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All Journal Indonesian Journal of Electronics and Instrumentation Systems IPTEK The Journal of Engineering Indonesian Journal of Applied Informatics Indonesian Journal of Electrical Engineering and Computer Science Jurnal Nasional Aplikasi Mekatronika, Otomasi dan Robot Industri (AMORI)
Noviyanti Santoso
Institut Teknologi Sepuluh Nopember
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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 1 
Hybrid Support Vector Machine to Preterm Birth Prediction Noviyanti Santoso; Sri Pingit Wulandari
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 8, No 2 (2018): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (475.058 KB) | DOI: 10.22146/ijeis.35817

Abstract

Preterm birth is one of the major contributors to perinatal and neonatal mortality. This issue became important in health research area especially human reproduction both in developed and developing country. In 2015 Indonesia rank fifth as the country with the highest number of premature babies in the world. The ability to reduce the number of preterm birth is to reduce risk factors associated with it. This research will be made the prediction model of preterm birth using hybrid multivariate adaptive regression splines (MARS) and Support Vector Machine (SVM). MARS used to select the attributes which suspected to affect premature babies. The result of this research is prediction model based on hybrid MARS-SVM obtains better performance than the other models
Modelling of Payout Ratio: A Panel Regression Analysis for Indonesian Listed Bank Noviyanti Santoso; Rizka Widya Permatasari; Lucia Aridinanti
IPTEK The Journal of Engineering Vol 6, No 1 (2020)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23378557.v6i2.a7211

Abstract

The Indonesian economy is a bank-based economy, where the economy relies on the existence of the banking sector as a source of financing, so a healthy and efficient banking system is the key to success in the sustainability of national economic development. The company's financial performance can be improved by going public. In companies that go public, dividends are one of the motivations of investors to invest their funds in the capital market, because it is a form of return on investor investment and an increase in wealth. The purpose of this study is determining the best model of the dividend payout ratio (DPR) in the banking sector by predictor variables such as ROI, DER, ROE, PER, and CAR using panel regression analysis. Based on the results of the analysis it was concluded that the factors that influenced the banking sector DPR were ROI and CAR with a good model of 86,7%.
APLIKASI R-SHINY UNTUK SENTIMENT ANALYSIS TERHADAP ULASAN RESTORAN DI SINGAPURA MENGGUNAKAN METODE NAÏVE BAYES Mashuri Mashuri; Noviyanti Santoso; Anggie Calista; Fahrila Annasiyah; Fifi Dwi Haryanti; Kharin Octavian Ranto; Yofani Kurnia Putri
Jurnal Nasional Aplikasi Mekatronika, Otomasi dan Robot Industri (AMORI) Vol 2, No 2 (2021)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213560.v2i2.11627

Abstract

Perkembanganateknologi yangpesat memberikan banyak kemudahan bagi penggunanya saat ini.Kehadirannya memberikan kemudahan akses pada banyak bidang. Seperti kemudahan dalam melakukan pemilihanrestoran yang dapat dilakukan dalam genggaman dan dapat diakses dimana saja secara cepat dan praktis. Pemilihansebuah restoran menjadi hal yang sangat mudah dilakukan, karena tersedia banyak rekomendasi pilihan dan ulasandari pengunjung sebelumnya terhadap restoran tersebut, yang dapat digunakan oleh orang lain dalam menentukanrestoran yang akan dikunjungi. Setiap individu pasti memiliki pandangan atau pendapat yang berbeda mengenairestoran-restoran tersebut. Pendapat dan opini dari pengguna dapat dijadikan masukan bagi restoran untukmeningkatkan kualitasnya. Sentiment analysis digunakan untuk memperoleh pendapat atau opini dari para penggunasuatu platform internet dari penyedia restoran tersebut. Maka dari itu akan dibuat sebuah aplikasi sentiment analysisberbasis website dengan menggunakan R-Shiny melalui program RStudio. Project ini dapat mempermudahpenggunanya untuk melihat ulasan beberapa restoran secara keseluruhan, sehingga dapat menjadi dasarpertimbangan dalam pemilihan restoran. Website ini akan menunjukkan sentiment analysis terkait teks, yang beradapada kalimat maupun opini yang ditulis. Sehingga bisa melihat apakah puas atau tidak puas dengan menggunakanaplikasi ini secara instan dan mudah.
Integration of synthetic minority oversampling technique for imbalanced class Noviyanti Santoso; Wahyu Wibowo; Hilda Hikmawati
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 1: January 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i1.pp102-108

Abstract

In the data mining, a class imbalance is a problematic issue to look for the solutions. It probably because machine learning is constructed by using algorithms with assuming the number of instances in each balanced class, so when using a class imbalance, it is possible that the prediction results are not appropriate. They are solutions offered to solve class imbalance issues, including oversampling, undersampling, and synthetic minority oversampling technique (SMOTE). Both oversampling and undersampling have its disadvantages, so SMOTE is an alternative to overcome it. By integrating SMOTE in the data mining classification method such as Naive Bayes, Support Vector Machine (SVM), and Random Forest (RF) is expected to improve the performance of accuracy. In this research, it was found that the data of SMOTE gave better accuracy than the original data. In addition to the three classification methods used, RF gives the highest average AUC, F-measure, and G-means score.
Deteksi Dini Financial Distress Pada Perusahaan Sektor Teknologi di Bursa Efek Indonesia Menggunakan Artificial Neural Network dan Support Vector Machine Noviyanti Santoso; Ni Luh Eva Pradnyaningsih; Fausania Hibatullah
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i2.98588

Abstract

Abstrak : Kondisi ekonomi dan geopolitik di Indonesia diperkirakan akan memburuk pada beberapa tahun kedepan yang disebabkan oleh beberapa faktor diantaranya inflasi dan biaya operasional yang tinggi. Hal ini berdampak pada minat investor dalam berinvestasi pada perusahaan. Salah satu perusahaan yang paling berdampak besar adalah perusahaan sektor teknologi. Industri teknologi di Indonesia menghadapi tantangan pada pangsa pasar yang relatif rendah dibandingkan pasar global dimana banyak saham teknologi di Indonesia masih tertinggal jauh dibandingkan negara-negara maju. Akibat hal tersebut investor lebih memilih berinvestasi pada emiten yang minim risiko. Penurunan ini memengaruhi kemampuan perusahaan-perusahaan teknologi untuk menarik investasi yang dibutuhkan untuk bertahan dan berkembang. Beberapa perusahaan di sektor teknologi telah mengalami perubahan signifikan dalam kinerja keuangan mereka, menunjukkan adanya potensi kesulitan keuangan. Kesulitan keuangan terjadi ketika kinerja keuangan perusahaan menurun dari waktu ke waktu, yang pada gilirannya memengaruhi stabilitas sistem keuangan dan sumber daya manusia perusahaan. Oleh karena itu, penelitian ini bertujuan untuk memprediksi apakah perusahaan-perusahaan di sektor teknologi di Indonesia akan mengalami kesulitan keuangan di masa depan atau tidak dengan menggunakan metode Artificial Neural Network dan Support Vector Machine. Hasil penelitian menunjukkan bahwa ANN lebih unggul dalam memprediksi kinerja keuangan perusahaan dengan akurasi sebesar 95,65%, sensitivitas mencapai 100%, dan F1 Score yaitu 80%, lebih lanjut rasio PER memiliki pengaruh besar dalam memprediksi risiko ini. Selain itu, aplikasi berbasis web yang dikembangkan menggunakan Streamlit memungkinkan pengguna untuk mendeteksi dini kondisi keuangan perusahaan. =====================================================Abstract : The economic and geopolitical conditions in Indonesia are expected to deteriorate in the coming years due to several factors, including inflation and high operational costs. This affects investor interest in investing in companies. One of the most significantly impacted sectors is technology companies. The technology industry in Indonesia faces challenges with a relatively low market share compared to the global market, where many technology stocks in Indonesia lag significantly behind those in developed countries. As a result, investors prefer to invest in issuers with minimal risk. This decline affects the ability of technology companies to attract the investment needed to survive and grow. Some companies in the technology sector have experienced significant changes in their financial performance, indicating potential financial difficulties. Financial difficulties occur when a company's financial performance declines over time, which in turn affects the stability of the financial system and the company's human resources. Therefore, this study aims to predict whether technology companies in Indonesia will experience financial distress in the future using Artificial Neural Network and Support Vector Machine methods. The results of the study indicate that ANN outperforms other models in predicting the financial performance of companies with the accuracy reach 95,65%, perfect sensitivity of 100%, and F1 Score is 80%, with the PER ratio having a significant impact on forecasting this risk. Additionaly, the web-based application developed using Streamlit enables users to detect companies financial conditions early.
Co-Authors Anggie Calista Eko Wahyu Wibowo Fahrila Annasiyah Fausania Hibatullah Fifi Dwi Haryanti Hilda Hikmawati Kharin Octavian Ranto Lucia Aridinanti Mashuri Mashuri Ni Luh Eva Pradnyaningsih Rizka Widya Permatasari Sri Pingit Wulandari Yofani Kurnia Putri