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All Journal EDUNOMIC : JURNAL ILMIAH PENDIDIKAN EKONOMI FAKULTAS KEGURUAN DAN ILMU PENDIDIKAN SMATIKA InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan) 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 JURSIMA (Jurnal Sistem Informasi dan Manajemen) JATI (Jurnal Mahasiswa Teknik Informatika) INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System JURSIMA Jurnal Sistem Informasi dan Manajemen Jurnal Nasional Teknik Industri, Sistem Informasi dan Teknik Informatika
Odi Nurdiawan
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Social Sciences Other

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KLASIFIKASI PENERIMA BANTUAN SOSIAL DENGAN ALGORITMA RANDOM FOREST UNTUK PENANGANAN COVID 19 Abdur Rosid; Odi Nurdiawan; Gifthera Dwilestari
JURSIMA (Jurnal Sistem Informasi dan Manajemen) Vol 10 No 2 (2022): Jursima Vol. 10 No. 2, Agustus Tahun 2022
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i2.398

Abstract

The Covid 19 outbreak has an impact on the community so that there are family heads who cannot work in general. The policy pursued by the central government is to provide assistance to workers who have salaries below 5 million and other programs. The obstacles faced to the community are not exactly recipients of assistance in accordance with the criteria set by the government. The criteria set by the government are workers who have salaries below 5 million. The purpose of the study can model the recipients of social assistance that is on target, so that the assistance can be useful in the time of the Covid 19 pandemic. This method of approaching research uses knowladge data discovery with the first stage of data obtained by social services in 2020 the second stage of data classification based on the riteri that has been established. The third stage of preprocessing is used to clean up noise data, stage four of the random forest model by using rapid miner tool version 9.9. Stage six discussion of the results of the model produced from random forest. The results expected in the study get a good model so that it becomes a recommendation in determining the recipients of sosial assistance
IMPLEMENTASI ALGORITMA FP-GROWTH UNTUK MENUNJANG KEPUTUSAN PERSEDIAN BARANG DI CV INDOTECH JAYA SENTOSA KOTA CIREBON Iman Nurrohmat; Odi Nurdiawan; Agus Bahtiar
JURSIMA (Jurnal Sistem Informasi dan Manajemen) Vol 10 No 2 (2022): Jursima Vol. 10 No. 2, Agustus Tahun 2022
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i2.421

Abstract

Indotech Jaya Sentosa is a company engaged in trading in the form of computers and network infrastructure. Currently experiencing problems in managing inventory for its customers. These obstacles include the frequent occurrence of overcapacity in storage warehouses that exceeds the number of requests, even more so in the era of the Covid-19 pandemic. This study aims to provide a solution to the problems at CV Jaya Sentosa, namely by applying a technique or algorithm to support decisions in managing its merchandise inventory. The approach taken is to use a data mining approach involving the FP-Growth algorithm method. FP-Growth Algorithm is a method to find the pattern of relationship between one or more items in a dataset. While the steps taken to the data mining approach include business understanding, data understanding, data preparation, data modeling, data evaluation and deployment. The final result of this research is expected to be able to apply association rules where these rules can be used as a reference in predicting what kind of inventory should be held to facilitate inventory management.
PENERAPAN MACHINE LEARNING UNTUK MENENTUKAN KELAYAKAN KREDIT MENGGUNAKAN METODE SUPPORT VEKTOR MACHINE Syafi'i Syafi'i; Odi Nurdiawan; Gifthera Dwilestari
JURSIMA (Jurnal Sistem Informasi dan Manajemen) Vol 10 No 2 (2022): Jursima Vol. 10 No. 2, Agustus Tahun 2022
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i2.422

Abstract

Credit is one of the services provided by banks, credit risk that occurs in the provision of credit loans, in the case that the customer is unable to pay the loan received is always considered by the bank, and supervises the customer to reduce risk. The main risk for banks and financial institutions is to differentiate creditors who have the potential for bad loans, this crisis is a concern for financial institutions about credit risk. SUPPORT VEKTOR MACHINE algorithm is an algorithm used to form a decision tree. The decision tree is a very powerful and well-known classification and prediction method. The richer the information or knowledge contained by the training data, the accuracy of the decision tree will increase. The SUPPORT VEKTOR MACHINE algorithm classification method can determine the credit worthiness of the national civil capital capitals as evidenced by the performance table data consisting of the AUC results, Acuracy results. The results of the application of machine learning using the vector machine support algorithm against cooperative data in KPRI "RUKUN" SMKN 1 Lemahabang to determine creditworthiness based on the results of the Performance Vector from the Support Vector Machine algorithm resulted in smooth prediction, smooth true 130, prediction of jammed, true jam 72, current prediction true jam 41, prediction of jammed true jam 332. The accuracy rate of the performance vector of the support vector algorithm is 80.34%. .
RANCANG BANGUN SISTEM INFORMASI PENDATAAN INVENTARIS BERBASIS WEB PADA SERVER PT JASA MARGA (PERSERO) TBK. CABANG PALIKANCI nur syarief abdullah; Arif Rinaldi Dikananda; 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.v10i3.464

Abstract

PT Jasa Marga (Persero) Tbk. The Palikanci branch is a toll gate support company, but computer Inventory Data Collection which includes servers and is still considered manual data collection so that it consumes a lot of paper and is very vulnerable to being lost and takes a lot of time to collect lost or torn data. To emphasize and learn in understanding the problems as described, the formulation of the problem that researchers can explain is to design a computerized information system for data collection of Jasa Marga's server inventory, create a database for data collection of server inventory items for managers to carry out their work. The purpose of the research is to find out, develop and create an ongoing asset collection application system into the PHP and HTML programming language using the MySQL database. So that researchers can draw conclusions in processing inventory data collection server by implementing applications that have been designed and built in a systematic and structured manner, so that the level of damage in the process of carrying out data collection for seafarers can be resolved.
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.
KLASIFIKASI PENERIMA BANTUAN SOSIAL DENGAN ALGORITMA RANDOM FOREST UNTUK PENANGANAN COVID 19 Abdur Rosid; Odi Nurdiawan; Gifthera Dwilestari
JURSIMA Vol 10 No 2 (2022): Jursima Vol. 10 No. 2, Agustus Tahun 2022
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i2.398

Abstract

The Covid 19 outbreak has an impact on the community so that there are family heads who cannot work in general. The policy pursued by the central government is to provide assistance to workers who have salaries below 5 million and other programs. The obstacles faced to the community are not exactly recipients of assistance in accordance with the criteria set by the government. The criteria set by the government are workers who have salaries below 5 million. The purpose of the study can model the recipients of social assistance that is on target, so that the assistance can be useful in the time of the Covid 19 pandemic. This method of approaching research uses knowladge data discovery with the first stage of data obtained by social services in 2020 the second stage of data classification based on the riteri that has been established. The third stage of preprocessing is used to clean up noise data, stage four of the random forest model by using rapid miner tool version 9.9. Stage six discussion of the results of the model produced from random forest. The results expected in the study get a good model so that it becomes a recommendation in determining the recipients of sosial assistance
IMPLEMENTASI ALGORITMA FP-GROWTH UNTUK MENUNJANG KEPUTUSAN PERSEDIAN BARANG DI CV INDOTECH JAYA SENTOSA KOTA CIREBON Iman Nurrohmat; Odi Nurdiawan; Agus Bahtiar
JURSIMA Vol 10 No 2 (2022): Jursima Vol. 10 No. 2, Agustus Tahun 2022
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i2.421

Abstract

Indotech Jaya Sentosa is a company engaged in trading in the form of computers and network infrastructure. Currently experiencing problems in managing inventory for its customers. These obstacles include the frequent occurrence of overcapacity in storage warehouses that exceeds the number of requests, even more so in the era of the Covid-19 pandemic. This study aims to provide a solution to the problems at CV Jaya Sentosa, namely by applying a technique or algorithm to support decisions in managing its merchandise inventory. The approach taken is to use a data mining approach involving the FP-Growth algorithm method. FP-Growth Algorithm is a method to find the pattern of relationship between one or more items in a dataset. While the steps taken to the data mining approach include business understanding, data understanding, data preparation, data modeling, data evaluation and deployment. The final result of this research is expected to be able to apply association rules where these rules can be used as a reference in predicting what kind of inventory should be held to facilitate inventory management.
PENERAPAN MACHINE LEARNING UNTUK MENENTUKAN KELAYAKAN KREDIT MENGGUNAKAN METODE SUPPORT VEKTOR MACHINE Syafi'i Syafi'i; Odi Nurdiawan; Gifthera Dwilestari
JURSIMA Vol 10 No 2 (2022): Jursima Vol. 10 No. 2, Agustus Tahun 2022
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i2.422

Abstract

Credit is one of the services provided by banks, credit risk that occurs in the provision of credit loans, in the case that the customer is unable to pay the loan received is always considered by the bank, and supervises the customer to reduce risk. The main risk for banks and financial institutions is to differentiate creditors who have the potential for bad loans, this crisis is a concern for financial institutions about credit risk. SUPPORT VEKTOR MACHINE algorithm is an algorithm used to form a decision tree. The decision tree is a very powerful and well-known classification and prediction method. The richer the information or knowledge contained by the training data, the accuracy of the decision tree will increase. The SUPPORT VEKTOR MACHINE algorithm classification method can determine the credit worthiness of the national civil capital capitals as evidenced by the performance table data consisting of the AUC results, Acuracy results. The results of the application of machine learning using the vector machine support algorithm against cooperative data in KPRI "RUKUN" SMKN 1 Lemahabang to determine creditworthiness based on the results of the Performance Vector from the Support Vector Machine algorithm resulted in smooth prediction, smooth true 130, prediction of jammed, true jam 72, current prediction true jam 41, prediction of jammed true jam 332. The accuracy rate of the performance vector of the support vector algorithm is 80.34%. .
Co-Authors Abdul Jamalul'ain Abdur Rosid Ade Irma Purnamasari agus bahtiar Ahmad Bahrum Maula Rahman Ananda Rafly Aria Pratama Arif Nur Hakim Arif Rinaldi Dikananda Budi Saputra Chaerudin, Chaerudin Cindyk Irawanto Dicky Miftakhul Rizki Dwi Teguh Afandi Dyana Fitri Ega Salsa Nugraha Eka Wisnu Ramadhona Fidya Arie Pratama Gifthera Dwilestari Iman Nurrohmat Irvan Himawan Liyanda Pangestu Martanto Martanto Melawati Melawati Mulyawan Mulyawan nur syarief abdullah Ruli Herdiana Saeful Anwar Shofian Yunus Syafi'i Syafi'i Teguh Abdi Mangun Tio Prasetiya Tio Prasetya