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All Journal Jurnal Ilmiah Informatika Komputer Ekonomi Bisnis ILKOM Jurnal Ilmiah
Eka Patriya
Gunadarma University
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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Implementation of Artificial Neural Network (ANN) in the Image Recognition of Offline Cursive Handwriting Fitrianingsih Fitrianingsih; Diana Tri Susetianingtias; Dody Pernadi; Eka Patriya; Rini Arianty
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

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

Abstract

Identifying a writing is an easy thing to do for human, but this does not apply to computers, in particular if it is handwriting. Handwriting recognition, especially cursive handwriting is a research in the area of image processing and pattern matching that is challenging to complete, following the different characteristics of each person's cursive handwriting style. In this study, the use of the ANN model will be implemented in performing offline handwriting image recognition. The cursive handwriting image that has been obtained is then preprocessed and segmented using bounding box rectangle and contour techniques. Evaluation of system performance using global performance metrics in this study resulted in a percentage of 93% where the bounding box and contour succeeded in determining the segmentation point correctly, so that the ANN model worked optimally.
ANALISIS KREDIT CALON DEBITUR MENGGUNAKAN METODE FUZZY TSUKAMOTO Eka Patriya; Ety Sutanty; Handayani Handayani; Meilani B. Siregar; Esti Setiyaningsih
Jurnal Ilmiah Informatika Komputer Vol 27, No 1 (2022)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2022.v27i1.6169

Abstract

Bank sebagai salah satu lembaga keuangan di Indonesia yang berbentuk bank memberikan jasa keuangan dengan menggunakan prinsip-prinsip perbankan. Bank SENDIRI menyediakan berbagai jenis fasilitas kredit, salah satunya Kredit Multi Guna. Saat ini proses analisis pengajuan kredit di Bank dilakukan dengan menggunakan Sistem Electronic Loan, akan tetapi ketika sistem bermasalah maka proses analisis kredit dilakukan dengan cara manual oleh analis. Tentu saja hal ini mengakibatkan proses analisis kredit membutuhkan waktu. Pada penelitian ini peneliti mengimplementasikan diimplementasikan penggunaan metode fuzzy tsukamoto dalam menganalisis kelayakan kredit calon debitur Bank. Proses analisis kredit pada penelitian ini menggunakan 3 variabel yaitu pekerjaan, Debt Service Ratio (DSR) yang merupakan perbandingan antara angsuran kredit dengan penghasilan, serta kolektabilitas. Masing-masing variabel memiliki 3 himpunan fuzzy dan aturan yang terbentuk adalah sebanyak 27 aturan. Kelayakan KMG calon debitur pada penelitian ini menggunakan hasil dari proses defuzzifikasi. Hasil ujicoba menunjukkan, implementasi fuzzy tsukamoto berdasarkan variabel, DSR, dan kolektabilitas berhasil menghasilkan keputusan kelayakan fasilitas kredit calon debitur Bank dari hasil defuzzifikasi. Hasil penelitian ini diharapkan dapat mempermudah analis dalam melakukan proses analisis pemutusan kredit calon debitur Bank.
Implementation of the prophet model in COVID-19 cases forecast Rodiah Rodiah; Eka Patriya; Diana Tri Susetianingtias; Ety Sutanty
ILKOM Jurnal Ilmiah Vol 14, No 2 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i2.1219.99-111

Abstract

One of the steps to understanding this pandemic is to look at the spread of the data by predicting an increase in cases in various countries so that prevention can be carried out as early as possible. One way to see fluctuations in COVID-19 pandemic data is to predict the rate of cases using forecasting methods so that conclusions can be drawn on the spread of COVID-19 pandemic data around the world to be processed using statistical models. This study will implement the use of the Prophet Model in seeing the rate of development of COVID-19 in the world using four features in the forecasting process such as the number of confirmed cases, the number of cases of recovered patients, the number of cases of death, and the number of active cases. The results of this study produce forecasting data on the number of cases of the COVID-19 pandemic that can be viewed daily, weekly, and even monthly. Forecasting results show the first spike at the end of March until the number of cases reached around 10,275,800 million as of June 29, 2020, where the number of cases grew exponentially until June 29, 2020. The case rate of growth in many instances experienced significant growth until the end of October, touching the number in the range of 34,507,150 million as of October 25, 2020. After June 29, 2020, a very high spike was different from the increase in cases in the previous months. Forecasting results show no point decline because historical data on the number of daily confirmed cases of the COVID-19 pandemic has not decreased. The forecasting results in this study are expected to be able to systematically predict events or events that will occur in the COVID-19 pandemic around the world with the help of valid periodic data so that some information can be obtained for preventive measures related to the COVID-19 pandemic.
PERAMALAN HARGA SAHAM PENUTUPAN INDEKS HARGA SAHAM GABUNGAN (IHSG) MENGGUNAKAN ALGORITMA LONG SHORT TERM MEMORY (LSTM) Eka Patriya; Andriansyah Latif; Handayani Handayani
Jurnal Ilmiah Ekonomi Bisnis Vol 28, No 2 (2023)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/eb.2023.v28i2.7964

Abstract

Investasi merupakan suatu kegiatan yang dilakukan untuk uang kepada suatu produk investasi untuk mendapatkan keuntungan (benefit) dengan harapan secara imbal balik mendapat keuntungan yang lebih besar di masa depan. Saham sebagai bentuk kegiatan investasi yang dapat menjadi alternatif sumber dana bagi para investor baik perusahaan atau pun individual. Seorang investor saham dituntut untuk bisa melakukan analisis dari indikator yang dapat mempengaruhi pergerakan saham. Indeks Harga Saham Gabungan (IHSG) merupakan salah satu indikator yang perlu diperhatikan dalam berinvestasi. IHSG merupakan refleksi dari kinerja keseluruhan saham perusahaan dan aktifitas kinerja ini dicatat di Bursa Efek Indonesia (BEI). BEI akan mencatat saham yang mengalami kenaikan dan penurunan.  Penelitian ini melakukan peramalan saham berdasarkan harga penutupan saham IHSG menggunakan Long Short Term Memory (LSTM). Evaluasi kinerja model LSTM dalam melakukan peramalan menggunakan Root Mean Square Error (RMSE). Model LSTM yang dibentuk dapat digunakan untuk melakukan peramalan harga penutupan saham, sehingga dapat menjadi pertimbangan para investor untuk melakukan investasi saham. Invesitasi saham dapat dilakukan salah satunya dengan melihat nilai pergerakan IHSG yang mencerminkan nilai kinerja saham di pasar keuangan.
Combination of YOLOv3 Algorithm and Blob Detection Technique in Calculating Nile Tilapia Seeds Diana Tri Susetianingtias; Eka Patriya; Rini Arianty
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1634.317-325

Abstract

Baby Fish counting must be counted accurately so it will not cause any loss, especially for fish seeds or fingerlings that have a small size. Generally, people still use conventional counting methods that produce low accuracy values. This research will make a Nila Baby Fish fingerlings counter program using the YOLOv3 algorithm and Blobb detection technique. The annotation data process will use LabelImg, and the dataset training will use Google COLABoratory with the Darknet framework in an online environment. Images that will predict in this program will be called and detected with an object detector. The object with a confidence score of more than 0.3 will be converted into a blob. The blob value will be forwarded to the output layer for scaling the bounding box objects. The output of this program is the predicted image, blob value, prediction time, and the number of Nila seeds. The model performance is evaluated using a confusion matrix and got a 98.87% for accuracy score.
Co-Authors Andriansyah Latif Diana Tri Susetianingtias Dody Pernadi Esti Setiyaningsih Ety Sutanty Ety Sutanty Fitrianingsih Fitrianingsih Handayani Handayani Handayani Handayani Meilani B. Siregar Rini Arianty Rodiah Rodiah