Garuda - Garba Rujukan Digital

p-Index From 2020 - 2025

5.083

P-Index

This Author published in this journals

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

Author-ID : 796912

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

Published : 44 Documents Claim Missing Document

Claim Missing Document

Articles

1 2 3 4 5

Dice Similarity and TF-IDF for New Student Admissions Chatbot Muhammad Riko Anshori Prasetya; Arif Mudi Priyatno
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 1 No. 1 (2022)
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

CS is one of the most important functions of any client-related organization, whether a business or a school (customer service). Notably from the committee responsible for student selection, CS, on the other hand, has a very limited capacity to be handled by humans, which can reduce university satisfaction. Therefore, we require technological assistance, which in this case takes the form of an AI-based chatbot. The objective of this study is to design and develop a chatbot system utilizing NLP (natural language processing) to aid the CS of the new student admissions committee at Pahlawan Tuanku Tambusai University in answering questions from prospective new students. The employed method is dice similarity weighted by TFIDF. The results of the conducted tests indicated that the recall rate was 100 percent and the precision reached 76.92 percent. The evaluation results indicate that the chatbot can effectively respond to questions from prospective students.

Analisis Performa Link Stability dari Faktor Kecepatan untuk Dinamisasi Zona pada Zone Routing Protocol Muhsin Bayu Aji Fadhillah; Radityo Anggoro; Arif Mudi Priyatno
Jurnal Rekayasa Elektrika Vol 16, No 3 (2020)
Publisher : Universitas Syiah Kuala

Zone dynamization is carried out in the Zone Routing Protocol to allow the adaptation of the routing protocol to VANET network conditions. Zone dynamization is accomplished by periodically updating the radius within a configured time period. The value of link stability from the factors that influence network conditions is used as a reference in the radius value’s renewal process. From the test and simulation results, speed is the most dominant factor in link stability composition. Comparison between ZRP and zone dynamics against traditional ZRP shows better performance than ZRP with zonal dynamics when measured from metric analysis of packet delivery ratio, delay, and routing overhead. The increase in ZRP performance can occur because the zoning dynamics carried out make ZRP more adaptive to network conditions so that it does not work too proactively or reactively.

Penanganan Imputasi Missing Values pada Data Time Series dengan Menggunakan Metode Data Mining Muhammad Riko Anshori Prasetya; Arif Mudi Priyatno; Nurhaeni
Jurnal Informasi dan Teknologi 2023, Vol. 5, No. 2
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v5i2.324

Pengumpulan data untuk perkiraan cuaca menjadi sangat penting untuk dilakukan untuk meningkatkan kualitas dari perkiraan cuaca tetapi seringkali data yang didapatkan untuk melakukan perkiraan cuaca tersebut terdapat data yang hilang (missing values). Untuk mengatasi permasalahan missing values, metode yang paling umum dilakukan adalah dengan melakukan sebuah imputasi terhadap missing values tersebut. Agar dapat melakukan imputasi pada data yang terdapat missing values tersebut dibutuhkan suatu metode imputasi. Pada penelitian ini, metode imputasi yang dilakukan adalah dengan menggunakan metode konvensional yaitu dengan menggunakan mean dan nilai maksimum dan metode data mining yang menggunakan KNN dan Neural Network. Dari ujicoba yang dilakukan didapatkan jika Metode KNN memiliki nilai RMSE yang terendah.

Pendampingan Pengelolaan Keuangan Berbasis Syariah Pada Masyarakat Pelaku Usaha Mitra BWM Fataha Di Kampung Maredan Barat Kecamatan Tualang Wahyu Febri Ramadhan Sudirman; Mohd Winario; Zubaidah Assyifa; Arif Mudi Priyatno; Muhammad Syaipudin
CARE: Journal Pengabdian Multi Disiplin Vol. 1 No. 1 (2023)
Publisher : Fakultas Agama Islam Universitas Pahlawan Tuanku Tambusai

Pengabdian ini dilaksanakan bagi nasabah Bank Wakaf Mikro (BWM) Fataha, Kampung Maredan, Kecamatan Tualang, Kabupaten Siak, tujuan dari pengabdian masyarakat ini adalah untuk mengetahui tingkat pemahaman Nasabah Bank Wakaf Mikro (BWM) Fataha Kampung Maredan Barat Kecamatan Tualang tentang Akad-Akad Pembiayaan Syariah dan mengetahui tingkat keberhasilan pengenalan nasabah Bank Wakaf Mikro Fataha Kampung Maredan Barat Kecamatan Tualang. Metode yang digunakan dalam kegiatan pengabdian masyarakat ini a dalah dengan penyuluhan, presentasi dan diskusi. Hasil pengabdian menunjukkan bahwa: Program pengabdian kepada masyarakat nasabah BWM Fataha, Kecamatan Tualang, Perawang ini dapat diselenggarakan dengan baik dan berjalan dengan lancar sesuai dengan rencana kegiatan yang telah disusun, hasil dari pengabdian ini disimpulakan bahwa: Pertama, Nasabah BWM Fataka pemahaman tentang transaksi keuangan syariah beragam, ada yang sudah faham, ada yang masih ragu-ragu, bahkan ada yang belum faham, Kedua, Ketercapaian tujuan program kegiatan pengabdian kepada masyarakat nasabah BWM Fataha keseluruhan program yang telah dilakukan dengan kolaborasi antara pemilik usaha dan pengabdi telah dilakukan semua dan sesuai dengan roundown acara maupun waktu yang telah ditentukan sebelumnya.

Deteksi Bot Spammer Twitter Berbasis Time Interval Entropy dan Global Vectors for Word Representations Tweet’s Hashtag Arif Mudi Priyatno; Muhammad Mirza Muttaqi; Fahmi Syuhada; Agus Zainal Arifin
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol. 5 No. 1 (2019): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v5i1.1382

Bot spammer merupakan penyalahgunaan user dalam menggunakan Twitter untuk menyebarkan pesan spam sesuai dengan keinginan user. Tujuan spam mencapai trending topik yang ingin dibuatnya. Penelitian ini mengusulkan deteksi bot spammer pada Twitter berbasis Time Interval Entropy dan global vectors for word representations (Glove). Time Interval Entropy digunakan untuk mengklasifikasi akun bot berdasarkan deret waktu pembuatan tweet. Glove digunakan untuk melihat co-occurrence kata tweet yang disertai Hashtag untuk proses klasifikasi menggunakan Convolutional Neural Network (CNN). Penelitian ini menggunakan data API Twitter dari 18 akun bot dan 14 akun legitimasi dengan 1.000 tweet per akunnya. Hasil terbaik recall, precision, dan f-measure yang didapatkan yaitu 100%; 100%, dan 100%. Hal ini membuktikan bahwa Glove dan Time Interval Entropy sukses mendeteksi bot spammer dengan sangat baik. Hashtag memiliki pengaruh untuk meningkatkan deteksi bot spammer. Spam spammers are users' misuse of using Twitter to spread spam messages in accordance with user wishes. The purpose of spam is to reach the required trending topic. This study proposes detection of bot spammers on Twitter based on Time Interval Entropy and global vectors for word representations (Glove). Time Interval Entropy is used to classify bot accounts based on the tweet's time series, while glove views the co-occurrence of tweet words with Hashtags for classification processes using the Convolutional Neural Network (CNN). This study uses Twitter API data from 18 bot accounts and 14 legitimacy accounts with 1000 tweets per account. The best results of recall, precision, and f-measure were 100%respectively. This proves that Glove and Time Interval Entropy successfully detects spams, with Hash tags able to increase the detection of bot spammers.

Risk Tolerance: Heuristic Bias Towards Investment Decision Making Wahyu Febri Ramadhan Sudirman; Mohd Winario; Arif Mudi Priyatno; Zubaidah Assyifa
Journal of Theoretical and Applied Management (Jurnal Manajemen Teori dan Terapan) Vol. 16 No. 2 (2023)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jmtt.v16i2.47471

Objective: This study aims to examine how risk tolerance influences the role of overconfidence bias and availability bias in investment decision-making. Because of the complexities of the investment decision-making process, this study attempts to investigate psychological variables in the investment decision-making process. Design/Methods/Approach: This study used the Structural Equation Modeling Partial Least Squares (SEM-PLS) analytic approach using the SmartPLS 3 program and survey data provided online to stock investors, with a total of 303 samples obtained. The study applied CMB preventive techniques to decrease common method bias (CMB). Findings: The results indicate a positive and significant mediating role of risk tolerance on the effect of overconfidence bias and availability bias toward investment decision-making. Originality/Value: This research seeks to explore the process of making investment decisions by taking into account the psychological aspects of investors by using a more comprehensive Bounded rationality theory point of view. This study tested the mediation mechanism of risk tolerance in bridging the influence of heuristic bias on investment decision-making, which has not been explored much by previous studies. Practical/Policy implication: The findings can guide investors to consider how they make biased investment decisions and help investment managers assess the appropriate level of investment risk.

Comparison Random Forest Regression and Linear Regression For Forecasting BBCA Stock Price Arif Mudi Priyatno; Lailatul Syifa Tanjung; Wahyu Febri Ramadhan; Putri Cholidhazia; Putri Zulia Jati; Fahmi Iqbal Firmananda
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

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.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v16i3.16502

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 Arif Mudi Priyatno; Wahyu Febri Sudirman; R. Joko Musridho; Fazilla Amalia
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

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.

A SYSTEMATIC LITERATURE REVIEW: RECURSIVE FEATURE ELIMINATION ALGORITHMS Arif Mudi Priyatno; Triyanna Widiyaningtyas
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5015

Recursive feature elimination (RFE) is a feature selection algorithm that works by gradually eliminating unimportant features. RFE has become a popular method for feature selection in various machine learning applications, such as classification and prediction. However, there is no systematic literature review (SLR) that discusses recursive feature elimination algorithms. This article conducts a SLR on RFE algorithms. The goal is to provide an overview of the current state of the RFE algorithm. This SLR uses IEEE Xplore, ScienceDirect, Springer, and Scopus (publish and publish) databases from 2018 to 2023. This SLR received 76 relevant papers with 49% standard RFEs, 43% strategy RFEs, and 8% modified RFEs. Research using RFE continues to increase every year, from 2018 to 2023. The feature selection method used simultaneously or for comparison is based on a filter approach, namely Pearson correlation, and an embedded approach, namely random forest. The most widely used machine learning algorithms are support vector machines and random forests, with 19.5% and 16.7%, respectively. Strategy RFE and modified RFE can be referred to as hybrid RFEs. Based on relevant papers, it is found that the RFE strategy is broadly divided into two categories: using RFE after other feature selection methods and using RFE simultaneously with other methods. Modification of the RFE is done by modifying the flow of the RFE. The modification process is divided into two categories: before the process of calculating the smallest weight criteria and after calculating the smallest weight criteria. Calculating the smallest weight criteria in this RFE modification is still a challenge at this time to obtain optimal results.

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

Title

Found 44 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 44 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 44 Documents
Search