Muslimat NU dari Pengurus Pimpinan Pusat, Pimpinan Provinsi, Pengurus Cabang, Hingga Pengurus ranting dan anak ranting. Dalam percepatan teknologi informasi di era global, setiap pengurus organisasi memiliki peran penting dalam menerapkan teknologi terkini untuk menjalankan roda organisasi. Pencapaian sungguh luar biasa dengan adanya fasilitas seperti internet dan alat komunikasi. Memudahkan pengurus dalam mengakses informasi.
Co-Authors Achmad Noerkhaerin Putra Achmad Ridwan, Achmad Afrinis, Nur Agus Zainal Arifin Amalia, Fazilla Annisa Yulia Zarti Arnes Yuli Vandika Baskoro Nugroho Cholidhazia, Putri Dini Adni Navastara, Dini Adni Eha Renwi Astuti Evan Tanuwijaya Fahmi Iqbal Firmananda Fahmi Syuhada Febi Yanto Firmanda, Fahmi Iqbal Hermawati, Sela Hidayat Hidayat Iis Afrianty Jati, Putri Zulia Joko Musridho, Raja Joni Joni Kasini, Kasini Lasepa, Wanda Librianty, Nany Librianty Lidya Ningsih Mardiyah, Suci Melvi Lesmana Alim 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 Muttaqi, Muhammad Mirza Ningsih, Yunia Nur Afrinis Nurhaeni Nurhaeni Nurnasrina Nurnasrina Pedro Masdy Prasetya, M. Riko Anshori Putri Cholidhazia Putri, Elen Sri Ega R. Joko Musrodho Radityo Anggoro, Radityo Raja Joko Musridho Rarasmaya Indraswari Riska Ananda Riska Rizqi, Eka Roshifita Rizqon Jamil Farhas Rusnedy, Hidayati Safni Marwa Sari, Efti Novita Sari, Resy Kumala Saru Reza Sudirman, Wahyu Febri Suwanto Sanjaya Syaipudin, Muhamamd Syuhada, Fahmi Tanjung, Lailatul Syifa Triyanna Widiyaningtyas Wahyu Febri Ramadhan Sudirman Wiratmo, Agung Yadi, Hebry Andri Zahara, Alfiatun Zubaidah Assyifa