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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) STTAL POSTGRADUATE - INTERNATIONAL CONFERENCE At-Tadbir : jurnal ilmiah manajemen JOURNAL OF APPLIED BUSINESS ADMINISTRATION

Budi Prasetiyo

Unknown Affiliation

Author-ID : 3097745

Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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Articles

Penerapan Dizcretization dan Teknik Bagging Untuk Meningkatkan Akurasi Klasifikasi Berbasis Ensemble pada Algoritma C4.5 dalam Mendiagnosa Diabetes Mirqotussa’adah Mirqotussa’adah; Much Aziz Muslim; Endang Sugiharti; Budi Prasetiyo; Siti Alimah
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 8, No. 2 Agustus 2017
Publisher : Institute for Research and Community Services, Udayana University

In the field of health, data mining can be used to predict a disease from patient medical record data, diabetes. There are several data mining models which one is classification. In the access field, there are many branches that are developing the decision tree (decision tree). One popular decision tree is C4.5. In this study, the data used were pima indian diabetes dataset taken from UCI machine learning repository. In this dataset all attributes are of continuous numeric type and for combined continuous data discretization is used. Accuracy is very important in the classification, ensemble method is a method used to improve the accuracy of classification algorithm by building some classifier of training data. From the research results, by applying discretization and bagging techniques to ensemble-based classification on C4.5 algorithm can increase the accuracy of 6.26%. With an initial accuracy of 68.61%, after applied discretization and bagging techniques to 74.87%..

INVESTIGASI DAMPAK PENERAPAN SISDM PADA KINERJA KARYAWAN Budi Prasetiyo; Pristiyono Pristiyono; Muhammad Yasin
JOURNAL OF APPLIED BUSINESS ADMINISTRATION Vol 4 No 1 (2020): Journal of Applied Business Administration - Maret 2020
Publisher : Politeknik Negeri Batam

Tujuan dari penelitian ini adalah untuk melihat dampak penerapan SISDM pada kinerja karyawan di Krisna Beach Hotel 1 Pangandaran. Penelitian ini dilakukan di Krisna Beach Hotel 1 Pangandaran, untuk mendapatkan jawaban tentang kecenderungan menurunnya kinerja karyawan. Salah satu penyebab turunnya kinerja, diduga karena masalah di SISDM. Kuisioner dibagikan kepada semua karyawan Krisna Beach Hotels 1 Pangandaran. Data diolah dengan metode analisis regresi linier sederhana. Hasil penelitian ini menunjukkan dampak penerapan SISDM pada kinerja karyawan Krisna Beach Hotel 1.

PENERAPAN BUDAYA KERJA KAIZEN DI PT X KABUPATEN BANDUNG BARAT Budi Prasetiyo; Ryan Supu Tauhid
At-Tadbir : jurnal ilmiah manajemen Vol 3, No 2 (2019): At-Tadbir : jurnal ilmiah manajemen
Publisher : Islamic University of Kalimantan MAB Banjarmasin

Kaizen is a Japanese culture that means continuing to improve and improve quality in the workplace. This culture is applied by many Japanese companies in the world, especially manufacturing companies. ISO 9001 is a quality management control system (Total Quality Management / TQM) issued by the Internatinal Organization for Standardization. ISO 9001 emphasizes for organizations starting from top management to lower level in carrying out continuous improvement and improvement of work quality. The auxiliary tool used in this research is Quality Control Circle (QCC) which uses the PDCA concept and 7 tools. The QCC method is a method that can be used to control product quality and reduce the number of defective products. The results of this study are that the Kaizen work culture has not run effectively even though the impact on productivity and employee performance is very influential and from the results of this QCC it is evident that with the change in the new Operational Procedure Standards it can reduce the total percentage of defective goods compared to the previous periodKeywords: Kaizen, ISO 9001, Total Quality Management, PDCA, 7 Tools, Quality Control Circle

MODEL OPTIMIZATION OF PATROL ASSIGNMENT AT NORTH NATUNA SEA TO SUPPORT OPERATION TASK OF INDONESIAN NAVY Budi Prasetiyo; Ayip Riva'i; Azi Wardiana
STTAL POSTGRADUATE - INTERNATIONAL CONFERENCE Vol 6 No 1 (2022): Indonesia Naval Technology College STTAL Postgraduate International Conference -
Publisher : Indonesian Naval Technology College STTAL

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

The South China Sea conflict is a regional security issue that has not yet reached the point of completion, and isprone to disrupting regional stability in the future. There are 3 (three) basic things that are the main reasons whycountries are involved in the South China Sea conflict. First, it contains enormous natural resources, including oiland gas, biodiversity and fisheries and other marine resources. To operate patrol boats and logistics vessels sothat the distribution of liquid logistics support is appropriate, timely, and in the right place, in solving the problemof liquid logistics distribution, the "Linear Programming" approach will be used to optimization the distribution ofliquid logistics for patrol boats in the Security Operations sector sea. From the description of the formulation of theproblem above, this research was carried out by aiming at the objectives to be achieved, including: formulating alogistic distribution optimization model and optimizing the ability of Naval Warship B9 in supporting the elementsof the title at sea. Data processing in this research activity is data that has been obtained from data collectionactivities and processed using the help of an excel solver. the results of the running of the spreadsheet solverprogram, the running results stated that in order to carry out operations in the Natuna sea for one year it required7 Naval Warship (1 K3; 5 K4; and 1 K5), then the central point of operation is sector A, the optimal refuel point isin R17 because it is closest to operation in sector A.Keywords: Distribution logistics, spreadsheet solver, Linear Programming.

Deep Learning Model Implementation Using Convolutional Neural Network Algorithm for Default P2P Lending Prediction Tiara Lailatul Nikmah; Jumanto Jumanto; Budi Prasetiyo; Nina Fitriani; Much Aziz Muslim
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26366

Peer-to-peer (P2P) lending is one of the innovations in the field of fintech that offers microloan services through online channels without intermediaries. P2P lending facilitates the lending and borrowing process between borrowers and lenders, but on the other hand, there is a threat that can harm lenders, namely default. Defaults on P2P lending platforms result in significant losses for lenders and pose a threat to the overall efficiency of the peer-to-peer lending system. So it is essential to have an understanding of such risk management methods. However, designing feature extractors with very complicated information about borrowers and loan products takes a lot of work. In this study, we present a deep convolutional neural network (CNN) architecture for predicting default in P2P lending, with the goal of extracting features automatically and improving performance. CNN is a deep learning technique for classifying complex information that automatically extracts discriminative features from input data using convolutional operations. The dataset used is the Lending Club dataset from P2P lending platforms in America containing 9,578 data. The results of the model performance evaluation got an accuracy of 85.43%. This study shows reasonably decent results in predicting p2p lending based on CNN. This research is expected to contribute to the development of new methods of deep learning that are more complex and effective in predicting risks on P2P lending platforms.

Co-Authors Ayip Riva'i Azi Wardiana Endang Sugiharti Jumanto Unjung Mirqotussa’adah Mirqotussa’adah Much Aziz Muslim Much Aziz Muslim Muhammad Yasin Nina Fitriani Pristiyono Pristiyono Ryan Supu Tauhid Siti Alimah Tiara Lailatul Nikmah

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