Article Per Year (5 Year)
p-Index From 2021 - 2026
4.207
P-Index
This Author published in this journals
All Journal EDUNOMIC : JURNAL ILMIAH PENDIDIKAN EKONOMI FAKULTAS KEGURUAN DAN ILMU PENDIDIKAN SMATIKA InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Information System for Educators and Professionals : Journal of Information System Information Management For Educators And Professionals (IMBI) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Informasi dan Komputer Jurnal Manajemen Informatika dan Sistem Informasi JURSIMA (Jurnal Sistem Informasi dan Manajemen) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Experimental Student Experiences JURSIMA Jurnal Nasional Teknik Industri, Sistem Informasi dan Teknik Informatika EDUNOMIC : JURNAL ILMIAH PENDIDIKAN EKONOMI FAKULTAS KEGURUAN DAN ILMU PENDIDIKAN IPSSJ
Odi Nurdiawan
Unknown Affiliation
Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

Published : 32 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 32 Documents
Search

 1   2   3   4 
ANALISA PEMBELIAN SEPEDA MENGGUNAKAN ALGORITMA APRIORI PADA TOKO SEPEDA BRADEN BIKE Dicky Miftakhul Rizki; Odi Nurdiawan; Saeful Anwar
JURSIMA (Jurnal Sistem Informasi dan Manajemen) Vol 10 No 3: Jursima Vol.10 No.3
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i3.465

Abstract

The store is a place for trading activities that provide all daily necessities with a special type of goods. Braden Bike Shop is a store that sells a variety of bicycle products and accessories, but the data collection of sales transactions for goods that have been sold is usually written on sheets of paper and collected paper that has been sold and rewrites items that have been sold manually to new paper to record sales reports every month with the current system, The purpose of this study is to find the rules of the combination of items by looking at the relationships of two or more variables, The method used is the A priori Algorithm Method in data mining techniques, namely the association rule or association rule used using a minimum support of 10% and a minimum of confidence of 50%, The results obtained are 12 rules 2 itemsets and 2 rules 3 itemssets following sales for 1 year using a priori algorithms, namely categories Aviator_GN, Exotic_GN, Interbike_GN, Fastron_GN, Polygon_GN, Seat Covers, Anti-Slip_AS Grips and Bell_AS. Results obtained based on manual calculations and using Rapid Miner software have results above the minimum support of 10%and confidence of 50%.
RANCANG BANGUN SISTEM INFORMASI PERSEDIAAN BARANG BERBASIS WEB PADA PT PARAGON FURNITAMA INDUSTRY Arif Rinaldi Dikananda; Shofian Yunus; Saeful Anwar; Odi Nurdiawan
JURSIMA (Jurnal Sistem Informasi dan Manajemen) Vol 10 No 3: Jursima Vol.10 No.3
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i3.474

Abstract

PT. Paragon Furnitama Industry is one of the companies engaged in the production of fabric and leather sofas, back seats, and chair cushions, at this time the inventory process is still done manually because it still uses records in books and Microsoft Excel, the process sometimes finds several problems including data redundancy, discrepancies in stock of goods with records, and providing long reports because data validation is needed first. So that the information received by the parties concerned is very difficult to obtain quickly. To emphasize and study the problems as described, the problem formulation that researchers can explain is to design an inventory information system so that the company's performance is getting better. The Design and Build of this Goods Inventory Information System is built based on a website. The design of the information system uses the Software Development Life Cycle (SDLC) with the waterfall method so that this design system is expected to improve performance and performance, especially those related to processing inventory data to making inventory reports at PT. Paragon Furnitama Industry.
Irvan Himawan PREDIKSI HARGA SAHAM DENGAN ALGORITMA REGRESI LINIER DENGAN RAPIDMINER Irvan Himawan; Odi Nurdiawan; Gifthera Dwilestari
JURSIMA (Jurnal Sistem Informasi dan Manajemen) Vol 10 No 3: Jursima Vol.10 No.3
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i3.475

Abstract

Stock investment in the capital market is very important for every company in the world. Stock prices in the capital market move very randomly, the highs and lows of stock prices are influenced by many factors. Therefore, it is necessary to predict the stock price so that it can help investors to see investment prospects in the future. In this study, the prediction of the stock price of BRI Bank with the BBRI stock code will be carried out, using an algorithm, namely Linear Regression on rapid miners. This Linear Regression Algorithm is the best algorithm to use because it is the most complex compared to other algorithms. Based on signaling theory, which are information signals needed by investors, the value of forecasting results that have been obtained can be used to consider investors' decisions that the stock has high or low risk in the future. Based on the theory of risk, this forecasting analysis helps investors to minimize losses. Stock prediction is one of the technical analysis. Stock buying and selling transactions without technicalities are gambling behavior and contain gharar or ambiguity. The impact of not using this technical analysis clearly resulted in transactions containing maisir and gharar which were clearly prohibited. The historical stock data used in the test was obtained from the finance.yahoo.com web page with the category PT. Bank Rakyat Indonesia Tbk, or with the issuer code BBRI shares. What will be used is annual data for the last 5 years in the form of time series accompanied by open, high, low and volume variables as independent variables and close as dependent variables. The algorithm used is multiple linear regression.
ANALISA KANKER PARU PARU DENGAN MENGGUNAKAN ALGORITMA K-NEARST NEIGHBOR Teguh Abdi Mangun; Odi Nurdiawan; Ade Irma Purnamasari
Jurnal Teknik Industri, Sistem Informasi dan Teknik Informatika Vol. 2 No. 2 (2023): Jurnal Tinsika
Publisher : Universitas Bakti Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kanker paru adalah salah satu jenis kanker yang paling mematikan di dunia. Menurut data dari World Health Organization (WHO), kanker paru merupakan jenis kanker yang paling banyak menyebabkan kematian di seluruh dunia. Karena itu, pengembangan metode klasifikasi yang akurat dan efektif untuk kanker paru sangat penting dalam upaya untuk meningkatkan deteksi dini dan pengobatan yang tepat. Tahapan penelitian analisis kanker paru-paru menggunakan algoritma k-Nearest Neighbor (k-NN), Tahap awal dalam penelitian ini adalah mengumpulkan data terkait kanker paru-paru, Setelah data terkumpul, langkah berikutnya adalah melakukan preprocessing data untuk membersihkan, Pemilihan nilai k, yaitu jumlah tetangga terdekat, merupakan langkah krusial dalam analisis kanker paru-paru, Setelah nilai k terpilih, model K-NN dilatih menggunakan data pelatihan untuk mempelajari hubungan antara atribut dan status kanker paru-paru. Hasil pada penelitian ini yaitu hasil akurasi yang didapat yaitu sebesar 80.40%.
Peningkatan Pemahaman Akuntansi Dengan Menggunakan Software Zahir Fidya Arie Pratama; Odi Nurdiawan
Edunomic : Jurnal Ilmiah Pendidikan Ekonomi Fakultas Keguruan dan Ilmu Pendidikan Vol 7 No 2 (2019): EDISI SEPTEMBER
Publisher : FKIP Unswagati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33603/ejpe.v7i2.2551

Abstract

STMIK IKMI have a student academic test when the students enter to STMIK IKMI, the result of the test is low. Low in this case will explains by categories the students get D 25% C 37% B 0% and all the students can not get A. The research uses kuasi eksperiments method with time series design that collaborate classroom action research. The result of research shows about pre test 1 until 4, the score of pre test 1 about 54,129, the score of pre test 2 about 55,548, the score of pre test 3 about 56,032, the score of pre test 4 about 56,097. Based on Kruskall Walls Test shows Asymp Sig score about 0,986 it means there is no significance differences between student perception for the first time and students understanding the materials. In second part of research the students learn accounting that use zahir accounting software for 6 meetings. In third part of research the students has a post test for 4 meetings and the results are the score of post test 1 62,6456, the results are the score of post test 2 70,065, the results are the score of post test 3 80,032, the results are the score of post test 4 86,742. The research analyze statistic test focus on pre test and post test by Kruskall Wall Test that shows Asymp Sig score about 0,000 it means there is a differences between the result of pre test and post test. This reality shows accounting learning by Zahir software gives the positive effect for improving (upgrading) student understanding about accounting.
Bibliometrik Analysis: Signal Preprocessing Techniques for Kualitas Sinyal Electrogram Odi Nurdiawan; Dadang Sudrajat; Fathurrohman; Ade Rizki Rinaldi
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study explores electroencephalogram (EEG) signal preprocessing techniques used in the early detection and diagnosis of epilepsy, aiming to enhance the quality and reliability of data used in clinical applications. Effective signal preprocessing techniques are crucial for minimizing artifacts and noise, which can obscure critical information in EEG signals. More accurate EEG signal processing allows for the identification of abnormal patterns associated with various neurological conditions, such as epilepsy, which heavily relies on this signal analysis for precise diagnosis. This study conducted a bibliometric analysis using a descriptive approach to identify research trends, geographic distribution, institutional contributions, and key authors in this field. Data was collected from the Scopus database using the keywords "electroencephalogram AND signal AND processing AND epilepsy". The analysis results show a significant increase in the number of publications related to EEG signal preprocessing techniques over the past five years, with major contributions from countries like China, India, and the United States, reflecting the high global interest and focus on this topic. Additionally, deep learning and machine learning techniques emerged as the most dominant methods in this research, indicating future trends in the development of increasingly sophisticated EEG signal processing technologies. The findings also suggest that using techniques such as artificial neural networks, convolutional neural networks (CNN), and deep learning can enhance the accuracy of epilepsy diagnosis and prediction, making a significant contribution to modern clinical practice. Moreover, this study emphasizes the importance of developing and integrating more advanced preprocessing techniques to improve the effectiveness of EEG signal detection and classification, which is expected to enhance diagnostic outcomes and patient management with neurological disorders. This study provides valuable contributions to the development of medical diagnostic technologies, particularly for neurological disorders such as epilepsy, and highlights the need for further research to optimize these techniques for broader clinical application.
PENINGKATAN MODEL ANALISIS SENTIMEN MELALUI ALGORITMA NAIVE BAYES BERDASARKAN DATA KOMENTAR YOUTUBE Syarif Hidayat, Deden; Odi Nurdiawan; Fadhil M.Basysyar; Muhamad Sulaeman
Jurnal Manajemen Informatika dan Sistem Informasi Vol. 8 No. 1 (2025): MISI Januari 2025
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/misi.v8i1.1413

Abstract

Penerapan kebijakan subsidi Bahan Bakar Minyak (BBM) berbasis QR Code untuk kendaraan roda empat, yang dimulai pada 1 Oktober 2024, telah memunculkan berbagai tanggapan dari masyarakat. Kebijakan ini bertujuan untuk memastikan distribusi BBM bersubsidi lebih tepat sasaran, namun menghadapi kritik terutama terkait kompleksitas proses pendaftaran QR Code dan pembatasan kriteria kendaraan yang memenuhi syarat. Penelitian ini bertujuan untuk menganalisis sentimen masyarakat terhadap kebijakan tersebut dengan menggunakan data komentar dari video YouTube berjudul "Simak! Aturan Baru Kriteria Penggunaan BBM Bersubsidi - SIP 02/09" yang diunggah pada kanal YouTube Seputar iNews RCTI. Metode yang digunakan adalah analisis sentimen berbasis algoritma Naive Bayes. Proses preprocessing data mencakup pembersihan teks, tokenisasi, penghilangan stopwords, dan stemming untuk memastikan data yang dianalisis bersih dan terstruktur. Dataset dibagi menjadi data pelatihan (70%) dan data uji (30%) untuk membangun serta mengevaluasi model. Model menunjukkan akurasi sebesar 79,40%, dengan performa yang lebih baik dalam mengenali sentimen negatif dibandingkan positif. Hasil penelitian menunjukkan bahwa mayoritas komentar memiliki sentimen negatif, mencerminkan ketidakpuasan masyarakat terhadap kebijakan ini. Penelitian ini menyoroti pentingnya strategi komunikasi yang lebih efektif dari pemerintah untuk meningkatkan pemahaman dan penerimaan masyarakat terhadap kebijakan yang diimplementasikan. Selain itu, hasil penelitian ini juga membuka peluang untuk pengembangan lebih lanjut dalam pemanfaatan analisis sentimen berbasis komputasi untuk mendukung pengambilan keputusan dalam studi kebijakan publik.
Convolutional Neural Networks for Classification Motives and the Effect of Image Dimensions Siti Aisyah; Rini Astuti; Fadhil M Basysyar; Odi Nurdiawan; Irfan Ali
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5623

Abstract

Although Indonesian batik patterns vary by location, they usually depict local customs and cultures. Each batik has a unique quality and, to correctly identify the batik designs, you need to understand the design patterns. However, many people struggle to identify and categorize these kinds of motivation because they don't have the requisite knowledge, understanding, or access to sufficient information. This study used photo data to classify batik patterns into 15 different groups. Batik Kawung, Megamendung, Lasem, Pole, Machete, Gills, Nutmeg, Karaswasih, Cendrawasih, Geblek Renteng, Bali, Betawi, and Dayak are all included in this category. 1,350 images were used in the research. Google supports the collection of data. To provide the highest level of precision and to evaluate how image dimensions affect the classification of batik designs, this study employs convolutional neural networks (CNNs). The results of this study show that Multi-Layer Perceptron (MLP) is a well-liked deep learning method for data classification, especially in domains where picture classification is involved. The size of the images utilized affects the accuracy of computational neural network (CNN) algorithms. The results showed that the test using training data comparisons of 60%, 30% and 10% resulted in a 01.89% loss of 1.18% and a 100% improvement in accuracy.
Implementation of Data Mining to Predict Graduation of SMK Al Huda Kedungwungu Students Using the Naïve Bayes Classifier Algorithm Odi Nurdiawan
Experimental Student Experiences Vol. 2 No. 2 (2023): April
Publisher : LPPM Institut Studi Islam Sunan Doe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58330/ese.v1i4.202

Abstract

The purpose of prediction is to become decision makers and make policies. Understanding the uncertainties and risks that may arise can be considered when making plans. By making these predictions, planners and decision makers will be able to consider other alternatives, so they can take advantage of student graduation data. The algorithm that will be used is the Naive Bayes Classifier Algorithm which is a simple probability classification method based on the application of Bayes' theorem with the assumption that explanatory variables are independent, clues and supporting data in predicting student graduation, namely student behavior, school exams, grades. In practice, the application of the Naive Bayes method applies data train to produce the probability of each criterion for different classes, so that the probability value of these criteria can be optimized to determine predictions of student graduation quickly and efficiently based on the classification carried out using the Naive Bayes method, then from the results of testing with the Naive Bayes method the results obtained an accuracy value of 76 .25%, so this result has very good accuracy. That way this method can be applied in predicting student graduation.
DETEKSI POLA CANDLESTICK MENGGUNAKAN YOLOV8 UNTUK ANALISIS TEKNIKAL BERBASIS CITRA Maulana Manshur; Odi Nurdiawan; Arif Rinaldi Dikananda; Fathurrohman
Integrative Perspectives of Social and Science Journal Vol. 3 No. 04 April (2026): Integrative Perspectives of Social and Science Journal
Publisher : PT Wahana Global Education

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Perkembangan teknologi computer vision dan deep learning telah meningkatkan analisis data keuangan, khususnya dalam pengenalan pola candlestick untuk pengambilan keputusan investasi. Namun, identifikasi pola secara manual cenderung subjektif, tidak konsisten, dan rentan terhadap kesalahan, terutama pada pola dengan kemiripan visual tinggi. Penelitian ini bertujuan mengevaluasi kinerja model YOLOv8 dalam mendeteksi pola candlestick secara otomatis serta menganalisis kemampuannya dalam membedakan pola yang memiliki kemiripan morfologis tinggi. Metode yang digunakan adalah pendekatan kuantitatif eksperimental dengan dataset sebanyak 4.435 citra yang diperoleh dari Roboflow. Model dilatih menggunakan konfigurasi standar YOLOv8 selama 100 epochs. Evaluasi dilakukan menggunakan metrik precision, recall, F1-score, dan mean Average Precision (mAP) pada rentang IoU 0.5–0.95. Hasil penelitian menunjukkan bahwa model mencapai precision 0.877, recall 0.898, F1-score 0.88, dan mAP sebesar 0.903. Model mampu mendeteksi pola dengan baik, namun performa menurun pada pola dengan kemiripan visual tinggi. Dengan demikian, YOLOv8 dinilai efektif untuk pengembangan sistem analisis teknikal berbasis citra yang lebih objektif dan efisien.
Co-Authors Ade Irma Purnamasari Ade Rizki Rinaldi agus bahtiar Ahmad Bahrum Maula Rahman Ananda Rafly Aria Pratama Arif Nur Hakim Arif Rinaldi Dikananda Aris Pratama Putra Bani Nurhakim Budi Saputra Chaerudin, Chaerudin Cindyk Irawanto Dadang Sudrajat Dicky Miftakhul Rizki Dwi Teguh Afandi Dyana Fitri Ega Salsa Nugraha Eka Wisnu Ramadhona Ezar Qotrunnada Fadhil M Basysyar Fadhil M.Basysyar Fathurrohman, Fathurrohman Fidya Arie Pratama Gifthera Dwilestari Iman Nurrohmat Irfan Ali Irvan Himawan Jayawarsa, A.A. Ketut Liyanda Pangestu Maulana Manshur Melawati Melawati Muhamad Sulaeman Mulyawan Mulyawan nur syarief abdullah Rini Astuti Ruli Herdiana Saeful Anwar Shofian Yunus Siti Aisyah Syafi'i Syafi'i Syarif Hidayat, Deden Teguh Abdi Mangun Tio Prasetiya Tio Prasetya